Multi-Swarm Optimization for Dynamic Combinatorial Problems: A Case Study on Dynamic Vehicle Routing Problem

Khouadjia, Mostepha; Alba, Enrique; Jourdan, Laëtitia; Talbi, El‐Ghazali

doi:10.1007/978-3-642-15461-4_20

Cited by 21 publications

(5 citation statements)

References 15 publications

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“…For PSO, a multi-swarm algorithm based on the island model was proposed for the DVRP [130]. Particles from different swarms migrate regularly to others to maintain diversity.…”

Section: Multiple Population Methodsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

475

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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“…For PSO, a multi-swarm algorithm based on the island model was proposed for the DVRP [130]. Particles from different swarms migrate regularly to others to maintain diversity.…”

Section: Multiple Population Methodsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

475

213

View full text Add to dashboard Cite

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“…Khouadjia et al [48] present a multi-swarm based optimization algorithm for the VRP with dynamic requests. The method consists of a particle swarm optimization approach with interacting swarms, thereby maintaining population diversity.…”

Section: Parallelization Approachesmentioning

confidence: 99%

Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing

Ritzinger

Puchinger

2013

Hybrid Metaheuristics

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Abstract. Recent developments in telematics, such as the wide spread use of positioning services and mobile communication technologies, allow the exact monitoring of vehicles. These advances build the basis for automatic real-time fleet management systems. To be successful such systems have to rely on optimization algorithms for solving dynamic and stochastic vehicle routing problems based on ingredients such as historical data, stochastic modeling, machine learning, fast shortest-path calculation, fast construction heuristics, and exact and (meta)heuristic optimization methods. This book documents the growing interest in and success of hybrid metaheuristics. They are often used to solve complex and large real-world optimization problems, combining advantages from various fields of computer science and mathematical optimization. Within this chapter the application of such methods for the dynamic and stochastic vehicle routing problem is studied. After a general introduction in this field, the main commonalities of dynamic and stochastic vehicle routing problems are described and a short overview of classical algorithms for these problems is given. Then, in the third part hybrid metaheuristics for dynamic problems vehicle routing problems are be described. The third part focusses on stochastic problems. The fourth part examines the combination of dynamic and stochastic problems. The chapter is concluded with an outlook towards future developments in the field as well as promising open research areas. IntroductionIn todays globalized economy fast, reliable but also flexible supply chains are among the main factors for successful enterprises. In real-world applications it is most often the case that some information about the future is available (stochastic information) and that known information is revealed over time (dynamic information). For example, an estimation for the customer demand is given at the beginning, whereas

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“…In order to assure a possibility of direct comparison of the obtained results with our previous work and with other literature results [15,16,13,17,18,19,20], the cut-off time is set in the middle of a depot's working hours.…”

Section: Benchmark Configuration and Optimization Settingsmentioning

confidence: 99%

Dynamic Vehicle Routing Problem: A Monte Carlo approach

Okulewicz,

Mańdziuk

2020

Preprint

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In this work we solve the Dynamic Vehicle Routing Problem (DVRP). DVRP is a modification of the Vehicle Routing Problem, in which the clients' requests (cities) number and location might not be known at the beginning of the working day Additionally, all requests must be served during one working day by a fleet of vehicles with limited capacity. In this work we propose a Monte Carlo method (MCTree), which directly approaches the dynamic nature of arriving requests in the DVRP. The method is also hybridized (MCTree+PSO) with our previous Two-Phase Multi-swarm Particle Swarm Optimization (2MPSO) algorithm.Our method is based on two assumptions. First, that we know a bounding rectangle of the area in which the requests might appear. Second, that the initial requests' sizes and frequency of appearance are representative for the yet unknown clients' requests. In order to solve the DVRP we divide the working day into several time slices in which we solve a static problem. In our Monte Carlo approach we randomly generate the unknown clients' requests with uniform spatial distribution over the bounding rectangle and requests' sizes uniformly sampled from the already known requests' sizes. The solution proposal is constructed with the application of a clustering algorithm and a route construction algorithm.The MCTree method is tested on a well established set of benchmarks proposed by Kilby et al. [1,2] and is compared with the results achieved by applying our previous 2MPSO [3] algorithm and other literature results. The proposed MCTree approach achieves a better time to quality trade-off then plain heuristic algorithms. Moreover, a hybrid MCTree+PSO approach achieves better time to quality tradeoff then 2MPSO for small optimization time limits, making the hybrid a good candidate for handling real world scale goods delivery problems.

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Multi-Swarm Optimization for Dynamic Combinatorial Problems: A Case Study on Dynamic Vehicle Routing Problem

Cited by 21 publications

References 15 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing

Dynamic Vehicle Routing Problem: A Monte Carlo approach

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