A Hybrid Ant Colony Optimization and Simulated Annealing Algorithm for Multi-Objective Scheduling of Cellular Manufacturing Systems

A Hybrid Ant Colony Optimization and Simulated Annealing Algorithm for Multi-Objective Scheduling of Cellular Manufacturing Systems

Aidin Delgoshaei (University Putra Malaysia, Seri Kembangan, Malaysia) and Ahad Ali (Lawrence Technological University, Southfield, USA)
Copyright: © 2020 |Pages: 40
DOI: 10.4018/IJAMC.2020070101


During the last 2 decades, there have been many manufacturing companies in various industries that used the advantages of cellular manufacturing layouts. However, determining the best schedule for cellular layouts considering uncertain product demands is a big concern for scientists. In this research, a multi-objective decision-making model is proposed in the process of dynamic cellular production planning where the market demands are uncertain. In this regard, a non-linear mixed integer programming model is developed. The complexity of the model is high to consider the model as NP-hard. Therefore, a hybrid Ant colony Optimization and Simulated Annealing Algorithms are proposed to solve the problem. Then, the Taguchi method is used to estimate appropriate sets of parameters of the proposed algorithm. The results demonstrated that the proposed algorithm can generate the best part-routes of products in terms of time, cost and load variance in a reasonable time. The algorithm is then used for a cellular production plant which is the producer of heavy vehicles parts.
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1. Introduction

The acquisition of group technology is one of the models of designing of industrial units that covers a wide range of various industries. Accordingly, machines in the factory are located in actual or virtual cells. The major obligation in group arrangement is to distinguish the segments categories. All parts of the same class which have similar characteristics, must be produced by present equipment in a particular cell. Each cell is able to provide all the essential needs for the manufacturing of a class of components which are assigned to that cell. The application of cellular production planning has many privileges such as decreasing the cost of machinery triggering, the cost of material transportation at the factory, the cost of machine repairs, the speed of production (compared to the job-shop model). In a model of cellular manufacturing, machinery is classified based on similarity in products design in cells. In the following, the class of products that have the most similarity with each other are located in a specific cell. Figure 1 shows a cellular production plan.

Figure 1.

Layout of a cellular manufacturing system


Benefits of using group technology is in its more flexibility in comparison with the flow-shop model and having the higher speed rate than the job-shop model. While flexibility in the zone of cells and the machinery displacement in the cells can lead to a widespread amount of production and a variety of performance depending on the range of plant’s products. But the utilization of cellular production plans can also have some disadvantages too. The presence of parallel machines, when they are used, can cause the variance so that the amount of queue which is in front of the materials ahead of the machine is so high while other parallel equipment is left over.

The objective of this research is to provide a mathematical model that can boost several goals simultaneously. The current models each pursue only one purpose but in the real world, managers and decision makers do not concentrate on just one goal (like cost reduction) but consider several goals at the same time (like considering on-time delivery and profits and increased client’s satisfaction and reduced transportation cost and so on). This is why a multi-objective mathematical model will be used in this investigation which the main targets are as follows: 1. Cost reduction; 2. Intracellular variance reduction; 3. Production time reduction. The restrictions in this model will be achieved according to the constraints of the real world. In the meantime, the extended constraints by other researchists will also be used. This research can examine the planning of dynamic cellular production systems by considering several targets at the same time. This category can help the organization’s managers to provide an effective and closer production program to the real world. On the other hand, taking into account the dynamic conditions of product demand in the market, causes the model to be more effective in the real world. Thus, the above model will be able to use clustering techniques in a flexible way in order to provide the best layout of cells in the production environment.


2. Literature Review

2.1. The Advent of Cellular Production Models

The first models of grouping (clustering) were introduced in the 80’s (1980) by King. According to this, parts with more similarity were found to have a higher weight. Further, the subject of clustering was expanded and used in the grouping of machinery and the formation of products category.

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