Search

Night deliveries are increasingly used by motor carriers to cut costs, but many carriers struggle to secure night truck drivers because many drivers dislike night driving. Monetary incentives are offered to drivers to accept night loads, but the incentive system currently used in practice is inefficient, as it not only pays more incentives than necessary but also does not incentivize the right drivers, both of which increase costs. This paper develops a new incentive system that allows carriers to secure night drivers at lower costs. Based on the interviews conducted with seven motor carriers, as well as the analysis of large night delivery data, we propose a bi-level programming approach that (1) generates a unique incentive for each driver based on his/her night driving performance, and (2) implicitly aligns the incentive paid to each driver with his/her reservation price. Simulation experiments conducted with real-world data showed that by using our approach carriers can not only avoid over-incentivizing drivers, but also (1) incentive only the right set of drivers who can bring cost savings and (2) encourage other drivers to improve night driving. Results also showed that our approach may give cost savings of up to 2.5% over the current system. [ABSTRACT FROM AUTHOR]

Summary: By controlling the network, the Internet of Things (IoT)‐connected software‐defined network (SDN) limits the scalability of IoT devices. Since SDN depends on a centralized controller that attackers can easily affect, it is incredibly susceptible to attacks. Secure access control to the SDN controller was the focus of the prior methods for controller scalability and restricted trust management. A framework called Safeguard Authentication Dynamic Access Control (SANDMAC) is suggested to safeguard and offer useful services to enterprises. Authentication confirms legitimacy after all users and applications have been registered. To improve network security, policies let users grant access to account attributes, legal activities, and temporal components. The administrator lessens conflicts between the methods by validating and saving the policies in the database. The services are provided to dependable customers using the forensic‐based investigation algorithm, depending on the quality of service and software level agreements requirements, decreasing reaction times and maximizing resource usage. Performance comparisons between the new and previous efforts are validated using a variety of parameters, and the proposed work is validated using the iFogSim application. According to the findings, SANDMAC significantly raises key performance indicators. SANDMAC specifically keeps false positives at 3.5% and accomplishes a low response time of 60 ms for roughly 800 authorized accesses. SANDMAC is a better option because of these enhancements, which result in longer network lifetimes and more dependable data transmission. [ABSTRACT FROM AUTHOR]

Current Predictive Adaptive Cruise Control (PACC) systems pose significant challenges. These include poor collaborative optimisation between economic velocity and the powertrain system, conservative energy-saving strategies and underutilisation of map data due to real-time constraints. A computationally efficient collaborative optimisation method is proposed for economic following velocity and gear shifting based on Bi-level optimisation theory. Based on the Bi-level theory, economic following velocity and gear shifting are decoupled and hierarchically planned. The lower-level subproblem constructs the objective function based on a dynamic-weight safety distance model within the Model Predictive Control (MPC) framework, solving the economic following velocity by the Continuous Generalized Minimal Residual Method (C/GMRES). The upper-level subproblem solves the economic gear shifting by the optimal control law based on the economic following velocity. Finally, comparative experiments are conducted between the proposed PACC algorithm, a standard Adaptive Cruise Control (ACC) algorithm, and a benchmark Dynamic Programming-Model Predictive Control (DP-MPC) PACC algorithm. The results show that the proposed PACC algorithm reduces fuel consumption by approximately 5.76% compared to the ACC. Compared to the benchmark algorithm, the proposed PACC algorithm also demonstrates significantly improved computational efficiency while achieving comparable energy savings. [ABSTRACT FROM AUTHOR]

The highly plastic nature of Alveolar Macrophage (AM) plays a crucial role in the defense against inhaled particulates and pathogens in the lungs. Depending on the signal, AM acquires either the classically activated M1 phenotype or the alternatively activated M2 phenotype. In this study, we investigate the metabolic shift in the activated phases of AM (M1 and M2 phases) by reconstructing context specific Genome-Scale Metabolic (GSM) models. Metabolic pathways such as pyruvate metabolism, arachidonic acid metabolism, chondroitin/heparan sulfate biosynthesis, and heparan sulfate degradation are found to be important driving forces in the development of the M1/M2 phenotypes. Additionally, we formulated a bilevel optimization framework named MetaShiftOptimizer to identify minimal modifications that shift one activated state (M1/M2) to the other. The identified reactions involve metabolites such as glycogenin, L-carnitine, 5-hydroperoxy eicosatetraenoic acid, and leukotriene B4, which show potential to be further investigated as significant factors for developing efficient therapy targets for severe respiratory disorders in the future. Overall, our study contributes to the understanding of the metabolic capabilities of the M1 and M2 phenotype of AM and identifies pathways and reactions that can be potential targets for polarization shift and also be used as therapeutic strategies against respiratory diseases. [ABSTRACT FROM AUTHOR]

To promote peer‐to‐peer trading in a distribution system with the franchise owned by the concerned distribution company respected, a so‐called "source‐grid‐load‐storage" (SGLS) integrated project is promoted in China. Given this background, this paper proposes a multi‐stage joint optimization model to optimize the participating strategy for SGLS‐IPs in electricity energy and ancillary service markets. A multi‐energy flow park model is presented with electricity, gas, and heat included. A two‐stage model for optimal participating strategy of SGLS‐IPs in electricity and ancillary service markets is then presented. Through scenario analysis of a sample system, the attained revenue of an SGLS‐IP in different markets is evaluated. Additionally, a bilevel Stackelberg game model is introduced for internal electric vehicle clusters (EVCs) within an SGLS‐IP, with the upper level representing an SGLS‐IP, and the lower level representing EV users engaging in the game. The effectiveness of the model is validated through case studies. Simulation results demonstrate that reasonable market participation and pricing strategies contribute to the efficient allocation of resources within a SGLS‐IP and a win‐win outcome for both the SGLS‐IP entity and EV users. [ABSTRACT FROM AUTHOR]

With the increasing penetration rate of renewable energy in the power system, it becomes increasingly important to coordinate adequacy, flexibility, and environmental protection. The development of the power market provides diverse signals for power investors and plays an increasingly significant role in the decarbonization of the power system. However, due to the interdependent operations of various power markets, measuring the multifaceted value of power generation within traditional market mechanisms poses considerable challenges. Herein, a bi‐level optimization model is proposed to integrate the capacity remuneration mechanism (CRM), energy market (EM), and carbon emission trading (CET). The adequacy, flexibility, and environmental revenue expectations of different generation resources through synergistic transactions are derived using Lagrange multipliers. Furthermore, the bilevel optimization model is converted to its equivalent single‐level scheme based on the Karush–Kuhn–Tucker optimality conditions. Finally, several case studies are conducted to demonstrate the effectiveness of the proposed model, along with comparisons and sensitivity analyses to study the principles behind synergistic transactions and the impact of some key parameters. [ABSTRACT FROM AUTHOR]

It is not uncommon that supply chain partners carry out cooperative advertising in green production involving green and dirty products (i.e., substitute products). Besides the advertising decisions, they need to jointly make many other decisions, such as substitute products' production quantities, wholesale prices, and retail prices. Practice and literature have shown that manufacturers-retailers' joint decision-making is of paramount importance yet challenging. This decision-making difficulty is compounded by governments' carbon tax policies and financial subsidies. To facilitate firms in making decisions, this study examines the joint decision-making mechanism involving local governments' carbon taxes and subsidies. To overcome the limitations of the relevant literature addressing one product and relatively fewer decisions, we include both dirty and green products and consider diverse decisions, including technology selection, production quantities, wholesale prices, and retail prices for both products. Additionally, we consider the retailers' advertising investment decisions for both products and the manufacturers' ratios of advertising investment paid to retailers. Capitalizing on decision interactions, we develop a Stackelberg game-based bilevel optimization model. Caused by the large number of decisions and their interactions, solving the game model analytically is barely possible. Consequently, we propose an algorithm of nested particle swarm optimization (NPSO). We perform numerical examples to show how the game model and the NPSO can help firms make complex joint decisions with many interactions. We also carry out sensitivity analysis based on which managerial insights are drawn. [ABSTRACT FROM AUTHOR]

In this article, real-time pricing (RTP) for a smart grid with complicated non-convex and/or non-smooth constraints is explored. First, piecewise linear functions are adopted to approximate any kinds of utility function and RTP is formulated into bilevel non-convex programming. Then, a 0–1 relaxation method is used to linearize sparse constraints and piecewise linear utility functions, to obtain mixed integer linear programming. Finally, a distributed simultaneous perturbation stochastic approximation (DSPSA) method is designed to solve the bilevel non-convex programming. The RTP strategy is generalizable because sparse constraints and non-differentiable and/or non-concave utility functions are widespread in practice, and the piecewise linear functions approximation method is applicable to all kinds of utility function, not just to non-smooth and/or non-concave utility functions. In addition, the 0–1 linear relaxation and stochastic approximation method make the bilevel non-convex programming easily solvable by DSPSA, which converges rapidly while maintaining the high accuracy of solutions. [ABSTRACT FROM AUTHOR]

Robots are attracing increasing attention in the machining field owing to their great flexibility, vast workspace and low cost. Functional redundancy exists when robots are engaged in milling tasks. Redundancy resolution be considered not only in milling robot motion planning, but also in workpiece layout. A two-step motion planning method is proposed to optimize the trajectory and the workpiece set-up simultaneously to improve planning efficiency with less computational burden. In the first stage, a bilevel optimization technique is proposed that can plan the motion from a global perspective and search the optimal workpiece layout. In the second step, based on a novel smoothness index, a modified whale optimization algorithm combined with the differential evolution algorithm (DE-WOA) is proposed to resolve redundancy under robot and machining error constraints. Experiments demonstrate that the proposed method can enhance the quality of surface machining while avoiding the impact of path length on computational complexity. [ABSTRACT FROM AUTHOR]

Shipping, the backbone of economic development, poses a significant environmental threat. Many government agencies have implemented regulations to mitigate ship air pollution. Three commonly used methods for compliance during berthing are marine diesel oil, scrubber, and shore power. Scrubber and shore power have greater emission reduction potential but can be costlier than marine diesel oil. To encourage their adoption, we optimize the subsidy plan through a bi-level mixed integer programming model where the government at the upper level minimizes the total subsidy amount while ship operators at the lower level choose the most cost-effective energy supply. The problem's complexity arises from the interdependence in the bi-level structure and the nonlinearity in the model. We address this by first converting the model into an equivalent single-level form and then reformulating the model by linearization. Numerical experiments are conducted to assess the model's performance. Results suggest that the promotion of scrubber or shore power starts with large ships in the initial stage. Increasing the number of ships with these technologies reduces subsidies. Additionally, each subsidy corresponds to a specific utilization range, allowing the government to adjust amounts based on target utilization levels. [ABSTRACT FROM AUTHOR]

Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models. However, this paper challenges this view through empirical and theoretical analysis. We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models; (ii) over-constraining task diversity may incur the risk of under-fitting or over-fitting during training; and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty. Based on this insight, we design a novel task sampler, called Adaptive Sampler (ASr). ASr is a plug-and-play module that can be integrated into any meta-learning framework. It dynamically adjusts task weights according to task diversity, task entropy, and task difficulty, thereby obtaining the optimal probability distribution for meta-training tasks. Finally, we conduct experiments on a series of benchmark datasets across various scenarios, and the results demonstrate that ASr has clear advantages. The code is publicly available at https://github.com/WangJingyao07/Adaptive-Sampler. [ABSTRACT FROM AUTHOR]

This paper develops a bilevel problem to address the interdiction and fortification of the healthcare network in Golestan province, Iran. We explore a bilevel hierarchical covering interdiction/fortification problem (BHCIFP) to analyze the dynamics between interdiction and fortification operations, referral systems for healthcare services, capacitated health facilities, and budgetary constraints for both defense and offense. The defender (leader) aims to minimize the establishment cost of protective equipment and subsequent healthcare costs post-interdiction while maximizing patient coverage. Conversely, the attacker (follower) aims to disrupt the healthcare network by interdicting facilities, thereby increasing the total travel distance for patients. To solve this NP-hard problem, we employ a hybrid approach that integrates the tabu search algorithm (TS) for the leader’s problem with an exact method for the follower’s problem. A case study of Golestan’s healthcare network is presented to validate the model and hybrid approach. The model effectively minimizes travel distances by strategically locating defense bases, considering both the leader’s and attacker’s decisions. Comparative evaluations of the proposed hybrid TS and two other metaheuristic approaches are conducted on different case study instances and random instances. Numerical results demonstrate that the novel bilevel TS approach provides superior quality solutions in a reasonable time, establishing it as a competitive method for the proposed model. [ABSTRACT FROM AUTHOR]

Bilevel optimization problems embed the optimality of a subproblem as a constraint of another optimization problem. We introduce the concept of near-optimality robustness for bilevel optimization, protecting the upper-level solution feasibility from limited deviations from the optimal solution at the lower level. General properties and necessary conditions for the existence of solutions are derived for near-optimal robust versions of general bilevel optimization problems. A duality-based solution method is defined when the lower level is convex, leveraging the methodology from the robust and bilevel literature. Numerical results assess the efficiency of exact and heuristic methods and the impact of valid inequalities on the solution time. [ABSTRACT FROM AUTHOR]

The bidirectional uncertainty of source and load creates scarcity in the reserve resources of the distribution network. Therefore, it is highly significant for the safe and economic operation of the system to harness spare energy storage capacity from prosumers to provide reserves. This paper proposes a bi-layer optimal scheduling model of "distribution network–prosumer group–prosumer" that considers the flexible reserve resources of a prosumer. The upper layer is the "distribution network–prosumer group" optimization model, in which the distribution network sets the electricity price and reserve price according to its own economic benefit and sends it to the prosumer group and guides it to participate in the scheduling of the resources of the prosumer. The lower layer is the "prosumer group–prosumer" optimization model, where the prosumer group incentivizes the prosumer to adjust its energy storage charging and discharging plans through prices and mobilize its own resources to provide flexible reserve resources. The results show that the optimal method proposed in this paper can fully utilize flexible reserve resources from prosumers, improve the economy of distribution network operations, and reduce the pressure of providing reserves using the upper grid. [ABSTRACT FROM AUTHOR]

This paper addresses the facility location problem under a mill pricing policy, integrating customers' behavior through the concept of preferences. The problem is modeled as a bilevel optimization problem, where the existence of ties in customers' preferences can lead to an ill-posed bilevel problem due to the possible existence of multiple optima to the lower-level problem. As the commonly employed optimistic and pessimistic strategies are inadequate for this problem, a specific approach is proposed bearing in mind the customers' rational behavior. In this work, we propose a novel formulation of the problem as a bilevel model in which each customer faces a lexicographic biobjective problem in which the preference is maximized and the total cost of accessing the selected facility is minimized. This allows for a more accurate representation of customer preferences and the resulting decisions regarding facility location and pricing. To address the complexities of this model, we apply duality theory to the lower-level problems and, ultimately, reformulate the bilevel problem as a single-level mixed-integer optimization problem. This reformulation incorporates big-M constants, for which we provide valid bounds to ensure computational tractability and solution quality. The computational study conducted allows us to assess, on the one hand, the effectiveness of the proposed reformulation to address the bilevel model and, on the other hand, the impact of the length of the customer preference lists and fixed opening cost for facilities on the computational time and the optimal solution. [ABSTRACT FROM AUTHOR]

Fuel cells and batteries are integral to achieving zero-emission maritime transport, delivering an efficient energy solution for offshore vessels. In this paper, an innovative multi-objective, bi-level optimization method is proposed, to minimize operational costs and spatial requirements for vessels, while addressing the interrelated challenges of capacity allocation and energy management. The outer layer of this method optimizes for volume and economic efficiency, determining the ideal power capacity configuration. The inner layer leverages an algorithm to enhance economic performance, with a focus on improving operational efficiency. This bi-layered method results in an energy management strategy that aligns with the hourly energy consumption patterns. A comprehensive analysis across four scenarios assesses the impact of varying electricity costs and ship classifications on the system's configuration. The study reveals that a modest 5% trade-off in economic efficiency can lead to a substantial 57% reduction in system volume, offering a viable and sustainable strategy for maritime operations. • A bi-layer optimization method is proposed to optimize configuration and control strategy in parallel. • The results indicate that that a modest 5% trade-off in economy can lead to substantial 57% reduction in volume. • The effects of various ship operating conditions and pricing environments on the optimization results are analyzed. [ABSTRACT FROM AUTHOR]

The importance of curbside management is quickly growing in a modernized urban setting. Dynamic allocation of curb space to different usages and dynamic pricing for those usages can help meet the growing demand for curb space more effectively and promote user turnover. To model curbside operations, we formulate a Stackelberg leader-follower game between a leader operating curbside spaces, who sets space allocation and pricing of each curbside usage, and multi-followers, one for each type of curbside usage, who accept the proposed prices or reject them in favor of outside options. The proposed model offers flexible adaptability to manage curb space usages characterized by high turnover rates, such as parking and ride-sourcing pickup and drop-off, alongside accommodating usages that require more permanent infrastructure allocation, such as micromobility stations. Furthermore, the proposed model is able to capture the sensitivity of users to both prices, which are determined solely by the operator, and the occupancy levels of the curb space, which are determined by the complex interactions between the curbside operator and the users. We model a Stackelberg leader-follower game as a bilevel nonlinear optimization problem and reconstruct the problem into a single-level convex program by applying the Karush-Kuhn-Tucker conditions, objective function transformation, and constraint linearization. Then, we develop a solution algorithm that leverages valid inequalities produced via Benders decomposition. We validate the practicability of the model and draw insights into curbside management using numerical experiments. History: This paper has been accepted for the Transportation Sci. Special Issue on the ISTTT25 Conference. Funding: This work was supported by the National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation [Grant 2046372]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0507. [ABSTRACT FROM AUTHOR]

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system. [ABSTRACT FROM AUTHOR]

Adverse selection is a version of the principal-agent problem that includes monopolist nonlinear pricing, where a monopolist with known costs seeks a profit-maximizing price menu facing a population of potential consumers whose preferences are known only in the aggregate. For multidimensional spaces of agents and products, Rochet and Choné (Econometrica, 1998) reformulated this problem as a concave maximization over the set of convex functions, by assuming agent preferences combine bilinearity in the product and agent parameters with a quasilinear sensitivity to prices. We characterize solutions to this problem by identifying a dual minimization problem. This duality allows us to reduce the solution of the square example of Rochet–Choné to a novel free boundary problem, giving the first analytical description of an overlooked market segment. [ABSTRACT FROM AUTHOR]

This study proposed a method for optimizing a multi-period freight network design problem with industrial relocation (MPNDP-IR). Firstly, a land-use transport interaction (LUTI) model is developed to quantitatively describe the intricate interactions between industrial relocation behavior and freight network upgrades. Then, the proposed LUTI model is integrated into a bilevel programming framework to effectively optimize the multi-period network investment strategies and maximize benefits from relocated industries and freight transshipment. Finally, taking the Yangtze River Economic Belt (YREB) as a case study, the study results indicate that the sensitivity to location utility significantly affects the scope of the beneficial impacts of freight network investment policies, thereby affecting the industrial relocation pattern. Among manufacturing activities, the raw material and chemical industry is most responsive to such network investment policies along an important railway corridor. Under the low-sensitivity scenario, the relocation pattern of such an industry aligns with a gradient relocation mode; while, under the high-sensitivity scenario, it tends to choose a "fly-in" mode. This study provides a decision support method tailored for the MPNDP-IR. It not only simulates the coevolution of different industrial activities and freight network upgrades but also evaluates the spatiotemporal impact of multi-period network investment policies in the context of industrial relocation. • Identify the trend of industrial relocation and regional attractiveness for relocated industries. • Extend the application of the LUTI model for multiperiod transportation policy. • Provide a spatial econometric method for analyzing for coevolution of industrial activities and transportation networks. • Optimize multiperiod freight network policies balancing the diverse interests of different stakeholders. [ABSTRACT FROM AUTHOR]

We introduce a Bi-level OPTimization (BiOPT) framework for minimizing the sum of two convex functions, where one of them is smooth enough. The BiOPT framework offers three levels of freedom: (i) choosing the order p of the proximal term; (ii) designing an inexact pth-order proximal-point method in the upper level; (iii) solving the auxiliary problem with a lower-level non-Euclidean method in the lower level. We here regularize the objective by a (p + 1) th-order proximal term (for arbitrary integer p ≥ 1 ) and then develop the generic inexact high-order proximal-point scheme and its acceleration using the standard estimating sequence technique at the upper level. This follows at the lower level with solving the corresponding pth-order proximal auxiliary problem inexactly either by one iteration of the pth-order tensor method or by a lower-order non-Euclidean composite gradient scheme. Ultimately, it is shown that applying the accelerated inexact pth-order proximal-point method at the upper level and handling the auxiliary problem by the non-Euclidean composite gradient scheme lead to a 2q-order method with the convergence rate O (k - (p + 1)) (for q = ⌊ p / 2 ⌋ and the iteration counter k), which can result to a superfast method for some specific class of problems. [ABSTRACT FROM AUTHOR]

A hybrid ship uses integrated generators, an energy storage system (ESS), and photovoltaics (PV) to match its propulsion and service loads, and together with optimal power and voyage scheduling, this can lead to a substantial improvement in ship operation cost, ensuring compliance with the environmental constraints and enhancing ship sustainability. During the operation, significant uncertainties such as waves, wind, and PV result in considerable speed loss, which may lead to voyage delays and operation cost increases. To address this issue, a distributionally robust optimization (DRO) model is proposed to schedule power generation and voyage. The problem is decoupled into a bi-level optimization model, the slave level can be solved directly by commercial solvers, the master level is further formulated as a two-stage DRO model, and linear decision rules and column and constraint generation algorithms are adopted to solve the model. The algorithm aims at minimizing the operation cost, limiting greenhouse gas (GHG) emissions, and satisfying the technical and operational constraints considering the uncertainty. Extensive simulations demonstrate that the expected total cost under the worst-case distribution is minimized, and compared with the conventional robust optimization methods, some distribution information can be incorporated into the ambiguity sets to generate fewer conservative results. This method can fully ensure the on-time arrival of hybrid ships in various uncertain scenarios while achieving expected operation cost minimization and limiting greenhouse gas (GHG) emissions. [ABSTRACT FROM AUTHOR]

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment. [ABSTRACT FROM AUTHOR]

In group decision making, the decision participants may exhibit various interaction behaviors, some of them are efficient at facilitating the efficiency of consensus reaching process. Motivated by the view of this, a bi-level consensus model with maximum return and minimum cost considering interaction and transaction behaviors (MRMC-IT) is proposed in the way of adopting the Choquet integral. The bi-level feedback mechanism based on the adjustment of opinions guarantees the interaction between the moderator in the upper level and the decision makers in the lower level. Meanwhile, a transaction mechanism of discourse weights is elicited to present the trading behaviors between the pairwise decision makers. Additionally, in order to solve the complex proposal, a bi-level interactive approach based on satisfactory degree is applied to obtain a satisfactory solution for all decision participants. Finally, an application of sewage discharge rights trading is conducted to demonstrate the validity and the rationality of the proposal. [ABSTRACT FROM AUTHOR]

In the planning of public charging facilities and the charging activity network of users, there is a decision-making conflict among three stakeholders: the government, charging station enterprises, and electric vehicle users. Previous studies have described the tripartite game relationship in a relatively simplistic manner, and when designing charging facility planning schemes, they did not consider scenarios where users' choice preferences undergo continuous random changes. In order to reduce the impacts of queuing phenomenon and resource idleness on the three participants, we introduce a bilateral matching algorithm combined with the dynamic Huff model as a strategy for EV charging selection in the passenger flow problem based on the three-dimensional activity network of time–space–energy of users. Meanwhile, the Dirichlet distribution is utilized to control the selection preferences on the user side, constructing uncertain scenarios for the choice of user charging activities. In this study, we establish a bilevel programming model that takes into account the uncertainty in social responsibility and user charging selection behavior. Solutions for the activity network and facility planning schemes can be derived based on the collaborative relationships among the three parties. The model employs a robust optimization method to collaboratively design the charging activity network and facility planning scheme. For this mixed-integer nonlinear multi-objective multi-constraint optimization problem, the model is solved by the NSGA-II algorithm, and the optimal compromise scheme is determined by using the EWM-TOPSIS comprehensive evaluation method for the Pareto solution set. Finally, the efficacy of the model and the solution algorithm is illustrated by a simulation example in a real urban space. [ABSTRACT FROM AUTHOR]

The power-to-gas (P2G) technology transforms the unidirectional coupling of power network and natural gas network into bidirectional coupling, and its operational characteristics provide an effective way for wind and solar energy accommodation. The paper proposes a bi-level optimal dispatch model for the integrated energy system with carbon capture system and P2G facility. The upper model is an optimal allocation model for coal-fired units, and the lower model is an economic dispatch model for the integrated energy system. Moreover, the upper model is solved by transforming the model into a mixed-integer linear programming problem and calling CPLEX, and the lower model is a multi-objective planning problem, which is solved by improving the small-habitat particle swarm algorithm. Finally, the simulation is validated by the MATLAB platform, and the results show that the simultaneous consideration of carbon capture system and P2G facility improves the economics of the integrated energy system and the capacity of wind and solar energy accommodation. [ABSTRACT FROM AUTHOR]

This paper proposes a multi‐objective evolutionary algorithm based on bilayered decomposition (MOEA/BLD) for solving constrained multi‐objective optimization problems. MOEA/D is an effective method for solving unconstrained multi‐objective optimization problems. It decomposes the objective space using weight vectors and simultaneously searches for solutions for the subproblems. However, real‐world applications impose many constraints, and these constraints must be handled appropriately when searching for good feasible solutions. The proposed MOEA/BLD treats such constraints as an additional objective function. Furthermore, in addition to the conventional weight vector, an augmented weight vector is introduced that decomposes the objective space and constraint violation space hierarchically. In the first stage, the objective space is decomposed by conventional weight vectors. In the next stage, the bi‐objective space consisting of the scalarizing function and constraint violation is decomposed by augmented weight vectors. The augmented weights are adjusted so that they decrease linearly in the search process as the search gradually moves from infeasible regions to feasible regions. The proposed algorithm is compared to several state‐of‐the‐art constrained MOEA/Ds using multi‐ and many‐objective problems. The results show that the proposed method outperforms existing methods, in terms of search performance, under various conditions. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Hazardous materials pose significant dangers during transportation due to their flammable and explosive properties. The consequences of accidents involving such materials are often severe and irreparable. A well‐designed hazardous materials transportation network can mitigate these risks. However, designing such a network presents two major challenges: quantifying the risk associated with hazardous materials transportation and addressing the hierarchical relationship between government and companies. To address these challenges, we enhance the accuracy of accident probability estimates and the comprehensiveness of accident consequence assessments, incorporating the uncertainty of accident outcomes. We propose a comprehensive risk assessment model and develop a bilevel programming model to reflect the hierarchical relationship. In this model, the government at the upper level aims to minimize the total risk, while companies at the lower level seek to minimize their total costs. The model is transformed using chance‐constrained programming and solved using heuristic algorithms. We apply the model to the highway network in Anhui province, China, to verify its validity. The results demonstrate that the model effectively manages the hierarchical relationship between government and companies, reduces the risk of hazardous materials transportation, and enhances the stability and safety of the transportation network. [ABSTRACT FROM AUTHOR]

The multi-leader–multi-follower game (MLMFG) involves two or more leaders and followers and serves as a generalization of the Stackelberg game and the single-leader–multi-follower game. Although MLMFG covers wide range of real-world applications, its research is still sparse. Notably, fundamental solution methods for this class of problems remain insufficiently established. A prevailing approach is to recast the MLMFG as an equilibrium problem with equilibrium constraints (EPEC) and solve it using a solver. Meanwhile, interpreting the solution to the EPEC in the context of MLMFG may be complex due to shared decision variables among all leaders, followers' strategies that each leader can unilaterally change, but the variables are essentially controlled by followers. To address this issue, we introduce a response function of followers' noncooperative game that is a function with leaders' strategies as a variable. Employing this approach allows the MLMFG to be solved as a single-level differentiable variational inequality using a smoothing scheme for the followers' response function. We also demonstrate that the sequence of solutions to the smoothed variational inequality converges to a stationary equilibrium of the MLMFG. Finally, we illustrate the behavior of the smoothing method by numerical experiments. [ABSTRACT FROM AUTHOR]

Convex bilevel optimization problems (CBOPs) exhibit a vital impact on the decision-making process under the hierarchical setting when image restoration plays a key role in signal processing and computer vision. In this paper, a modified double inertial extragradient-like approach with a line search procedure is introduced to tackle the CBOP with constraints of the CFPP and VIP, where the CFPP and VIP represent a common fixed point problem and a variational inequality problem, respectively. The strong convergence analysis of the proposed algorithm is discussed under certain mild assumptions, where it constitutes both sections that possess a mutual symmetry structure to a certain extent. As an application, our proposed algorithm is exploited for treating the image restoration problem, i.e., the LASSO problem with the constraints of fractional programming and fixed-point problems. The illustrative instance highlights the specific advantages and potential infect of the our proposed algorithm over the existing algorithms in the literature, particularly in the domain of image restoration. [ABSTRACT FROM AUTHOR]

With the burgeoning of remote sensing and space technology, multi-satellite collaborative mission planning, which is the key to achieving efficient Earth observation, has become increasingly intricate due to the expanding complexity and volume of observation missions. Addressing the multi-satellite collaborative mission planning problem, which is characterized by its two-stage decision-making process involving mission assignment and resource scheduling, this study investigates a comprehensive joint decision making that encompasses both mission assignment and resource scheduling and comprehensively optimizes the mission completion rate, the mission profit rate, and the satellite resource utilization rate. Considering the interaction of these decisions, we formulate the problem as a bilevel programming model from a game-theoretic perspective and propose a nested bilevel improved genetic algorithm (NBIGA) for its solution. Simulation experiments substantiate the applicability of the bilevel programming model in joint decision making for the stages of mission assignment and resource scheduling in multi-satellite collaborative mission planning, as well as the robustness of the NBIGA. A comparative analysis with the nested bilevel genetic algorithm (NBGA) confirms that the algorithm proposed in this study can achieve superior optimization outcomes and higher solving efficiency. [ABSTRACT FROM AUTHOR]

Under the background of the Energy Internet and the shared economy, it is of great significance to explore the collaborative planning strategies of multi-energy microgrids (MEMGs) and a shared energy storage operator (SESO) supported by shared energy storage resources. In this context, a joint planning method of SESO and MEMG alliances based on a dynamic game with perfect information is proposed in this paper. First, an upper-level model for energy storage capacity configuration and pricing strategy planning of SESO is proposed to maximize the total planning and operational income of SESO. Then, a lower-level model for the optimal configuration of MEMGs' alliance considering SES is proposed to minimize the total planning and operational costs of the MEMG alliance. On this basis, a solving algorithm based on the dynamic game theory with perfect information and the backward induction method is proposed to obtain the Nash equilibrium solution of the proposed bi-level optimization models. Finally, a case study with one SESO and an alliance consisting of five MEMGs is conducted, and the simulation results show that the proposed bi-level optimization method can increase SESO's net income by 1.47%, reduce the average planning costs for each MEMG at least by 1.7%, and reduce model solving time by 62.9% compared with other counterpart planning methods. [ABSTRACT FROM AUTHOR]

This paper introduces a deterministic algorithm to solve the discrete network design problem (DNDP) efficiently. This non‐convex bilevel optimization problem is well‐known as an non deterministic polynomial (NP)‐hard problem in strategic transportation planning. The proposed algorithm optimizes budget allocation for large‐scale network improvements deterministically and with computational efficiency. It integrates disjunctive programming with an improved partial linearized subgradient method to enhance performance without significantly affecting solution quality. We evaluated our algorithm on the mid‐scale Sioux Falls and large‐scale Chicago networks. We assess the proposed algorithm's accuracy by examining the objective function's value, specifically the total travel time within the network. When tested on the mid‐scale Sioux Falls network, the algorithm achieved an average 46% improvement in computational efficiency, compared to the best‐performing method discussed in this paper, albeit with a 4.17% higher total travel time than the most accurate one, as the value of the objective function. In the application to the large‐scale Chicago network, the efficiency improved by an average of 99.48% while the total travel time experienced a 4.34% increase. These findings indicate that the deterministic algorithm proposed in this research improves the computational speed while presenting a limited trade‐off with solution precision. This deterministic approach offers a structured, predictable, and repeatable method for solving DNDP, which can advance transportation planning, particularly for large‐scale network applications where computational efficiency is paramount. [ABSTRACT FROM AUTHOR]

This article proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short‐term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this article, the model training is formulated as a bi‐level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower‐level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full connected Neural Network (FNN) to generate interval boundary. In upper‐level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper‐parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state‐of‐the‐art algorithms, achieving a 15% reduction in prediction error and a 20% decrease in computational time. [ABSTRACT FROM AUTHOR]

This paper is concerned with the value function approach to multiobjective bilevel optimization which exploits a lower-level frontier-type mapping in order to replace the hierarchical model of two interdependent multiobjective optimization problems by a single-level multiobjective optimization problem. As a starting point, different value-function-type reformulations are suggested and their relations are discussed. Here, we focus on the situations where the lower-level problem is solved up to efficiency or weak efficiency, and an intermediate solution concept is suggested as well. We study the graph-closedness of the associated efficiency-type and frontier-type mappings. These findings are then used for two purposes. First, we investigate existence results in multiobjective bilevel optimization. Second, for the derivation of necessary optimality conditions via the value function approach, it is inherent to differentiate frontier-type mappings in a generalized way. Here, we are concerned with the computation of upper coderivative estimates for the frontier-type mapping associated with the setting where the lower-level problem is solved up to weak efficiency. We proceed in two ways, relying, on the one hand, on a weak domination property and, on the other hand, on a scalarization approach. Illustrative examples visualize our findings and some flaws in the related literature. [ABSTRACT FROM AUTHOR]

Most implicit sentiment sentences in aspect level implicit sentiment analysis lack emotional words, but there are still potential emotional clues. Current research mainly utilizes contextual information and external knowledge of text for semantic enhancement, thereby improving implicit sentiment analysis. However, in these models, the semantic correlation between words decays as the distance between the two increases, leading to weak and difficult to capture emotional cues at long distances, presenting ambiguity in emotional polarity and reducing the performance of sentiment analysis. To solve this problem, in this paper, we propose aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement. Firstly, the semantic wave-based aspect word context information enhancement method uses the fluctuation property of semantic wave to perform weight adaptive enhancement of semantically related long-distance context information. This method essentially utilizes a sine wave function to significantly reduce the weight of words that are close but semantically unrelated, thereby allowing the weight of words that are close and semantically similar to be relatively reflected, in order to solve the problem of hiding emotional clues at long distances. Then, the implicit sentiment analysis method based on supervised contrastive pre-training is used to capture potential sentiment cues from context information and embed them into the sentence vector by weak feature learning of implicit sentiment. Finally, multi-feature interaction-based knowledge enhancement method is used to focus on the context content of sentences that are more related to background knowledge in a feature interaction way, and the information scaling factor is combined to enhance the dependency between aspect words and sentiment cues, thereby solving the problem of weak features in long-distance emotional clues and accurately identifying the emotional polarity of aspect words. The experiment shows that the model proposed in this paper has made significant improvements on the Restaurant and Laptop datasets in SemEval 2014 Task 4, with an accuracy increase of 3.38% and 4.57% in implicit emotions, respectively, achieving the best performance so far. At the same time, generalization and robustness testing experiments were conducted on the model, achieving preferable performance improvement. [ABSTRACT FROM AUTHOR]

This study focuses on solving the problem of metro first/last mile, studying the method for designing microcirculation bus routes and coordinating schedules considering the impact of shared bicycles. First, we propose a bilevel mixed-integer programming model for designing microcirculation bus routes and coordinating schedules considering the impact of shared bicycles. The upper-level model minimizes the weighted sum of the travel time cost of passengers and the operating cost of public transport enterprises, and the lower-level model maximizes the number of passengers served by microcirculation bus routes. Then, an improved genetic algorithm is developed to solve the model, called the Monte Carlo adaptive genetic algorithm (M-GAI). Finally, the proposed model and algorithm are evaluated using the case study in the area near the Fubao metro station of Shenzhen Metro Line 3. Results show that if the impact of shared bicycles is not considered, the passenger demand will be greater than the actual value, and the operating cost of public transport enterprises will be increased by 36%. Compared with GAI, the average number of iterations of M-GAI is reduced by 31%, and the objective function value is decreased by 4%. In addition, when the number of routes increases, the average waiting time of passengers is shortened, the average attendance rate of microcirculation buses increases, and the average empty distance of each vehicle is shortened. However, the operating cost of public transport enterprises will increase with the number of routes. Finally, when weight factors α and β are 0.6 and 0.4, respectively, and the sum of the travel time cost of passengers and the operating cost of public transport enterprises reach optimal. [ABSTRACT FROM AUTHOR]

Current unsupervised domain adaptation methods for intelligent fault diagnosis focus on learning domain-invariant representations under covariate shift assumption. However, the covariate shift assumption is usually unsatisfied when each fault class in different domains consists of multiple modes with skewed proportions, which is common in industrial scenarios. Imbalanced data from multiple modes cause the presence of within-domain class imbalance and between-domain label distributional shift. This paper introduces a novel subpopulation shift that further considers the domain shift from a subpopulation perspective, i.e., that covariate shift and label distributional shift across domains are caused by shifts in the multiple modes. To address this issue, a label propagation-based unsupervised domain adaptation is proposed based on a realistic expansion assumption. We apply the theoretical analysis of the proposed method with a bi-level optimization strategy adapted from meta-learning. Using joint optimization of a teacher model and a student model, the label propagation-based model-agnostic meta-learning (LPMAML) not only propagates supervision information from the source to the target but also adjusts the teacher's strategy throughout the student's learning process. To alleviate the noise caused by label distributional shift, we integrate a sampling-based alignment method that aligns the empirical label distributions across the two domains into LPMAML. Experimental results on three bearing datasets show that the proposed method has impressive generalization ability under covariate, label distributional, and subpopulation shifts. The proposed method offers consistent improvements to unsupervised domain adaptation (UDA) methods. Compared with the vanilla UDA methods, the average diagnosis accuracies of the proposed method on the label distributional shift benchmark and subpopulation shift benchmark are improved by 8.21% and 7.63%, respectively. [ABSTRACT FROM AUTHOR]

This article introduces a class of Nash games among Stackelberg players (NASPs), namely, a class of simultaneous noncooperative games where the players solve sequential Stackelberg games. Specifically, each player solves a Stackelberg game where a leader optimizes a (parametrized) linear objective function subject to linear constraints, whereas its followers solve convex quadratic problems subject to the standard optimistic assumption. Although we prove that deciding if a NASP instance admits a Nash equilibrium is generally a Σ2p -hard decision problem, we devise two exact and computationally efficient algorithms to compute and select Nash equilibria or certify that no equilibrium exists. We use NASPs to model the hierarchical interactions of international energy markets where climate change aware regulators oversee the operations of profit-driven energy producers. By combining real-world data with our models, we find that Nash equilibria provide informative, and often counterintuitive, managerial insights for market regulators. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03418. [ABSTRACT FROM AUTHOR]

The increasing energy crisis and environmental problems promote the development of distributed energy systems (DESs) that utilize combined heat and power technology, renewable energy technology, and waste heat recovery technology to meet various load requirements. Although there is existing work and research on a variety of DESs, most previous studies tend to focus on energy production with little consideration of a distributed energy network and its energy loss, which results in large errors in energy-efficiency calculations and performance analyses. In this paper, a new DES model is proposed with full consideration of an energy network and the full use of solar energy, terrestrial heat, and exhaust gases. At the same time, an effective bi-level optimization method is also proposed for the daily operation of the DES in order to improve the system performance and benefits in the energy, the economy, and the environment. Specifically, the co-generation energy station and water heating network in the DES are optimized separately with two different optimization models. The first-level optimization model is used to seek the optimal values of water mass flow rate and water temperature of the water heating network, and the second-level optimization model is built to determine the optimal energy purchasing strategy and the optimal energy outputs of the co-generation energy station. In order to verify the advantages and effectiveness of the proposed system and method, a contrastive simulation study is undertaken to provide a comparison of the global optimization method. Simulation results show that energy loss, energy cost, and energy consumption of the DES using the bi-level optimization operation strategy only account for 22.51%, 33.42%, and 51.31% of the quantities of the global optimization method, respectively. The hourly curves of the optimal operating variables also demonstrate that the proposed bi-level optimization method can improve the operating stability of the DES better than the global optimization method. [ABSTRACT FROM AUTHOR]

This research introduces an enhanced approach for multiframe super-resolution (SR) that incorporates a bilevel optimization technique for learning the space variable weights parameter in the second-order nonlocal term. This novel approach effectively preserves image edges and ensures accurate reconstruction of image textures. The main contributions of this work include the establishment of the existence and uniqueness of the solution within a well-posed framework and a bilevel optimization procedure to compute the weights $ \alpha $. The experimental results demonstrate the efficiency and accuracy of the proposed method compared to existing super-resolution approaches. These findings highlight the practical effectiveness and potential of the proposed approach in real-world applications. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to minimize the overall travel costs for passengers while simultaneously maximizing the operational revenue for the transportation company. This is achieved through the optimization and adjustment of various factors, such as the intervals between regular bus and subway services, the duration of vehicle stops at each station, and the pricing structure for subway and shared bicycle usage. By enhancing the efficiency of passenger travel, we have successfully bolstered the company's operational profits. In contrast to prior research, this paper comprehensively considers the dual uncertainties associated with both bus operations and shared bicycle operations within a cooperative system. By establishing a coordinated dual‐level optimization model for regular bus, subway, and bike‐sharing networks under dual uncertainty conditions, we employed convex combination techniques to unify the dual uncertain variables into a single objective, which was then incorporated into a chance‐constrained bilevel programming model. Ultimately, we utilized KKT conditions to transform the model from a bilevel to a single level for resolution. This paper centers its research on the collaborative system comprising the Nanchang Metro Line 1, Bus Route 520, Bus Route 211, and the adjacent region hosting a cluster of shared bicycles. By leveraging Python programming, optimization models, empirical data on traffic flow and stoppage times, and OD data, we conducted an optimization analysis to solve the problem at hand. According to the optimization results, passenger waiting time, passenger transfer time, and passenger on board time are effectively reduced by 6.81%, 18.29%, and 23.92%. At a confidence level of 95%, the resulting time level results in a 12.44% reduction in total travel time. The average subway fare increased by 18.12%, the average shared bicycle fare decreased by 19.12%, and the total cost of travel expenses increased by 16.68%. The final total cost of travel was reduced by 4.06%, and the business operating income was increased by 13.10%. The comprehensive optimization results have effectively fulfilled the objectives of the bilevel optimization model, thereby confirming the rationality and practicality of the optimization approach. The research outcomes hold significant practical implications for facilitating the efficient and cooperative development of urban transportation networks, ultimately enhancing the convenience of residents' travel experiences. [ABSTRACT FROM AUTHOR]

This article discusses research on time-of-use compensation pricing strategies for load aggregators based on user demand response. The study aims to address the issue of mismatch between purchased electricity and market bid volume, which results in punitive costs for companies. The researchers propose a tiered pricing strategy to adjust power distribution and establish a Stackelberg leader-follower game model to promote a mutually beneficial relationship between load aggregators and controllable load users. Through case studies, the paper examines the operational profits of load aggregators and the power adjustment behaviors of controllable load users under tiered and fixed compensation pricing schemes. The results indicate that tiered compensation pricing significantly reduces punitive costs and enhances user participation in demand response. The article also discusses a dynamic compensation pricing model for controllable load power adjustment in power systems, including interactions between load aggregators and controllable load users. A case study is presented to validate the model's effectiveness, and future work will explore the integration of artificial intelligence and machine learning technologies to further optimize demand response strategies. [Extracted from the article]

An optimal credit term decision in supply chain finance often needs to be made in a dynamic way considering the varying market demand among other factors. We study the dynamic credit term optimization problem (DCTOP), where a supplier determines the credit term in conjunction with its production and inventory decision, while anticipating a buyer's order quantity in a leader-follower game setting. The DCTOP is first approached to using a continuous time optimal control model, with analytical results characterizing the structural properties of the optimal solution. To complement the structural properties, we then develop a discrete time bilevel programming model to provide computationally tractable and implementable numerical solutions. A comprehensive computational study shows significant advantage of our optimal solutions over the heuristic credit term rules in practice, and provides managerial insights regarding the impacts of key problem parameters on the optimal solutions and coordination scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose two bilevel models to cope with problems concerning waste management. The first formulation stems from a multi-objective version of the problem present in the literature, while the second one improves upon the first by taking into account a theoretical result associated with the first formulation. For both models, we provide the optimistic and the pessimistic versions. We validate these formulations in a case study in the Pathum Thani province in Thailand. Moreover, we compare the results obtained by the bilevel formulation to those obtained by a multi-objective version of the problem. Finally, we provide our thoughts on future applications in this field. [ABSTRACT FROM AUTHOR]

We show how to solve a maximum clique problem on a given graph by an equivalent problem on an auxiliary graph. The transformation has interesting consequences in the bilevel setting. In fact, it allows to map a clique interdiction problem with edge interdiction into a clique interdiction problem with node interdiction. As a byproduct of the mapping, we can generalize to the edge interdiction problem complexity and algorithmic results that hold for the node interdiction problem. [ABSTRACT FROM AUTHOR]