Hybrid 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/s10852-005-9033-y

Cited by 103 publications

(85 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular, Teodorović and Pavković [26] adopted simulated annealing while Gendreau et al [27] used tabu search. Finally, an extended analysis on the behavior of different metaheuristics has been proposed by Bianchi et al [9].…”

Section: Literature Overviewmentioning

confidence: 99%

“…In particular, the cost saving allowed by a move is the difference between the approximated saving obtained removing the string of consecutive customers from its original position, and the approximated cost of inserting it somewhere else in the tour: Instead of evaluating the complete solution before and after the move, the saving and cost are computed only with respect to the immediate neighbors of the customers shifted. This method has been adopted also by Bianchi et al [9], who proposed also another approximation based on delta values calculated in a TSP-wise fashion, that is, considering the variation of the length of the a priori tour. Moreover, they extended this TSP-wise approximation also to the 3-opt local search [29].…”

Section: Literature Overviewmentioning

confidence: 99%

“…Following Bianchi et al [9], we focus on five of the most popular metaheuristics: tabu search (TS), simulated annealing (SA), genetic algorithm (GA), iterated local search (ILS), and ant colony optimization (ACO).…”

Section: Literature Overviewmentioning

confidence: 99%

“…This removes some of the ambiguity connected with the measure of the development effort and guarantees that equal attention is devoted to all metaheuristics under analysis. The last ones are exactly the implementations described in the literature: They are obtained considering the implementations and the values of the parameters reported by Bianchi et al [9]. To the best of our knowledge this paper represents the most recent and successful application of metaheuristics to the problem considered.…”

Section: Introductionmentioning

confidence: 98%

“…In the Metaheuristics Network, five academic groups and two companies, each specialized in the development and application of one or more of the above metaheuristics, joined their research efforts with the aim of gathering a deeper insight into the theory and practice of metaheuristics. For a detailed description of the metaheuristics developed by the Metaheuristics Network for the vehicle routing problem with stochastic demand, we refer the reader to Bianchi et al [9]. The availability of this reference makes the vehicle routing problem with stochastic demand, and the five metaheuristics above mentioned, particularly suitable to our analysis.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Pellegrini

Birattari

2011

Intelligent Computational Optimization in Engineering

Self Cite

View full text Add to dashboard Cite

Abstract. Metaheuristics are a class of effective algorithms for optimization problems. A basic implementation of a metaheuristic typically requires rather little development effort. With a significantly larger investment in the design, implementation, and fine-tuning, metaheuristics can often produce state-of-the-art results. According to the amount of development effort, we say that an implementation of a metaheuristic is either an out-of-the-box version or a custom one. The possibility of implementing metaheuristics in such a flexible way is one of the major strengths of these algorithms. Nonetheless, it also hides some possible catches. In particular, it should be noticed that results obtained with out-of-the-box implementations cannot be always generalized to custom ones, and vice versa. The goal of this analysis is to stress that these two ways of using metaheuristics are different. As a case study, we focus on the vehicle routing problem with stochastic demand and on five among the most successful metaheuristics-namely, tabu search, simulated annealing, genetic algorithms, iterated local search, and ant colony optimization. We show that the relative performance of these algorithms strongly varies whether one considers out-of-the-box implementations or custom ones, in which the parameters are accurately fine-tuned. Moreover, we underline the relevance of clearly stating the framework in which the results reported in the literature have been obtained. To this aim, we consider also an implementation of the same algorithms as described in the literature.

show abstract

Section: Literature Overviewmentioning

confidence: 99%

Section: Literature Overviewmentioning

confidence: 99%

Section: Literature Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Pellegrini

Birattari

2011

Intelligent Computational Optimization in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

A Concise Overview of Applications of Ant Colony Optimization

Stützle

López-Ibáñez

Dorigo

2011

Wiley Encyclopedia of Operations Research and Management Science

View full text Add to dashboard Cite

Ant colony optimization (ACO) [1-3] is a metaheuristic for solving hard combinatorial optimization problems inspired by the indirect communication of real ants. In ACO algorithms, (artificial) ants construct candidate solutions to the problem being tackled, making decisions that are stochastically biased by numerical information based on (artificial) pheromone trails and available heuristic information. The pheromone trails are updated during algorithm execution to bias the ants search toward promising decisions previously found. The article titled Ant Colony Optimization gives a detailed overview of the main concepts of ACO.Despite being one of the youngest metaheuristics, the number of applications of ACO algorithms is very large. In principle, ACO can be applied to any combinatorial optimization problem for which some iterative solution construction mechanism can be conceived. Most applications of ACO deal with NP-hard combinatorial optimization problems, that is, with problems for which no polynomial time algorithms are known. ACO algorithms have also been extended to handle problems with multiple objectives, stochastic data, and dynamically changing problem information. There are extensions of the ACO metaheuristic for dealing with problems with continuous decision variables, as well.This article provides a concise overview of several noteworthy applications of ACO algorithms. This overview is necessarily incomplete because the number of currently available ACO applications goes into the hundreds. Our description of the applications

show abstract