Production distribution planning in consumer goods industry

In this thesis, we present a hybrid solution methodology based on a MILP formulation and a simulation for production scheduling and distribution problem in dairy industry. An efficient solution method for perishable products is developed by considering stochastic factors in food industry. A real life dairy industry producing yoghurt is studied in detail in this study.In food industry, increasing variety of products causes more complex production process which requires flexibility and efficient assignment of resources. Production process of multiple products in more than one production sites and distribution of them involves many variables and constraints.Shelf life is one of the significant constraints for perishable products such as dairy, meat or bakery goods in food industry. However, shelf life issues are seldom accounted for in today?s production planning systems. This research is supported by an application in yoghurt production plant of a leading dairy product manufacturing company.In analytic models proposed to solve production planning problems, operation time is assumed as fixed values. However, uncertain factors such as breakdowns, operation time, delays of real systems cannot be correctly represented in analytic model. In addition, in yoghurt production process, the products differ from each other in features such as cup size, due dates, set up times, fat content etc. This variability enforces the scheduling methodologies practical for real world applications. To overcome with this problem, hybrid analytic-simulation approach is proposed by combining the analytic and simulation model. Analytic model is developed for decreasing the cost of setup, transportation, production, inventory and overtime. Simulation model is applied to insert the stochastic factors such as operation time, delays or machine failures in the model.In hybrid approach, operation time is considered as dynamic factor and it is adjusted by the results simulation and analytic model iteratively. Thus, more realistic solution is obtained for scheduling problem in food industry by performing the iterative hybrid analytic-simulation procedure.