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Reviewer Assignment Problem (RAP) is a crucial problem for the conference due to time constraints and inadequate availability of expert reviewers. A fair evaluation of paper is key to an author's success, paper quality, conference reputation, and productive usage of funds. Recent studies reflect on the issue of reviewer bias in bids favoring authors belonging to the top institution and higher authority. Existing Conference Management Systems (CMS) are solely dependent upon self-declared Conflict of Interest (CoI) made by the authors, and reviewers. In literature, existing studies considers topic similarity , potential CoI , and reviewer's workload as trivial factors for ensuring review quality. Other factors include the diversity and authority of a reviewer. Past studies propose several theoretical optimization models. In this paper, we first individually model the factors using the best possible strategy in a constrained-based optimization framework. We tried to propose a completely novel framework that can be practically implemented to improve upon the performance of existing CMS. We map the RAP to an equilibrium multi-job assignment problem. Moreover, we propose a meta-heuristic greedy solution to solve it using weighted matrix factorization. We re-define an assignment quality metric required to validate such assignments. A real conference assignment data set collected from EasyChair is used for a comparative study. The TPMS is used as a baseline because it also uses similar factors, and due to its integration with widely used Microsoft CMS. The results show that the mean assignment quality of the proposed method is superior to other benchmark RAP systems. [ABSTRACT FROM AUTHOR]

Reviewers play a significant role in academic peer review activities, including conference paper assignment and funding selection, because their evaluation of proposals impacts the final decision. Several studies have proposed reviewer selection strategies or reviewer evaluation methods for solving the problem of selecting appropriate reviewers. Identifying reviewers who are familiar with the proposals to be reviewed is the objective of the reviewer assignment problem. However, the majority of the existing studies ignore quantitative constraints with respect to the articles assigned to the reviewers during the review process. In this study, we propose a novel optimization model with several review condition constraints to address the reviewer assignment problem. In the proposed model, the expertise and research areas of the candidate reviewers and proposals are identified using semantic topic models, which are demonstrated to be effective when measuring the relevance of the reviewers with respect to the proposals to be reviewed; further, the computational efficiency is improved owing to the reduced representation dimensionality. Herein, an improved heuristic algorithm is proposed to match reviewers and papers based on specific topic areas, and candidate reviewers are assigned to each proposal under the global optimum condition based on their overall performance values. Subsequently, an empirical test is conducted using a conference reviewer dataset. The obtained results show that the proposed model can help the managers to efficiently and effectively select reviewers in terms of the convergence rate and convergence level when compared with several classic benchmarks. • A novel optimization model is proposed to address the reviewer assignment problem. • The expertise and profiles are identified with the semantic topic models. • An improved heuristic algorithm is proposed to assign reviewer with global optimum. • The effectiveness and robustness is validated in three stages of experiments. [ABSTRACT FROM AUTHOR]

Assigning patients to rooms is a fundamental task in hospitals and, especially, within wards. For this so-called patient-to-room assignment problem (PRA) many heuristics have been proposed with a large variety of different practical constraints. However, a thorough investigation of the problem's structure itself has been neglected so far. In this paper, we present insights about the basic, underlying combinatorial problem of PRA with a focus on minimizing the number of patient transfers which occur if patients have to change rooms during their stay. Particularly, we prove that in the case of double bedrooms, although minimizing the total number of transfers is NP -complete, each patient has to be transferred at most once, and provide upper bounds on the minimum total number of transfers. [ABSTRACT FROM AUTHOR]

In this paper, we present a variable sample-size operator extrapolation algorithm for solving a class of stochastic mixed variational inequalities. One distinctive feature of our algorithm is that it updates a single search sequence by solving a prox-mapping subproblem and computing an evaluation of the expected mapping at each iteration and hence it may significantly reduce computation load. In particular, the iteration sequence generated by our algorithm always belongs to the feasible region. We show that, under some moderate conditions, the proposed algorithm can achieve O (1 / T) ergodic convergence rate in terms of the expected restricted gap function, where T denotes the number of iterations. We derive some results related to the convergence rate of the Bregman distance between iterates and solutions, the iteration complexity, and the oracle complexity for the proposed algorithm when the sample size increases at a geometric rate. We also investigate the sublinear convergence rate in terms of the residual function under the generalized monotonicity condition. Numerical experiments on stochastic network game, stochastic sparse traffic assignment problems and sparse classification problem indicate that the proposed algorithm is promising compared with some existing algorithms. [ABSTRACT FROM AUTHOR]

This paper is concerned with a new variant of bin packing called priority-based bin packing with subset constraints (PBBP-SC). PBBP-SC is a variant of traditional bin packing (TBP). In a TBP instance, we are given a collection of items { t 1 , t 2 , ... t n } , where each item t i has size s i ∈ (0 , 1). The goal of TBP is to pack the items in as few unit-sized bins as possible. In a PBBP-SC instance, we are given a collection of n unit-size items and a collection of m bins of varying capacities. Associated with each item is a positive integer which is called its priority. The priority of an item indicates its importance in being included in a (possibly infeasible) packing. As with the traditional case, these items need to be packed in the fewest number of bins. What complicates the problem is the fact that each item can be assigned to only one of a select set of bins, i.e., the bins are not interchangeable. We investigate several problems associated with PBBP-SC. The first problem we study is the feasibility problem. This is the problem of checking if there is a feasible assignment to a given PBBP-SC instance. The second problem we study is the priority maximization problem. This is the problem of finding a maximum priority assignment of an infeasible PBBP-SC instance. The third problem we study is the bin minimization problem. This is the problem of finding an assignment with the fewest number of bins to pack all of the items in a feasible PBBP-SC instance. We derive a number of results from both the algorithmic and computational complexity perspectives for these problems. In particular, we show that both the feasibility and priority maximization problems are solvable in polynomial time. We also show that the bin minimization problem is log-APX-complete and cannot be solved in time o (1. 4 1 m) unless the Strong Exponential Time Hypothesis fails. [ABSTRACT FROM AUTHOR]

This paper considers combinatorial optimization problems with an objective of type bottleneck, so the objective is to minimize the maximum cost among all elements in a feasible solution. For these problems, the sensitivity of an optimal solution to changes in parameters has received much less attention in existing studies than the computation of an optimal solution. This paper introduces methods for computing upper and lower tolerances which measure the amount of cost change needed in an element inside and outside an optimal solution, respectively, before that solution becomes non-optimal. Our main contribution is the development of efficient computation methods for bottleneck versions of the Linear Assignment Problem and the Minimum Spanning Tree Problem. • We study sensitivity of combinatorial problems with bottleneck objective (CBPs). • We investigate the range of element costs that keep current solutions optimal. • We derive algorithms for two CBPs in particular. • We determine the elements belonging to optimal solutions of CBPs efficiently. [ABSTRACT FROM AUTHOR]

• Evaluates a crowdsourced delivery (CD) system where drivers have their own trip destinations. • Models CD problem with dedicated and CD drivers as set-partitioning problem. • Solves large-scale problem instances with novel decomposition heuristic. • Uses simulated-annealing-inspired approach for package swapping. • Finds that CD reduces operator costs but increases emissions. This paper presents a set-partitioning formulation and a novel decomposition heuristic (D-H) solution algorithm to solve large-scale instances of the urban crowdsourced shared-trip delivery (CSD) problem. The CSD problem involves dedicated vehicles (DVs) and shared personal vehicles (SPVs) fulfilling delivery orders, wherein the SPVs have their own trip origins and destinations. The D-H begins by assigning as many package delivery orders (PDOs) to SPVs as possible, where the D-H enumerates the set of routes each SPV can feasibly traverse and then solves a PDO-SPV-route assignment problem. For PDO-DV assignment and DV routing, the D-H solves a multi-vehicle routing problem with time-window, tour duration, and capacity constraints using an insertion heuristic. Finally, the D-H seeks potential solution improvements by switching PDOs between SPV and DV routes through a simulated annealing (SA)-inspired procedure. The D-H outperforms a commercial solver in terms of computational efficiency while obtaining near-optimal solutions for small problem instances. The SA-inspired switching procedure outperforms a large neighborhood search algorithm regarding run time, and the two are comparable regarding solution quality. Finally, the paper uses the D-H to analyze the impact of several relevant factors on city-scale CSD system performance, namely the number of participating SPVs and the maximum willingness to detour of SPVs. Consistent with the existing literature, we find that CSD can substantially reduce delivery costs. However, we find that CSD can increase vehicle miles traveled. Our findings provide meaningful insights for logistics practitioners, while the algorithms illustrate promise for large real-world systems. [ABSTRACT FROM AUTHOR]

This paper introduces a collaborative allocation model designed for multiple UAVs and diverse targets in maritime combat situations. The model incorporates factors such as distance, angle, interception rate, and recognition rate to comprehensively represent the UAVs' overall damage advantage against targets. Given the complexity of real-world environments and real-time demands, large-scale UAV swarm missions necessitate swift and effective responses. To address this, the paper proposes a Two-Stage Greedy Auction Algorithm, enabling the rapid and efficient completion of cooperative strike tasks within large-scale UAV swarms while preventing deadlock occurrences. In the initial allocation stage, the entropy weight method is utilized to assess task advantages, ensuring a rational allocation criterion for various metrics during the strike process. Subsequently, to enhance the overall effective strike rate within all constraints, a reassignment algorithm is designed based on effective strike benefit indices and the initial assignment result. Simulation results demonstrate the algorithm's quick and stable running time in small-scale and large-scale scenarios. [ABSTRACT FROM AUTHOR]

One of the most promising solutions to deal with huge data traffic demands in large communication networks is given by flexible optical networking, in particular the flexible grid (flexgrid) technology specified in the ITU-T standard G.694.1. In this specification, the frequency spectrum of an optical fiber link is divided into narrow frequency slots. Any sequence of consecutive slots can be used as a simple channel, and such a channel can be switched in the network nodes to create a lightpath. In this kind of networks, the problem of establishing lightpaths for a set of end-to-end demands that compete for spectrum resources is called the routing and spectrum allocation problem (RSA). Due to its relevance, RSA has been intensively studied in the last years. It has been shown to be NP-hard and different solution approaches have been proposed for this problem. In this paper we present several families of valid inequalities, valid equations, and optimality cuts for a natural integer programming formulation of RSA and, based on these results, we develop a branch-and-cut algorithm for this problem. Our computational experiments suggest that such an approach is effective at tackling this problem. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized guidance and target assignment (DGTA) strategy for reconfiguring swarms made up of hundreds of modules that have limited communication capabilities. This strategy provides a decentralized onboard computation method to address both near-optimal target assignment and collision-free trajectory planning for swarms of modules. To effectively estimate the transfer cost between modules and targets for target assignment, a multilayer perceptron (MLP) is developed via the backward generation method. The decentralized target assignment problem is solved by greedy algorithm according to the result of the efficient estimating solver. A decentralized model predictive control approach is proposed to guide swarm modules towards their assigned targets, while avoiding collisions with other modules. The feasibility of this guidance framework has been demonstrated through rigorous theoretical analysis. Numerical experiment confirms that the MLP-based method shows an accurate and real-time advantage for estimating transfer cost. Furthermore, the DGTA strategy has been shown to possess robustness and generalization ability, as evidenced by its successful application in a pinpoint assembly task involving 125 and 182 modules. The simulation results confirm the feasibility of this approach for in-orbit assembly application. [ABSTRACT FROM AUTHOR]

In this paper we consider a general class of rules called simple liability assignment rules under which the assignment of liabilities for losses arising out of interactions involving negative externalities can be coupled for some combinations of the levels of nonnegligence of the interacting parties and decoupled for other combinations, and explore the possibility of efficient assignment of liabilities in the presence of decoupling. The main result of the paper establishes that a simple liability assignment rule is efficient if and only if its structure is such that (i) whenever one party is negligent and the other is not then the negligent party is made to bear the full loss and the nonnegligent party bears none; (ii) whenever both parties are negligent they are made to together bear at least the full loss; and (iii) whenever both parties are nonnegligent they are made to together bear at most the full loss. Thus it follows that the assignment of liabilities under an efficient rule has to be coupled only when one party is negligent and the other is not and hence decoupling liability is not inconsistent with efficiency. [ABSTRACT FROM AUTHOR]

Design and verification of a distributed backward control approach which solves task assignment problem of an under-actuated nonlinear multi-agent system is investigated in this paper. This under-actuated nonlinear multi-agent system includes several quadrotors work together to transport a load aerially. All quadrotors are connected to the load by cables. In this paper, these cables are modelled as several series of masses, springs and dampers to consider their masses and curvatures. There is no real control on the load and cable masses. Therefore, a backward control approach is presented which is based on some virtual controls considered for the load and masses of the cables. By using this control approach, path tracking of the load is performed by controlling formation of the quadrotors during transportation. Solving consensus, formation control and task assignment problems in the case study in less than respectively one, six and five seconds, is the result of using the proposed approach. An Integral BackStepping-Sliding Mode controller is used for formation tracking of a multi-quadrotor system in the presence of external disturbances. Two theorems are presented to guarantee the stability and convergence of the proposed control systems. In addition, simulation example is used to illustrate the effectiveness of the presented control approach. [ABSTRACT FROM AUTHOR]

Revenue management (RM) is essential for a wide range of industries such as airlines, hotels, cruise lines, fashion, and seasonal retail. This paper focuses on the multi-perishable-product dynamic pricing (MPPDP) problem, a significant research field in RM, where a company sells multiple interactive and perishable products over a limited selling window without replenishment. Most studies in this field assume customer behavior, which is modeled by demand function, is known in advance. Even when considering uncertainty in customer behavior, most studies still assume the mathematical form or structural properties of the underlying demand function are known in advance. However, these assumptions are usually inconsistent with the actual market situation. Recently, Reinforcement Learning (RL), a potent technique for handling sequential decision-making problems, has been increasingly applied to solve complex dynamic pricing problems without relying on any assumption about demand functions. However, the curse of dimensionality poses a challenge for currently used centralized RL algorithms when solving the MPPDP problem due to the exponential expansion of the joint price space with the number of products. To address this issue, our paper proposes a distributed dynamic pricing framework and innovatively models the MPPDP problem as a Fully Cooperative Markov Game solved by Multi-Agent Reinforcement Learning (MARL). Additionally, we use counterfactual baselines to design appropriate agent-specific reward signals that facilitate faster learning for the agents in our established multi-agent cooperative system. Finally, two MARL-based distributed dynamic pricing algorithms, Counterfactual Q-learning, and Counterfactual DQN, are proposed for the MPPDP problem. Through the case studies on four computer-simulated markets, we show that our algorithms can alleviate the curse of dimensionality faced by centralized RL algorithms, expedite the learning process, and demonstrate satisfactory performance without relying on any assumption about demand functions. In conclusion, our work provides an effective MARL-based distributed dynamic pricing framework and algorithms for companies to efficiently price their multiple perishable products in modern highly uncertain markets. • Propose a distributed pricing framework for multi-perishable products. • Model multi-product dynamic pricing problem as a Fully Cooperative Markov Game. • Design agent-specific reward functions to tackle the credit assignment problem. • Propose two efficient and effective distributed dynamic pricing algorithms. [ABSTRACT FROM AUTHOR]

• Investigate assigning shipping requests to crowdsourcees with time and capacity constraints • Propose a centralized, deep reinforcement learning-based approach • Present new state space representation encompassing spatial-temporal and capacity information • Embed heuristics-guided action choice in DRL to preserve tractability and enhance efficiency • Integrate rule-interposing into DRL to further enhance training and implementation efficiency This paper investigates the problem of assigning shipping requests to ad hoc couriers in the context of crowdsourced urban delivery. The shipping requests are spatially distributed each with a limited time window between the earliest time for pickup and latest time for delivery. The ad hoc couriers, termed crowdsourcees, also have limited time availability and carrying capacity. We propose a new deep reinforcement learning (DRL)-based approach to tackling this assignment problem. A deep Q network (DQN) algorithm is trained which entails two salient features of experience replay and target network that enhance the efficiency, convergence, and stability of DRL training. More importantly, this paper makes three methodological contributions: 1) presenting a comprehensive and novel characterization of crowdshipping system states that encompasses spatial-temporal and capacity information of crowdsourcees and requests; 2) embedding heuristics that leverage information offered by the state representation and are based on intuitive reasonings to guide specific actions to take, to preserve tractability and enhance efficiency of training; and 3) integrating rule-interposing to prevent repeated visiting of the same routes and node sequences during routing improvement, thereby further enhancing the training efficiency by accelerating learning. The computational complexities of the heuristics and the overall DQN training are investigated. The effectiveness of the proposed approach is demonstrated through extensive numerical analysis. The results show the benefits brought by the heuristics-guided action choice, rule-interposing, and having time-related information in the state space in DRL training, the near-optimality of the solutions obtained, and the superiority of the proposed approach over existing methods in terms of solution quality, computation time, and scalability. [ABSTRACT FROM AUTHOR]

This research looks towards improving the accuracy and efficiency of orbit transfer speed increment estimation and task assignment for space patrol missions involving multiple platforms. First, we set up a quick prediction model for the increment in orbit transfer velocity. Then, a two-layered hybrid optimization framework is developed, with a deep neural network sub-model as the inner layer to find the optimal speed increase and the consensus-based bundle algorithm as the outer layer to solve the multi-task patrol sequence problem. Finally, the simulation and analysis of the patrol task assignment problem are conducted under two types of scenarios. The results show that the hierarchical optimization method proposed in this paper is three orders of magnitude faster than the traditional optimization method. In addition, the suggested hierarchical optimization method has significant promise in practical applications, where it can produce solutions more quickly to adapt to more complex patrol scenarios. • A multi-layered framework is established to handle the optimization of space patrol task assignment. • The deep neural network-based orbit-transfer speed increment calculator is proposed. • The consensus-based bundle algorithm-based task allocator achieves efficient assignments. • Calculating performance of the proposed method is evaluated. [ABSTRACT FROM AUTHOR]

An incidence of a graph G is a vertex-edge pair (v , e) such that the vertex v is incident with the edge e. A proper incidence k-coloring of a graph is a coloring of its incidences involving k colors so that two incidences (u , e) and (w , f) receive distinct colors if and only if u = w , or e = f , or u w ∈ { e , f }. In this paper, we present some idea of using the incidence coloring to model a kind of multi-frequency assignment problem , in which each transceiver can be simultaneously in both sending and receiving modes, and then establish some theoretical and algorithmic aspects of the incidence coloring. Specifically, we conjecture that if G is the Mycielskian of some graph then it has a proper incidence (Δ (G) + 2) -coloring. Actually, our conjecture is motivated by the " (Δ + 2) conjecture" of Brualdi and Quinn Massey in 1993, which states that every graph G has a proper incidence (Δ (G) + 2) -coloring, and was disproved in 1997 by Guiduli, who pointed out that the Paley graphs with large maximum degree are counterexamples (yet they are all known counterexamples to the " (Δ + 2) conjecture", and are not Mycielskians of any graph). To support our conjecture, we prove in this paper that if G is the Mycielskian of a graph H with | H | ≥ 3 Δ (H) + 2 , then we can construct a proper incidence (Δ (G) + 1) -coloring of G in cubic time, and if G is the Mycielskian of an incidence (Δ (H) + 1) -colorable graph H with | H | ≤ 2 Δ (H) , or the Mycielskian of an incidence (Δ (H) + 2) -colorable graph H with | H | ≥ 2 Δ (H) + 1 , then G has a proper incidence (Δ (G) + 2) -coloring. The minimum positive integer k such that the Mycielskian of a cycle or a path has a proper incidence k -coloring is also determined. [ABSTRACT FROM AUTHOR]

The matching problem is known since the beginning of the eighteenth century and Bayesian solutions have been proposed. In this paper, we present a Bayesian analysis of an experiment that also leads to the matching problem. Since in this paper we consider the order in which assignments are made and not only the number of matches, our approach is different from the literature on this problem. Our approach also considers that it is possible to have different abilities for the different objects that will be guessed. Hence, we have a parameter that measures the matching ability for each specific object or assignment. As a consequence, we need to have some replicates of the experiment and a prior distribution for each of the parameters. Conjugate prior distributions for the parameters are discussed. The frequentist solution has a particular Bayesian interpretation under a non-informative prior distribution. A real data set is analyzed using the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper presents one-to-one matching and assignment problems with externalities across pairs. Examples include as pairs figure skating competition and joint ventures in oligopolistic markets. In these models, players care not only about their partners but also about what rival pairs are formed. Therefore, it is important for a deviating pair to know which matching will be realized after it deviates from a matching (via an effectiveness function) in order to define pairwise stable matching. We find that the assortative matching may fail to be pairwise stable under an effectiveness function commonly used in matching and coalition formation literature. In contrast, it is pairwise stable under a swapping effectiveness function which is natural in our environments. We discuss two generalizations of our model, intrinsic preferences over partners and pair-specific match qualities. [ABSTRACT FROM AUTHOR]

This paper presents the crucial method for the results found at Beijing Institute of Technology and China Academy of Space Technology in the 11th Global Trajectory Optimization Competition (GTOC-11). To build 12 solar-power stations placed in the "Dyson Ring" orbit around the Sun, GTOC-11 focuses on the flyby trajectory design, low-thrust trajectory optimization, and target assignment problem. The mission with time and state constraints causes many difficulties in the design and optimization, which is performed in the following four steps. The first step is to design the orbital elements of the "Dyson Ring" by the grid search method. An approximate model is utilized to evaluate the minimum transfer time for each candidate at first, and the accurate solutions are obtained by the indirect method. The second step involves the Mother Ship trajectories design. A large quantity of earth-to-asteroid and asteroid-to-asteroid subsequences are generated by parallel beam search. The complete trajectories of Mother Ship are designed by connecting the subsequences using beam search. The third step involves the station assignment problem, which is required to design the asteroid transfer device activation epoch and the target station assignment. Based on the results above, the last step involves the rendezvous trajectories optimization to obtain the accurate thrust direction profile. Finally, the submitted and post-competition results are reported. • The simplified and accurate model are proposed to obtain the optimal transfer time. • The parallel beam search is developed to reduce the computational burden. • The station assignment problem is solved by coordinate descent method. [ABSTRACT FROM AUTHOR]

Let G be a simple graph. Suppose f is a mapping from V (G) to nonnegative integers. If, for any two adjacent vertices u and v of G , | f (u) − f (v) | ≥ 2 , then f is called a 2-distant coloring of G. In this paper, we introduce a relaxation of 2-distant coloring of a graph. Let t be a nonnegative integer. Suppose f is a mapping from V (G) to nonnegative integers. If adjacent vertices receive different integers and for each vertex u of G , the number of neighbors v of u with | f (v) − f (u) | = 1 is at most t , then f is called a t -relaxed 2-distant coloring of G. If t = 0 then f is just a 2-distant coloring of G. The span of f , denote by s p (f) , is the difference between the maximum and minimum integers used by f. The minimum span of a t -relaxed 2-distant coloring of G , is called t -relaxed 2-distant coloring span of G , denoted by s p 2 t (G). Suppose G is a graph. Let γ : V (G) → Z + be a function defined on V (G). A γ -relaxed 2-distant coloring of G with span k is a mapping f from V (G) to { 0 , 1 , ... , k } such that f (u) ≠ f (v) for any two adjacent vertices u and v and each vertex u of G has at most γ (u) neighbors v with | f (v) − f (u) | = 1. In this paper, we prove that, for any two positive integers t and k with k ≥ 2 , deciding if s p 2 t (G) ≤ k for a graph G is NP-complete, except the case when t = 1 and k = 2 which is polynomial-time solvable. Let t be any nonnegative integer. It is easy to see that s p 2 t (G) ≤ 6 for any planar graph G and s p 2 t (G) ≤ 4 for any outerplanar graph G. We prove that, for any two positive integers t and k with k ∈ { 2 , 3 , 4 , 5 } , deciding if s p 2 t (G) ≤ k for a planar graph G is NP-complete, except the case when t = 1 and k = 2 which is polynomial-time solvable. We present examples to illustrate that there is no constant integer t such that every planar graph has a t -relaxed 2-distant coloring of span 5 and there is no constant integer t such that every outerplanar graph has a t -relaxed 2-distant coloring of span 3. We introduce pseudo ear decomposition and simple pseudo ear decomposition of a graph and show that a graph is outerplanar if and only if it admits a simple pseudo ear decomposition. Using this result, we show that each outerplanar graph has a γ -relaxed 2-distant coloring with span 3, where γ (v) = ⌈ d (v) 2 ⌉ for each vertex v of G and the function γ is sharp in some sense, and that every triangle-free outerplanar graph has a 1-relaxed 2-distant coloring with span 3. [ABSTRACT FROM AUTHOR]

As an emerging manufacture technology, block copolymer directed self-assembly (DSA) is promising for via layer fabrication. Meanwhile, redundant via insertion is considered as an essential step for yield improvement. For better reliability and manufacturability, in this paper, we first concurrently consider DSA guiding template cost assignment with multiple redundant via and dummy via insertion. Firstly, by analyzing the structure property of guiding templates, we propose a building-block based solution expression to discard redundant solutions. Then, honoring the compact solution expression, we construct a conflict graph with dummy via insertion, and then formulate the problem to an integer linear programming (ILP). In addition, to optimize the guiding template cost, we incorporate it into the objective of ILP by introducing vertex weight and edge weight in conflict graph. To make a good trade-off between solution quality and runtime, we relax the ILP to an unconstrained nonlinear programming (UNP). Finally, a line search optimization algorithm is proposed to solve the UNP. Experimental results verify the effectiveness of our new solution expression and the efficiency of our proposed algorithm. Specifically, our guiding template cost optimization method can save 18% total guiding template cost. • In this journal version, we first use multiple redundant via insertion technique to further improve the manufacture rate and insertion rate for the DSA guiding template and redundant via insertion problem. In Section 1, we present the role of multiple redundant via insertion by illustrating an example as Figure 4. • In this journal version, we first consider the cost of DSA guiding template. Complex templates with more holes would introduce larger overlays than regular templates with less holes. Considering guiding template cost can reduce overlays. • For the DSA guiding template assignment with multiple redundant via and dummy via insertion (DMRD) problem, we prove its NP complexity in Section 2.3. • In Section 3.2, we add the introduction of our graph reduction technique. • In Section 3.3, we add the introduction and relationship of guiding template cost, building-block cost and template edge weight. • In Section 4.3, we prove the local convergence and analyze the efficiency of our unconstrained nonlinear programming (UNP) solver. • In Section 4.4, we extend our ILP formulation in conference paper to consider guiding template cost, which is a quadratic integer programming (QIP). Furthermore, the QIP also can be solved by our UNP solver. • In Section 5, we add experimental result comparison of our ILP solver with previous works. And we add the comparison of our UNP solver and the ILP solver in TCAD'17 [18] on nine much larger benchmarks in Table 2. In addition, 1 we compare the guiding template cost between our UNP solver with guiding template cost and without guiding template cost in Section 5.3. [ABSTRACT FROM AUTHOR]

• A novel supervisory information is adopted to solve reviewer assignment problem. • The supervisory information helps machine to understand field relationships. • Reviewer assignment problem is treated as a sentence pair modeling issue. Assigning appropriate reviewers to a manuscript from a pool of candidate reviewers is an important task in the academic community. Recent researches have focused mainly on text processing methods based on natural language processing, such as topic model, word embedding and so on. However, it is difficult for the computer to understand the research fields of reviewers and manuscripts. In this paper, a novel supervisory information that is expressed as sentence pairs constructed by titles and abstracts is adopted to solve reviewer assignment problem. We propose a sentence pair modeling-based reviewer assignment (SPM-RA) method, which models the relationship of sentence pairs by supervising information. The supervisory information makes the model accurately learn the field features of reviewers and manuscripts. Firstly, we construct the training set by the field relationship between title and abstract. We use TF-IDF sampling to solve the problem of unbalanced data set. Then, we use neural network models, such as the BERT (bidirectional encoder representations from transformers), CNN (convolution neural network), biLSTM (bidirectional long short term memory), or various combinations of them to do modeling sentence pair and learn the field features of the paper through the training set. Finally, we predict the similarity between reviewers and manuscripts by training model. We evaluate SPM-RA on two real datasets and compare its performance to that of seven existing methods. The experimental results show that SPM-RA improves the recommendation precision by at least 18% on the public dataset. [ABSTRACT FROM AUTHOR]

Reallocating resources to get mutually beneficial outcomes is a fundamental problem in various multi-agent settings. While finding an arbitrary Pareto optimal allocation is generally easy, checking whether a particular allocation is Pareto optimal can be much more difficult. This problem is equivalent to checking that the allocated objects cannot be reallocated in such a way that at least one agent prefers her new allocation to her old one, and no agent prefers her old allocation to her new one. We consider the problem for two related types of preference relations over sets of objects. In the first part of the paper we focus on the setting in which agents express additive cardinal utilities over objects. We present computational hardness results as well as polynomial-time algorithms for testing Pareto optimality under different restrictions such as two utility values or lexicographic utilities. In the second part of the paper we assume that agents express only their (ordinal) preferences over individual objects, and that their underlying preferences are additively separable. In this setting, we present characterizations and polynomial-time algorithms for possible and necessary Pareto optimality. [ABSTRACT FROM AUTHOR]

• Based on the model decomposition approach, two new parallel algorithmic frameworks are proposed. • The parallel algorithm B-ADMM is developed by employing the concept of bush. • Integrating the parallel block coordinate descent (PBCD) method and the B-ADMM algorithm, then a bi-level parallel algorithm PBCD-ADMM is developed. • To validate the two algorithms proposed with much large-scale networks. This paper introduces two novel parallel algorithmic frameworks to address the user equilibrium traffic assignment problem (UE-TAP). Most of the existing solution algorithms for the UE-TAP are executed in a sequential manner. This study endeavors to explore parallel computing methods based on model decomposition. Considering that the TAPs can be decomposed based on their origins, thus, following the spirit of the alternating direction method of multipliers (ADMM), a new parallel algorithm B-ADMM is proposed, which integrates the concept of the bush. Subsequently, the convergence of the proposed algorithm is rigorously proven. To enhance the algorithmic parallelism while maintaining the convergence efficiency of the B-ADMM algorithm, this paper further employs the parallel block coordinate descent (PBCD) method to improve the B-ADMM algorithm. We develop a bi-level parallel algorithm PBCD-ADMM, in which the independent origins/bushes are separated into several blocks, and the origin/bush-based restricted subproblems in each block can be solved in parallel. Furthermore, for the given bush belonging to a block, the bush links can also be grouped into several sub-blocks based on the original link-blocking scheme. Thus, these link-based subproblems in each sub-block can also be solved in parallel. A numerical experiment is conducted to validate the proposed algorithms, which indicates that the two new parallel algorithms perform better in terms of convergence speed compared with the original ADMM algorithm. [ABSTRACT FROM AUTHOR]

• Scenario where vehicles adhere to or deviate from the system optimal route guidance. • Reinforcement learning approach to dynamic system optimal assignment problem. • Multi-agent reinforcement learning to assess the impact of non-compliance. • Exploration of the balance between system efficiency and individual user preferences. We consider a scenario where the transportation management center (TMC) guides future autonomous vehicles (AVs) toward optimal routes, aiming to bring the network in line with the system optimal (SO) principle. However, achieving this requires a joint decision-making process, while users may be non-compliant with the TMC's route guidance for personal gain. This paper models a future transportation network with a microscopic simulation, to introduce a novel concept of mixed equilibrium. In this framework, AVs follow the TMC's SO route guidance, while users can dynamically choose to either comply or manually override this autonomy based on their own judgment. We initially model a fully compliant scenario, where the centralized Q-network, analogous to a TMC, is trained using reinforcement learning (RL) to minimize total system travel time (TSTT), providing optimal routes to users. Subsequently, we extend the problem setting to a multi-agent reinforcement learning (MARL) scenario, where users can comply or deviate from the TMC's guidance based on their own decision-making. Through neural fictitious self-play (NFSP), we employ a modulating hyperparameter to investigate the impact of varying degrees of non-compliance on the overall system. Results indicate that our RL approach holds significant potential for addressing the dynamic system optimal assignment problem. Remarkably, the TMC's route guidance retains the essence of SO while integrating some level of non-compliance. However, we also demonstrate that dominant user-centric decision-making may lead to system inefficiencies while creating disparities among users. Our framework serves as an innovative tool in an AV-dominant future, offering a realistic perspective on network performance that aids in formulating effective traffic management strategies. [ABSTRACT FROM AUTHOR]

The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed. However, deploying these approaches for large-scale networks poses significant challenges. In this paper, we leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate model for traffic assignment, specifically user equilibrium traffic assignment problems. Our model integrates an adaptive graph attention mechanism with auxiliary "virtual" links connecting origin–destination node pairs, This integration enables the model to capture spatial traffic patterns across different links, By incorporating the node-based flow conservation law into the overall loss function, the model ensures the prediction results in compliance with flow conservation principles, resulting in highly accurate predictions for both link flow and flow-capacity ratios. We present numerical experiments on urban transportation networks and show that the proposed heterogeneous graph neural network model outperforms other conventional neural network models in terms of convergence rate and prediction accuracy. Notably, by introducing two different training strategies, the proposed heterogeneous graph neural network model can also be generalized to different network topologies. This approach offers a promising solution for complex traffic flow analysis and prediction, enhancing our understanding and management of a wide range of transportation systems. • A novel heterogeneous GNN-based approach is proposed for static traffic assignment. • GNN model includes "real" roadway links and "virtual" links connecting OD pairs. • Flow conservation loss is added as a physics-based term in the loss function. • Model is generalizable to different network topologies and OD demands. [ABSTRACT FROM AUTHOR]

The proportionality condition is a standard approach to dealing with the non-uniqueness issue in the user equilibrium (UE) traffic assignment problems (TAP). Although the proportionality condition can reduce the degree of arbitrariness, it remains unclear how much arbitrariness remains and whether it can meaningfully affect model outcomes and relevant decisions that depend on them. The answers to these questions are impeded by the lack of an efficient algorithm that can find the exact maximum entropy UE path flow solution for networks of practical size. In this paper, we fill this gap by developing a high-performance augmented Lagrangian algorithm that effectively exploits the special problem structure. Our numerical results reveal that there are a considerable number of links with non-negligible arbitrariness in the solution generated by the proportionality condition, and that this problem becomes worse if the level of congestion increases in the network. Since about a decade ago, many practitioners have relied on state-of-the-art traffic assignment tools based on the proportionality condition to perform select link analysis, among other applications. The results reported herein are a reminder that their toolbox may need reevaluation and perhaps an upgrade. • The problem of finding path flow solutions for maximum entropy user equilibrium (MEUE) is studied. • Introduced a novel algorithm for exact MEUE path flow in practical size networks. • Investigated arbitrariness in order-2 proportionality (O2P) versus MEUE solutions. • Results show a substantial number of links with significant arbitrariness in O2P solutions. [ABSTRACT FROM AUTHOR]

Contrastive learning-based deep multi-view clustering methods have become a mainstream solution for unlabeled multi-view data. These methods usually utilize a basic structure that combines autoencoder, contrastive learning, or/and MLP projectors to generate more representative latent representations for the final clustering stage. However, existing deep contrastive multi-view clustering ignores two key points: (i) the latent representations projecting from one or more layers of MLP or new representations directly obtained from autoencoder fail to mine inherent relationship inner-view or cross-views; (ii) more existing frameworks only employ a one or dual-contrastive learning module, i.e., view- or/and category-oriented, which may result in the lack of communication between latent representations and clustering assignments. This paper proposes a new composite attention framework for contrastive multi-view clustering to address the above two challenges. Our method learns latent representations utilizing composite attention structure, i.e., Hierarchical Transformer for each view and Shared Attention for all views, rather than simple MLP. As a result, the learned representations can simultaneously preserve important features inside the view and balance the contributions across views. In addition, we add a new communication loss in our new dual contrastive framework. The common semantics will be brought into clustering assignments by pushing clustering assignments closer to the fused latent representations. Therefore, our method will provide a higher quality of clustering assignments for the segmentation problem of unlabeled multi-view data. The extensive experiments on several real data demonstrate that the proposed method can achieve superior performance over many state-of-the-art clustering algorithms, especially the significant improvement of an average of 10% on datasets Caltech and its subsets according to accuracy. [ABSTRACT FROM AUTHOR]

This paper investigates the uplink resource allocation issue for radio access network (RAN) slicing with a focus on three primary fifth generation (5G) use cases, namely ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC). Most of the existing resource allocation schemes for RAN slicing were designed to address the use cases in downlink communications. However, these schemes are not applicable to the uplink RAN slicing, even though the primary use cases also entail stringent requirements in the uplink. To this end, we aim to develop a new, fairness-aware uplink resource allocation scheme for RAN slicing, catering to the three 5G use cases. Firstly, we formulate a new uplink resource allocation optimization problem that maximizes a sum logarithmic utility for ensuring proportional-fair effective capacity distribution and resource allocation among RAN slices, subject to the distinct quality of service (QoS) requirements of the uRLLC, eMBB and mMTC slices. Then, an efficient, hierarchical resource allocation framework is then proposed to solve the problem deterministically, whereby resource block assignment is first performed at the base station via a demand-oriented greedy algorithm, followed by power allocation using a bisection method at individual user equipment. Results show that the proposed scheme outperforms the baseline schemes with fairness and QoS provisioning performance gains of up to 44.3% and 19.17% respectively. [ABSTRACT FROM AUTHOR]

Existing task allocation algorithms often fail to consider communication load and do not meet the scalability requirements of large-scale systems in practical applications. Furthermore, many general network simplification algorithms are centralized and not designed for task allocation scenarios, thus losing their superiority when applied to solve task allocation problems in distributed systems. Inspired by recent network simplification algorithms, this paper introduces a novel network simplification algorithm called bid-based distributed broken-motifs (BDBM), which simplifies the communication network by reducing the number of closed-loop triangles. The BDBM algorithm keeps those communication edges that facilitate the deconfliction based on the initial bids, thus avoiding the surge in the number of convergence iterations caused by the simplification. In addition, CBBA is distributed and requires only two iterations to complete the communication network simplification. Theoretical analysis confirms that the simplified network of BDBM remains connected and can be used in combination with improved task allocation algorithms based on consensus-based bundle algorithm (CBBA). In terms of experiments, we conducted comprehensive experiments in different settings and found that the proposed algorithm does not lead to worse allocation solutions. The statistical results also show that BDBM outperforms state-of-the-art network simplification algorithms in terms of solving efficiency and scalability in assisting CBBA solve large-scale complex task allocation problems. • The BDBM algorithm reduces the communication load by suspending some edges. • The BDBM algorithm preserves some edges that facilitate conflict resolution. • BDBM can be used in combination with any CBBA-based task allocation algorithm. • BDBM can improve the solving efficiency of CBBA without reducing the solution quality. • BDBM performs better in solving large-scale complex task allocation problems. [ABSTRACT FROM AUTHOR]

Achieving accurate and real-time perception of environmental targets in complex traffic scenes based on visual sensors is a challenging research problem in the field of autonomous driving technology. In methods to date, it is difficult to effectively balance the detection accuracy and speed. To this end, this paper proposes an interactive and lightweight visual detection algorithm – YRDM (Your Region Decision-Making) – based on the concepts of efficient mining and utilisation of target feature information, lightweight network structure, and optimisation of label allocation for highly practical detection of ambient targets in autonomous driving scenarios. First, a two-stage algorithm architecture consisting of four low-parameter subnetworks is constructed with the goal of efficiently mining and utilising target feature information, and the accuracy and effectiveness of the algorithm are balanced through the interaction of information between the subnetworks. Second, in order to further improve the detection speed, lightweight convolution is introduced into the structure of the YRDM network to construct the DSC3 module, which allows lightweight processing of the subnetwork structure. Finally, by converting the label assignment problem into an optimal transport problem, adaptation to the global nature of the samples by YRDM is improved, allowing better detection accuracy. The algorithm is tested with two major public datasets, BDD100K and KITTI, and a large number of experimental results show that the comprehensive performance of YRDM is better than other existing algorithms. In addition, ablation experiments and mobile terminal device deployment experiments further demonstrate the effectiveness and real-time performance of this algorithm. • Proposed a two-stage detection algorithm called your region decision-making (YRDM). • YRDM addressed challenges of complex traffic and limited computing resources. • YRDM achieved 57.8 % accuracy (BDD100K); 89.6 % (KITTI) with 2.98 GFLOPs. • Info interaction in low-parameter subnets boosts accuracy. • Lightweight convolution and DSC3 enhanced speed and efficiency. [ABSTRACT FROM AUTHOR]

This paper discusses the use of unmanned surface vehicles (USVs) in the ocean for traversing multiple targets and proposes a novel multiple targets traversing algorithm that takes into account the multi-level priority of target assignment and path optimization. The algorithm includes a bundled genetic optimization target assignment method and a fast marching-Q-Learning path planning method. Comparative studies with existing methods show that the proposed algorithm have efficient performance, where the calculation time for target assignment has been reduced by 45.64%, and the path length has been further optimized by 15.3%. Considering the velocities of USVs navigation, path turning motion and multi-level priorities constraints are satisfied, and collision risk is avoided in dynamic ocean environment. Hence, the proposed multiple targets traversing algorithm plays an important role in the ocean engineering field of USVs. • Bundled-genetic target assignment method: The core of this method is to use bundling mechanism to solve target assignment efficiency problem, including improving the speed of target assignment, satisfying USV velocity and multi-level priority constraints. • FM-Q-Learning path planning method: This method utilizes FM's reward function to solve path optimization problem, including improving path convergence speed, decreasing path length, and smoothing the paths through navigation restriction. • Simulation of remote sensing satellite background: This strategy can reflect the experimental results in a real environment, which helps to verify the applicability of theoretical algorithms in practical situations. [ABSTRACT FROM AUTHOR]

The design of dynamic tolling algorithms for managed lanes is complex and challenging, in part due to the often conflicting and differing objectives of the operator, travelers and the regulator. In this paper, we propose a framework for personalized pricing of managed lanes that combines elements of prediction, optimization, and personalization to dynamically determine displayed tolls and personalized discounts to travelers. The proposed approach involves an online bi-level optimization formulation consisting of coupled system-level and user-level optimization problems. The system optimization problem uses real-time traffic predictions (from a simulation-based dynamic traffic assignment model) to periodically determine displayed tolls and a discount control policy that maximize a multi-component system-level objective (considering different stakeholders with flexible policy hyperparameters). The user optimization problem consistently determines a personalized discount to offer to each individual traveler based on individual-specific preferences subject to the displayed toll and discount policy in effect. At the heart of both the system and user optimization problems is an individual-specific behavioral model (of choice between the managed lane and general purpose lanes) that incorporates state dependence (habit and loyalty formation), unobserved inter- and intra-consumer heterogeneity, and corrections for price endogeneity. Rigorous closed-loop simulations of an operational managed lane facility using real data demonstrate that the proposed personalized dynamic tolling approach improves operator revenue and managed lane usage, traveler benefits and equity, and reduces congestion relative to the existing tolling policy. • Propose a framework for personalized tolling of managed lanes. • Displayed tolls and personalized discounts are determined in real-time. • A multi-component objective function incorporates the interests of the operator, the travelers and the regulator. • Behavioral model incorporates state dependence, unobserved inter- and intra-consumer heterogeneity, and corrects for price endogeneity. • Improves operator revenue and managed lane usage, traveler benefits and equity, and reduces congestion relative to status quo. [ABSTRACT FROM AUTHOR]

Traditionally, freight airlines assign individual aircraft to each flight and sequentially determine the cargo itineraries for each order. This may limit the option of cargo itineraries, and lead to mismatches between cargo demands and freighter capacities. To overcome this limitation, we introduce the concepts of through cargo connection and short through cargo connection. A through cargo connection requires cargoes to stay on the same aircraft between two connected flights, and a short through cargo connection is a through connection that needs less time than the standard cargo transshipment time. By considering the through cargo connections and short through cargo connections, we can generate the complete set of all possible cargo itineraries, so as to improve the overall schedule efficiency and robustness, and reduce the cargo ground handling cost. In this paper, two mathematical models (the connection- and string-based models) are proposed to solve the aircraft tail assignment problem (TAP) and cargo routing problem (CRP) simultaneously with through cargo considerations. We developed a column generation framework to solve the LP relaxation of the string-based model, and a diving heuristic with reoptimization strategy to find a good IP solution in a reasonable time. The proposed solution methods are tested using industrial cases from the largest freight airline in China. The computational experiments revealed that the integrated models could bring up to 5.04% ∼ 10.31% profit growth, and the through cargo savings are tripled compared with the baseline solutions. By analyzing the computational performance of two models, we recommend that airlines can apply connection-based model only if the problems are not very large and the computational time is sufficient; otherwise, the string-based model is more robust in terms of problem scales and computational time. • The aircraft tail assignment and cargo routing problems were solved simultaneously. • The concepts of through cargo and short through cargo connections were proposed. • The connection- and string-based models were proposed to solve the problem. • A column generation-based heuristic was developed to solve the problem. [ABSTRACT FROM AUTHOR]

Let G be a simple connected graph. For any two vertices u and v , let d (u , v) denote the distance between u and v in G. A radio- k -labeling of G for a fixed positive integer k is a function f which assigns to each vertex a non-negative integer label such that for every two vertices u and v in G , | f (u) − f (v) | ⩾ k − d (u , v) + 1. The span of f is the difference between the largest and smallest labels of f (V). The radio- k -number of a graph G , denoted by r n k (G) , is the smallest span among all radio- k -labelings admitted by G. A cycle C n has diameter d = ⌊ n / 2 ⌋. In this paper, we combine a lower bound approach with cyclic group structure to determine the value of rnk (C n) for k ⩾ n − 3. For d ⩽ k < n − 3 , we obtain the values of rnk (C n) when n and k have the same parity, and prove partial results when n and k have different parities. Our results extend the known values of r n d (C n) and r n d + 1 (C n) shown by Liu and Zhu (2005), and by Karst et al. (2017), respectively. [ABSTRACT FROM AUTHOR]

• Considering operator learning/forgetting effects in a cellular manufacturing system. • Employing a robust optimization approach to handle the considered uncertainty. • Linearizing the proposed mixed integer nonlinear programming model. • Conducting a statistical and sensitivity analysis and providing managerial insights One of the most critical issues in manufacturing systems is the operator management. In this paper, the operator assignment problem is studied within a cellular manufacturing system. The most important novelty of this research is the consideration of operator learning and forgetting effects simultaneously. The skill level of an operator can be increased/decreased based on the time spent on a machine. Moreover, the issues related to operators like hiring, firing, and salaries are considered in the proposed model. The parameters are considered to be uncertain in this model, and a robust optimization approach is developed to handle it. Using this approach, the model solution remains feasible (or even optimal) for different levels of parameter uncertainty. To verify and validate the proposed model, some numerical instances are randomly generated and solved using GAMS. A statistical analysis is also conducted on the results of the objective function values of linear and nonlinear models, followed by some managerial insights. [ABSTRACT FROM AUTHOR]

• The paper includes a primer on differential variational inequalities (DVI). • The theory of DVI is applied to the study of dynamic user equilibrium in continuous time. • The DUE problem is articulated as a DVI and fixed-point problem. • These formulations are explored using optimal control theory. • A continuous-time computational scheme is developed then applied to two example problems. This paper is pedagogic in nature, meant to provide researchers a single reference for learning how to apply the emerging literature on differential variational inequalities to the study of dynamic traffic assignment problems that are Cournot-like noncooperative games. The paper is presented in a style that makes it accessible to the widest possible audience. In particular, we apply the theory of differential variational inequalities (DVIs) to the dynamic user equilibrium (DUE) problem. We first show that there is a variational inequality whose necessary conditions describe a DUE. We restate the flow conservation constraint associated with each origin-destination pair as a first-order two-point boundary value problem, thereby leading to a DVI representation of DUE; then we employ Pontryagin-type necessary conditions to show that any DVI solution is a DUE. We also show that the DVI formulation leads directly to a fixed-point algorithm. We explain the fixed-point algorithm by showing the calculations intrinsic to each of its steps when applied to simple examples. [ABSTRACT FROM AUTHOR]

This paper introduces a new distributed approach to solve multi-objective planning problem applied to multimodal transport network planning (MTNP) problem. In this problem, the commodities should be transported on the international network by at least two different transport modes. The main goal is to find the best multimodal transportation modes and itineraries. The aim of the new approach has assured us that a distributed optimization. We split the MTNP problem into two sub-problems. These sub-problems are the assignment and the planning problems. Each sub-problem is solved at a corresponding layer. Each layer is executed by an agent. These agents interact, collaborate and communicate together to solve the MTNP problem. In this paper, we contribute by introducing a multi-layer distributed approach to solve real case's problems. Firstly, we define the MTNP problem as a distributed constraint satisfaction multi-criteria optimization problem (DCSMOP). Secondly, we show that the split of the main problem reduces the computational complexity and the communication between the planner agent and the modes agents lead to faster convergence. The experimental results are proof of this work efficiently. This method proves their efficiency, according to the complexity of the problem and the exchange of information, the computational time and the solution quality. [ABSTRACT FROM AUTHOR]

Highlights • In this paper a Redundancy Allocation problem (RAP) for a queueing system is modeled. • Optimal number of repairmen and redundant servers are found by which queueing and maintenance costs are minimized. • Four random search algorithms are developed to deal with different structures of solution space. • Two algorithms for unimodal objective functions and two algorithms for multimodal objective functions are developed. Abstract The Redundancy Allocation Problem (RAP) is becoming all the time more important in system reliability design. Therefore, RAP has been studied vastly for different systems and under various assumptions. One of the important systems that RAP can be applied to them is queuing systems. In many applications, reliability of queuing systems need to be improved with regard to some queuing and reliability cost constraints. Despite the importance of queuing systems in real world situations, RAP of these systems have not gained attention in the literature. Therefore, RAP of a queuing system with maintenance considerations and including queueing costs in the modeling of RAP is considered in this paper. To find optimal solutions of the problem, four random search algorithms are developed and compared based on the problem structures. According to the experiments, for unimodal solution spaces algorithms 1 and 2 and for multimodal solution spaces algorithms 3 and 4 are recommended. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses a scheduling problem involving a continuously-divisible cumulative resource with limited capacity. During its processing, each task requests a part of this resource, which lies between a minimum and a maximum requirement. A task is finished when a certain amount of energy is received by it within its time window. This energy is received via the resource and an amount of resource is converted into an amount of energy with an increasing and pseudo-linear efficiency function. The goal is to minimize the resource consumption. The paper focuses on an event-based mixed integer linear program, providing several valid inequalities, which are used to improve the performance of the model. Furthermore, we give a minimal description of the polytope of all feasible assignments to the on/off binary variable for a single activity along with a dedicated separation algorithm. Computational experiments are reported in order to show the effectiveness of the results. [ABSTRACT FROM AUTHOR]

Highlights • We introduce a spatial queueing link model to consider the effects of spatial queueing and blocking back. • We develop a novel equilibrium model with capacity constraints, queueing delays and blocking back. • The modelling framework is further integrated in a traffic assignment and control problem. • We prove existence of equilibrium results under a variety of scenarios and compare different control policies. • With blocking back, existence of an equilibrium depends on the adopted merge models. Abstract This paper introduces a steady-state, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queueing delays and explicit bounds on queue storage capacities. The model is a quasi-dynamic model. The link model at the heart of this quasi-dynamic equilibrium model is a spatial queueing model, which takes account of the space taken up by queues both when there is no blocking back and also when there is blocking back. The paper shows that if this quasi-dynamic model is utilised then for any feasible demand there is an equilibrium solution, provided (i) queue storage capacities are large or (ii) prices are used to help impose capacity restrictions; the prices either remove queueing delays entirely or just reduce spatial queues sufficiently to ensure that blocking back does not occur at equilibrium. Similar results, but now involving the P 0 control policy (introduced in Smith (1979a, 1987)) and two new variations of this policy (i.e., the spatial P 0 control policy, and the biased spatial P 0 control policy) are obtained. In these results, the control policies allow green-times to vary in response to prices as well as spatial queueing delays. These three policies are also tested on a small simple network. In these tests, the biased spatial version of P 0 is much the best in reducing equilibrium delays (on this simple network). The paper further illustrates how the spatial queueing model works on simple networks with different merge models; it is demonstrated that equilibrium may be prevented by certain (fixed ratio) merge models. It is also shown in this case that equilibrium may be imposed on just the controlled area itself by a variety of (merge model, gating strategy) combinations. Opportunities for developing such combined gating and merging control strategies are finally discussed. [ABSTRACT FROM AUTHOR]

Highlights • In this paper, the redundancy allocation problem (RAP) is revisited. • A new redundancy strategy called K-mixed is implemented on the system. • A new mathematical model is developed and solved by GA. • Results demonstrate the K-Mixed strategy outperforms all the previous strategies in RAP. Abstract This paper revisits the redundancy allocation problem, a well-known problem in reliability optimization, and implements a novel redundancy strategy, called K-mixed, to improve system reliability. The K-mixed strategy is a general form of a mixed strategy. The mixed strategy is a combination of active and standby redundancy strategies which was introduced in 2014. Initially, the mathematical formulation for calculating the reliability of the K-mixed strategy is investigated and then its power and efficiency are evaluated against different test problems and a famous benchmark problem. In order to solve the proposed benchmark problem, an efficient genetic algorithm is developed and the results are compared with those reported elsewhere. It is found that the proposed K-mixed strategy results in higher reliability in most situations than would otherwise be achieved by its mixed counterpart. It is concluded that by using the K-mixed strategy in an optimization model, system designers have the opportunity to select the best strategy for each subsystem from among all the different strategies available to design systems with maximum reliability. [ABSTRACT FROM AUTHOR]

This paper focuses on the order acceptance and scheduling problem with worker–operation assignment considering the precedence constraint in the divisional seru production system, which is a new production type derived from Japan and can achieve responsiveness, flexibility, and efficiency simultaneously. With limited production capacity and workers with different skill sets and skill levels, the problem considered in this paper includes three sub-problems: (i) assigning workers to each seru , (ii) making the order acceptance and scheduling plan, and (iii) allocating workers to operations for each order. Subsequently, a nonlinear integer programming model is established for improving the total net revenue, and a bi-level nested heuristic algorithm is designed due to its intractable computation. Computational experiments are made finally, and results show that the objective value obtained by the bi-level nested heuristic algorithm is 3% better than the bi-level genetic algorithm, and the running time is shortened by 99.23%. That is, the proposed bi-level nested heuristic algorithm can achieve better results and higher efficiency for divisional seru order acceptance and scheduling problems. • A new production system, seru , is introduced and the divisional seru order acceptance and scheduling problem is studied. • The integer programming model for the studied problem is presented, and a bi-level nested heuristic algorithm is designed. • The experimental results indicate that the proposed solution methodology is effective and robust. [ABSTRACT FROM AUTHOR]

Binary numbers converted into stochastic bitstreams enable computationally complex arithmetic to be performed using stochastic computing (SC). The fault-tolerant nature of stochastic computing has made it useful in a large number of applications. However, stochastic circuit synthesis allows for a larger solution space than classical logic synthesis. SC circuits are synthesized using heuristics in previous methods. In this paper, a novel exact synthesis method via satisfiability (SAT) is proposed to obtain an optimal SC circuit represented by majority-inverter graphs (MIGs). To accelerate the algorithm for executing the exact synthesis, we try to decompose the problem vector by using a cube assignment method for scalability. Moreover, we propose a hybrid algorithm based on both exact synthesis and heuristics. The experimental results indicate that the proposed hybrid approach can reduces the area by 14%, the delay by 9%, and the mean absolute error by 7%. • An exact synthesis method is proposed for stochastic circuit synthesis. • The proposed method can be used to reduce the solution space. • The combined exact and heuristic method is scalable. [ABSTRACT FROM AUTHOR]

Taken the optimal assignment, trajectory optimization and collision avoidance into consideration, this research focuses on the fast-planning method for swarm safe-migration along Halo orbit. The swarm safe-migration planning problem is an inherently high-dimensional nonlinear problem. Repeatedly solving the bottom layer's collision-free trajectory optimization problem causes heavy computation burden in the traditional two-layer optimization framework. To reduce the computation cost and keep the optimization performance, an adaptive surrogate model-based swarm safe-migration planning method is developed in this paper. The surrogates fitted to the collision-avoidance constraints are investigated and the input variables of the surrogates are analyzed and selected for improved efficiency. In particular, a classifier-based surrogate management strategy is designed for the swarm safe-migration problems to improve the optimization capability of the proposed algorithm. Finally, the proposed method is applied to two real-world swarm migration problems, i.e. , a reconfiguration problem with geometric constraint and an assignment optimization problem. Numerical results indicate that, compared with the optimal result from the widely-used Genetic Algorithm method, our method could obtain a similar solution within only about 4% of the computational time. • An adaptive surrogate model-based swarm safe-migration planning method is proposed. • The surrogate fitted to the collision-avoidance constraint are investigated and the inputs of surrogates are analyzed for better approximation. • A classifier-based strategy is particularly designed to improve the optimization capability. [ABSTRACT FROM AUTHOR]

In this paper, the traditional system optimum dynamic traffic assignment (SO-DTA) problem is solved by distributed multi-agent dynamics. The goal of SO-DTA is to optimally control the route choice of each user to minimize the total travel time by all users over the assignment time period. It is beneficial for reducing the congestion of the traffic network with time-varying demand. Different from the traditional SO-DTA which is formulated and solved in a centralized scheme, we aim at solving it from a multi-agent perspective in a communication network. Based on the cell transmission model, two connector-based relaxed SO-DTA models are provided in a multi-agent system framework for the traffic network with a single destination and multiple destinations, respectively. In the provided models, each cell connector is treated as an agent, which could exchange information with its adjacent connectors to accomplish the system optimization objective. Then, a collective neurodynamic system equipped with the proposed distributed protocol is used to solve general network optimization problems including the above SO-DTA models as special cases. The convergence analysis is further given for the proposed algorithm. Numerical studies over various traffic networks are presented to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Airport runway assignment and gate assignment are both crucial problems for large multirunway airports. A cooperative schedule for the runway and gate can have a significant effect on airport ground operation efficiency and safety. This paper focuses on the joint assignment problem of runways and gates considering parking time differences and airline preferences. A quadratic model is established to reduce taxiing fuel consumption loss and increase the robustness of gate assignment. An improved branch-and-price algorithm is proposed by utilizing an improved pulse algorithm and various acceleration strategies to improve the solution performance. The actual flight data at an international hub airport are utilized to validate the proposed model. The results indicate that the joint assignment model can effectively reduce ground taxiing loss, optimize gate assignment robustness, and increase the utilization rate of contact gates. The proposed algorithm also achieves better computational accuracy and efficiency. • Proposed joint runway–gate assignment model can reduce the taxiing loss and gate robustness loss. • The model optimizes the taxiing and gate robustness while ensuring airline's preference. • A better solution is obtained by an improved branch-and-price method. • Experiments are designed to verify the value of the joint model. [ABSTRACT FROM AUTHOR]

• This paper formulates the task assignment and route planning of multiple AGVs in an intelligent warehouse as a fixed-destination Multiple Depot Traveling Salesman Problem (MDTSP). Considering the layout of the automated warehouse and the driving characteristics of the AGVs, a MILP model is developed to minimize the total completion time. A series of valid inequalities are presented to strengthen the model by analyzing the characteristics of the problem. • We develop a hyper-heuristic that uses a novel selection strategy based on the improved Multi-Armed Bandits algorithm called Co-SLMAB. It applies the Exponential Monte Carlo with counters (EMCQ) as the acceptance criterion. To our knowledge, ours is the first study to investigate the suitability of a reinforcement learning-based hyper-heuristic (RLHH) method for solving the multi-AGVs task assignment and scheduling problem. • We introduce a novel scheduling approach that optimizes racks' allocation between AGVs and the handling sequence in the RMFS. Then, an efficient method for conflict-free AGV path planning is proposed, which takes different collision avoidance measures depending on the situation. Practicality is achieved by integrating task scheduling and path planning for multi-AGV systems. • We demonstrate the superior performance of the RLHH on various problem instances through a comparative analysis with other algorithms. Furthermore, we analyze the efficiency of the proposed algorithm based on real-life warehouse layouts and perform a sensitivity analysis on AGV configurations. Numerical investigations reveal that the proposed approach greatly enhances the productivity of RMFS and the coordination of AGVs in an actual intelligent warehouse scenario. Globally, e-commerce warehouses have begun implementing robotic mobile fulfillment systems (RMFS), which can improve order-picking efficiency by using automated guided vehicles (AGVs) to realize operations from parts to pickers. AGVs depart from their initial points, move to a target rack position, and subsequently transport racks to picking stations. The AGVs return the racks to their original positions after the workers pick them up. When all tasks are completed, the AGVs return to their starting point. In this context, the main challenge is the task assignment and route planning of multiple AGVs to minimize travel times. We formulate a mixed-integer linear programming (MILP) model with valid inequalities to solve small problem instances optimally. We introduce a reinforcement learning (RL)-based hyper-heuristic (HH) framework to solve large instances to near-optimality. A typical HH framework comprises two levels: high-level heuristics (HLH) and low-level heuristics (LLH). The framework starts from an initial solution and improves iteratively through LLHs, while the HLH invokes a selection strategy and an acceptance criterion to generate a new solution. We propose a novel selection strategy based on the improved Multi-Armed Bandits algorithm called Co-SLMAB and Exponential Monte Carlo with counters (EMCQ) as the acceptance criterion. The corresponding collision avoidance rules are then formulated for different conflicts to construct a conflict-free traveling route for AGVs. Besides testing the proposed framework's effectiveness in real-life warehouse layouts, we perform extensive computational experiments and a thorough sensitivity analysis. The results show that (i) the proposed valid inequalities aid in obtaining better lower bounds and significantly speed up the solution process; (ii) the Co-SLMAB-HH framework is quite competitive compared to CPLEX, outperforming the other tested hyper-heuristics and the problem-specific heuristic regarding convergence and computation time; and (iii) a pool of LLHs consisting of a wide range of different operators is advantageous over a limited set of simple operators while solving problems using hyper-heuristics. [ABSTRACT FROM AUTHOR]

Park-and-ride systems have the potential to improve the efficiency of transportation networks by providing targeted shared mobility services. The design of a park-and-ride system depends on its role in regards to the broader transportation network. Reconfigurable park-and-ride systems aim to provide complementary shared mobility services in the context of varying travel demand scenarios, such as special events, network maintenance operations or non-recurrent perturbations. The design of reconfigurable park-and-ride systems involves the location of access and egress hubs for shared mobility services. We study an extended version of this hub location problem with integrated fleet assignment decisions. We consider stochastic scenarios representative of varying travel demand and traffic conditions on the network and propose a two-stage stochastic integer programming hub location formulation for this problem. First-stage variables represent hub location decision while second-stage variables represent both scenario-based transportation flows and fleet assignment decisions. The latter represent shared mobility service vehicles and they are modeled as integer variables. We develop solution methods to solve this two-stage stochastic integer programming hub location formulation. Exact approaches based on the L-shaped method are proposed with single- and multi-cut configurations. Valid inequalities along with a tight lower bound for the generation of optimality cuts are presented. We also develop a matheuristic to solve larger problem instances. We report numerical results on problem instances based on real data of the city of Lyon, France. We show how stochastic scenarios representative of varying demand and traffic conditions can be generated from such data. Our experiments demonstrate the benefits of this integrated modeling approach for designing efficient reconfigurable park-and-ride systems while considering fleet assignment decisions. • In this paper, we consider a park-and-ride problem with feet assignment. • We consider scenarios representative of varying demand and traffic conditions. • We propose a two-stage stochastic integer programming hub location formulation. • We develop a matheuristic to solve larger problem instances. • We report numerical results on problem instances based on real data of the city of Lyon, France. • We show how stochastic scenarios can be generated from such data. • We show the benefits for designing efficient reconfigurable park-and-ride systems. [ABSTRACT FROM AUTHOR]