An exact algorithm to solve the vehicle routing problem with stochastic demands under an optimal restocking policy

•We present an exact method to solve vehicle routing problem with stochastic demands.•We lower approximate the optimal restocking policy to bound recourse function.•We enhance the Integer L-shaped method by redefining lower bounding functionals.•We can solve problems with 100 customers having arbitr...

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Vydáno v:European journal of operational research Ročník 273; číslo 1; s. 175 - 189
Hlavní autoři: Salavati-Khoshghalb, Majid, Gendreau, Michel, Jabali, Ola, Rei, Walter
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 16.02.2019
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ISSN:0377-2217, 1872-6860
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Abstract •We present an exact method to solve vehicle routing problem with stochastic demands.•We lower approximate the optimal restocking policy to bound recourse function.•We enhance the Integer L-shaped method by redefining lower bounding functionals.•We can solve problems with 100 customers having arbitrary discrete demands. This paper examines the Vehicle Routing Problem with Stochastic Demands (VRPSD), in which the actual demand of customers can only be realized upon arriving at the customer location. Under demand uncertainty, a planned route may fail at a specific customer when the observed demand exceeds the residual capacity. There are two ways to face such failure events, a vehicle can either execute a return trip to the depot at the failure location and refill the capacity and complete the split service, or in anticipation of potential failures perform a preventive return to the depot whenever the residual capacity falls below a threshold; overall, these return trips are called recourse actions. In the context of VRPSD, a recourse policy which schedules various recourse actions based on the visits planned beforehand on the route must be designed. An optimal recourse policy prescribes the cost-effective returns based on a set of optimal customer-specific thresholds. We propose an exact solution method to solve the multi-VRPSD under an optimal restocking policy. The Integer L-shaped algorithm is adapted to solve the VRPSD in a branch-and-cut framework. To enhance the efficiency of the presented algorithm, several lower bounding schemes are developed to approximate the expected recourse cost.
AbstractList •We present an exact method to solve vehicle routing problem with stochastic demands.•We lower approximate the optimal restocking policy to bound recourse function.•We enhance the Integer L-shaped method by redefining lower bounding functionals.•We can solve problems with 100 customers having arbitrary discrete demands. This paper examines the Vehicle Routing Problem with Stochastic Demands (VRPSD), in which the actual demand of customers can only be realized upon arriving at the customer location. Under demand uncertainty, a planned route may fail at a specific customer when the observed demand exceeds the residual capacity. There are two ways to face such failure events, a vehicle can either execute a return trip to the depot at the failure location and refill the capacity and complete the split service, or in anticipation of potential failures perform a preventive return to the depot whenever the residual capacity falls below a threshold; overall, these return trips are called recourse actions. In the context of VRPSD, a recourse policy which schedules various recourse actions based on the visits planned beforehand on the route must be designed. An optimal recourse policy prescribes the cost-effective returns based on a set of optimal customer-specific thresholds. We propose an exact solution method to solve the multi-VRPSD under an optimal restocking policy. The Integer L-shaped algorithm is adapted to solve the VRPSD in a branch-and-cut framework. To enhance the efficiency of the presented algorithm, several lower bounding schemes are developed to approximate the expected recourse cost.
Author Gendreau, Michel
Jabali, Ola
Salavati-Khoshghalb, Majid
Rei, Walter
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Issue 1
Keywords Routing
Lower bounding functionals
Optimal restocking policy
Integer L-shaped algorithm
Stochastic demands
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Snippet •We present an exact method to solve vehicle routing problem with stochastic demands.•We lower approximate the optimal restocking policy to bound recourse...
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SubjectTerms Integer L-shaped algorithm
Lower bounding functionals
Optimal restocking policy
Routing
Stochastic demands
Title An exact algorithm to solve the vehicle routing problem with stochastic demands under an optimal restocking policy
URI https://dx.doi.org/10.1016/j.ejor.2018.07.039
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