A decision support system for production scheduling in an ion plating cell

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Abstract

Production scheduling is one of the major issues in production planning and control of individual production units which lies on the heart of the performance of manufacturing organizations. Traditionally, production planning decision, especially scheduling, was resolved through intuition, experience, and judgment. Machine loading is one of the process planning and scheduling problems that involves a set of part types and a set of tools needed for processing the parts on a set of machines. It provides solution on assigning parts and allocating tools to optimize some predefined measures of productivity. In this study, Ion Plating industry requires similar approaches on allocating customer's order, i.e. grouping production jobs into batches and arrangement of machine loading sequencing for (i) producing products with better quality products; and (ii) enabling to meet due date to satisfy customers. The aim of this research is to develop a Machine Loading Sequencing Genetic Algorithm (MLSGA) model to improve the production efficiency by integrating a bin packing genetic algorithm model in an Ion Plating Cell (IPC), such that the entire system performance can be improved significantly. The proposed production scheduling system will take into account the quality of product and service, inventory holding cost, and machine utilization in Ion Plating. Genetic Algorithm is being chosen since it is one of the best heuristics algorithms on solving optimization problems. In the case studies, industrial data of a precious metal finishing company has been used to simulate the proposed models, and the computational results have been compared with the industrial data. The results of developed models demonstrated that less resource could be required by applying the proposed models in solving production scheduling problem in the IPC.

Introduction

Production scheduling is one of the major issues in production planning and control of individual production units which lies on the heart of the performance of manufacturing organizations. Planning is a pivotal task in today's competitive marketplace. Traditionally, production planning decisions were resolved through intuition, experience, and judgment (Barbaro & Ramani, 1986). Effective production planning can result in reduction of manpower, work-in-process inventory cost, and other production costs by minimizing machine idle time and increase the number of on-time job deliveries. In this connection, the need for efficient scheduling has increased rapidly in recent decades owing to market demands for product quality, flexibility, and order flow time. Although scheduling has been heavily concerned by manufacturers, it is still a typically human domain. Stoop and Wiers (1996) have mentioned that the task of scheduling production units can become very complicated. Humans are not well equipped to control or optimize large and complex systems, and the relations between actions and effects are difficult to assess. Hence, techniques and information systems are commonly regarded as means to improve scheduling. Researchers have begun to develop production scheduling models over the last three decades. Most of the results obtained from the previous publications on production scheduling were focused on processing time of machine and cost of the resources allocated (i.e. manpower, electricity, and support equipment). Chen (2004) has proposed branch and bound exact solution algorithms for solving scheduling problems by analyzing the cost associated with the job schedule and the cost of the resources allocated. Gue, Nemhauser, and Padron (1997) have introduced the concept of ‘almost continuous time’ into a large-scale production scheduling problem while minimizing the penalty on both work-in-process job and lateness jobs simultaneously in its proposed objective function.

However, in most realistic situations, while the manufacturers are dealing with production scheduling problems, additional considerations are required to process jobs in order to obtain better quality of product and service which previous researchers have paid less attention on. Customers have continuously demanded quality of products and services in the recent global era, especially in Ion Plating (IP) industry. IP is a manufacturing process used to deposit elements onto a substrate via a reactive gas. A negative charge is applied to the substrate to ionize the reactive gas. Coating material is vaporized into the glow discharge of the ionized gas. The coating vapor are ionized and accelerated toward the substrate surface where they will be adhered and form a coating. IP can use vacuum evaporation sputtering or arc vaporization as the source of the depositing material (Mattox, 2000). IP manufacturing is mainly based on the concept of batch production which means that several number of parts will be processed in an IP machine simultaneously. Nowadays, IP has been widely applied in Watch and Clock industry which provides tremendous advantages on prolonging product lifetime, reducing production cost and so on. However, the quality of coating will significantly depend on the combination of different part types allocated in the IP machine, while the quality of service is heavily depended on the machine loading sequencing of the corresponding machines.

Bin Packing (BP) problem is a NP hard problem which consists of a finite set of items with different sizes and a set of bins with same capacity. The aim of the BP problem is to partition the items between the bins so that the sum of item sizes in each bin is less than or equal to the capacity of bin (Garey & Johnson, 1979). Recently, most of BP problems reported in literature mainly concentrated on number of bins being used (i.e. utilization) and cost function due to the major focus on traditional grouping problems, i.e. storage of goods and allocation of products etc. (Falkenauer, 1996, Kang and Park, 2003). However, customers have continuously demanded quality of products and services in the recent global era. While considering the production scheduling problem in IP industry, similar approach is required on allocating production jobs into batches for producing better quality products and enabling to meet due date to satisfy customers. Hence, Chan, Au, and Chan (2005) proposed a Bin Packing Genetic Algorithm (BPGA) model which has taken into account the quality of product and service in solving BP problem, and also to improve the production efficiency by reducing the production unit cost in the IP process. The BPGA is acted as a decision support tool to provide decision making information for packing production jobs in order to optimize the scheduling efficiency of the Ion Plating Cell (IPC). The BPGA focuses on combination of part types taken into account quality of coating, utilization, due date (quality of service), and frequency of machining operations required (see details in Chan et al., 2005).

Machine loading is one of the process planning and scheduling problems that involves a set of part types and a set of tools needed for processing the parts on a set of machines. It provides solution on assigning parts and allocating tools to optimize some predefined measures of productivity. In order to improve the production efficiency of the IPC, the proposed MLSGA model has considered several critical issues as objectives to measure the system performance including: tardiness, earliness, and machine utilization. Non-production time will be considered in order to improve the adaptability of the MLSGA model to the real life situation in IPC. The proposed MLSGA model is used to arrange the allocated production batches into their corresponding IP machines. This model has considered five criteria of the system performance: number of tardy job(s), maximum tardiness time, number of early job(s), maximum earliness time, and machine utilization index. The computational results of the MLSGA model provide decision support information in machine loading operations in the IPC. All the available jobs can be processed with the minimum inventory carrying cost (i.e. minimizing the number of early jobs and the maximum earliness time), and satisfy customers by providing on-time delivery.

In the rest of this paper, literature review on production scheduling will be given in Section 2. These include genetic algorithms applied in different production problems, and machine loading problem with multi-criterion models. The methodology on the proposed MLSGA model will be presented in details in Section 3. Next, the case studies on the proposed model applied onto a precious metal finishing company will be illustrated. Conclusion of the entire study will be presented in Section 5.

Section snippets

Literature review

In this section, three major research issues are being discussed based on the findings from previous researchers, namely, production scheduling, genetic algorithms, and machine loading algorithms. At first, general definitions of production scheduling will be stated, and then different approaches on solving production scheduling will be discussed. Secondly, it will focus on research work in applying GAs on production problems. For the issue of machine loading algorithms, it will illustrate

Objective function

Multi-objective genetic algorithm has been applied in the MLSGA model. The objective function has considered inventory holding cost, on-time delivery, and machine utilization. These three major criteria could be used to measure the system performance in order to provide decision support information to analyze their strategies. The equation below was the objective function defined by integrating the real situation in IPC and experience from Production Manager of a chosen precious metal finishing

Case study

In this case study, a precious metal finishing company has been chosen that its major outputs are electroplating and ion plating products. Its customers are mainly in Watch & Clock Industry. The case study was focused on arranging the machine loading operations by using the model of MLSGA in the IPC of the company. There are three major types of coating produced in the IPC, i.e. Ion Plating Gold (IPG), Ion Plating Stainless Steel (IPS), and Ion Plating Black (IPBK). In this study, three IP

Conclusion

Upon the completion of this study, a machine loading sequencing (MLSGA) has been elaborated. By adopting the developed models, the production schedule can be automatically generated. The manual operation and human decision making process will almost be eliminated, and hence both error source and chance of making packing mistakes in the production operations will be reduced considerably. After determining the job allocation with the BPGA model, the obtained results are employed by the MLSGA

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