Uncapacitated and capacitated dynamic lot size models for an integrated manufacturer-buyer production system

This research aims to develop the uncapacitated and capacitated dynamic lot size models for an integrated manufacturer-buyer production system. Of the capacitated dynamic lot size models, the production capacity of the manufacturer is constrained to meet the realistic problems. To perform an investigation these production systems, it was assumed to be a single-product problem with a planned finite horizon based upon the forecasting demands from the buyer. It was also assumed that the manufacturer pays for the transportation cost.; There are two objectives of this research. First, to find uncapacitated and capacitated algorithms that generate optimal production and transportation schedules for the production system in order to minimize the system cost. Secondly, to find uncapacitated and capacitated heuristics that generate near-optimal production and transportation schedules in a very short period of time.; In an integrated two-echelon production system, the production and delivery schedules dynamically affect each other. To determine the optimal production and delivery schedules with time-varying discrete demand, we evaluate these two schedules simultaneously by minimizing the system variable cost of manufacturing setup cost, transportation costs, order cost and inventory holding costs for both the manufacturer and the buyer. Dynamic programming techniques are used to formulate the uncapacitated and capacitated algorithms in this paper.; On a set of randomly generated problem instances, the results show that the heuristics have very low deviation from the algorithms. This indicates that the heuristics have very promising results for production systems. A special case of a capacitated heuristic was also investigated in which the demands can be split into different production setup periods. The portion of the research is important because the results of a capacitated heuristic can be improved when the number of manufacturing setups are reduced by splitting demand into different manufacturing setup periods. Consequently, it could result in reducing the system cost by compensating the increased inventory cost from the split demand. The numerical results show that the split-demand capacitated heuristic can reduce the system cost in almost one-third of cases from a set of randomly generated problem instances.