Abstract
In this paper, it is presented a heuristic method for solving the multi-objective flow shop problem. The work carried out considers the simultaneous optimization of the makespan and the flow time; both objectives are essential in measuring the production system's performance since they aim to reduce the completion time of jobs, increase the efficiency of resources, and reduce waiting time in queue. The proposed method is an adaptation of multi-objective Newton's method, which is applied to problems with functions of continuous variables. In this adaptation, the method seeks to improve a sequence of jobs through local searches recursively. The computational experiments show the potential of the proposed method to solve medium-sized and large instances compared with other existing literature methods.
Highlights
In a flow shop environment, J jobs must be processed on a set of N machines following the same order
The flow shop problem (FSP) is classified as NP-hard for most of the classic problems, for example [1]: F2 || ∑ cj, an FSP with two machines and with the aim of minimizing the sum of the completion time of all the jobs; F2 ||LM, an FSP
One can mention other works that adopt the generic algorithm for the FSP [7, 8, 9, 10, 11]
Summary
In a flow shop environment, J jobs must be processed on a set of N machines following the same order. Widmer and Hertz (1989) [2] proposed a heuristic method to solve the problem to minimize the makespan This method consists of two phases: the first phase considers an initial sequence based on a solution to the traveling salesman problem, and the second phase consists of improving this solution using tabu search techniques. Proposed a multi-objective evolutionary search algorithm; the authors solve a traveling salesman problem and employ a genetic algorithm to minimize the makespan, flow time, and downtime. Pasupathy et al (2006) [6] proposed a multiobjective genetic algorithm, using local search techniques and minimizing makespan and flow time. This algorithm makes use of the principle of non-dominance in conjunction with an agglomeration metric. One can mention other works that adopt the generic algorithm for the FSP [7, 8, 9, 10, 11]
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