Using parallel & distributed computing for real-time solving of vehicle routing problems with stochastic demands

This paper focuses on the Vehicle Routing Problem with Stochastic Demands (VRPSD) and discusses how Parallel and Distributed Computing Systems can be employed to efficiently solve the VRPSD. Our approach deals with uncertainty in the customer demands by considering safety stocks, i.e. when designing...

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Veröffentlicht in:	Annals of operations research Jg. 207; H. 1; S. 43 - 65
Hauptverfasser:	Juan, Angel A., Faulin, Javier, Jorba, Josep, Caceres, Jose, Marquès, Joan Manuel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.08.2013 Springer Springer Nature B.V
Schlagworte:	Algorithms Business and Management Combinatorics Computer networks Computer simulation Customers Demand Distributed processing Distributed processing (Computers) Marketing Mobile communication systems Monte Carlo simulation Operations research Operations Research/Decision Theory Optimization Parallel processing Probability distribution Random variables Routing Safety stocks Small & medium sized enterprises-SME Stochastic analysis Studies Theory of Computation Traveling salesman problem Vehicles Wireless communication systems Spain Probabilistic algorithms Parallel and distributed computing Vehicle routing problem with stochastic demands Heuristics Monte Carlo simulation
ISSN:	0254-5330, 1572-9338
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Abstract	This paper focuses on the Vehicle Routing Problem with Stochastic Demands (VRPSD) and discusses how Parallel and Distributed Computing Systems can be employed to efficiently solve the VRPSD. Our approach deals with uncertainty in the customer demands by considering safety stocks, i.e. when designing the routes, part of the vehicle capacity is reserved to deal with potential emergency situations caused by unexpected demands. Thus, for a given VRPSD instance, our algorithm considers different levels of safety stocks. For each of these levels, a different scenario is defined. Then, the algorithm solves each scenario by integrating Monte Carlo simulation inside a heuristic-randomization process. This way, expected variable costs due to route failures can be naturally estimated even when customers’ demands follow a non-normal probability distribution. Use of parallelization strategies is then considered to run multiple instances of the algorithm in a concurrent way. The resulting concurrent solutions are then compared and the one with the minimum total costs is selected. Two numerical experiments allow analyzing the algorithm’s performance under different parallelization schemas.
AbstractList	This paper focuses on the Vehicle Routing Problem with Stochastic Demands (VRPSD) and discusses how Parallel and Distributed Computing Systems can be employed to efficiently solve the VRPSD. Our approach deals with uncertainty in the customer demands by considering safety stocks, i.e. when designing the routes, part of the vehicle capacity is reserved to deal with potential emergency situations caused by unexpected demands. Thus, for a given VRPSD instance, our algorithm considers different levels of safety stocks. For each of these levels, a different scenario is defined. Then, the algorithm solves each scenario by integrating Monte Carlo simulation inside a heuristic-randomization process. This way, expected variable costs due to route failures can be naturally estimated even when customers' demands follow a non-normal probability distribution. Use of parallelization strategies is then considered to run multiple instances of the algorithm in a concurrent way. The resulting concurrent solutions are then compared and the one with the minimum total costs is selected. Two numerical experiments allow analyzing the algorithm's performance under different parallelization schemas. This paper focuses on the Vehicle Routing Problem with Stochastic Demands (VRPSD) and discusses how Parallel and Distributed Computing Systems can be employed to efficiently solve the VRPSD. Our approach deals with uncertainty in the customer demands by considering safety stocks, i.e. when designing the routes, part of the vehicle capacity is reserved to deal with potential emergency situations caused by unexpected demands. Thus, for a given VRPSD instance, our algorithm considers different levels of safety stocks. For each of these levels, a different scenario is defined. Then, the algorithm solves each scenario by integrating Monte Carlo simulation inside a heuristic-randomization process. This way, expected variable costs due to route failures can be naturally estimated even when customers' demands follow a non-normal probability distribution. Use of parallelization strategies is then considered to run multiple instances of the algorithm in a concurrent way. The resulting concurrent solutions are then compared and the one with the minimum total costs is selected. Two numerical experiments allow analyzing the algorithm's performance under different parallelization schemas.[PUBLICATION ABSTRACT] This paper focuses on the Vehicle Routing Problem with Stochastic Demands (VRPSD) and discusses how Parallel and Distributed Computing Systems can be employed to efficiently solve the VRPSD. Our approach deals with uncertainty in the customer demands by considering safety stocks, i.e. when designing the routes, part of the vehicle capacity is reserved to deal with potential emergency situations caused by unexpected demands. Thus, for a given VRPSD instance, our algorithm considers different levels of safety stocks. For each of these levels, a different scenario is defined. Then, the algorithm solves each scenario by integrating Monte Carlo simulation inside a heuristic-randomization process. This way, expected variable costs due to route failures can be naturally estimated even when customers' demands follow a non-normal probability distribution. Use of parallelization strategies is then considered to run multiple instances of the algorithm in a concurrent way. The resulting concurrent solutions are then compared and the one with the minimum total costs is selected. Two numerical experiments allow analyzing the algorithm's performance under different parallelization schemas. Keywords Vehicle routing problem with stochastic demands * Parallel and distributed computing * Monte Carlo simulation * Probabilistic algorithms * Heuristics
Audience	Academic
Author	Jorba, Josep Marquès, Joan Manuel Juan, Angel A. Caceres, Jose Faulin, Javier
Author_xml	– sequence: 1 givenname: Angel A. surname: Juan fullname: Juan, Angel A. organization: Department of Computer Science, Multimedia, and Telecommunication, Open University of Catalonia – sequence: 2 givenname: Javier surname: Faulin fullname: Faulin, Javier email: javier.faulin@unavarra.es organization: Department of Statistics and OR, Public University of Navarre – sequence: 3 givenname: Josep surname: Jorba fullname: Jorba, Josep organization: Department of Computer Science, Multimedia, and Telecommunication, Open University of Catalonia – sequence: 4 givenname: Jose surname: Caceres fullname: Caceres, Jose organization: Department of Computer Science, Multimedia, and Telecommunication, Open University of Catalonia – sequence: 5 givenname: Joan Manuel surname: Marquès fullname: Marquès, Joan Manuel organization: Department of Computer Science, Multimedia, and Telecommunication, Open University of Catalonia
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SubjectTerms	Algorithms Business and Management Combinatorics Computer networks Computer simulation Customers Demand Distributed processing Distributed processing (Computers) Marketing Mobile communication systems Monte Carlo simulation Operations research Operations Research/Decision Theory Optimization Parallel processing Probability distribution Random variables Routing Safety stocks Small & medium sized enterprises-SME Stochastic analysis Studies Theory of Computation Traveling salesman problem Vehicles Wireless communication systems
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Title	Using parallel & distributed computing for real-time solving of vehicle routing problems with stochastic demands
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