Dynamic Algorithms for Order Acceptance and Capacity Planning within a Multi-Project Environment

We consider the dynamic order acceptance and capacity planning problem under limited regular and non-regular resources. The goal consists of maximizing the expected profits of the accepted projects within a finite problem horizon. Capacity planning is a useful tool to support tactical decisions such as due-date quotation, price quotation and hiring non-regular capacity. The way the projects are planned affects their payout time and as a consequence, the reinvestment revenues, as well as the available capacity for future arriving projects. Since actual characteristics of project proposals are only revealed upon arrival, dynamic solution approaches are more likely to obtain good results. For this reason, this paper considers dynamic heuristics, such as approximate dynamic programming algorithms and investigates their suitability to solve the problem. We perform simulation experiments to compare the performance of our algorithms to methods commonly used in practice.