An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand

The multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is much harder to tackle than other traditional vehicle routing problems (VRPs), due to the uncertainty in customer demands and potentially conflicted objectives. In this paper, we present an improved multi-objective learn...

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Vydáno v:Knowledge-based systems Ročník 230; s. 107378
Hlavní autoři: Niu, Yunyun, Kong, Detian, Wen, Rong, Cao, Zhiguang, Xiao, Jianhua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 27.10.2021
Elsevier Science Ltd
Témata:
Algorithms
Chromosomes
Compensation
Customers
Decision making
Decision trees
Evolutionary algorithms
Heuristic
Learnable evolution model
Machine learning
Multi-objective evolutionary algorithm
Objectives
Route planning
Stochastic demand
Uncertainty
Vehicle routing
Vehicle routing problems
Learnable evolution model
Multi-objective evolutionary algorithm
Vehicle routing problems
Stochastic demand
ISSN:0950-7051, 1872-7409
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Shrnutí:The multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is much harder to tackle than other traditional vehicle routing problems (VRPs), due to the uncertainty in customer demands and potentially conflicted objectives. In this paper, we present an improved multi-objective learnable evolution model (IMOLEM) to solve MO-VRPSD with three objectives of travel distance, driver remuneration and number of vehicles. In our method, a machine learning algorithm, i.e., decision tree, is exploited to help find and guide the desirable direction of evolution process. To cope with the key issue of ”route failure” caused due to stochastic customer demands, we propose a novel chromosome representation based on priority with bubbles. Moreover, an efficient nondominated sort using a sequential search strategy (ENS-SS) in conjunction with some heuristic operations are leveraged to handle the multi-objective property of the problem. Our algorithm is evaluated on the instances of modified Solomon VRP benchmark. Experimental results show that the proposed IMOLEM is capable to find better Pareto front of solutions and also deliver superior performance to other evolutionary algorithms.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.107378