The general employee scheduling problem: an integration of MS and AI
Computers and Operations Research - Special issue: Applications of integer programming
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Tabu search for total tardiness minimization in flowshop scheduling problems
Computers and Operations Research
Tabu Search
Multiobjective Metaheuristics for the Bus Driver Scheduling Problem
Transportation Science
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A multi-objective model for environmental investment decision making
Computers and Operations Research
Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems
Computers and Operations Research
Computers and Operations Research
Hi-index | 0.00 |
This paper proposes a new tabu search algorithm for multi-objective combinatorial problems with the goal of obtaining a good approximation of the Pareto-optimal or efficient solutions. The algorithm works with several paths of solutions in parallel, each with its own tabu list, and the Pareto dominance concept is used to select solutions from the neighborhoods. In this way we obtain at each step a set of local nondominated points. The dispersion of points is achieved by a clustering procedure that groups together close points of this set and then selects the centroids of the clusters as search directions. A nice feature of this multi-objective algorithm is that it introduces only one additional parameter, namely, the number of paths. The algorithm is applied to the permutation flowshop scheduling problem in order to minimize the criteria of makespan and maximum tardiness. For instances involving two machines, the performance of the algorithm is tested against a Branch-and-Bound algorithm proposed in the literature, and for more than two machines it is compared with that of a tabu search algorithm and a genetic local search algorithm, both from the literature. Computational results show that the heuristic yields a better approximation than these algorithms.