Multiobjective large scale job sequencing optimization based on a synergy of compensatory and non compensatory methods

During the last years, the manufacturing industry is experiencing an increasing trend in the utilization of mixed-model assembly lines (MMALs). These lines, often adhering to the Just-In-Time production system, produce diversified products efficiently, maintaining least possible inventories. In this paper, the sequencing of an already balanced large scale double MMAL, manufacturing cars, is modeled as an integer mathematical programming problem and optimized over four objective functions. Two classical multiobjective methods are implemented to solve the mathematical integer programming problem, as well as a synergy of them. The former two are: the compensatory one, global criterion method, and the non-compensatory one, method of satisfactory goals, proposed by Benson. In addition, the model is solved using a combination of the aforementioned methods, as proposed by the authors, which is coupling certain characteristics from both. All three methods make use of the preferences, extracted from a real decision maker, responsible for the management of the production, who is enabled to choose the most preferred among the optimal solutions. Finally, the results, obtained from the implementation of the three separate methods, are assessed with regard to their effectiveness and efficiency.