Proposition of two multiple criteria models applied to Dynamic Multi-objective Facility Layout Problem based on Ant Colony Optimization

Chen, G. Y.; Rogers, Kevin

doi:10.1109/ieem.2009.5373095

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…This study adopted the concept, and each rating is converted into numerical values. According to Chen [37], the value assigned to each rating is subjective and does not have particular rule, depend on the level of importance of the closeness for each department. However, as some ratings are denoted different needs, such as A (absolutely need to be close to each other) or X (cannot be close to each other), many studies used a high deviation of number.…”

Section: Adjacency Functionmentioning

confidence: 99%

“…However, as some ratings are denoted different needs, such as A (absolutely need to be close to each other) or X (cannot be close to each other), many studies used a high deviation of number. In this study, however, followed the study conducted by Chen [37] and assigned the closeness score in such ways: 6 to A, 5 to E, 4 to I, 3 to O, 2 to U and -1 to X.…”

Section: Adjacency Functionmentioning

confidence: 99%

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Solving Multi-Objective Paired Single Row Facility Layout Problem Using Hybrid Variable Neighborhood Search

Maghfiroh

2021

jti

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The footwear industry is distinguished by its manual assembly line and a high proportion of shared workstation configuration. This study focuses on a subset of the single row facility layout problem known as the paired single row facility layout problem. As one of type of single-row facility layout, the paired single row facility layout problem cannot be solved quickly. Further, different objectives also need to be considered in the decision-making process. Therefore, multi-objective approaches are proposed to minimize the penalty of material handler usage while maximizing the adjacency function based on each workstation's closeness rating. A Single Row Facility Layout is an NP-hard problem; this problem also belongs to the NP-hard problem class. As a result, we propose a hybrid method combining variable neighborhood search (VNS) and genetic algorithm (GA) to solve the problem of obtaining the optimal configuration of a multi-objective paired single-row assembly line. A heuristic approach was used to create the schematic representation solution. To obtain the neighborhood solutions, a hybrid VNSGA was used. The schematic representation solution employs crossover and variable neighborhood descent. Using the concept of VNS, the neighborhood was changed in each generation.

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Section: Adjacency Functionmentioning

confidence: 99%

Section: Adjacency Functionmentioning

confidence: 99%

Solving Multi-Objective Paired Single Row Facility Layout Problem Using Hybrid Variable Neighborhood Search

Maghfiroh

2021

jti

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“…It is only recently that researchers have been proposing multi-objective approaches for DFLP. Chen and Rogers [18] were the first ones who proposed a multi-objective dynamic facility layout model to search about several features of the facility layout problems such as time and distance-based objective as well as the adjacencybased objective. They applied a meta-heuristic optimization algorithm called ant colony optimization to solve the MODFLP.…”

Section: A Brief Literature Reviewmentioning

confidence: 99%

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

Hosseini

Seifbarghy

2016

RAIRO-Oper. Res.

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This paper proposes an integrated approach for dynamic facility layout problem considering the material handling equipment (MHE). The objectives of this problem are minimization of the fixed costs of MHE, minimization of material handling cost (MHC) and minimization of machine rearrangement costs (MRC). To be more realistic, MHE fixed costs, MRC and MHC, which might be of different importance to decision maker, are considered separately in three objective functions. An integrated model is proposed which is able to simultaneously select the MHE along with the arranging and re-arranging facilities. The model belongs to the class of multi-objective nonlinear mathematical programming models. Considering the NP-hard nature of the model and inspiring the existing water flow like algorithm, a novel Pareto-based meta-heuristic algorithm called multi-objective water flow like algorithm (MOWFA)is developed to solve the problem. We comprehensively discuss the parameter tuning of the algorithms utilizing Taguchi method. Finally, the performance of the proposed MOWFA is evaluated against two well-known meta-heuristic algorithms called non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA). Computational results indicate the greater efficiency of the algorithm compared to the two addressed algorithms for solving the given multi-objective problem.

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“…To face this challenge, biology and nature can provide useful insights, especially the self-organization phenomenon. Shan et al (2007), Sharma et al (2009), Lu et al (2008) Lazzerini et al (1999, Gao and Chen (2008) Lv and Lu (2009), Dong et al (2007) Layout optimization Jain and Sharma (2005), Sun and Teng (2002), Chen and Rogers (2009), Corry andKozan (2004) Wang et al (2008), Kulkarni andShanker (2007) Ning et al (2004), Ohmori et al (2010), Lei et al (2003), Pham et al (2007a), Yamada et al (2003) Manufacturing scheduling Arnaout et al (2008), Chen et al (2008), Xu et al (2009), Blum and Sampels (2004), Gravel et al (2002) Qiu et al 2009, Aggoune et al (2001), Tharumarajah (1998) Shi et al (2009, Zhang and Wu (2008), Pham et al (2007b), Cicirello and Smith (2001a), Cicirello and Smith (2001b), Xia and Wu (2005) Self-organization applied to multi-agent systems allows several self-n properties to be achieved (Leitão, 2008a):…”

Section: Applicability Of Bio-inspired Systems In Manufacturingmentioning

confidence: 99%

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

Leitão

Barbosa

Trentesaux

2012

Engineering Applications of Artificial Intelligence

135

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The current market's demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature's insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.

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Proposition of two multiple criteria models applied to Dynamic Multi-objective Facility Layout Problem based on Ant Colony Optimization

Cited by 5 publications

References 12 publications

Solving Multi-Objective Paired Single Row Facility Layout Problem Using Hybrid Variable Neighborhood Search

Solving Multi-Objective Paired Single Row Facility Layout Problem Using Hybrid Variable Neighborhood Search

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

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

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