A production and distribution framework: Manufacturer dominates

This paper presents a two-stage supply chain problem at the operational level involving a manufacturer and a third-party logistics provider (3PL provider). The integrated problem consists of two subproblems: An m-machine permutation flow shop with inventory considerations for the manufacturer and a routing problem for the 3PL provider. Both agents face tardiness penalty costs in case of late delivery to the customer locations. Assuming imperfect information sharing between the two agents and independence in their own decisions, we investigate the scenario in which the manufacturer dominates the negotiation.

In this scenario, the manufacturer imposes the number and composition of vehicles as well as the vehicle departure dates on the 3PL provider. However, lacking the information regarding the actual delivery times he is forced to estimate delivery times at the customer locations in the context of his planning. Following the planning of the manufacturer, the 3PL provider decides on the optimal routing of the vehicles. Both agents aim to minimise their total costs. After formulating the problems as a mixed integer linear program, two metaheuristic algorithms are proposed to solve the scenario. The performance of the heuristics is evaluated on a set of randomly generated instances. The results show that the genetic algorithm is the most performing method.