Genetic algorithm using a modified backward pass heuristic for the dynamic facility layout problem *

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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A new soft computing algorithm based on cloud theory for dynamic facility layout problem

RAIRO - Operations Research ◽

10.1051/ro/2020127 ◽

2020 ◽

Author(s):

Mostafa Zandieh ◽

Seyed Shamsodin Hosseini ◽

parham azimi ◽

Mani Sharifi

Keyword(s):

Material Handling ◽

Simulated Annealing Algorithm ◽

Facility Layout ◽

Planning Horizon ◽

Test Problems ◽

Layout Problem ◽

Facility Layout Problem ◽

Dynamic Facility Layout Problem ◽

Material Handling Costs ◽

Cloud Theory

This paper deals with dynamic facility layout problem (DFLP) in a plant which is concerned with determining the best position of machines in the plant during a multi-period planning horizon. The material handling costs and machines rearrangement costs are used to determine the best layout. In addition to positions of machines, the details of transportation such as type of transporters and sequence of transportation operations have a direct effect on MHC. Therefore, it is more realistic to consider the transportation details during DFLP optimization. This paper proposes a new mathematical model to simultaneously determine the best position of machines in each period and to plan the transportation operations. Minimizing sum of MHC and MRC is considered as the objective function. A new hybrid meta-heuristic approach has been developed by combining modified genetic algorithm and cloud-based simulated annealing algorithm to solve the model. Finally, the proposed methodology is compared with two meta-heuristics on a set of test problems.

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

Neural Information Processing. Theory and Algorithms - Lecture Notes in Computer Science ◽

10.1007/978-3-642-17537-4_78 ◽

2010 ◽

pp. 642-651 ◽

Cited By ~ 4

Author(s):

Kazi Shah Nawaz Ripon ◽

Kyrre Glette ◽

Mats Høvin ◽

Jim Torresen

Keyword(s):

Genetic Algorithm ◽

Facility Layout ◽

Multiple Objectives ◽

Pareto Optimal ◽

Pareto Optimal Solutions ◽

Optimal Solutions ◽

Layout Problem ◽

Facility Layout Problem ◽

Dynamic Facility Layout Problem

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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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