Distributed planning problems are commonly found in today's production environment because of supply chain integration. Different factories vertically partner up and work collaboratively to increase their overall competitiveness. Although they are collaborating, factories are independent entities belonging to different companies in many practical situations. They individually face their own production capacity constraints, maximizing their own benefits, and planning production to satisfy their customer orders, especially in fulfilling those committed ones. This scenario is similar to some factories producing electrical home appliances in Mainland China. The objective of this paper is to study the collaboration strategy that they adopted, and propose a new strategy for better collaboration. For simulation, a new hybrid genetic algorithm with exhaustive searching for fine local searching is proposed to determine the production schedule in the factories. The proposed algorithm is compared with a set of well known benchmarking problems, and the results demonstrate that it outperforms many other existing algorithms. Moreover, a number of numerical examples have also been run to demonstrate the strength of the new collaboration strategy in minimizing the tardiness in new orders, meanwhile avoiding tardiness in committed orders.
- Distributed planning
- genetic algorithm
- supply chain collaboration
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering