Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost

•Addressed a VRP variant that considers stochastic demands and weight-related cost.•Adopted the a priori optimization solution concept to deal with the problem.•Proposed a novel and flexible recourse strategy.•Implemented three ALNS heuristics to solve the problem.•Generated test instances and bench...

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Vydáno v:Transportation research. Part E, Logistics and transportation review Ročník 85; s. 69 - 89
Hlavní autoři: Luo, Zhixing, Qin, Hu, Zhang, Dezhi, Lim, Andrew
Médium: Journal Article
Jazyk:angličtina
Vydáno: Exeter Elsevier India Pvt Ltd 01.01.2016
Elsevier Sequoia S.A
Témata:
A priori optimization
Adaptive large neighborhood search
Approximation
Automotive components
Benchmarks
Demand
Dynamic programming
Heuristic
Route optimization
Route selection
Routing
Searching
Stochastic demands
Stochastic models
Stochasticity
Studies
Transportation
Transportation problem (Operations research)
Weight-related cost
Routing
Adaptive large neighborhood search
A priori optimization
Weight-related cost
Stochastic demands
ISSN:1366-5545, 1878-5794
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Shrnutí:•Addressed a VRP variant that considers stochastic demands and weight-related cost.•Adopted the a priori optimization solution concept to deal with the problem.•Proposed a novel and flexible recourse strategy.•Implemented three ALNS heuristics to solve the problem.•Generated test instances and benchmark results for future researchers of the problem. The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers.
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ISSN:1366-5545
1878-5794
DOI:10.1016/j.tre.2015.11.004