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Facility layout design using virtual multi-agent system

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Abstract

This paper presents a novel approach to the facility layout design problem based on multi-agent society where agents’ interactions form the facility layout design. Each agent corresponds to a facility with inherent characteristics, emotions, and a certain amount of money, forming its utility function. An agent’s money is adjusted during the learning period by a manager agent while each agent tries to tune the parameters of its utility function in such a way that its total layout cost can be minimized in competition with others. The agents’ interactions are formed based on market mechanism. In each step, an unoccupied location is presented to all applicant agents, for which each agent proposes a price proportionate to its utility function. The agent proposing a higher price is selected as the winner and assigned to that location by an appropriate space-filling curve. The proposed method utilizes the fuzzy theory to establish each agent’s utility function. In addition, it provides a simulation environment using an evolutionary algorithm to form different interactions among the agents and makes it possible for them to experience various strategies. The experimental results show that the proposed approach achieves a lower total layout cost compared with state of the art methods.

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Abbreviations

\({\vartheta _{ij}}\) :

= (c ij )(f ij )

ω i :

Layout cost of ith agent for each area unit

d ij :

The distance between facility i and facility j in a completed layout

b :

The learning rate of money

p i :

Price proposed by ith agent

M ij :

ith agent’s money in jth iteration

\({\mu_{\tilde {M}}}\) :

The degree of membership of an agent in the set of rich agents

\({\mu_{\tilde {\lambda }x}}\) :

The degree of membership of a location in the set of middle horizontal positions

\({\mu_{\tilde {\lambda }y}}\) :

The degree of membership of a location in the set of middle vertical positions

\({\mu_{\tilde {\lambda }}}\) :

The degree of membership of a location in the set of central locations

\({\mu_{\tilde {R}}}\) :

The degree of membership of an agent in the set of high risk-taking agents

μ α :

The degree of membership of an agent in the set of highly attracted agents

\({\beta_{\tilde {F}}}\) :

Membership shrink parameter of \({\tilde {F}}\) , \({0\leq \beta_{\tilde {F}}\leq 1}\) . In other words, it represents the effectiveness of \({\tilde {F}}\) on final inference

\({\gamma_{\tilde {F}}}\) :

The shape parameter of \({\tilde {F}}\)

n :

Number of facilities and it is equal to the number of agents

S :

Sweep band width

RX :

Horizontal plant length

RY :

Vertical plant length

A :

Total assigned area

SF :

The vector of selected facilities; it consists of agents who bought the previous locations offered to them

SU :

The vector of suppliant agents, it is the complementary vector of SF

FA :

The vector of facilities’ area

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Hamed Tarkesh, Arezoo Atighehchian & Ali S. Nookabadi

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  1. Hamed Tarkesh
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  2. Arezoo Atighehchian
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  3. Ali S. Nookabadi
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Correspondence to Ali S. Nookabadi.

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Tarkesh, H., Atighehchian, A. & Nookabadi, A.S. Facility layout design using virtual multi-agent system. J Intell Manuf 20, 347–357 (2009). https://doi.org/10.1007/s10845-008-0109-1

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  • DOI: https://doi.org/10.1007/s10845-008-0109-1

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