| Managers of manufacturing and service facilities often face situations where incoming demand exceeds available capacity. In such situations a firm can accept, reject or renegotiate the order in order to match the demand and supply. Order acceptance research studies this decision, both at the firm and supply chain level, in order to help the firm select orders that meet their business objectives. Prior research has focused on developing order acceptance models for diverse business situations. This thesis builds upon prior research by incorporating rejection techniques in the development of order acceptance models.;A tunable two-step order acceptance model consisting of a screening step and a detailed evaluation step is proposed. A set of 53 single-step acceptance models, built using four acceptance procedures and six sequencing methods, are analyzed in a simulation study. The best performing single-step models are modified by adding a screening step and evaluated for performance using a set of metrics including profit, acceptance rates, rejection rates, revenues, costs, service levels, average queue length, workload and flow time. The best performing models are then analyzed under changing industry, customer and business conditions in order to evaluate the robustness of the model.;An integrated model using the minimum tardiness beam search procedure for shop floor scheduling and a slack based acceptance model using the apparent tardiness cost (ATC) dispatch procedure for shop-floor scheduling are identified as the best single-step models. Additional results confirm that the performance of the single-step models can be improved further by using screening which increased profits of the integrated model by an average of 26.92% and of the slack model by about 24.28%. |
