Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
A vehicle routing problem with stochastic demand
Operations Research
American Journal of Mathematical and Management Sciences - Modern digital simulation methodology, III
Tabu Search
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Stochastic Vehicle Routing Problem with Restocking
Transportation Science
A Rollout Policy for the Vehicle Routing Problem with Stochastic Demands
Operations Research
Ant Colony Optimization
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
High-Performance Local Search for Task Scheduling with Human Resource Allocation
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
The consultation timetabling problem at Danish high schools
Journal of Heuristics
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In practical applications, one can take advantage of metaheuristics in different ways: To simplify, we can say that metaheuristics can be either used out-of-the-box or a custom version can be developed. The former way requires a rather low effort, and in general allows to obtain fairly good results. The latter implies a larger investment in the design, implementation, and fine-tuning, and can often produce state-of-the-art results. Unfortunately, most of the research works proposing an empirical analysis of metaheuristics do not even try to quantify the development effort devoted to the algorithms under consideration. In other words, they do not make clear whether they considered out-of-the-box or custom implementations of the metaheuristics under analysis. The lack of this information seriously undermines the generality and utility of these works. The aim of the paper is to stress that results obtained with out-of-the-box implementations cannot be always generalized to custom ones, and vice versa. 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 algorithm, 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.