A Study of Greedy, Local Search, and Ant Colony Optimization Approaches for Car Sequencing Problems

Abstract

This paper describes and compares several heuristic approaches for the car sequencing problem. We first study greedy heuristics, and show that dynamic ones clearly outperform their static counterparts. We then describe local search and ant colony optimization (ACO) approaches, that both integrate greedy heuristics, and experimentally compare them on benchmark instances. ACO yields the best solution quality for smaller time limits, and it is comparable to local search for larger limits. Our best algorithms proved one instance being feasible, for which it was formerly unknown whether it is satisfiable or not. 1. Random choice (Rand): choose randomly a car within the set of candidates.

Definición y Evaluación de la dificultad del problema de secuenciación de unidades homogéneas

“…Para ello, multipes técnicas de resolución para resolver el CSP están presentes en la literatura: la programación por restricciones (Bergen, Van Beek, & Carchrae, 2001), la programación matemática lineal (Drexl & Kimms, 2001;Gravel, Gagne, & Price, 2005), los métodos ad hoc (Drexl, Kimms, & Matthießen, 2006), la optimización de colonia de hormigas (Gottlieb, Puchta, & Solnon, 2003;Solnon, 2000), Branch & Bound (Fliedner & Boysen, 2008), los algoritmos voraces (Gottlieb et al, 2003) o la búsqueda local (Puchta & Gottlieb, 2002) son unos ejemplos.…”

Tackling the Industrial Car Sequencing Problem Using GISMOO Algorithm

“…In this way, the PI allows us to find out if it is good to put a car of class v and colour Colors in position i, according to how it dominates (or not) the other candidate classes. A tie is broken by using an extension of a strategy introduced in (Gottlieb et al, 2003), which favours a better distribution of car classes in the sequence under construction. For more details on these subjects, see (Zinflou et al, 2008a).…”

Algorithmes évolutionnaires pour l'ordonnancement industriel :

“…L'algorithme sera par la suite bonifié par l'addition d'une heuristique gourmande [Gottlieb et al 2003]. Gravel et al [2005] [Benoit 2007;Briant et al 2007;Cordeau et al 2007;Gavranovic 2007;Ribiero et al 2007], un algorithme d'optimisation par colonie de fourmis (ACO) ] et un algorithme génétique (AG) [Jaszkiewicz et al 2004].…”