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Economic globalization and the rapid development of the Internet make logistics systems more and more diversified, people and enterprises have greatly increased their requirements for logistics systems, and fourth party logistics has received more and more attention from people and related enterprises. In order to further study the routing problem under uncertain stochastic environments, this paper considers the fourth party logistics routing problem from a single manufacturer to multiple distributors with uncertain times and random supplies under the complete information symmetry scenario and symmetric transportation volume decision space. Then, an uncertain stochastic programming model is established with the minimum total cost as its core objective, and the total transportation time, manufacturer's supply, and distributor's demand as constraints. In order to solve the optimal path of the above problems, this paper transforms the uncertain stochastic programming model into a classical mathematical programming model based on the distribution functions of uncertain time and random supply. Finally, two numerical examples are given to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Environmental concerns have called for several measures to be taken within the logistics and transportation fields. Among these measures is the adoption of electric vehicles instead of diesel-operated vehicles for personal and commercial-delivery use. The optimized routing of electric vehicles for the commercial delivery of products is the focus of this paper. We study the effect of several practical challenges that are faced when routing electric vehicles. Electric vehicle routing faces the additional challenge of the potential need for recharging while en route, leading to more travel time, and hence cost. Therefore, in this work, we address the issue of electric vehicle routing problem, allowing for partial recharging while en route. In addition, the practical mandate of the time windows set by customers is also considered, where electric vehicle routing problems with soft time windows are studied. Real-life experience shows that the delivery of customers' demands might be uncertain. In addition, real-time traffic conditions are usually uncertain due to congestion. Therefore, in this work, uncertainties in customers' demands and traffic conditions are modeled and solved using fuzzy methods. The problems of fuzzy real-time, fuzzy demand, and electric vehicle routing problems with soft time windows are addressed. A mixed-integer programming mathematical model to represent the problem is developed. A novel two-phase solution approach is proposed to solve the problem. In phase I, the classical genetic algorithm (GA) is utilized to obtain an optimum/near-optimum solution for the fuzzy demand electric vehicle routing problem with soft time windows (FD-EVRPSTW). In phase II, a novel fuzzy real-time-adaptive optimizer (FRTAO) is developed to overcome the challenges of recharging and real-time traffic conditions facing FD-EVRPSTW. The proposed solution approach is tested on several modified benchmark instances, and the results show the significance of recharging and congestion challenges for routing costs. In addition, the results show the efficiency of the proposed two-phase approach in overcoming the challenges and reducing the total costs. [ABSTRACT FROM AUTHOR]

This paper systematically constructs a multi-project and a multi-objective human resource scheduling mathematical model in construction projects and puts forward an improved genetic algorithm to solve it by aiming at the problems existing in the process of human resource scheduling and optimization. Specifically, first, the basic mathematical models of GPRs and resource-constrained construction project human resource scheduling problem (RCWSP/GPRs) are established, and the multi-project equilibrium problem is extended. Then, an improved inter-cluster separation (ICS) algorithm is proposed and used to solve the RCWSP/GPRs problem. Finally, on this basis, the mathematical model of multi-project and multi-objective human resource scheduling problem and the solution method based on multi-objective-integrated circuit are proposed. At the same time, the resource-constrained multi-project and multi-skill human resource scheduling problem and the integer programming mathematical model under the generalized priority relationship are proposed. Also, the simulation results verify the accuracy of the proposed algorithm and model. [ABSTRACT FROM AUTHOR]

This paper discusses a particular situation in project management in which an analyst attempts to prioritize several independent activities to handle all of them one by one in such a way that there would be no precedence relationships over the activities. The novelty of this research is that the structure of prioritized activities is linear in arrangement which can be considered as a combinatorial optimization problem. The paper formulates a mathematical model and applies it to two real cases in the oil and gas industry. In addition, a row generation procedure is developed to solve largescale problems and the computational results for the problem instances of size up to 300 activities are reported. The results demonstrate the applicability and efficiency of the proposed methodology. [ABSTRACT FROM AUTHOR]

In the modern era, graph theory is considered a useful tool for quantification and simplification of various dynamic components in complex systems. By representing elements as nodes and their connections as edges, graph theory can transform anything from urban planning to computer data into a meaningful mathematical language. Nowadays, numerous practical applications have been designed and developed based on graph theory. Graph theory is a branch of discrete mathematics that aims to describe and solve problems with discrete structures using points and edges. One of the problems concerning graphs is the Eulerian path problem. This research demonstrates that this problem can also be investigated from the perspective of Operations Research (OR). In a more general sense, the Eulerian path problem is a routing problem. This paper presents a pure mathematical model to describe the relationship between the variables of the Eulerian path problem. One of the features of the proposed mathematical model is its solvability by most optimization software. Finally, several numerical examples are provided to enhance the understanding of this model, and they are solved using the proposed approach. All the analyses in this research are conducted using one of the most advanced optimization software, MATLAB. The proposed mathematical model provides a systematic and efficient approach to discover Eulerian paths in specific graphs, contributing to the advancement of graph theory and its practical applications. [ABSTRACT FROM AUTHOR]

I present a method for developing extensible and modular computational models without sacrificing serial or parallel performance or source code readability. By using a generic simulation cell method I show that it is possible to combine several distinct computational models to run in the same computational grid without requiring any modification of existing code. This is an advantage for the development and testing of computational modeling software as each submodel can be developed and tested independently and subsequently used without modification in a more complex coupled program. Support for parallel programming is also provided by allowing users to select which simulation variables to transfer between processes via a Message Passing Interface library. This allows the communication strategy of a program to be formalized by explicitly stating which variables must be transferred between processes for the correct functionality of each submodel and the entire program. The generic simulation cell class presented here requires a C++ compiler that supports variadic templates which were standardized in 2011 (C++11). The code is available at: https://github.com/nasailja/gensimcell for everyone to use, study, modify and redistribute; those that do are kindly requested to cite this work. [ABSTRACT FROM AUTHOR]

Software trustworthiness is an important research field in software engineering. In order to appropriately evaluate it, some different measurement approaches have been proposed, which have important guiding significance for improving software trustworthiness. Recently, we have investigated attributes-based approaches. That is, how to maximize trustworthy degree of some software satisfying a given threshold by adjusting every attribute value such that the cost is minimal, i.e., the sum of all attribute values is as small as possible. The work is helpful to improve the software quality under the same cost. This paper continues this work and considers a reallocation approach to dealing with the problem that the threshold and the minimal constraints of every attribute values dynamically increase. In this process, the costs of trustworthiness improvement should be ensured to be minimal. For this purpose, we firstly define a reallocation model by mathematical programming. Then we introduce the notion of growth function. Based on this, a polynomial reallocation algorithm is designed to solve the above reallocation model. Finally, we verify our work on spacecraft softwares and the results show that this work is valid. [ABSTRACT FROM AUTHOR]

Once trusted, automated vehicles (AVs) will gradually appear in urban areas. Such a transition is an opportunity in transport planning to control undesired impacts and possibly mitigate congestion at a time when both conventional vehicles (CVs) and AVs coexist. This paper deals with the complex transport decision problem of designing part of the network that is exclusive for AVs through a nonlinear programming model. The objective function minimises the costs of travel times where vehicles circulate under user equilibrium. The model evaluates the benefits of having an AVs-dedicated infrastructure and the associated costs from the detouring of CVs. Three planning strategies are explored: incremental, long-term and hybrid planning. The first creates a subnetwork evolving incrementally over time. The second reversely designs a subnetwork from the optimal solution obtained at a ratio of 90% AVs. The third limits the incremental planning towards that optimal long-term solution. The model is applied to the city of Delft, in the Netherlands. Two scenarios are analysed, with and without AV-dedicated roads, at several AV penetration rates. We find that implementing dedicated roads for AVs reduces the overall costs and congestion up to 16%. However, CV detouring is inevitable at later network stages, increasing the total distance travelled (up to 8%) and congestion in the surroundings of AV subnetworks. Concerning the planning strategies, incremental planning is appropriate for starting in the initial stages and is the strategy that most tackles CV detouring. The hybrid or the long-term strategies are more suitable to be applied after a ratio of 50% AVs, and the hybrid planning is the strategy that most reduces delay. [ABSTRACT FROM AUTHOR]

Inventory–production coordination for customer orders is becoming increasingly important for companies to increase customer responsiveness and achieve economic purposes. In this paper, the joint optimization of inventory and production is considered for stochastic customer orders to maximize the throughput. Demands of customer orders dynamically arrive at the inventory department, and each incoming order consists of multiple product types with random workloads. To process the workloads, certain amounts of a common raw material are required and need to be drawn from the inventory department. A customer order will be lost if there do not exist enough raw materials in the inventory department. With the necessary materials, workloads of accepted orders will be assigned to a set of unrelated parallel servers to be processed in the production department. This paper intends to maximize the effective throughput through proper coordination of the inventory and the production departments. For this problem, system bottlenecks are identified and analyzed, and mathematical programming models are developed to determine the optimal throughput and the corresponding inventory and production policies. Several special cases are also explored to provide intuitive insights into the relationship between the system parameters and optimal throughput. Relationships between key model parameters and effective throughput are identified through sensitivity analysis and further validated by the results of computational experiments. Note to Practitioners—Coordination of production and inventory is crucial for system efficiency improvement. In order to better operate the system and respond to customer demands, this paper explores such coordination for stochastic customer orders to achieve the maximum effective throughput. Particularly, we consider the case where production is constrained by material inventory. System bottlenecks for various cases are identified, which can facilitate system diagnosis and performance evaluation. Based on the bottleneck analysis, an optimization problem is formulated to provide the optimal throughput and policy. Several important practical cases are also discussed, including single product type, identical/uniform servers, and speed-identical/uniform product types. Besides, sensitivity analysis is conducted to show how the changes of parameters such as expected workload and server speed will affect throughput performance. In the future research, more complex production environments and cost-related constraints will be further considered to help practitioners achieve desirable system outputs. [ABSTRACT FROM AUTHOR]

In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

Large varieties of mathematical programming models have been developed to provide decision support to the design engineer. The paper deals with some of the problems and offers practical solutions. [ABSTRACT FROM AUTHOR]

One of the most common systems in non-automated warehouses, is drive-in pallet racking with a shared storage policy (which is usually based on the duration-of-stay). Such scheme targets towards an efficient use of storage space, since its operation costs are directly related to the size and layout of the warehouse. In this paper, two mathematical programming models and two greedy-randomised based heuristics for finding (nearly) optimal storage and retrieval operation sequences for this type of storage system are proposed. The computational effectiveness of the proposed approaches is measured by considering two sets of synthetic instances. The obtained results show that the proposed heuristics are not only able to compute high-quality solutions (as observed when being compared with the optimal solutions attained by the mathematical programming models), but it is also capable of providing solutions in very short running times even for large instances for which the mathematical programming model failed to find feasible solutions. At the light of these results, the best heuristic is also tested using a rolling-horizon planning strategy in a real-world case study, obtained from a Chilean company. It turns out that the attained results are more effective than the company's current storage policy. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of partitioning a complete weighted graph into complete subgraphs, each having the same number of vertices, with the objective of minimizing the sum of edge weights of the resulting subgraphs. This NP-complete problem arises in many applications such as assignment and scheduling-related group partitioning problems and micro-aggregation techniques. In this paper, we present a mathematical programming model and propose a complementary column generation approach to solve the resulting model. A dual based lower bounding feature is also introduced to curtail the notorious tailing-off effects often induced when using column generation methods. Computational results are presented for a wide range of test problems. [ABSTRACT FROM AUTHOR]

In the process of cement equipment manufacturing, the demand of rectangle pieces of steel structure is very large. The traditional manual nesting, which is simply cutting by hand-making according to the arrangement of the number and size, causes the low efficiency and material wasting. To solve the problem above, this paper proposes an optimizing model for nesting problem of rectangle pieces. Firstly, with the aim of the maximum utilization ratio of the sheet, the optimization mathematical model for nesting problem of rectangle pieces is established. The lowest horizontal line searching algorithm is described in detail. Secondly, the mathematical model is solved to get the optimal solution by the combination of genetic algorithm and the lowest horizontal line searching algorithm. In the solution process, this paper presents the methods of gene encoding and decoding, definition of fitness function, the design of genetic operators and the design of algorithm operating parameters. Finally, we use one sheet as an example to illustrate the proposed model and algorithm process. Experimental results have shown that the proposed approach is able to achieve rectangle pieces nesting with the maximum material utilization ratio. [ABSTRACT FROM AUTHOR]

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency. [ABSTRACT FROM AUTHOR]

Classification algorithms can suffer from performance degradation when the class distribution is unbalanced. This paper develops a two-step approach to evolving ensembles using genetic programming (GP) for unbalanced data. The first step uses multiobjective (MO) GP to evolve a Pareto-approximated front of GP classifiers to form the ensemble by trading-off the minority and the majority class against each other during learning. The MO component alleviates the reliance on sampling to artificially rebalance the data. The second step, which is the focus this paper, proposes a novel ensemble selection approach using GP to automatically find/choose the best individuals for the ensemble. This new GP approach combines multiple Pareto-approximated front members into a single composite genetic program solution to represent the (optimized) ensemble. This ensemble representation has two main advantages/novelties over traditional genetic algorithm (GA) approaches. First, by limiting the depth of the composite solution trees, we use selection pressure during evolution to find small highly-cooperative groups of individuals for the ensemble. This means that ensemble sizes are not fixed a priori (as in GA), but vary depending on the strength of the base learners. Second, we compare different function set operators in the composite solution trees to explore new ways to aggregate the member outputs and thus, control how the ensemble computes its output. We show that the proposed GP approach evolves smaller more diverse ensembles compared to an established ensemble selection algorithm, while still performing as well as, or better than the established approach. The evolved GP ensembles also perform well compared to other bagging and boosting approaches, particularly on tasks with high levels of class imbalance. [ABSTRACT FROM AUTHOR]

Problems related to the extremization of functions have been studied for quite a long time not only by Russian experts but also by the world's leading experts in the field of applied mathematics. It should be noted that, nowadays, not all problems on this topic have a solution, despite the ongoing active research in mathematical modeling and mathematical programming. A serious work is underway on a deeper study of the properties of extremizable functions. This is especially topical in the context of our country's transition to a knowledge economy and, as a first step towards this, to the digital economy, when powerful supercomputers with performance of hundreds and thousands of petaflops have arisen and quantum computers begin to occur. The paper is a survey of results associated with a new class of functions that are useful in extremization problems and is based on joint work of the authors. [ABSTRACT FROM AUTHOR]

The year 2020 saw the world turned upside down by the coronavirus pandemic. Countless human activities were suspended or cancelled as the virus spread across the globe. In this paper, we show how the regular season matches of Ecuador's professional football league were rescheduled due to the disruption caused by the pandemic. As with many others, this league had to reschedule its remaining games to fit within in a much shorter period of time than originally planned. To address this problem, we developed two mathematical models that designed new match calendars. The first one, a round assignment model, rescheduled the various rounds in the season still to be played while the second one, a day assignment model, took the solutions of the first model as input to assign the matches within each round to specific days. The implementation of our models secured a well-balanced number of days off before each match across all of the teams. Also, it enabled the league to conclude a full season without cancelling any matches or changing the schedule format, unlike what occurred in many other leagues, and won the approval of all stakeholders including league officials, players, team coaches, the TV broadcaster and fans. [ABSTRACT FROM AUTHOR]

An optimal zero‐sequence voltage injection‐based common‐mode voltage reduction pulse‐width modulation (CMVRPWM) for the reduction of common‐mode voltages (CMVs) in both amplitude and third‐order component is proposed in this paper. On the basis of general symmetrical switching patterns of CMVRPWM, a constrained mathematical programming model is established with the objective of optimizing the volt‐second value of CMV in each switching period. By solving the mathematical model, the limitation of the CMV amplitude and the reduction of third‐order harmonic in CMV can be achieved simultaneously, contributing to better CMV suppression and common‐mode filter design in the applications of two‐level voltage source inverters (2L‐VSIs). The optimal zero‐sequence voltage under the given target is obtained, and the calculation method of the switching pattern and modulation wave is studied. Simulation and experimental results show that the proposed method can restrict the CMV amplitude within one‐sixth of the DC bus voltage, and the third‐order component in CMV is effectively reduced compared with the conventional methods. [ABSTRACT FROM AUTHOR]

As train speed increases, the aerodynamic noise gradually exceeds the wheel-rail noise and becomes the main noise source of high-speed trains. The aerodynamic noise affects the interior environment of a train in two ways: turbulent wall pressure fluctuation (TWPF) and acoustic wall pressure fluctuation (AWPF). In this paper, a hybrid aerodynamic model for noise analysis of high-speed trains based on the computational fluid dynamics (CFD) method and the Lighthill's acoustic analogy (LAA) algorithm, so-called CFD-LAA method, is established and solved by using the finite-element acoustic simulator of Actran software. The noise contributions of both the TWPF and the AWPF are examined and discussed. The method presented in this paper may be a valuable reference for revealing the generation mechanism of aerodynamic noises and evaluating their effects on a train interior noise. [ABSTRACT FROM AUTHOR]

This paper studies a periodic review inventory model with random supply capacity and demand, where the retailer is loss-averse. For the single-period problem, it is shown that the retailer will not order unless the initial inventory level is less than a critical value, and the order-up-to level is generally not a constant. Moreover, the critical value and optimal order quantity are only dependent on the random demand and independent on the random capacity. We also investigate the impacts of loss aversion, price and cost on the optimal order quantity and maximum expected utility. Then a dynamic programming approach is used to analyse the retailer’s ordering policy in the multi-period case. In each period, if the initial inventory level is above a given value, an order will not be placed. Otherwise, the upper bound on the optimal order quantity is given. The numerical experiments are conducted to illustrate the optimal ordering policies. [ABSTRACT FROM AUTHOR]

Project management is a process that schemes and controls the project life cycle via the easiest and the best way to achieve project goals. Project managers always aim to simultaneously handle conflicting goals in the organization. In this paper, a new mathematical model is proposed that simultaneously minimizes total cost and completion time and maximizes the quality in the project management decision problem. Contractual penalty cost and contractual reward cost with a new method are the other consideration in the proposed model. In the projects, the relation between time and direct cost is a nonlinear function. Hence, a linearization technique is presented with attention to variable change and piecewise linearization, in which nonlinear function is converted to the linear programming model. On the other hand, in real conditions according to uncertainty in environmental situations and incomplete information, there can be ambiguity in parameters and variables of the problem. The uncertainty of the parameters and variables is expressed with fuzzy sets theory and fuzzy mathematical programming. The other aim of this paper is to introduce a modified version of fully fuzzy multi-objective linear programming for the problem. For analyzing a fully fuzzy time–cost–quality project management model, a practical example of the literature is provided. By examining the results of the model with conflicting objectives, two scenarios are presented to explore the interactions of conflicting objectives on the project, and the results are reported. [ABSTRACT FROM AUTHOR]

Shipbrokers play a key role in maritime industry by acting as intermediates between shipping companies and the market. They undertake various chartering, buying or selling operations. In this paper, we propose a mathematical programming approach for the evaluation and selection of shipbrokers. Specifically, the score of each ship broker is a composite measure that is derived by aggregating a set of performance criteria, e.g., reputation, etc. The developed mathematical programming models enable the aggregation and weighting of the criteria. We employ three optimization models to explore the effect of different weighting schemes on the scores and ranking of the shipbrokers. The models that provide a common set of weights for all the shipbrokers establish the appropriate ground for comparisons among them. Also, our models facilitate the incorporation of user priorities over the criteria in the form of weight restrictions. The proposed approach is illustrated by assessing seven shipbroker offers for selling a dry-bulk ship using four criteria, namely revenue, brokerage fee, brokerage time and terms & conditions. [ABSTRACT FROM AUTHOR]

The analytic hierarchy process (AHP) is one of the most popular multi-criteria decision-making (MCDM) methods, and so is its extension fuzzy analytic hierarchy process (FAHP). However, the FAHP, unlike the AHP, easily handles the trusted weights by the consistency index (CI) or consistency ratio (CR). We need to first derive the consistent fuzzy pairwise comparison matrix (FPCM) by the transitivity axiom and then drive fuzzy weights. We also need a flexible mechanism for users to control the spread of fuzzy weights under tolerable consistency. In this paper, we propose a novel model based on mathematical programming to derive rational fuzzy weights of the FAHP and provide a parameter for decision-makers to control the spread of fuzzy weights. Three examples are used to demonstrate the proposed method and compared with others to validate and justify the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the resource costs of agricultural water use and simulate the environmental and economic impacts of their recovery. To this end, we develop a socio-hydrology-inspired, dynamic, protocol-based modular approach that interconnects economic and hydrologic modeling via two-way feedback protocols. The hydrologic module is populated with the AQUATOOL model, the Decision Support System used in Spanish river basins; while the economic module is populated with an ensemble of four Mathematical Programming Models (MPMs) that capture human agency and responses. This allows us to sample uncertainty and provide a range for resource costs estimates and the environmental and economic impacts of their recovery, rather than a point estimate. Methods are illustrated with an application to the Órbigo Catchment, a sub-basin of the Douro River Basin in Spain. Our results suggest significant resource costs (a 34–62% increase in existing charges, depending on the model) with non-trivial impacts on income (2–27% reduction) and the environment (water savings range between 6% and 69%), while the impact on tax revenue is ambiguous yet potentially significant (between − 2.3 million EUR/year and 5 million EUR/year). [ABSTRACT FROM AUTHOR]

This paper presents a comparative study of six multiobjective evolutionary algorithms (MOEAs) with the node-depth encoding (NDE) which have been used to solve the service restoration in distribution systems. The study has been divided into three steps: (1) the MOEAs have been evaluated taking into account the switching operations necessary to find adequate restoration plans considering multiple nonlinear constraints and objective functions; (2) the MOEAs have been employed to solve four different datasets with 3860, 7720, 15,440 and 30,880 buses, respectively; (3) comparisons have been performed using the hypervolume indicator and the results obtained with each approach are statistically compared using Kruskal-Wallis nonparametric tests and multiple comparisons. In addition, this paper provides a comprehensive evaluation of six combinations of MOEAs based on NDE and our objective is to identify the features of each approach that consistently produce best results applied to network reconfiguration for service restoration in distribution systems. Simulations results have shown that MOEA based on NDE with crowding distance and strength pareto found good configurations with low switching operations and explored the search space better than others approaches used in this paper, approximating better the pareto-optimal front. [ABSTRACT FROM AUTHOR]

Simulation optimisation has gained a great attention due to its success in the design of complex manufacturing systems. In this paper, we look at manufacturing as a special class of queueing systems and propose the Discrete Event Optimisation (DEO) methodology, which provides a formal way to develop integrated mathematical models for the simultaneous simulation and optimisation. In the case, the obtained model is a mixed integer linear programming model; the methodology provides a formal way to generate approximations of them. The analytical properties of DEO models are analysed for the first time in the framework of sample path optimisation and mathematical programming. The methodology represents a reference for the use of mathematical programming as a way to model simulation optimisation for queueing systems. The applicability of the DEO methodology to complex problems is showed using the task and buffer allocation problem in a production line. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a set of scalar optimization subproblems and optimizes them in a collaborative manner. Subproblems and solutions are two sets of agents that naturally exist in MOEA/D. The selection of promising solutions for subproblems can be regarded as a matching between subproblems and solutions. Stable matching, proposed in economics, can effectively resolve conflicts of interests among selfish agents in the market. In this paper, we advocate the use of a simple and effective stable matching (STM) model to coordinate the selection process in MOEA/D. In this model, subproblem agents can express their preferences over the solution agents, and vice versa. The stable outcome produced by the STM model matches each subproblem with one single solution, and it tradeoffs convergence and diversity of the evolutionary search. Comprehensive experiments have shown the effectiveness and competitiveness of our MOEA/D algorithm with the STM model. We have also demonstrated that user-preference information can be readily used in our proposed algorithm to find a region that decision makers are interested in. [ABSTRACT FROM AUTHOR]

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively. [ABSTRACT FROM AUTHOR]

The aim of this study is to provide a balanced distribution of air traffic controller workload (ATCW) across airspace sectors taking into account the complexity of airspace sectors and the factors affecting ATCW, both objective and perceived. Almost all the studies focusing on the airspace sectorisation problem use heuristic or metaheuristic algorithms in dynamic simulation environments instead of a mathematical modelling approach. The paper proposes a multi-objective mixed integer mathematical model for airspace sectorisation. The model is applied to the upper, en-route level of Turkish airspace. Geographical information systems (GIS) are used to advantage for airspace analysis. The multi-objective model developed in this paper is scalarised by using the conic scalarisation method. For solving the scalarised problem, the CPLEX and DICOPT solvers of GAMS software are implemented. Finally, the optimal sector boundaries of Turkish airspace are defined. [ABSTRACT FROM AUTHOR]

This paper addresses a real‐life rescheduling problem of a pipe‐laying support vessel (PLSV) fleet in charge of subsea oil well connections. The short‐term schedule of these vessels is subject to uncertainties inherent to its operations, resulting in ships idleness or delays in oil production. The objective of this study is to develop methods to support a Brazilian oil and gas company in overcoming impacts caused by operational disruptions, while reaching its planned production level. The PLSV rescheduling problem was treated as an identical parallel machine scheduling problem, where the machines represent the vessels and the jobs are the activities for the subsea well connections. We propose a mathematical programming model and a method based on the iterated local search (ILS) metaheuristic to solve the problem. This paper contributes to this by considering simultaneously setup times, machine eligibility, release dates, due dates, and machine availability. Both methods were applied on 10 instances based on real PLSV data. Taking into account an objective function that measures the operational impact on schedules, the ILS provided an average improvement above 91% in schedules when compared to the initial solution provided by the studied company. The ILS outperformed a mathematical programming model for the problem, in eight instances, within a 30‐minute execution time limit, fitting to the company process. [ABSTRACT FROM AUTHOR]

Proactive scheduling aims at the generation of robust baseline schedules, which has been studied for many years with the assumption that activity splitting is not allowed. In this paper, we focus on the proactive resource-constrained project scheduling problem in which each activity can be split at discrete time instants under the constraints of a maximum number of splitting and a minimum period of continuous execution. In this problem, setup times are also considered. A mathematical model is established and analyzed, of which two properties and one lemma are proposed. As the problem is proved to be ${\text{NP}}$ -hard in the strong sense, for solving the model, we develop a genetic algorithm (GA) in which the two proposed properties and the lemma are applied as local search operators. After linearizing the proposed model, we use a commercial mathematical programming solver as a benchmark to solve the problem. From the computational results, we find that the developed GA is effective and efficient in solving the defined problem, and activity splitting improves robustness. With the growth of the maximum number of splitting, the decline in the minimum execution time, the decrease in the setup times, and the extension of the project due date, robustness increases. [ABSTRACT FROM AUTHOR]

Single-valued neutrosophic numbers (SVNNs) are very much useful to express uncertain environments. In real-life problems, there are many situations where players of a matrix game can not assess their payoffs by using ordinary fuzzy sets or intuitionistic fuzzy sets. In these situations, single-valued trapezoidal neutrosophic numbers (SVTNNs) play a vital role in game theory, as it includes indeterminacy in the information besides truth and falsity. The objectives of this paper are to explore matrix games with SVTNN payoffs and to investigate two different solution methodologies. To solve such games, a pair of neutrosophic mathematical programming problems have been formulated. In the first approach, the two neutrosophic mathematical programming models are converted into interval-valued multi-objective programming problems by using a new ranking order relation of SVTNNs. Finally, the reduced problems are solved using the weighted average approach and utilizing LINGO 17.0 software. It is worth mentioning that the values of the game for both the players are obtained in SVTNN forms, which is desirable. In the second approach, each neutrosophic mathematical programming model is transformed into a crisp one by using the idea of α -weighted possibility mean value for SVTNNs. A market share problem and another numerical example are illustrated to show the validity and applicability of the proposed approaches. [ABSTRACT FROM AUTHOR]

Multi-manned assembly lines are often designed to produce big-sized products, such as automobiles and trucks. In this type of production lines, there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones. In this paper, such a class of problems, namely, the multi-manned assembly line balancing problem is addressed, with the objective of minimising the cycle time.Amixed-integer mathematical programming formulation is proposed for the considered problem. This model has the primary objective of minimising the cycle time for a given number of workstations and the secondary objective of minimising the total number of workers. Since the addressed problem is NP-hard, two meta-heuristic approaches based on the simulated annealing algorithm have been developed: ISA and DSA. ISA solves the problem indirectly while DSA solves it directly. The performance of the two algorithms are tested and compared on a set of test problems taken from the literature. The results show that DSA outperforms ISA in term of solution quality and computational time. [ABSTRACT FROM AUTHOR]

To enhance the overall performance of supply chains, coordination among production and distribution stages has recently received an increasing interest. This paper considers the coordinated scheduling of production and transportation in a two-stage assembly flowshop environment. In this problem, product components are first produced and assembled in a two-stage assembly flowshop, and then completed final products are delivered to a customer in batches. Considering the NP-hard nature of this scheduling problem, two fast heuristics (SPT-based heuristic and LPT-based heuristic) and a new hybrid meta-heuristic (HGA-OVNS) are presented to minimise the weighted sum of average arrival time at the customer and total delivery cost. To guide the search process to more promising areas, the proposed HGA-OVNS integrates genetic algorithm with variable neighbourhood search (VNS) to generate the offspring individuals. Furthermore, to enhance the effectiveness of VNS, the opposition-based learning (OBL) is applied to establish some novel opposite neighbourhood structures. The proposed algorithms are validated on a set of randomly generated instances, and the computation results indicate the superiority of HGA-OVNS in quality of solutions. [ABSTRACT FROM PUBLISHER]

This paper studies the scheduling problem of minimising total weighted earliness and tardiness penalties on identical parallel machines against a restrictive common due date. This problem is NP-hard in the strong sense and arises in many just-in-time production environments. A fast ruin-and-recreate (FR&R) algorithm is proposed to obtain high-quality solutions to this complex problem. The proposed FR&R algorithm is tested on a well-known set of benchmark test problems that are taken from the literature. Computational results provide evidence of the efficiency of FR&R, which consistently outperform existing algorithms when applied to benchmark instances. This work provides a viable alternative approach for efficiently solving this practical but complex scheduling problem. [ABSTRACT FROM PUBLISHER]

The flow shop scheduling problem with blocking has important applications in a variety of industrial systems but is under-represented in the research literature. In this paper, a modified fruit fly optimisation (MFFO) algorithm is proposed to solve the above scheduling problem for makespan minimisation. The MFFO algorithm mainly contains three key operators. One is related to the initialisation scheme in which a problem-specific heuristic is adopted to generate an initial fruit fly swarm location with high quality. The second is concerned with the smell-based search in which a neighbourhood strategy is designed to generate a new location. To further enhance the exploitation of the proposed algorithm considered, a speed-up insert-neighbourhood-based local search is applied with a probability. Finally, the last is for the vision-based search in which an update criterion is proposed to induce the fruit fly into a better searching space. The simulation experimental results demonstrated the efficiency of the proposed algorithm, in spite of its simple structure, in comparison with a state-of-the-art algorithm. Moreover, new best solutions for Taillard’s instances are reported for this problem, which can be used as a basis of comparison in future studies. [ABSTRACT FROM PUBLISHER]

• The model allocates safety stocks in supply chains at minimum cost. • An MIQCP reformulation with piecewise approximation greatly improves computational efficiency. • The piecewise function yields an improved estimation for the fill rates. • The GSM is extended to handle non-normally distributed demands. • Real-world case studies solved to optimality within few seconds of computational time. This paper presents a Multi-Echelon Inventory Optimization (MEIO) framework, based on the Guaranteed-Service Model (GSM), to allocate safety stocks across a supply chain with several locations and products, minimizing costs while meeting service level objectives. Extending previous work by Achkar et al. (2023), this paper enhances the Mixed-Integer Quadratically Constrained Program (MIQCP) with a highly efficient solution approach. The model introduces a piecewise linear approximation, significantly improving computational efficiency and the accuracy of the approximation for the fill rate function. It also introduces a different and more efficient approach to account for stochastic lead times using a discrete function. Moreover, an extension of the approach to account for non-normally distributed demands is proposed. The model is applied to several instances of a real-world case study from a pharmaceutical company, with more than 7300 product-location combinations, showing that optimal solutions can be obtained within few seconds of computational time. [ABSTRACT FROM AUTHOR]

In this paper, we considered a multi-objective stochastic transportation problem where the supply and demand parameters follow extreme value distribution having three-parameters. The proposed mathematical model for stochastic transportation problem cannot be solved directly by mathematical approaches. Therefore, we converted it to an equivalent deterministic multi-objective mathematical programming problem. For solving the deterministic multi-objective mathematical programming problem, we used an e-constraint method. A case study is provided to illustrate the methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a new multi-objective interior search algorithm (MOISA) for solving multi-objective optimization problems. Multi-objective complex mathematical models need to be solved by meta-heuristic algorithms in such a way that Pareto-optimal solutions are obtained; therefore, a new algorithm is presented in this paper for solving such models. The process of the interior search algorithm (ISA) is based on principles of interior design and decoration. This algorithm divides all elements, except the most suitable one, into two groups. In the first group, which is called the artistic composition group, algorithm changes the composition of elements to achieve a more desirable view. In the second group, which is called the mirror group, the algorithm places a mirror between the group elements and the most suitable element to find a better view. This paper uses the principles of the ISA in conjunction with the concepts of the non-dominated sorting and crowding distance to present the proposed MOISA, which is capable of obtaining near-optimal non-dominated solutions from solution space and identifying accurate Pareto fronts. To evaluate the performance of the foregoing algorithm, the related results of solving six models and a maximal covering location-allocation model are compared with several standard multi-objective evolutionary algorithms in terms of different metrics. This comparison shows that the results of the proposed MOISA are better than those obtained from other tested algorithms. Based on the solved numerical examples, the algorithm presented in this paper has many advantages over existing algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the study of optimality conditions (both necessary and sufficient) for a weakly quasi e-Pareto solution to a multiobjective optimization problem by using a minimax programming approach. To establish necessary conditions for approximate solutions of minimax programming problems under a suitable constraint qualification, we use some advanced tools of variational analysis and generalized differentiation. Sufficient conditions for such solutions to the considered problem are also provided by using generalized convex functions defined in terms of the limiting subdifferential for locally Lipschitz functions. In addition, some duality results for minimax programming problems are also provided. [ABSTRACT FROM AUTHOR]

The problem of optimal flow production planning at the operational scheduling stage is being studied, using the example of the out-of-furnace department of a converter-based steel-making production in the iron metallurgy industry. To solve this problem, a linear integer programming model is proposed, which fully describes the specifics of the investigated technological processes. A major advantage of this approach is its scalability for solving related optimization problems in the industry of plant logistics, as well as flexibility in adapting to changes and fine-tuning the system of constraints and objective function. The software implementation of the developed model forms the basis of the operational scheduling module of the optimal flow production planning system, which is used for a large-scale computational experiment on real-world data. [ABSTRACT FROM AUTHOR]

In this paper, we study the quadratic assignment problem with a rank-one cost matrix (QAP-R1). Four integer-programming formulations are introduced of which three are assumed to have partial integer data. Unlike the standard quadratic assignment problem, some of our formulations can solve reasonably large instances of QAP-R1 with impressive running times and are faster than some metaheuristics. Pairwise relative strength of the LP relaxations of these formulations are also analyzed from theoretical and experimental points of view. Finally, we present a new metaheuristic algorithm to solve QAP-R1 along with its computational analysis. Our study offers the first systematic experimental analysis of integer-programming models and heuristics for QAP-R1. The benchmark instances with various characteristics generated for our study are made available to the public for future research work. Some new polynomially solvable special cases are also introduced. Summary of Contribution: This paper aims to advance our knowledge and ability in solving an important special case of the quadratic assignment problem. It shows how to exploit inherent properties of an optimization problem to achieve computational advantages, a strategy that was followed by researchers in model building and algorithm developments for decades. Our computational results attest to this time-tested general philosophy. The paper presents the first systematic computational study of the rank one quadratic assignment problem, along with new mathematical programming models and complexity analysis. We believe the theoretical and computational results of this paper will inspire further research on the topic and will be of significant value to practitioners using rank one quadratic assignment models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-step bloodmobile collection planning framework. The first step is the annual planning to determine weeks of collection at each mobile site in order to ensure regional self-sufficiency of blood supply. The second step is the detailed weekly planning to determine days of collections at each mobile site and to form corresponding transfusion teams. Only key resource requirements are considered for annual planning while detailed resource requirements and transportation times are considered for weekly planning. Two Mixed Integer Programming models are proposed for annual planning by assuming fixed or variable mobile collection frequencies. A new donation forecast model is proposed based on population demographics, donor generosity, and donor availability. A new concept of bloodmobile collection configurations is proposed for compact and efficient mathematical modeling of weekly planning in order to minimize the total working time. Field data from the French Blood Service (EFS) in the Auvergne-Loire Region are used to design numerical experiments and to assess the efficiency of the proposed models. [ABSTRACT FROM PUBLISHER]

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time. [ABSTRACT FROM AUTHOR]

In this paper, a new group of optimization algorithms named Human Strategy Algorithm (HS) is proposed which is inspired by human strategies to problem solving. The main idea of HS is based on human actions to find the problem’s optima by means of accessible instruments. As the environment of an unknown problem assumed to be a black box, it is supposed that the environment of our problem is a dark room occupied by several men named blind men. The main mission of these men is to look for the optimum solution. Each man has at least one instrument as his assistance. Like real life, the instrument might be any tool such as stick, billy, rope, stone, yoyo, sweep. Any instrument by its unique features is suitable in some situations. In fact, this algorithm maps problem space and searches agents to dark room and people, respectively. In this paper, one sample algorithm of the group of human strategy, YOYO Blind Man Algorithm (YOYO-BMA), is introduced which uses yoyos as men’s accessible instruments. The performance of the YOYO-BMA is evaluated on a set of benchmark problems provided for CEC’2010 Special Session and Competition on Large-Scale Global Optimization (Tang et al. 2010). The results show superior performance of proposed algorithm in comparison with others. Moreover, the problem of designing urban traffic network is solve to evaluate the algorithm using a real complex problem. [ABSTRACT FROM AUTHOR]

This paper introduces two-dimensional loading time-dependent vehicle routing problem and proposes a bi-objective mathematical model. This problem assesses the process of distributing the rectangular-shaped demanded items over an urban environment; it does not, however, allow items to be loaded on top of each other. In addition to the above assumptions, the presented model also satisfies the first-in-first-out property in the time-dependent vehicle routing problem. Given the NP-hard nature of the problem, a method called elitist non-dominated sorting local search is developed to obtain its solutions. To evaluate the performance of the proposed algorithm, the solutions of this algorithm for small-scale problem instances are compared with the results of an exact method. For the medium-scale problem instances, results of NSGA-II and SPEA2 are used as the basis of comparison. The computational results demonstrate the good performance of the proposed method. [ABSTRACT FROM AUTHOR]