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A multi-compartment vehicle routing problem with time windows for urban distribution - A comparison study on particle swarm optimization algorithms

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 133卷 95-106页

作者： Chen, Jiumei Shi, Jing Chongqing Technol & Business Univ Sch Business Planning Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Chongqing Key Lab Elect Commerce & Supply Chain S Chongqing 400067 Peoples R China Univ Cincinnati Coll Engn & Appl Sci Dept Mech & Mat Engn Cincinnati OH 45221 USA

In this paper, we introduce a multi-compartment vehicle routing problem with time window (MCVRPTW) arising from urban distribution, which essentially reflects the "last mile" delivery challenge for modern logistics. The distinguishing feature of MCVRPTW is that products must be transported in independent vehicle compartments because they cannot be mixed together due to differences in their characteristics. Based on the mathematical formulation established for the optimization problem, we propose and compare two solution approaches, with one being the hybrid particle swarm optimization (HPSO) with simulated annealing, and the other being the conventional particle swarm optimization (PSO). Based on Solomon's vehicle routing problem with time windows (Solomon, 1987), experimental instances with 25 customers, 50 customers and 100 customers are developed to investigate the performance of the proposed solution approaches. The results indicate that both approaches are reasonably efficient for the MCVRPTW. Moreover, the HPSO algorithm has overall better performance, particularly in delivering the best solution, for all cases, and the HPSO algorithm becomes more efficient than the PSO algorithm as the problem size increases. On the other hand, the PSO algorithm shows a slight edge in terms of the worst solution and standard deviation.

关键词： multi-compartment vehicle routing problem vehicle routing problem with time windows Hybrid particle swarm optimization Simulated annealing Urban distribution

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A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands

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COMPUTERS & OPERATIONS RESEARCH 2010年第11期37卷 1886-1898页

作者： Mendoza, Jorge E. Castanier, Bruno Gueret, Christelle Medaglia, Andres L. Velasco, Nubia Ecole Mines Nantes IRCCyN Equipe Syst Logist & Prod CNRSUMR 6597 F-44307 Nantes 3 France Univ Los Andes COPA Dept Ind Engn Bogota Colombia

The multi-compartment vehicle routing problem (MC-VRP) consists of designing transportation routes to satisfy the demands of a set of customers for several products that, because of incompatibility constraints, must be loaded in independent vehicle compartments. Despite its wide practical applicability the MC-VRP has not received much attention in the literature, and the few existing methods assume perfect knowledge of the customer demands, regardless of their stochastic nature. This paper extends the MC-VRP by introducing uncertainty on what it is known as the MC-VRP with stochastic demands (MC-VRPSD). The MC-VRPSD is modeled as a stochastic program with recourse and solved by means of a memetic algorithm. The proposed memetic algorithm couples genetic operators and local search procedures proven to be effective on deterministic routing problems with a novel individual evaluation and reparation strategy that accounts for the stochastic nature of the problem. The algorithm was tested on instances of up to 484 customers, and its results were compared to those obtained by a savings-based heuristic and a memetic algorithm (MA/SCS) for the MC-VRP that uses a spare capacity strategy to handle demand fluctuations. In addition to effectively solve the MC-VRPSD, the proposed MA/SCS also improved 14 best known solutions in a 40-problem testbed for the MC-VRP. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： multi-compartment vehicle routing problem Stochastic demands Evolutionary algorithms Memetic algorithms

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A hybrid iterated local search algorithm for the multi-compartment vehicle routing problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期45卷 257-268页

作者： Hou, Yan-e Wang, Chunxiao Wang, Congran Fan, Gaojuan Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng Peoples R China Henan Univ Coll Comp & Informat Engn Kaifeng Peoples R China

multi-compartment vehicle routing problem (MCVRP) is an extension of the classical capacitated vehicle routing problem where products with different characteristics are transported together in one vehicle with multiple compartments. This paper deals with this problem, whose objective is to minimize the total travel distance while satisfying the capacity and maximum route length constraints. We proposed a hybrid iterated local search metaheuristic (HILS) algorithm to solve it. In the framework of iterated local search, the current solutionwas improved iteratively by five neighborhood operators. For every obtained neighborhood solution after the local search procedure, a large neighborhood search-based perturbation method was executed to explore larger solution space and get a better neighborhood solution to take part in the next iteration. In addition, the worse solutions found by the algorithm were accepted by the nondeterministic simulated annealing-based acceptance rule to keep the diversification of solutions. Computation experiments were conducted on 28 benchmark instances and the experimental results demonstrate that our presented algorithm finds 17 new best solutions, which significantly outperforms the existing state-of-the-art MCVRP methods.

关键词： multi-compartment vehicle routing problem hybrid metaheuristic iterated local search large neighborhood search simulated annealing

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Learning Ant Colony Algorithm for Solving Green multi-compartment vehicle routing problem with Simultaneous Pickup and Delivery 7

Learning Ant Colony Algorithm for Solving Green Multi-compar...

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7th International Conference on Automation Electronics and Electrical Engineering

作者： Geng, Yan Guo, Ning Bin Qian Kunming Univ Sci & Technol Informat Engn & Automat Inst Kunming Yunnan Peoples R China

ISBN: (纸本)9798350377040;9798350377033

To solve the Green multi-compartment vehicle routing problem with Simultaneous Pickup and Delivery (GMCVRPSPD), a mathematical model is established with the objective of minimizing total costs. The model consists of vehicle operating costs and fuel consumption costs. A Learning Ant Colony Optimization algorithm (LACO) is proposed to solve GMCVRPSPD. Firstly, in the global search stage of LACO, three different heuristic methods are used to generate the initial solution. These solutions are used to initialize the pheromone matrix, providing a better starting point for the algorithm's subsequent search process. A pheromone update mechanism is designed to effectively enhance the convergence speed of the algorithm. Secondly, in the local search of LACO: a local learning strategy is designed, which selects the neighborhood operation suitable for the current search stage from the six neighborhood operations designed, and dynamically updates the selection probability value according to the contribution value generated by the neighborhood operation. In addition, a rapid evaluation strategy is designed to reduce the calculation time in the local search process. Finally, the effectiveness of LACO in solving GMCVRPSPD problem is verified by simulation experiment and algorithm comparison.

关键词： multi-compartment vehicle routing problem Simultaneous Pickup and Delivery Green Learning Mechanism Ant Colony Optimization Algorithm

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An Enhanced Ant Colony Algorithm with Variable Neighborhood Descent for multi-compartment vehicle routing problem with Time Limits 41

An Enhanced Ant Colony Algorithm with Variable Neighborhood ...

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第41届中国控制会议

作者： Ning Guo Bin Qian Jing Na Rong Hu Jian-Lin Mao Academy of Information Engineering and Automation Kunming University of Science and Technology Academy of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The multi-compartment vehicle routing problem(MCVRP) is applicable in many areas,such as fuel transportation,garbage collection and food *** colony(ACO) algorithm has been recognized as a prominent evolutionary methodology due to its excellent performance of global *** this paper,an enhanced ant colony algorithm,namely EACO,is presented to minimize the total distance of the MCVRP with time limits(TLs),which is a typical NP-hard combinatorial optimization problem with strong practical application ***,a new pheromone concentration matrix(NPCM) is developed to learn both the information of two successive customers and the customer ***,to improve the quality of initial solutions,three heuristic rules are adopted to initialize the NPCM and the ***,after the ACO-based exploration,a problem-dependent local search with the technique of variable neighborhood descent is presented to perform *** results on benchmark data sets demonstrate the effectiveness of the proposed EACO.

关键词： ant colony algorithm multi-compartment vehicle routing problem time limits variable neighborhood descent

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A three-dimensional ant colony optimization algorithm for multi-compartment vehicle routing problem considering carbon emissions

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APPLIED SOFT COMPUTING 2022年 127卷

作者： Guo, Ning Qian, Bin Na, Jing Hu, Rong Mao, Jian-Lin Kunming Univ Sci & Technol Sch Informat Engn & Automation Kunming 650500 Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Peoples R China

A three-dimensional ant colony optimization algorithm (TDACO) is proposed to solve the multi -compartment vehicle routing problem (MCVRP) arising in many industries, such as petrol station replenishment, cold chain logistics, and waste collection. The objective of this problem to be minimized is the total transportation cost including the consideration of carbon emissions (TC_CE). In TDACO, a new three-dimensional pheromone concentration matrix is first presented to learn and accumulate the valuable information from excellent individuals or ants. Then, to make the algorithm have high-quality search in the initial stage, five heuristic rules are designed to initialize the pheromone concentration matrix and construct the initial population or colony. In addition, the vehicle capacity rate and carbon emissions are brought into the state transition rule to reasonably guide the global search towards the promising solutions or regions. Moreover, an adaptive local search with the speed-up search strategy and the first move strategy is devised to perform effective exploitation from the promising regions. Finally, comparative experiments on benchmark problem instances with different scales are conducted, and the superior performance of the proposed TDACO is verified. (C) 2022 Elsevier B.V. All rights reserved.

关键词： multi-compartment vehicle routing problem Carbon emissions Ant colony algorithm Three-dimensional pheromone concentration matrix Adaptive local search

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An improved estimation of distribution algorithm for multi-compartment electric vehicle routing problem

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Journal of Systems Engineering and Electronics 2021年第2期32卷 365-379页

作者： SHEN Yindong PENG Liwen LI Jingpeng Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computing Science and Mathematics University of StirlingStirling FK94LAUK

The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process,are employed.A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost,distribution cost,time window penalty cost and charging service *** solve the problem,an estimation of the distribution algorithm based on Lévy flight(EDA-LF)is proposed to perform a local search at each iteration to prevent the algorithm from falling into local *** results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA *** addition,when comparing with existing algorithms,the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale *** experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles.

关键词： multi-compartment vehicle routing problem electric vehicle routing problem(EVRP) soft time window multiple charging type estimation of distribution algorithm(EDA) Lévy flight

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multi-compartment waste collection vehicle routing problem with bin washer

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 189卷

作者： Masmoudi, M. Amine Baldacci, Roberto Mancini, Simona Kuo, Yong-Hong Int Univ Rabat Rabat Business Sch Rabat Morocco Hamad Bin Khalifa Univ HBKU Coll Sci & Engn CSE POB 5825 Doha Qatar Univ Palermo Dept Engn Palermo Italy Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China Univ Hong Kong HKU Musketeers Fdn Inst Data Sci Hong Kong Peoples R China

This paper introduces a new variant of the multi-compartment Waste Collection problem (MCWCP), which we call the MC-WCP with Bin Washer (MC-WCP-BW). The problem involves a fleet of compressed refuse collection trucks equipped with a bin washer, which compresses each waste type in the corresponding compartment of the vehicle. The vehicles can also wash the bins. Separation sites and water refilling stations are considered in the problem. A subset of the bins must be washed when emptied, which is not mandatory for the others. The problem is modeled using a mixed-integer linear programming formulation incorporating multiple visits to separation sites and water refilling stations. An efficient Hybrid Variable Neighborhood Search (HVNS) algorithm is developed and evaluated on a set of instances from the literature and newly generated instances. Computational experiments show that our proposed algorithm can identify solutions of better quality in a shorter computational time, as compared with the current state-of-the-art algorithms. The potential benefits of the compaction operation under several scenarios are also examined via extensive analyses.

关键词： Transportation multi-compartment vehicle routing problem Waste collection Hybrid metaheuristics Bin washer

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A logic-based Benders decomposition approach for a fuel delivery problem with time windows, unsplit compartments, and split deliveries

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第1期325卷 100-117页

作者： Melo, Rafael A. Ribeiro, Celso C. Urrutia, Sebastian Vansteenwegen, Pieter Univ Fed Bahia Inst Comp BR-40170115 Salvador BA Brazil Univ Fed Fluminense Inst Comp BR-24210240 Niteroi RJ Brazil Molde Univ Coll Fac Logist Molde Norway Katholieke Univ Leuven Leuven Inst Mobil CIB Leuven Belgium

We consider a single-period fuel delivery problem in which a distribution company has to transport multiple types of fuel from a depot to a set of service stations using a heterogeneous set of multi-compartment vehicles. Among other characteristics, the problem includes time windows, a limit on the duration of each route, unsplit compartments, and split deliveries. We propose two mixed integer programming (MIP) formulations and a logic-based Benders decomposition approach. The first formulation is an arc-based model while the second is based on the possible trips. The logic-based Benders decomposition follows a trip-based principle and breaks down the problem into a generalized assignment master problem and a subproblem responsible for identifying violated feasibility cuts implicated by the time-related constraints. It takes advantage of the problem-specific characteristics that allow efficiently solving the resulting subproblems. The logic-based Benders decomposition also serves as a heuristic, which works by limiting the number of trips generated throughout the process. Symmetry breaking constraints and preprocessing procedures are also proposed to help solving the formulations. The computational experiments using synthetic instances show that the logic-based Benders decomposition outperforms the other formulations and is very effective in solving the considered benchmark instances. It solved to optimality instances with up to 20 customers. The MIP heuristic obtained solutions within 7.2% of the optimal cost for all but one of the tested instances with up to 25 customers. Furthermore, it proved to be a viable approach for medium-sized instances where the exact logic-based Benders decomposition encountered difficulties.

关键词： routing multi-compartment vehicle routing problem Fuel delivery MIP formulations Benders decomposition

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multi-compartment vehicle selection and delivery strategy with time windows under multi-objective optimization

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ALEXANDRIA ENGINEERING JOURNAL 2023年 85卷 146-159页

作者： Lv, Chenghong Xu, Jianxin Kunming Univ Sci & Technol Fac Management & Econ Kunming 650093 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Met & Energy Engn Kunming 650093 Yunnan Peoples R China

The main research topic of this article is the distribution strategy problem for multi-compartment and single cabin vehicles under multi-objective conditions, namely the time dependence of multi-compartment vehicle selection problem TDMCVRPTWs. This article uses NSGA-II algorithm and improved Large Scale Domain Search (ILNS) to fuse, and uses the fusion algorithm to experiment with Solomon dataset. In order to verify the advantages of improved large-scale domain search (ILNS), this article uses 100 customers from Solomon to verify. The ILNS algorithm will be compared with algorithms such as ACS, HAC, HVANS, and HABC. Experiments have shown that the improved large-scale domain search (ILNS) has good computational performance. The experimental data of three modes adapted by 100 customers of Solomon show that multi-compartment vehicle fleets have significant advantages under transportation costss, while the deterioration costs advantages of single compartment vehicle and multi-compartment vehicle combined fleets are prominent. In terms of total cost, a mixed fleet is more advantageous than a single multi-compartment vehicle fleet.

关键词： multi-compartment vehicle routing problem SOM clustering Improved large-scale domain search algorithm multi-objective optimization Time dependence vehicle type selection algorithm

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