Price negotiation for capacity sharing in a two-factory environment using genetic algorithm

Uncertain and lumpy demand forces capacity planners to maximize the profit of individual factory by simultaneously taking advantage of outsourcing to and/or being outsourced from its supply chain and even competitors. This study develops a resource-planning model of a large manufacturer with two profit-centered factories. The proposed model enables a collaborative integration for resource and demand sharing which is highly attractive to the high-tech industries against the challenges of short product life cycle, intensive capital investment and decreasing marginal profit. Each of the individual factories applies an economic resource-planning model and a genetic algorithm to improve its objective while purchasing extra capacity requirement from its peer factory or selling extra capacity of resources to the others through a negotiation algorithm. This study makes a contribution in successfully building a mutual negotiation model for a set of customer tasks to be realized by the negotiating parties, each with private information regarding company objectives, cost and price. Experimental results reveal that near-optimal solutions for both of the isolated (a single factory) and negotiation-based (between two factories) environments are obtained.