A two-stage preference-based evolutionary multi-objective approach for capability planning problems

  • Authors:

  • Jian Xiong;Ke-wei Yang;Jing Liu;Qing-song Zhao;Ying-wu Chen

  • Affiliations:

  • Department of Management Science and Engineering, College of Information System and Management, National University of Defense Technology, Changsha, 410073 Hunan, PR China and School of Engineerin ...;Department of Management Science and Engineering, College of Information System and Management, National University of Defense Technology, Changsha, 410073 Hunan, PR China and Department of Comput ...;School of Engineering and Information Technology, University of New South Wales at the Australian Defence Force Academy, ACT 2600, Australia;Department of Management Science and Engineering, College of Information System and Management, National University of Defense Technology, Changsha, 410073 Hunan, PR China;Department of Management Science and Engineering, College of Information System and Management, National University of Defense Technology, Changsha, 410073 Hunan, PR China

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

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Abstract

As a type of long-term planning problems, capability planning problems (CPPs) have received considerable attention in the defense and military area. In this paper, we model CPPs as a type of project scheduling problems, referred to as multi-mode resource investment project scheduling problems (MRIPSPs). The makespan and the cost are simultaneously considered. To deliver decision support, a two-stage approach is developed considering both operational and strategic perspectives. At both levels, knowledge of experts or preference of decision makers is utilized. By integrating domain knowledge at the operational level and preference information at the strategic level into the optimization algorithm, a two-stage preference-based multi-objective evolutionary algorithm is proposed. A hypothetical case with 16 tasks is studied. The experimental results show that by focusing computational efforts on the sub-regions where experts or decision makers are interested, we can obtain the solutions which are not only closer to the true Pareto front in objective space, but also hold good characteristics in decision space.