چكيده لاتين
In todayʹs competitive world, determining the sequence and effective timing of production products is a necessity for survival in the market environment. Scheduling is a tool for optimal use of available resources. Resources and tasks in scheduling may have different types and with the development of the industrial world, the relevant resources become more critical. Scheduling these resources leads to increasing the efficiency and utilization of capacity, reducing the time required to complete the work and ultimately increasing the profitability of an organization. Effective scheduling of resources such as machines and manpower is a must in todayʹs competitive environment. The importance of delivering the parts to the customer on time, as well as the variety and extent of the manufacturing process, requires this planning problem to try to implement it in the best possible way. This research is in the category of Flexible flow shop problems and has NP Hard complexity class. This research tries to optimize the schedule and logistics delays in the metal line of Part Rubber Company to feed the polymer and packaging line. The results of this research are also used for similar systems (Flexible flow shop). Since the primary materials and components as well as the production parts in the metal line need internal transportation between departments and lines for moving, in this research, the transportation and logistics of materials and parts are also considered. Therefore, this research pursues the three goals of minimizing early and late times, transportation times, and logistics delays. In addition, line stoppages that may occur due to untimely arrival of raw materials, preparation times, and breakdowns of devices or molds are considered with uncertainty conditions to formulate a model based on assumptions and close to reality. The studied model has been implemented in 6 different dimensional groups, as an exact solution in GAMS software, and as a meta-heuristic algorithm (SA) solution in MATLAB software. The obtained results show that the exact solution method is able to implement and optimize the model up to the dimensions of 24 defined tasks and the number of 6 means of transportation, with a value of 1% Gap in about half an hour. In order to clarify the effectiveness of the model, the Gantt chart of the problem is also examined. On the other hand, the results state that the first and third objective functions can have an opposite effect on each other, and with the increase of one objective function, the other objective function will have a relative decrease. For example, by limiting the first objective function and increasing it to 50 minutes, the third objective function is reduced by 31 minutes. Also, the results of meta-heuristic algorithm show that there is a 9% increase in the total values of the three objective functions compared to the exact solution method. Of course, the results presented in chapter 4 state that this increase goes through a decreasing process as the dimensions of the problem get bigger. Also, the average duration of problem solving with 8550 percent reduction compared to the exact solution method is very important in the implementation of problems with more tasks than the defined dimensional groups.
