Generalized quadratic multiple knapsack problem and two solution approaches


COMPUTERS & OPERATIONS RESEARCH, vol.43, pp.78-89, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 43
  • Publication Date: 2014
  • Doi Number: 10.1016/j.cor.2013.08.018
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.78-89
  • Keywords: Generalized Quadratic Multiple Knapsack, Problem (G-QMKP), F-MSG, Genetic Algorithm (GA), Production with plastic injection, Combinatorial optimization, GENETIC ALGORITHM, OPTIMIZATION
  • Eskisehir Osmangazi University Affiliated: Yes


The Quadratic Knapsack Problem (QKP) is one of the well-known combinatorial optimization problems. If more than one knapsack exists, then the problem is called a Quadratic Multiple Knapsack Problem (QMKP). Recently, knapsack problems with setups have been considered in the literature. In these studies, when an item is assigned to a knapsack, its setup cost for the class also has to be accounted for in the knapsack. In this study, the QMKP with setups is generalized taking into account the setup constraint, assignment conditions and the knapsack preferences of the items. The developed model is called Generalized Quadratic Multiple Knapsack Problem (G-QMKP). Since the G-QMKP is an NP-hard problem, two different meta-heuristic solution approaches are offered for solving the G-QMKP. The first is a genetic algorithm (GA), and the second is a hybrid solution approach which combines a feasible value based modified subgradient (F-MSG) algorithm and GA. The performances of the proposed solution approaches are shown by using randomly generated test instances. In addition, a case study is realized in a plastic injection molding manufacturing company. It is shown that the proposed hybrid solution approach can be successfully used for assigning jobs to machines in production with plastic injection, and good solutions can be obtained in a reasonable time for a large scale real-life problem. (C) 2013 Elsevier Ltd. All rights reserved.