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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Bibliographic Details
Published in:Annals of operations research Vol. 207; no. 1; pp. 43 - 65
Main Authors: Juan, Angel A., Faulin, Javier, Jorba, Josep, Caceres, Jose, Marquès, Joan Manuel
Format: Journal Article
Language:English
Published: Boston Springer US 01.08.2013
Springer
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-011-0918-z