𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Preemptive scheduling with changeovers: Using column generation technique and genetic algorithm

✍ Scribed by Ewa Figielska


Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
331 KB
Volume
37
Category
Article
ISSN
0360-8352

No coin nor oath required. For personal study only.

✦ Synopsis


This paper considers the problem of the scheduling of preemptive jobs on unrelated parallel machines, which differs from those discussed in the literature in that it includes changeovers of machines as well as temporary constraints of resources. This problem is complicated to such an extent that even its mathematical formulation seems impossible. Its solution calls therefore for the introduction of some heuristics. The paper presents a two-stage heuristic integrating the column generation technique with a genetic algorithm for the purpose of minimizing the makespan and the total cost of changeovers. The quality of this heuristic is evaluated by comparing the solutions to a lower bound on the objective function optimal value. An integer-linear programming procedure determining the lower bound is proposed. Extensive experimental study shows that the two-stage heuristic presented is effective for medium-size problems with strong temporary resource constraints in the case of the total cost of changeovers being not in excess of 10% of the makespan cost.


πŸ“œ SIMILAR VOLUMES


Project scheduling using a genetic algor
✍ Tomoya Ikeuchi; Yoshitomo Ikkai; Dai Araki; Takenao Ohkawa; Norihisa Komoda πŸ“‚ Article πŸ“… 1998 πŸ› John Wiley and Sons 🌐 English βš– 252 KB πŸ‘ 2 views

Genetic algorithms (GA) have been widely used to solve planning problems. However, they require one to determine the optimal values of many genetic parameters, such as population sizes, crossover probability, mutation probability, and so on. To make matters worse, the most suitable combination of pa

Generator maintenance scheduling using a
✍ K.P. Dahal; C.J. Aldridge; J.R. McDonald πŸ“‚ Article πŸ“… 1999 πŸ› Elsevier Science 🌐 English βš– 622 KB

In this paper we consider the problem of generator maintenance scheduling (GMS) in power systems. A genetic algorithm with a fuzzy evaluation function is proposed in order to overcome some of the limitations of conventional modelling and solution methods. A test GMS problem is formulated with a rel