| Abstract: | As the global industry environment changes drastically, business competition is no longer a business to business, but one supply chain competes with another supply chain in the same industry. Today, the competitiveness in some Taiwanese industries is inferior to that in their corresponding foreign industries. In the face of the global competition, enterprises must strive to survive. The division of work, coordination and integration in the industrial supply chain encounter a serious challenge with the fast change. All members in the upper, middle, and down streams of a supply chain should closely cooperate together to establish effective coordination mechanisms. These coordination mechanisms can integrate the planning and management activities of these members in all aspects of the supply, production and distribution. The purpose is to reduce inventory risks and cost pressures in order to achieve mutually beneficial goals. The issue of coordination mechanisms has received increasing attention from practitioners. This paper proposes an integrated production and distribution mathematical programming model with quantity discounts coordination mechanisms in a multi-level supply chain. In the proposed model, the multi-level arborescent structure, multiple members in each level, and the finite planning horizon are considered. Under the constraint of supply capacity and the satisfaction of the total demand of all members in each period, appropriate order decisions are made in order to minimize the total supply chain cost, including the total ordering cost, the total purchasing cost, the total transportation cost, and the total inventory holding cost. A typical example is solved by a nonlinear mathematical programming software (i.e., LINGO) and a genetic algorithm software (i.e., Gene Hunter), respectively. In the experimental design, this paper first uses the typical example to analyze the impact of the genetic algorithm parameters (i.e., population size, mutation rate ... etc.) on the optimal solution. Then, this study uses 10 test problems to compare the solutions and required CPU times obtained from the genetic algorithm and LINGO. Finally, the case of Taiwanese agricultural product chains is studied and accompanied by explanation. The results of the case study can provide important references for practical and academic persons who intend to design and implement supply chain coordination mechanisms. |
