Integration of process planning and scheduling—A modified genetic algorithm-based approach

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Abstract

Traditionally, process planning and scheduling for parts were carried out in a sequential way, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved greatly. In this paper, a new integration model and a modified genetic algorithm-based approach have been developed to facilitate the integration and optimization of the two functions. In the model, process planning and scheduling functions are carried out simultaneously. In order to improve the optimized performance of the modified genetic algorithm-based approach, more efficient genetic representations and operator schemes have been developed. Experimental studies have been conducted and the comparisons have been made between this approach and others to indicate the superiority and adaptability of this method. The experimental results show that the proposed approach is a promising and very effective method for the integration of process planning and scheduling.

Introduction

Process planning and scheduling are two of the most important sub-systems in manufacturing systems. A process plan specifies what raw materials or components are needed to produce a product, and what processes and operations are necessary to transform those raw materials into the final product. The outcome of process planning is the information required for manufacturing processes, including the identification of the machines, tools, and fixtures. Typically, most jobs may have a large number of alternative process plans. Process planning is the bridge of the product design and manufacturing. Scheduling plans receive process plans as their input and their task is to schedule the operations on the machines while satisfying the precedence relations given in the process plans. It is the link of the two production steps which are the preparing processes and putting them into action [1]. Although there is a strong relationship between process planning and scheduling, the integration of them is still a challenge in both research and applications.

In traditional approach, process planning and scheduling were carried out sequentially, where scheduling was done separately after the process plan had been generated. This approach has become an obstacle to enhance the productivity and responsiveness of manufacturing systems, and it may cause the following problems [2], [3]:

(1) In a manufacturing organization, process planning function works in static. It considers the resources on the shop floor in an ideal way. Process planners assume unlimited resources on the shop floor and plan for the most recommended alternative process [4]. This may lead to the process planners favoring to select the desirable machines repeatedly. Therefore, the generated process plans are somewhat unrealistic and cannot be readily executed on the shop floor [5]. Accordingly, the resulting optimum process plans often become infeasible when they are carried out in practice at the later stage.

(2) Even if, in the planning phase, process planners consider the current resources on the shop floor, the constraints considered in the process planning phase may have already changed greatly because of the time delay between planning phase and execution phase. This may lead to the optimized process plan infeasibility. Investigations have shown that 20–30% of the total production plans in a given period have to be modified to adapt to the dynamic changing of a production environment [3].

(3) Scheduling plans are often determined after process plans. In the scheduling phase, scheduling planners have to consider the determined process plans. Fixed process plans may drive scheduling plans to end up with severely unbalanced resource load and create superfluous bottlenecks.

(4) In most cases, both for process planning and scheduling, a single criterion optimization technique is used for determining the best solution. However, the real production environment is best represented by considering more than one criterion simultaneously [3]. Furthermore, the process planning and scheduling may have conflicting objectives. Process planning emphasizes the technological requirements of a task, while scheduling involves the timing aspects of it. If there is no appropriate coordination, it may create conflicting problems.

To overcome these problems, there is thus a major need for an integrated process planning and scheduling system. The integration of the two functions may introduce significant improvements to the efficiency of the manufacturing facilities through elimination or reduction in scheduling conflicts, reduction of flow-time and work-in-process, improvement of production resources utilization and adaptation to irregular shop floor disturbances [5]. Without the integration of process planning and scheduling (IPPS), a true computer integrated manufacturing system (CIMS), which strives to integrate the various phases of manufacturing in a single comprehensive system, may not be effectively realized.

The remainder of this paper is organized as follows. Section 2 introduces a literature survey of the problem. IPPS is discussed in Section 3. A modified genetic algorithm (GA) for IPPS is given in Section 4. Experimental studies and discussion are reported in Section 5. Section 6 is the conclusion.

Section snippets

Literature survey

In the early studies of CIMS, it has been found that the IPPS is very important to the development of CIMS [6], [7]. Chryssolouris and Chan [8], [9] were the first to propose the preliminary idea of the IPPS. Beckendorff [10] used alternative process plans to improve the flexibility of manufacturing systems. Khoshnevis and Chen [11] introduced the concept of dynamic feedback into the IPPS. The integration model proposed by Zhang and Larsen [12], [13] extended the concepts of alternative process

Proposed integration model

In this section, the IPPS model is introduced. This model is illustrated in Fig. 1.

The basic integration methodology of this model is to utilize the advantages of NLPP (alternative process plans) and DPP (hierarchical approach). This integration model is based on the concurrent engineering principle where the computer aided process planning (CAPP) and scheduling systems are working simultaneously. In the whole integration decision-making phase, this model gives expression to interactive,

Flow chart of proposed approach

Fig. 3 shows flow chart of the proposed method. First, the CAPP system gives alternative process plans. They are optimized by GA and the near optimal process plans are found. The next step is to select s near optimal process plans. And then, the integration of process plan and scheduling is optimized by GA. Finally, the optimized process plan for each job and the scheduling plan can be determined.

Encoding and decoding

Each chromosome in process planning population consists of two parts with different lengths as

Experimental studies and discussion

Some experiments have been conducted to measure the adaptability and superiority of the proposed GA-based integration approach. The approach is compared with a hierarchical approach and other methods. The performance of the approach is satisfactory from the experimental results and comparisons.

Conclusion

In the traditional approach, process planning and scheduling were regarded as two separate tasks and performed sequentially. However, the functions of the two systems are usually complementary. Therefore, the research on the integration of process planning and scheduling is necessary. The research presented in this paper developed a new integration model with a modified GA-based approach have been developed to facilitate the integration and optimization of these two systems. With integration

Acknowledgements

The authors would like to thank Professor Yiming Rong from Worcester Polytechnic Institute for his helpful comments and suggestions. The authors would like to thank anonymous referees whose comments helped a lot to improve this paper. This research work is supported by the National Basic Research Program of China (973 Program) under Grant no.2004CB719405, the National High-Tech Research and Development Program of China (863 Program) under Grant nos.2007AA04Z107 and 2006AA04Z131.

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