Elsevier

Expert Systems with Applications

Volume 36, Issue 9, November 2009, Pages 11875-11887
Expert Systems with Applications

A Hybrid Fuzzy Knowledge-Based Expert System and Genetic Algorithm for efficient selection and assignment of Material Handling Equipment

https://doi.org/10.1016/j.eswa.2009.04.014Get rights and content

Abstract

Material Handling (MH) is one of the key issues for every production site and has a great impact on manufacturing costs. The core concern in the design of a MH system is selecting the most suitable equipment for every MH operation and optimising them totally in order to attain an optimum solution. This paper presents a hybrid method for the selection and assignment of the most appropriate Material Handling Equipment (MHE). In the first phase, the system selects the most appropriate MHE types for every MH operation in a given application using a Fuzzy Knowledge-Based Expert System consisting of two sets of rules: Crisp Rules and Fuzzy Rules. In the second phase, a Genetic Algorithm (GA) searches throughout the feasible solution space, constituting of all possible combinations of the feasible equipment specified in the previous phase, in order to discover optimum solutions. The validity of the methodology developed in this paper is proved through the use of a real problem. Finally a comparison of the method with the other available publicised methods reveals the effectiveness of this hybrid approach.

Introduction

A Material Handling (MH) system is responsible for transporting materials between workstations with minimum obstruction and joins all workstations and workshops in manufacturing systems by acting as a basic integrator (Sujono & Lashkari, 2007). According to our definition, “MH is the art of implementing movement economically and safely” (Apple, 1972). The key role of a MH system in industry is apparent simply because without it the movement of materials between processes is impossible and production therefore could not be accomplished.

Additionally, the MH cost is a substantial component of the total costs in manufacturing. Tompkins et al. (1996) estimated that in a typical manufacturing operation, 25% of the number of employees, 55% of all plant area, and 87% of production time are assigned to MH and it accounts for between 15% and 70% of the total cost of manufacturing a product.

In summary, an efficient MH system greatly improves the competitiveness of a product through the reduction of handling cost, enhances the production process, increases production and system flexibility, provides effective utilisation of manpower and decreases lead time (Chan, 2002, Chu et al., 1995). Having an efficient and cost-effective MH system necessitates designing the entire MH system at once even though it may comprise several subsystems. The selection and configuration of Material Handling Equipment (MHE) types are the key subsystems in the design of a MH system (Chan, 2002, Park, 1996).

Since the 1970s research concentrating on the selection and assignment of MHE has been carried out and significant achievements have been attained. The vast majority of the research has addressed only the selection problem whilst there only a small amount aimed at developing methods for the resolution of both the selection and assignment problem and to reach a comprehensive solution for the whole MH problem.

This paper presents a novel two-phase method employing a Hybrid Fuzzy Knowledge-Based Expert System and Genetic Algorithm to practically solve both the selection and assignment of MHE problem. In the first phase a Fuzzy Expert System is used to identify the best MHE types for every handling operation with their appropriateness factors whilst in the second phase a GA investigates throughout the feasible solution space to select a number of optimal solutions.

This paper is organised as follows. Section 2 presents an overview of some of the related literature. In Section 3 the MHE selection and assignment problem is reviewed in a total view. While the methodology framework is presented in Sections 4 The methodology framework, 5 Phase I, 6 Phase II, respectively, discuss the first and the second phase of the method in detail. The software developed is outlined in Section 7, and the capacity analysis together with the comparison of the method with other techniques is discussed in Section 8. Finally the last section concludes the discussion of this paper.

Section snippets

Background

The computer assisted methods utilised for solving the problem, in a total view, can be classified into the following three groups (Welgama & Gibson, 1995):

  • (1)

    Analytical methods.

  • (2)

    Knowledge-based methods.

  • (3)

    Hybrid approaches (a combination of both analytical and knowledge-based methods).

In this section the more recent and pertinent literature on the MHE selection and assignment is reviewed.

MHE selection and assignment problem

The selection of an appropriate equipment type for a handling operation is a process for finding the equipment that’s capability is closely matched to the operation attributes while all the surrounding constraints are met.

By considering the existence of the broad spectrum of models and brands of equipment types together with their capabilities of carrying out a relatively wide range of movements in various conditions with diverse levels of efficiency, the selection problem is extremely wide and

The methodology framework

The proposed method consists of two phases, the Selection Phase and the Assignment Phase. The two-phase procedure of this paper is illustrated in Fig. 2.

Phase I – Selection Phase is a reasoning system using two sets of rules: crisp rules and fuzzy rules. Through the crisp rules the feasibility of the handling equipment for a particular handling operation is assessed. The result can be either positive or negative, represented by 1 or 0, and nothing else. The fuzzy rules subsequently calculate

Phase I

The architecture of the Fuzzy Knowledge-Based Expert System employed in this phase is presented in Fig. 3.

Phase II

The task of this phase is to search throughout the massive solution space, which is constituted of the entire possible combinations of the feasible equipment identified for every handling operation in the last phase, to discover a few optimal solutions. To carry out the task, in the methodology presented in this paper a Genetic Algorithm was employed.

Implementation of the methodology

The method developed in this paper thus far, has been coded using Microsoft Visual C# version 2005 within the .Net Framework operating under the Windows XP Professional operating system.

The interfaces of the provided software are demonstrated in Fig. 10. At the commencement step the required data regarding the problem under study is entered and then documented in the associated tables of an MS Access Database. The final outputs and the partial conclusions achieved during the Selection and

Test and results

The assessment of the methodology’s operation was carried out using the associated data for a real production site.

The problem selected to test out the capability of the method was a production line with 29 MH operations connecting 20 origin and destination points; 16 of them were workstations and the rest four points were stores. The whole production line was constituted of a main line with seven workstations and two minor pre-preparation lines with three and two workstations.

The software was

Comparison of the method with the others

To show the novelty of the proposed method, it is compared with the other methods developed in this domain.

There are only three previous research works whose objectives are, to some extent, similar to this research objective. They are the methods proposed by Hassan et al., 1985, Welgama and Gibson, 1995 and Cho and Egbelu (2005), called DESIGNER and described in detail earlier in Literature Review section.

A concise comparison of the three methods accompanied by the hybrid system is presented in

Conclusion

In this paper, a Hybrid Fuzzy Knowledge-Based Expert System and Genetic Algorithm method was developed for the selection and assignment of the most suitable MHE for all the MH operations in a production system. The method resolves the problem through a two-phase procedure. In the first phase, the inference engine of the expert system assesses the crisp and fuzzy rules of the fuzzy knowledge base, respectively, by the crisp and fuzzy attributes values of every handling operation to identify a

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