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Process deterioration is a leading cause of mediocre production plan adherence and hence not satisfying customers' demand. Process setup and quality improvement are two common factors that return the production system to its designated status. The proposed research integrates production control, capital investment, and quality control aspects to enhance the reliability and productivity of the production-inventory system. To achieve this goal, a mathematical model is formulated to decide the optimal production run length (OPRL), cost of setup, and cost of process quality improvement. It is assumed that the production process starts in an in-control state with a fixed defective rate and may shift to an out-of-control state with a linear increase of defective rate. A numerical example is provided to demonstrate model practicability and to derive managerial insights. For instance, investment in setup cost reduction and quality improvement can achieve a 61.07% reduction in the total cost. [ABSTRACT FROM AUTHOR]

With the technology advancement, equipment used in a number of important economic sectors such as discrete manufacturing, oil/gas production, defense, and health care, become more and more capital intensive. Condition based maintenance (CBM) is a promising approach to overcome the failures and the interruption of these equipment. CBM executes the necessary maintenance actions just-in-time and only when there is evidence of abnormal health condition. The maintenance decision making in CBM recommends cost and performance efficient policy based on the fitted degradation process. In this paper, we consider a component exhibiting a continuous monotone degradation. The degradation process is random and modelled as a Gamma process. The maintenance policy is of condition based type and follows the two threshold control limit policy with a preventive limit, a switching limit, and a given failure limit. The inspection cost is non-negligible. Therefore, the component monitoring is periodic. Our objective is to minimize the annual expected maintenance cost subject to a constraint on the minimum average availability. The decision variables are the preventive limit, the switching limit, and the time between inspections. We derive exact results for the key performance indicators but since they are numerically involved we propose a new simple (i.e., it involves basic spreadsheet calculations) and accurate approximation of the annual average maintenance cost and of the average availability. The empirical results show that the two threshold control policy achieves a yearly cost savings up to 9% compared the single threshold policy. Moreover, the optimization results show a balancing phenomenon, which stabilizes the optimal expected number of inspections per cycle as the degradation per unit of time increases. Moreover, we find that doubling the corrective maintenance cost increases the optimal annual average cost by a small factor of 13%. These results show the benefit of jointly considering the preventive limit, the switching limit, and the time between inspections in the maintenance optimization. [ABSTRACT FROM AUTHOR]

Supply chain studies have been key areas for optimization across many sectors. However, with the changing landscape of geo-economic factors in the Middle East, coupled with increased complexities within the supply chain network and growing competitiveness amongst industries, optimization studies have naturally become more complex overtime. This thesis considers a case study for a major lubrication company in Saudi Arabia. The case study addresses a single-sourcing network redesign problem for a four-level supply chain consisting of suppliers, plants, distribution centers (DC's) and markets. The demand, land prices, and energy prices are uncertain parameters with DC-to-market dependent lead times. The objective is to determine the optimal number and locations of plants and DC's, the assignment of each plant-DC and DCmarket, which minimizes the system-wide location, transportation, and inventory costs for each scenario. The problem is formulated as mixed integer nonlinear programming models (MINLP). Finally, a multi criteria decision-making approach is developed to decide the best warehouse type for the six different local markets. [ABSTRACT FROM AUTHOR]