Abstract:
In supply chains, the decisions regarding production, transportation and pricing are often handled separately. In this thesis study, a nonlinear programming model (NLP) is generated to optimize the production, transportation, and pricing decisions in a supply chain where substitute products are produced in multiple factories and sold at several markets. Under the cooperative competition of substitute products, these decisions are given centrally to maximize the total profit. Demands for the substitute products are realized as functions of their prices where the market shares are expressed as market share attraction models from the marketing literature. The NLP model is solved with different parameter settings and sensitivity analysis on the input parameters is made to provide managerial insights. Finally, a decision support system (DSS) is developed to provide an efficient, effective and flexible decision making environment. The DSS includes a relational database for input and output data, a model base that includes the generated NLP model and a graphical user interface that provides interaction between the user, the database, and the solver for the NLP model.
