A priori optimization with recourse for the vehicle routing problem with hard time windows and stochastic service times

The vehicle routing problem with hard time windows and stochastic service times (VRPTW-ST) introduced by Errico et al. (2013) in the form of a chance-constrained model mainly differs from other vehicle routing problems with stochastic service or travel times considered in the literature by the prese...

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Veröffentlicht in:European journal of operational research Jg. 249; H. 1; S. 55 - 66
Hauptverfasser: Errico, F., Desaulniers, G., Gendreau, M., Rei, W., Rousseau, L.-M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 16.02.2016
Elsevier Sequoia S.A
Schlagworte:
A-priori optimization
Algorithms
Column generation
Decision making models
Mathematical programming
Recursive algorithms
Route optimization
Service time
Stochastic models
Stochastic programming
Studies
Transportation problem (Operations research)
Vehicle routing problem
Service time
Column generation
Vehicle routing problem
A-priori optimization
Stochastic programming
ISSN:0377-2217, 1872-6860
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Zusammenfassung:The vehicle routing problem with hard time windows and stochastic service times (VRPTW-ST) introduced by Errico et al. (2013) in the form of a chance-constrained model mainly differs from other vehicle routing problems with stochastic service or travel times considered in the literature by the presence of hardtime windows. This makes the problem extremely challenging. In this paper, we model the VRPTW-ST as a two-stage stochastic program and define two recourse policies to recover operations feasibility when the first stage plan turns out to be infeasible. We formulate the VRPTW-STas a set partitioning problem and solve it by exact branch-cut-and-price algorithms. Specifically, we developed efficient labeling algorithms by suitably choosing label components, determining extension functions, and developing lower and upper bounds on partial route reduced cost to be used in the column generation step. Results on benchmark data show that our methods are able to solve instances with up to 50 customers for both recourse policies.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2015.07.027