Abstract
The aim of this paper is to analyse, model, and solve the rescheduling problem in dynamic permutation flow shop environments while considering several criteria to optimize. Searching optimal solutions in multiobjective optimization problems may be difficult as these objectives are expressing different concepts and are not directly comparable. Thus, it is not possible to reduce the problem to a single-objective optimization, and a set of efficient (nondominated) solutions, a so-called Pareto front, must be found. Moreover, in manufacturing environments, disruptive changes usually emerge in scheduling problems, such as machine breakdowns or the arrival of new jobs, causing a need for fast schedule adaptation. In this paper, a mathematical model for this type of problem is proposed and a restarted iterated Pareto greedy (RIPG) metaheuristic is used to find the optimal Pareto front. To demonstrate the appropriateness of this approach, the algorithm is applied to a benchmark specifically designed in this study, considering three objective functions (makespan, total weighted tardiness, and steadiness) and three classes of disruptions (appearance of new jobs, machine faults, and changes in operational times). Experimental studies indicate the proposed approach can effectively solve rescheduling tasks in a multiobjective environment.
Highlights
Scheduling in production systems addresses the problem of sequencing the manufacturing of a series of jobs assigned to different machines in a production environment subject to certain requirements
E restarted iterated Pareto greedy (RIPG) algorithm requires the configuration of 5 input parameters: the size of the set of solutions, k, nsel, nneigh, and the restart threshold
Experimental Design. e lack of a common scheme to assess rescheduling problems in a permutation flow shop problem (PFSP) leads to the generation of databases that permit to work with diverse disruptions in the production environment and evaluate a multiobjective optimization problem. e main database to assess PFSPs was established by Taillard [61] whose instances were initially generated to be applied into single-objective environments, minimization of the completion time of the jobs
Summary
Scheduling in production systems addresses the problem of sequencing the manufacturing of a series of jobs assigned to different machines in a production environment subject to certain requirements. Every objective can be measured in different units or have different meanings, making them incomparable They do not allow a possible optimal solution for all criteria to be obtained. E methods used to solve multiobjective optimization problems are usually based on scalar [9] or metaheuristic techniques [10]. All of them try to convert a multiobjective problem into a single one, by modifying the weights of the objective functions at each process step in order to find the Pareto-front solution set. E limitations of current algorithms are usually analysed to solve problems with a greater number of objective functions and subsequently design new techniques which allow the specific problems to be efficiently tackled [27, 28]. Comparative results are obtained, and the discussion and the main conclusions of the paper are shown
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