Metaheuristics for the Vehicle Routing Problem with Stochastic Demands

Bianchi, Leonora; Birattari, Mauro; Chiarandini, Marco; Manfrin, Max; Mastrolilli, Monaldo; Paquete, Luís; Rossi-Doria, Olivia; Schiavinotto, Tommaso

doi:10.1007/978-3-540-30217-9_46

Cited by 36 publications

(41 citation statements)

References 8 publications

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“…Route failure and recourse action: Route failure is said to be occurred if the total demand exceeds the vehicle capacity and the preventive restocking policy [4,19] is employed.…”

Section: Methodsmentioning

confidence: 99%

“…These equations are used to recursively determine the objective value of the planned vehicle route and the optimal sequence of decisions after customers are served [4] .…”

Section: Methodsmentioning

confidence: 99%

“…Savelsbergh and Goetschalckx [15] simplified the recourse to achieve computational efficiency assuming at most one failure in a route. All subsequent publications (e.g., by [10,13] ) have maintained the simple recourse policy defined in [8] with the exception of [3,4,19] that using preventive restocking and the recourse of [5] that is to terminate the route and impose a penalty such as lost revenue and/or emergency deliveries.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies in development of metaheuristics for VRPSD were done by [4] . They considered basic implementation of five metaheuristics for single vehicle: Iterated Local Search, Tabu Search (TS), Simulated Annealing, Ant Colony Optimization and Evolutionary Algorithm (Genetic Algorithm (GA)) that found better solution quality in respect to cyclic heuristic.…”

Section: Introductionmentioning

confidence: 99%

“…They considered basic implementation of five metaheuristics for single vehicle: Iterated Local Search, Tabu Search (TS), Simulated Annealing, Ant Colony Optimization and Evolutionary Algorithm (Genetic Algorithm (GA)) that found better solution quality in respect to cyclic heuristic. Instead of the work of [4,10,20] , the work on the application of GA and TS for VRPSD are lacking in the literature, although it is widely known that GA has been proven effective and successful in a wide variety of combinatorial optimization problems, including certain types of VRP; and as known also that TS, the approach that dominates the list of successful algorithms, is a robust, efficient and effective approach to the general VRP family of problem [12,14] and often outperforms other heuristic techniques in terms of computational speed and solution quality [14] .…”

Section: Introductionmentioning

confidence: 99%

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Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search

Ismail¹,

Irhamah²

2008

J. of Mathematics and Statistics

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This study considers a version of the stochastic vehicle routing problem where customer demands are random variables with known probability distribution. A new scheme based on a hybrid GA and Tabu Search heuristic is proposed for this problem under a priori approach with preventive restocking. The relative performance of the proposed HGATS is compared to each GA and TS alone, on a set of randomly generated problems following some discrete probability distributions. The problem data are inspired by real case of VRPSD in waste collection. Results from the experiment show the advantages of the proposed algorithm that are its robustness and better solution qualities resulted.

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“…Route failure and recourse action: Route failure is said to be occurred if the total demand exceeds the vehicle capacity and the preventive restocking policy [4,19] is employed.…”

Section: Methodsmentioning

confidence: 99%

“…These equations are used to recursively determine the objective value of the planned vehicle route and the optimal sequence of decisions after customers are served [4] .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search

Ismail¹,

Irhamah²

2008

J. of Mathematics and Statistics

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Metaheuristics for Stochastic Problems

Hvattum

Esbensen

2011

Wiley Encyclopedia of Operations Research and Management Science

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A large number of publications have been written about metaheuristics and their use in solving deterministic optimization problems. As metaheuristic techniques become more widespread in the production of decision support systems, it is clear that they also need to cope with problems, based on real‐world applications, that contain noise and uncertainty. An increasing amount of work is being put into the study of metaheuristics for stochastic problems, and into how metaheuristics must be adapted to efficiently handle different sources of stochasticity. We give examples of how metaheuristics have been used to solve stochastic problems, thereby illustrating the additional considerations that are required in this context.

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