An exact penalty function method for optimising QAP formulation in facility layout problem. Issue 10 (19th May 2017)

• Record Type: Journal Article
• Authors: Zhou, Jingyang; Love, Peter E.D.; Teo, Kok Lay; Luo, Hanbin
• Is Part Of: International journal of production research. Volume 55:Issue 10(2017)
• Page Start: 2913
• Page End: 2929
• Publication Date: 2017-05-19
• Subjects: facility layout -- QAP -- nonlinear programming -- continuous optimization -- exact penalty function method -- backtracking search algorithm; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2016.1229068
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : A quadratic assignment problem (QAP), which is a combinatorial optimisation problem, is developed to model the problem of locating facilities with material flows between them. The aim of solving the QAP formulation for a facility layout problem (FLP) is to increase a system's operating efficiency by reducing material handling costs, which can be measured by interdepartmental distances and flows. The QAP-formulated FLP can be viewed as a discrete optimisation problem, where the quadratic objective function is optimised with respect to discrete decision variables subject to linear equality constraints. The conventional approach for solving this discrete optimisation problem is to use the linearisation of the quadratic objective function whereby additional discrete variables and constraints are introduced. The adoption of the linearisation process can result in a significantly increased number of variables and constraints; solving the resulting problem can therefore be challenging. In this paper, a new approach is introduced to solve this discrete optimisation problem. First, the discrete optimisation problem is transformed into an equivalent nonlinear optimisation problem involving only continuous decision variables by introducing quadratic inequality constraints. The number of variables, however, remains the same as the original problem. Then, an exact penalty function method is applied to convert this transformed continuous optimisation problem into an unconstrained continuous optimisation problem. An improved backtracking search algorithm is then developed to solve the unconstrained optimisation problem. Numerical computation results demonstrate the effectiveness of the proposed new approach.