This paper presents a novel app roach for solving the multi-objective vehicle routing problem (MOVRP) using deep reinforcement learning. The MOVRP considered in this study involves two objectives: travel distance and ...
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ISBN:
(纸本)9789819947546;9789819947553
This paper presents a novel app roach for solving the multi-objective vehicle routing problem (MOVRP) using deep reinforcement learning. The MOVRP considered in this study involves two objectives: travel distance and altitude difference. To address this problem, we employ a decomposition strategy based on weights to decompose the multi-objectiveproblem into multiple scalar subproblems. We then use a pointer network to solve each subproblem and train the policy network's parameters using the policy gradient algorithm of reinforcement learning to obtain the Pareto front solutions of the entire problem. The proposed method provides an effective solution to the MOVRP, and experimental results demonstrate its superiority over traditional optimization methods in terms of solution quality and computational efficiency. Furthermore, the method exhibits strong generalization and adaptability, enabling it to handle multi-objective vehicle routing problems of varying sizes and features with significant flexibility and practicality. The proposed method's distinct advantages make it a promising solution to the MOVRP.
The complexity of the vehicleroutingproblems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of optimization. Recently, attentions have turned to multi-objective VRP...
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ISBN:
(纸本)9781479944675
The complexity of the vehicleroutingproblems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of optimization. Recently, attentions have turned to multi-objective VRPs with multi-objective Genetic Algorithms (MOGAs). MOGAs, thanks to its genetic operators such as selection, crossover, and/or mutation, constantly modify a population of solutions in order to find optimal solutions. However, given the complexity of VRPs, conventional crossover operators have major drawbacks. The Best Cost Route Crossover is lately gaining popularity in solving multi-objective VRPs. It employs a brute force approach to generate new children. Such approach may be unacceptable when presented with a relatively large problem instance. In this paper, we introduce a new crossover operator, called Partially Optimized Cyclic Shift Crossover (POCSX). A comparative study, between a MOGA based on POCSX, and a MOGA which is based on the Best Cost Route Crossover affirms the level of competitiveness of the former.
This paper deals with a problem in collaborative logistics which arises when a number of carriers, having both transferable and non-transferable utilities, form a coalition. The main application of the problem is last...
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This paper deals with a problem in collaborative logistics which arises when a number of carriers, having both transferable and non-transferable utilities, form a coalition. The main application of the problem is last-mile delivery in urban areas. We propose mathematical models to formulate both cases of coopera-tion and non-cooperation as multi-objective optimization problems in which carriers seek two objectives, including maximizing profit and increasing customer coverage. To allocate the coalition outcomes to par-ticipating carriers in the proposed cooperative game, we develop mathematical conditions to define a generalized core solution concept. In addition, we develop a generalized form of the well-known Shap-ley value ensued by a detailed discussion on the trust issue in these games. Thereafter, two methods of reaching a compromise among coalition members are proposed. Moreover, a heuristic algorithm and a full-enumeration method are developed to find Pareto-optimal solutions for the bi-objective coopera-tive game. In order to evaluate the efficacy of the proposed models and algorithms, a set of benchmark instances having up to 225 customers are devised. Computational results indicate that cooperation can lead to profit improvement ranging from 9.07% in small-size instances to 14.7% in large-size instances on average, without worsening the customer coverage.(c) 2022 Elsevier B.V. All rights reserved.
In the context of establishing emission control areas (ECAs) in many ports to meet the challenges posed by air pollution, the use of drone-carrying sniffers to perform emission monitoring missions has become a new mon...
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In the context of establishing emission control areas (ECAs) in many ports to meet the challenges posed by air pollution, the use of drone-carrying sniffers to perform emission monitoring missions has become a new monitoring mode for ECAs. The operational management problem of drones in ECAs, namely, drone scheduling problem (DSP), is eliciting the attention of researchers. To consider the influence of vessel traffic on the demand for drones, this study proposes a rental-based drone operation model. In the model, the number of drones used depends on the load of monitoring missions. Maximizing the cumulative monitoring reward and minimizing the use number of drones within the minimum monitoring rate constraint are used as optimization objectives to maximize the cost return of the rental-based operation model. The rental-based drone operation model is modeled as a multi-objective DSP (MDSP). Furthermore, we horizontally compare the characteristics of MDSP with those of many classical models in the field of operations research. Afterward, we reveal the similarities and differences between MDSP and previous models. We find that MDSP has the non-first-in-first-out property, whereas most of the advanced models have the first-in-first-out property, which leads to the failure of the developed efficient algorithms in solving MDSP. Therefore, this study innovatively designs four feasible multi-objective optimization methods for MDSP. Numerical experiments are conducted to evaluate the performance of the four methods in solving MDSP with different scales. In terms of theoretical implications, experimental results prove that the proposed methods for solving MDSP are feasible and effective In terms of practical implications, the proposed rental-based vessel monitoring operation model shows great potential for practical engineering.
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