Traditionally, process planning and scheduling were performed sequentially, where scheduling was done after process plans had been generated. Considering the fact that these two functions are usually complementary, it is necessary to integrate them more tightly so that the performance of a manufacturing system can be improved greatly. In this paper, an agent-based approach has been developed to facilitate the integration of these two functions. In the approach, the two functions are carried out simultaneously, and an optimization agent based on an evolutionary algorithm is used to manage the interactions and communications between agents to enable proper decisions to be made. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show the proposed approach has achieved significant improvement.
Process planning and scheduling used to link product design and manufacturing are two of the most important functions in a manufacturing system. A process plan specifies what manufacturing resources and technical operations/routes are needed to produce a product (a job). The outcome of process planning includes the identification of machines, tools and fixtures suitable for a job, and the arrangement of operations and processes for the job. Typically, a job may have one or more alternative process plans. With the process plans of jobs as input, a scheduling task is to schedule the operations of all the jobs on machines while precedence relationships in the process plans are satisfied. Although as mentioned above, there is a close relationship between process planning and scheduling, the integration of them is still a challenge in both research and applications (Sugimura, Hino, & Moriwaki, 2001).
In traditional approaches, process planning and scheduling were carried out in a sequential way. Scheduling was conducted after the process plan had been generated. Those approaches have become an obstacle to improve the productivity and responsiveness of manufacturing systems and to cause the following problems in particular (Kumar and Rajotia, 2003 and Saygin and Kilic, 1999):
(1)
In manufacturing practice, process planner plans jobs individually. 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 & Fernandes, 1996). This may lead to the process planners favoring to select the desirable machines for each job repeatedly. Therefore, the generated process plans are somewhat unrealistic and cannot be readily executed on the shop floor for a group of jobs (Lee & Kim, 2001). Accordingly, the resulting optimal process plans often become infeasible when they are carried out in practice at the later stage.
(2)
Scheduling plans are often determined after process plans. Fixed process plans may drive scheduling plans to end up with severely unbalanced resource load and create superfluous bottlenecks.
(3)
Even though process planners consider the restriction of the current resources on the shop floor, the constraints in the process planning phase may have already changed due to the time delay between the planning phase and execution phase. This may lead to the infeasibility of the optimized process plan. Investigations have shown that 20–30% of the total process plans in a given period have to be modified to adapt to the dynamic change in a production environment (Kumar & Rajotia, 2003).
(4)
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 & Rajotia, 2003). Furthermore, the process planning and scheduling may have conflicting objectives. Process planning emphasizes the technological requirements of a job, while scheduling involves 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 an Integrated Process Planning and Scheduling (IPPS) system. The IPPS introduces significant improvements to the efficiency of manufacturing resources 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 & 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 survey of the problem. Problem formulation is discussed in Section 3. The proposed agent-based approach for IPPS is given in Section 4. Experimental studies and discussion are reported in Section 5. Section 6 is the conclusion.
Considering the complementary roles of process planning and scheduling, the research has been conducted to develop an agent-based approach to facilitate the integration and optimization of these two systems. Process planning and scheduling functions are carried out simultaneously. An optimization agent based on an evolutionary algorithm has been developed to optimize and realize the proper decisions resulting from interactions between the agents. To verify the feasibility of the proposed approach, a number of experimental studies have been carried out to compare this approach with other previously developed approaches. The experimental results show that the proposed approach is very effective for the IPPS problem and achieves better overall optimization results.
With the new method developed in this work, it would be possible to increase the efficiency of manufacturing systems. One future work is to use the proposed method to practical manufacturing systems. The increased use of this approach will most likely enhance the performances of future manufacturing systems.
