Solution of facility layout problems with pickup/drop-off locations using random search techniques

Abstract

Due to non-polynomial hardness, the facility layout problem (FLP) becomes more critical when pickup/drop-off (P/D) locations are considered in the design of an open field layout under a manufacturing environment. This paper proposes an indigenous model of the facility layout problem based on random search techniques and its solution methodology using a genetic algorithm (GA), simulated annealing (SA) and a hybrid algorithm (HA). The paper illustrates the performance of different random search operating parameters in solving the facility layout problem considering P/D locations along the periphery of rectangular machine blocks. The preliminary experiments were carried out on three facility layout test problems having six, eight and ten machines in order to fix the different operating parameters such as crossover operator, crossover rate, initial temperature, temperature reduction factor, number of generations, population size, etc. The results of extensive preliminary experimentation were utilized to solve facility layout problems having 12 and 18 machines and, finally, were compared with the existing procedures in the literature. The experimental tables and related analysis performed via the solution methods by applying GA, SA and HA revealed that random-search-based modeling of FLP considering P/D and its solution as suggested in this paper is worth pursuing.

MIP model and elitist strategy hybrid GA–SA algorithm for layout design

“…Deb and Bhattacharyya (2005) reported that use of GA with SA algorithm (random search hybrid approach) could perform well for layout problems than GA and SA algorithm based methodologies reported in the literature. However, this study differs from Deb and Bhattacharyya (2005) by implementing elitist strategy in ESHGA. To test its effectiveness, problem set 1 and problem set 2 are taken for experimentation and the parameter values are taken from Table 6.…”

“…It consists of four test problem instances of 6 and 12 departments (Welgama and Gibson 1993), 7 departments (Wu and Appleton 2002) and 20 departments (Aiello et al 2002). In addition, to justify the proposed approach, it is compared with the hybrid GA based random search approach that use SA for local search (Deb and Bhattacharyya 2005). The problem set 2 consists of three test problem instances of 12 and 18 departments (Deb and Bhattacharyya 2005) and 62 department problem of Dunker et al (2003).…”

“…In addition, to justify the proposed approach, it is compared with the hybrid GA based random search approach that use SA for local search (Deb and Bhattacharyya 2005). The problem set 2 consists of three test problem instances of 12 and 18 departments (Deb and Bhattacharyya 2005) and 62 department problem of Dunker et al (2003). The details of test problem instances are given in the Table 5.…”

“…Then, ESHGA is tested with another three test problem instances of problem set 2 and the parameter values are taken from Table 6. ESHGA is compared with the random search hybrid approach proposed by Deb and Bhattacharyya (2005). To make the comparison fair and justified we suitably change our proposed model to use rectilinear distance metric similar to the model proposed by Kim and Goetschalckx (2005).…”

“…proposed the integration of cell layout and I/O location problem and they solved it by a heuristic procedure. Deb and Bhattacharyya (2005) solved layout problem considering fixed and variable I/O locations problem with rectilinear distance metric using GA based hybrid random search that uses SA as a local search technique. For the inter-cell layout and flow path design of CMS having predefined I/O stations a standard GA based procedure called integrated cell system layout was developed by Hu et al (2007).…”