Process planning and scheduling are two of the most important functions in the manufacturing system. Traditionally, process planning and scheduling were regarded as separate tasks performed sequentially, where scheduling was implemented after process plans had been generated. However, their functions are usually complementary. If the two systems can be integrated more tightly, greater performance and higher productivity of manufacturing system can be achieved. In this paper, a new hybrid algorithm (HA) based approach has been developed to facilitate the integration and optimization of these two systems. To improve the optimization performance of the approach, an efficient genetic representation, operator and local search strategy have been developed. Experimental studies have been used to test the performance of the proposed approach and to make comparisons between this approach and some previous works. The results show that the research on integrated process planning and scheduling (IPPS) is necessary and the proposed approach is a promising and very effective method on the research of IPPS.
Process planning and scheduling are two of the most important sub-systems in a manufacturing system. A process plan specifies raw materials or components needed to produce a product, processes and operations, which are necessary to transform those raw materials into the final product. The outcome of process planning includes the identification of machines, tools and fixtures suitable for a job and the arrangement of operations for a job. And, a job may have one or more alternative process plans. Process planning is the bridge of the product design and manufacturing. With the process plans of jobs as inputs, a scheduling task is to schedule the operations of all the jobs on machines while precedence relationships in the process plans are satisfied. Scheduling is the link of the two production steps, which are the preparing processes and putting them into action. Although there is a close relationship between process planning and scheduling, their integration is still a challenge in both research and applications (Sugimura et al., 2001).
In traditional approaches, process planning and scheduling were carried out in a sequential way, where scheduling was conducted separately after the process plans had been generated. Those approaches have become the obstacles to improve the productivity and responsiveness of the manufacturing systems and to cause the following problems (Kumar and Rajotia, 2002 and Kumar and Rajotia, 2003):
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Traditionally, in a manufacturing organization, the process planning function works in static. Process planner plans jobs separately. For each job, manufacturing resources on the shop floor are usually assigned on it without considering the competition for the resources from other jobs (Usher and Fernandes, 1996). This may lead to the process planners favoring to select the desirable resources for each job repeatedly. Therefore, the resulting optimum process plans often become infeasible when they are carried out in practice at the later stage (Lee and Kim, 2001).
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Even though process planners consider the restriction of the current resources on the shop floor, because of the time delay between planning phase and execution phase, the constraints considered in the planning phase may have already changed greatly, which may lead to the optimum process plans being infeasible (Kuhnle et al., 1994). Investigations have shown that 20–30% of the total production plans in a given period have to be rescheduled to adapt to dynamic changes in a production environment (Kumar and Rajotia, 2003).
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Traditionally, 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.
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In most cases, both for process planning and scheduling, a single criterion optimization technique is used to determine the best solution. However, the real production environment is best represented by considering more than one criterion simultaneously (Kumar and Rajotia, 2003). And, process planning emphasizes the technological requirements of a job, while scheduling attaches importance to the timing aspects and resource sharing of all jobs. If there is no appropriate coordination, it may create conflicting problems.
To overcome these problems, there is an increasing need for deep research and application of the IPPS system. The IPPS can introduce significant improvements to the efficiency of manufacturing through eliminating or reducing scheduling conflicts, reducing flow-time and work-in-process, improving production resources utilizing and adapting to irregular shop floor disturbances (Lee and Kim, 2001). Without 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 review. Problem formulation is discussed in Section 3. Hybrid algorithm for IPPS is proposed in Section 4. Experimental studies and discussions are reported in Section 5. Section 6 is conclusion.
Considering the complementarity of process planning and scheduling, the research has been conducted to develop a hybrid algorithm-based approach to facilitate the integration and optimization of these two systems. Process planning and scheduling functions are carried out simultaneously. To improve the optimization performance of the proposed approach, the efficient genetic representations, operator and local search strategy have been developed. To verify the feasibility of the proposed approach, three experimental studies have been carried out to compare this approach with other previous methods. The experimental results show that the research on IPPS is necessary and the proposed approach has achieved significant improvement.
