عنوان مقاله [English]
Due to the competitiveness of the market, manufacturers have been forced to increase their activity effectiveness and efficiency. The shortening of the life cycle and the period of product supply to the market have forced manufacturers to increase the efficiency of their activities and production processes. As regards, the scheduling process and sequencing of efficient operations in manufacturing environments is one of the strategic issue for survival in the competitive market. Workshop environments such as job shop and flow shop are used in many industrial and service processes. One of the most challenging scheduling problem is the open shop scheduling one, but researches on this realm has not paid much attention to human resources. When there is no limit to the processing route of any job on shop machines, this model is referred to as an open shop. The open shop scheduling problem is a strategic issue. However, in most of available schedules in the literature, only workshop equipment, such as machines, is considered as limited resources, but in reality we are confronted with limited human and machine resources. In this study, a mixed-integer programming model is presented for the bi-objective open shop scheduling problem with limited human and machine dual resources. Small-sized problem are solved by using the exact epsilon-constraint method. According to the complexity of solving and Np-hardness of this problem, we used two pareto-based meta-heuristics algorithms, including the Non-Dominated Sorting Genetic Algorithm (NSGAII) and Multi-objective Vibration Damping optimization (MOVDO). In order to analyze and comparison the algorithms , we used from four different indicators which are include: The number of members of the Pareto Front , Mean of ideal distance , Diversity Index , Space uniformity index and 30 problems in three-scale (small , medium , large ) problems have been generated. The computational results shows that the NSGAII is more functional and has better output in comparison to the other presented algorithm.