In the scheduling literature, estimation of job flowtimes has been an important issue since the late 1960s. The previous studies focus on the problem in the context of due date assignment and develop methods using aggregate information in the estimation process. In this study, we propose a new flowtime estimation method that utilizes the detailed job, shop and route information for operations of jobs as well as the machine imbalance information. This type of information is now available in computer-integrated manufacturing systems. The performance of the proposed method is measured by computer simulation under various experimental conditions. It is compared with the existing flowtime estimation methods for a wide variety of performance measures. The results indicate that the proposed method outperforms all the other flowtime estimation methods. Moreover, it is quite robust to changing shop conditions (i.e., machine breakdowns, arrival rate and processing time variations, etc.). A comprehensive bibliography is also provided in the paper.
In the job shop scheduling literature, estimation of job flowtimes has always been an important issue since the late 1960s. Because the flowtime estimation is used to assign order due dates, the problem has been mostly studied within the context of due date assignment. In several previous studies [1] and [2], the term due date assignment has been often used to describe the problem. However, beyond the objective of due date setting, accurate flowtime estimates are also needed for better management of the shop floor control activities, such as order review/release, evaluation of the shop performance, identification of jobs that requires expediting, leadtime comparisons, etc. All these application areas make the problem as important as other shop floor control activities (i.e., scheduling).
The research problem studied in this paper is the estimation of the jobs’ time spent in the system from their arrival until the completion of all processing activities. The difficulty of the problem stems from the dynamic and stochastic nature of the job shop environments (i.e., arrival of hot jobs, sudden machine breakdowns and variations in machining conditions, etc.) that precludes accurate predictions.
The existing studies in the literature examine the problem by identifying the key information sources required in flowtime estimation. The results indicate that job- and shop-related information are the key elements in the estimation process. Researchers (e.g. [3]) used these information sources in aggregate terms by ignoring the benefits of using more detailed shop and route congestion information in the flowtime estimation. Other important findings which motivated our study to develop a new flowtime estimation method are as follows.
First, previous studies indicate that total load on the route of an arriving job provides valuable information in flowtime estimation [3], [4], [5], [6] and [7]. We also expect that the distribution of the work load on the machines is as important as the total load itself. The load information of the machines nearer to the beginning of the route of the job would affect the flowtime of that job more than the load of the machine closer to the end of its route, because the system state can be considerably different when the job arrives at these machines for its later operations. Thus, splitting the route information in terms of operations of the job can improve the quality of the flowtime estimation. Second, previous research also indicate that consideration of total load of the jobs elsewhere in the shop (i.e. the jobs which are not currently at the machines on the route of the arriving job, but will visit them later for processing) is also important [8]. This is because these jobs will eventually bring additional workloads to the route of the arriving job. Hence, both timing and distribution of these so called “other jobs” should also be taken into account in the estimation process.
Third, as shown by several researchers, dispatching rules affect the performance of the flowtime estimation methods [3], [9], [10], [11], [12], [13] and [14]. For example, Ragatz and Mabert [3] use different flowtime estimation models for different dispatching rules. Finally, it is observed that the performance of the flowtime estimation methods are significantly affected by the load balance in the shop (e.g., [5]).
In this study, we develop a new method by using these four observations outlined above. Specifically, the proposed method estimates flowtimes by employing the detailed job, shop and route information for each operation of a job as well as considering the machine imbalance and dispatching rule information. Results indicate that it is quite effective in using these information sources to achieve better system performance.
The rest of this paper is organized as follows. In Section 2, we present a literature survey. In Section 3, basic structure and characteristics of the proposed method are described. The key components of the model are also discussed using an illustrative example. In Section 4, we define the experimental design and give the details of the simulation model. Computational requirements of the proposed study are discussed in Section 5. Results of the simulation experiments and statistical tests are presented in Section 6. Finally, the concluding remarks are made and further research directions are outlined in Section 7.
