In this paper, a new approach for due date assignment in a multi-stage job shop is proposed and evaluated. The proposed approach is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is a relatively new member of the genetic programming family. The primary objective of this research is to compare the performance of the proposed due date assignment model with several previously proposed conventional due date assignment models. For this purpose, simulation models are developed and comparisons of the due date assignment models are made mainly in terms of the mean absolute percent error (MAPE), mean percent error (MPE) and mean tardiness (MT). Some additional performance measurements are also given. Simulation experiments revealed that for many test conditions the proposed due date assignment method dominates all other compared due date assignment methods.
The importance of meeting promised delivery dates, or due dates, in manufacturing and service industry is recognized by practicing production managers and academic researchers (Ragartz & Mabert, 1984a). Recent trends in time based competition and inventory reduction require products to be completed in shorter time and with more reliable delivery dates. At the operational level, this can be made possible via better scheduling and due date management. Due date management always wants to increase due date performance. Due date performance depends not only on the scheduling procedure followed but also on the reasonability of the assigned due dates. There are two aspects of due date performance: “delivery reliability” and “delivery speed” (Hill, 1991). Delivery reliability which is also referred to as missed due date (Cheng & Jiang, 1998) is the ability to consistently meet promised delivery dates. Delivery speed is the ability to deliver orders to the customer with shortest lead times (Philipoom, 2000). In fact, production managers want neither early nor tardy jobs. They all want to meet the target due date. This is mainly because, an early job completion results in inventory carrying costs, such as storage and insurance costs while a tardy job completion results in penalties, such as loss of customer goodwill and damaged reputation. Hence meeting due dates tends to be the primary concern of the most production managers (Melnyk, Vickery, & Carier, 1986) including those of the job shops, which usually have process type of layouts and are suitable for high variety, low volume, make to order production (Chang, 1997). In summary, due date management’s main problem is to increase due date performance in terms of “delivery reliability” and “delivery speed” in order to avoid storage and/or insurance cost or loss of customer goodwill and damaged reputation.
Increasing due date reliability depends on assigning more reasonable due dates to the arriving jobs. This can be achieved only by estimating the flowtime of jobs more accurately and precisely (Kuo, Chang, & Huang, 2009). In fact, the flowtime prediction problem is really the crux of the due date management problem. The due date assignment process consists of making an estimate of flowtime for a job and then setting a due date on the basis of that estimate and some performance criteria (Ragartz & Mabert, 1984b). Because the flowtime estimation is used to assign order due dates, the problem has been mostly studied within the context of due date assignment (Sabuncuoglu & Comlekci, 2002).
In this study, a genetic programming technique which is known as gene expression programming (GEP) algorithm is employed to estimate flowtime of jobs in a multi-stage job shop. The main objective of this research is to compare the performance of the GEP with previously proposed due date assignment models (DDAM) from the literature with respect to some selected performance criteria. The main reason for this comparison is to find out the best DDAM that has the best due date performance in terms of “delivery reliability” and “delivery speed”. The rest of the paper is organized as follows: Section 2 presents the literature review and research outline; Section 3 gives a brief overview of GEP; Section 4 introduces the hypothetical job shop and gives information about the simulation model; Section 5 gives a brief explanation of the methodology; Section 6 states the performance measurements; Section 7 presents experimental results; Finally, the paper is concluded.
The main objective of this research is to compare the performance of the GEP based DDAM with previously proposed DDAMs with respect to some selected performance criteria. The main reason for this comparison is to find the best DDAM that has the best due date performance in terms of delivery reliability and delivery speed. From the results it is apparently seen that GEP has better performance than the compared conventional DDAMs with respect to selected performance measures. As a result we could assign more reasonable due dates by using GEP based DDAM. The most interesting result is that in spite of being static, GEP has considerable better performance than dynamic DDAMs (i.e., ADRES, DPPW) for many performance measures. This is an indication of the strength of the artificial intelligence based methods like GEP in modeling complex decision problems like due date assignment.
