Multi-objective Facility Layout Problem Research Articles

Solving multi objective facility layout problem by modified simulated annealing

This paper proposes a modified simulated annealing approach for solving multi objective facility layout problem. Multi objective facility layout problem is solved by using different objective weights which are generated by decision maker. Thus layout design process is decision maker dependent. However proposed approach in this paper makes layout design process independent of decision maker. Proposed approach can also incorporate any number of objectives that may be qualitative or quantitative in nature. Computational results show superiority of proposed heuristic approach than other approaches available in literature. Finally, comparison was also made with best solution qualities for the 15 single objective test problems of Nugxx series taken from QAPLIB. Comparisons were made in terms of the quality of the solutions obtained and the computational time required and this comparison shows efficiency and effectiveness of proposed modified simulated annealing algorithm.

SOLVING MULTI OBJECTIVE FACILITY LAYOUT PROBLEM USING ROBUST TABU SEARCH

ABSTRACT In this paper a robust tabu search based approach has been presented for solving multi objective facility layout problem. It can incorporate more than two objectives that may be qualitative or quantitative in nature. Computational results show superiority of proposed tabu search for solving multi objective facility layout problem, than other algorithms available in literature. Keywords Multi Objective facility layout problem, QAP, Tabu Search.

DYNAMIC FACILITY LAYOUT WITH MULTI-OBJECTIVES

A problem in multi-objective dynamic facility layout is achieving distance- and adjacency-based objectives for arranging facility layouts across multiple time periods. As a non-deterministic polynomial time-hard problem, it resembles the quadratic assignment problem (QAP), which can be solved through meta-heuristics such as ant colony optimization (ACO). This study investigates three multi-objective approaches coupled with ACO to solve this problem. As the experimental design, we apply the proposed methods to solve the dynamic facility layout problem (DFLP), multi-objective facility layout problem, and multi-objective DFLP based on data sets from the literature to test the quality of the solution. The results show that the proposed methods are effective for solving the problem.

A note on “Parallel variable neighborhood search for solving fuzzy multi-objective dynamic facility layout problem”

Abedzadeh et al. (Int J Adv Manuf Technol 65:197–211, 2013) proposed a mixed integer programming formulation for dynamic flexible bay layout problem with three objectives: minimizing material handling and relayout cost, maximizing adjacency ratio, and minimizing shape ratio difference. In this paper, we correct the formulation proposed by Abedzadeh et al. (Int J Adv Manuf Technol 65:197–211, 2013). This correction is mainly focused on the three objectives of their formulation. It is shown that the relayout cost was miscalculated, the adjacency ratio was improperly linearized, the third objective was incorrectly linearized and fuzzy programming technique for multi-objective linear programming problems was not applied correctly for this problem.

Modified simulated annealing based approach for multi objective facility layout problem

In this paper a modified simulated annealing (SA) based approach is presented for solving multi objective facility layout problem (MOFLP). It can incorporate more than two objectives that may be qualitative or quantitative in nature. Computational results show superiority of the proposed modified SA based approach for MOFLP over past approaches available in literature. To validate the proposed SA based approach, it is tested on instances taken from the literature which shows that the proposed approach provides promising results.

Adaptive variable neighborhood search for solving multi-objective facility layout problems with unequal area facilities

In this paper, we report the results of our investigation of an evolutionary approach for solving the unequal area multi-objective facility layout problem (FLP) using the variable neighborhood search (VNS) with an adaptive scheme that presents the final layouts as a set of Pareto-optimal solutions. The unequal area FLP comprises a class of extremely difficult and widely applicable optimization problems arising in diverse areas and meeting the requirements for real-world applications. The VNS is an explorative local search method whose basic idea is systematic change of neighborhood within a local search. Traditionally, local search is applied to the solutions of each generation of an evolutionary algorithm, and has often been criticized for wasting computation time. To address these issues, the proposed approach is composed of the VNS with a modified 1-opt local search, an extended adaptive local search scheme for optimizing multiple objectives, and the multi-objective genetic algorithm (GA). Unlike conventional local search, the proposed adaptive local search scheme automatically determines whether the VNS is used in a GA loop or not. We investigate the performance of the proposed approach in comparison to multi-objective GA-based approaches without local search and augmented with traditional local search. The computational results indicate that the proposed approach with adaptive VNS is more efficient in most of the performance measures and can find near-optimal layouts by optimizing multiple criteria simultaneously.

Non-greedy systematic neighbourhood search heuristic for multi-objective facility layout problem

In this paper a non-greedy systematic neighbourhood search heuristic is presented for solving multi-objective facility layout problem. It can incorporate more than two objectives that may be qualitative or quantitative in nature. Computational results show superiority of our proposed heuristic algorithm than other algorithms available in literature. Finally, comparison was also made with best solution qualities for the 15 single objective test problems of Nugxx series taken from QAPLIB and this comparison also shows efficiency and effectiveness of proposed heuristic algorithm.

Three-level AHP-based heuristic approach for a multi-objective facility layout problem

In real decision making problems, many conflicting objectives have to be taken into account. With increasing awareness of this, multi-objective problems have become more and more popular. Similarly, the design of the multi-objective facility layout problem (MOFLP) has generally been recognised as an important issue in the modern manufacturing system. The MOFLP is formulated as a quadratic assignment problem (QAP) which is NP-hard and solving MOFLP is a tough problem. A new three-level AHP-based heuristic algorithm for resolving the MOFLP is presented here. It also presents a new normalisation procedure (H-1), and a new heuristic method (H-2) for generating objective weights. The proposed approach consists of three levels. The first level applies AHP to generate paired comparison matrices, the consistency of matrix, to convert inconsistent matrices into consistent ones and then generate a qualitative objective matrix; the second level applies normalisation procedure (H-1) to normalise matrices of qualitative and quantitative objectives and the third level computes the objective weight for qualitative and quantitative objectives. An illustrative example is shown to demonstrate an application of the proposed methodology for solving MOFLP.

An improved heuristic approach for multi-objective facility layout problem

Multi-objective facility layout problem (mFLP) generates a different layout by varying objectives weights. Since the selection of objective weights in mFLP is critical, stages of designing layout having multiple objectives, the objective weights therefore play an important role in the layout design of mFLP. In practice, it is selected randomly by the layout designer based on his/her past experience that restricts the layout designing process completely designer dependent and thus the layout varies from designer to designer. This paper aims to resolve the issues of selecting the objective weight for each objective. We propose four methods to determine objective weight which makes the design process of mFLP completely designer independent.

A simulated annealing algorithm to find approximate Pareto optimal solutions for the multi-objective facility layout problem

In this article, we consider the facility layout problem which combines the objective of minimization of the total material handling cost and the maximization of total closeness rating scores. Multi-objective optimization is the way to consider the two objectives at the same time. A simulated annealing (SA) algorithm is proposed to find the non-dominated solution (Pareto optimal) set approximately for the multi-objective facility layout problem we tackle. The Pareto optimal sets generated by the proposed algorithm was compared with the solutions of the previous algorithms for multi-objective facility layout problem. The results showed that the approximate Pareto optimal sets we have found include almost all the previously obtained results and many more approximate Pareto optimal solutions.

Heuristic approach for solving the multi-objective facility layout problem

A new heuristic approach for the generation of preferred objective weights to solve the multi-objective facility layout problem is presented. By applying a multi-pass halving and doubling procedure, a paired comparison method based on the strength of preference among objectives given by the decision-maker is developed. Furthermore, a ‘prior test’ is proposed to examine the consistency of the paired comparison matrix. An efficient method to transform the inconsistent matrix into a consistent one so the result can closely approximate the decision-maker's original assessments is also offered. The geometric mean method is then employed to obtain the objective weights and the final solution. There are five phases in the proposed heuristic approach. The first generates a basic solution; the second involves constructing a paired comparison matrix by using the multi-pass halving and doubling procedure; the third identifies the consistency of the paired comparison matrix; the fourth transforms the inconsistent matrix into a consistent one; and the fifth generates the preferred weights and obtains the facility layout solution. An illustrative example is given to demonstrate an application of the proposed approach for solving the multi-objective facility layout problem.

A design approach to the multi-objective facility layout problem

A new multi-objective heuristic algorithm for resolving the facility layout problem is presented in this paper. It incorporates qualitative and quantitative objectives and resolves the problem of inconsistent scales and different measurement units. We consider suboptimal solutions since optimal methods are computationally unfeasible to large layout problems. In this paper, we develop the dominant index (DI) and incorporate it in our heuristic algorithm to guarantee the quality of proposed solutions. Moreover, a new measure of solution quality, dominant probability ( D p), is offered to determine the probability that one layout is better than the others. Computational results show that our proposed heuristic algorithm is an efficient method for obtaining good-quality solutions.