Abstract. Over the years, various approaches have been proposed in order to solve the multi-objective job-shop scheduling problemparticularly a hard combinatorial optimization problem. The paper presents an evaluation of job shop scheduling problem under multiple objectives (mean flow time, max lateness, mean tardiness, mean weighted tardiness, mean earliness, mean weighted earliness, number of tardy tasks). The formulation of the scheduling problem has been presented as well as the evaluation schedules for various optimality criteria. The paper describes the basic mataheuristics used for optimization schedules and the approaches that use domination method, fuzzy method, and analytic hierarchy proccess (AHP) for comparing schedules in accordance with multiple objectives. The effectiveness of the algorithms has been tested on several examples and the results have been shown. New search space for evaluation and generation of schedules has been created. The three-dimensional space can be used for the analysis and control of the production processes.
The paper presents contemporary artificial intelligence tools -evolution algorithms and random algorithms designed for the optimalisation of the production scheduling problem for multi-assortment short-series production. The essential idea of SZEZA method has been presented. SZEZA method has the ability of part control of the sequence of tasks. The efficiency of SZEZA has been compared with the efficiency of genetic algorithm AGHAR and with efficiency of other metaheuristic methods. Problems related with the further development of mentioned algorithms for the purpose of a choice and defining their optimal decision factors and constraints have been described.
In the paper the influence of route flexibility degree, open rate of operations and production type coefficient on makespan is discussed. The flexible job-open shop scheduling problem FJOSP (an extension of the classical job shop scheduling) is analyzed. For the analysis of the production process the GRASP (greedy randomized adaptive search procedure) and simulated annealing heuristic algorithms were used. Experiments with different levels of factors have been considered and compared. The presented algorithms have been tested, and illustrated with examples for serial route.
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