scholarly journals A novel meta-heuristic algorithm for multi-objective dynamic facility layout problem

2016 ◽  
Vol 50 (4-5) ◽  
pp. 869-890 ◽  
Author(s):  
Seyed Shamsodin Hosseini ◽  
Mehdi Seifbarghy
Keyword(s):  
Heuristic Algorithm ◽  
Facility Layout ◽  
Layout Problem ◽  
Facility Layout Problem ◽  
Dynamic Facility Layout Problem ◽  
Multi Objective

DYNAMIC FACILITY LAYOUT WITH MULTI-OBJECTIVES

2014 ◽  
Vol 31 (04) ◽  
pp. 1450027 ◽  
Author(s):  
GARY YU-HSIN CHEN ◽  
JU-CHIEH LO
Keyword(s):  
Quadratic Assignment Problem ◽  
Facility Layout ◽  
Quadratic Assignment ◽  
Data Sets ◽  
Multiple Time ◽  
Layout Problem ◽  
Facility Layout Problem ◽  
Dynamic Facility Layout Problem ◽  
Multi Objective

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.

scholarly journals Dynamic facility layout problem under uncertainty: a Pareto-optimality based multi-objective evolutionary approach

2011 ◽  
Vol 1 (4) ◽  
Author(s):  
Kazi Ripon ◽  
Kyrre Glette ◽  
Mats Hovin ◽  
Jim Torresen
Keyword(s):  
Pareto Optimality ◽  
Facility Layout ◽  
Multiple Objectives ◽  
Evolutionary Approach ◽  
Layout Problem ◽  
Facility Layout Problem ◽  
Dynamic Facility Layout Problem ◽  
Multi Objective ◽  
Time Period ◽  
Manufacturing Environments

AbstractIn this paper, we investigate an evolutionary approach to solve the multi-objective dynamic facility layout problem (FLP) under uncertainty that presents the layout as a set of Pareto-optimal solutions. Research examining the dynamic FLP usually assumes that data for each time period are deterministic and known with certainty. However, production uncertainty is one of the most challenging aspects in today’s manufacturing environments. Researchers have only recently modeled FLPs with uncertainty. Unfortunately, most solution methodologies developed to date for both static and dynamic FLPs under uncertainty focus on optimizing just a single objective. To the best of our knowledge, the use of Pareto-optimality in multi-objective dynamic FLPs under uncertainty has not yet been studied. In addition, the approach proposed in this paper is tested using a backward pass heuristic to determine its effectiveness in optimizing multiple objectives. Results show that our approach is an efficient evolutionary dynamic FLP approach to optimize multiple objectives simultaneously under uncertainty.

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