摘要
This paper studies a two stage supply chain with a dominant upstream partner. Manufacturer is the dominant partner and operates in a Just-in-Time environment. Production is done in a single manufacturing line capable of producing two products without stopping the production for switching from one product to the other. The manufacturer imposes constraints on the distributor by adhering to his favorable production schedule which minimizes his manufacturing cost. Distributor on the other hand caters to retailers＇ orders without incurring any shortages and is responsible for managing the inventory of finished goods. Adhering to manufacturer＇s schedule may lead to high inventory carrying costs for the distributor. Distributor＇s problem, which is to find an optimal distribution sequence which minimizes the distributor＇s inventory cost under the constraint imposed by the manufacturer is proved NP-Hard by Manoj et al. （2008）. Therefore, solving large size problems require efficient heuristics. We develop algorithms for the distribution problem by exploiting its structural properties. We propose two heuristics and use their solutions in the initial population of a genetic algorithm to arrive at solutions with an average deviation of less than 3.5% from the optimal solution for practical size problems.
This paper studies a two stage supply chain with a dominant upstream partner. Manufacturer is the dominant partner and operates in a Just-in-Time environment. Production is done in a single manufacturing line capable of producing two products without stopping the production for switching from one product to the other. The manufacturer imposes constraints on the distributor by adhering to his favorable production schedule which minimizes his manufacturing cost. Distributor on the other hand caters to retailers＇ orders without incurring any shortages and is responsible for managing the inventory of finished goods. Adhering to manufacturer＇s schedule may lead to high inventory carrying costs for the distributor. Distributor＇s problem, which is to find an optimal distribution sequence which minimizes the distributor＇s inventory cost under the constraint imposed by the manufacturer is proved NP-Hard by Manoj et al. （2008）. Therefore, solving large size problems require efficient heuristics. We develop algorithms for the distribution problem by exploiting its structural properties. We propose two heuristics and use their solutions in the initial population of a genetic algorithm to arrive at solutions with an average deviation of less than 3.5% from the optimal solution for practical size problems.
