Two Strategies Based on Meta-Heuristic Algorithms for Parallel Row Ordering Problem (PROP)

Abstract

Proper arrangement of facility layout is a key issue in management that influences efficiency and the profitability of the manufacturing systems. Parallel Row Ordering Problem (PROP) is a special case of facility layout problem and consists of looking for the best location of <em>n </em>facilities while similar facilities (facilities which has some characteristics in common) should be arranged in a row and dissimilar facilities should be arranged in a parallel row. As PROP is a new introduced NP-hard problem, only a mixed integer programming model is developed to formulate this problem. So to solve large scale instances of this problem, heuristic and meta-heuristic algorithms can be useful. In this paper, two strategies based on genetic algorithm (GA) and a novel population based simulated annealing algorithm (PSA) to solve medium and large instances of PROP are proposed. Also several test problems of PROP in two groups with different sizes that have been extracted from the literature are solved to evaluate the proposed algorithms in terms of objective function value and computational time. According to the results, in the first group of instances, both algorithms almost have equal performances, and in the second group PSA shows better performance by increasing the size of test problems.