A Genetic Algorithm to Find Pareto-optimal Solutions for the Dynamic Facility Layout Problem with Multiple Objectives

AbstractLayout planning in a manufacturing company is an important economical consideration. In the past, research examining the facility layout problem (FLP) generally concerned static cases, where the material flows between facilities in the layout have been assumed to be invariant over time. However, in today’s real-world scenario, manufacturing system must operate in a dynamic and market-driven environment in which production rates and product mixes are continuously adapting. The dynamic facility layout problem (DFLP) addresses situations in which the flow among various facilities changes over time. Recently, there is an increasing trend towards implementation of industrial robot as a material handling device among the facilities. Reducing the robot energy usage for transporting materials among the facilities of an optimal layout for completing a product will result in an increased life for the robots and thus enhance the productivity of the manufacturing system. In this paper, we present a hybrid genetic algorithm incorporating jumping genes operations and a modified backward pass pair-wise exchange heuristic to determine its effectiveness in optimizing material handling cost while solving the DFLP. A computational study is performed with several existing heuristic algorithms. The experimental results show that the proposed algorithm is effective in dealing with the DFLP.

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

Open Computer Science ◽

10.2478/s13537-011-0027-8 ◽

2011 ◽

Vol 1 (4) ◽

Cited By ~ 3

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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An Improved Genetic Algorithm Based Robust Approach for Stochastic Dynamic Facility Layout Problem

Discrete Dynamics in Nature and Society ◽

10.1155/2018/1529058 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Yunfang Peng ◽

Tian Zeng ◽

Lingzhi Fan ◽

Yajuan Han ◽

Beixin Xia

Keyword(s):

Genetic Algorithm ◽

Demand Uncertainty ◽

Facility Layout ◽

Search Space ◽

Adaptive Genetic Algorithm ◽

Stochastic Dynamic ◽

Layout Problem ◽

Facility Layout Problem ◽

Robust Approach ◽

Dynamic Facility Layout Problem

This paper deals with stochastic dynamic facility layout problem under demand uncertainty in terms of material flow between facilities. A robust approach suggests a robust layout in each period as the most frequent one falling within a prespecified percentage of the optimal solution for multiple scenarios. Mont Carlo simulation method is used to randomly generate different scenarios. A mathematical model is established to describe the dynamic facility layout problem with the consideration of transport device assignment. As a solution procedure for the proposed model, an improved adaptive genetic algorithm with population initialization strategy is developed to reduce the search space and improve the solving efficiency. Different sized instances are compared with Particle Swarm Optimization (PSO) algorithm to verify the effectiveness of the proposed genetic algorithm. The experiments calculating the cost deviation ratio under different fluctuation level show the good performance of the robust layout compared to the expected layout.

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