عملیات تولید سیستم پشتیبانی تصمیم گیری در زمان واقعی برای حل مشکلات تقاضای مواد تولید تصادفی

Nowadays, shop floor managers are facing numerous unpredictable risks in the actual manufacturing environment. These unpredictable risks not only involve stringent requirements regarding the replenishment of materials but also increase the difficulty in preparing material stock. In this paper, a real-time production operations decision support system (RPODS) is proposed for solving stochastic production material demand problems. Based on Poon et al. (2009), three additional tests are proposed to evaluate RFID reading performance. Besides, by using RPODS, the real-time status of production and warehouse operations are monitored by Radio Frequency Identification (RFID) technology, and a genetic algorithm (GA) technique is applied to formulate feasible solutions for tackling these stochastic production demand problems. The capability of the RPODS is demonstrated in a mould manufacturing company. Through the case study, the objectives of reducing the effect of stochastic production demand problems and enhancing productivity both on the shop floor and in the warehouse are achieved.

In make-to-order manufacturing environments, products are customized and production processes are started only upon receiving a customer’s order. In order to satisfy customer requirements and meet the delivery time punctually, it is necessary to handle several customers’ orders simultaneously and allocate them appropriate machines and production resources before production starts. Therefore, production scheduling and planning is an important process for avoiding delay in the production process and for improving manufacturing performance so as to fulfill customers’ needs (Chan et al., 2009 and Fayad and Petrovic, 2006). In general, different constraints are considered for formulating the most satisfactory production plan. These constraints are constant and predictable. However, in the actual manufacturing environment, shop floor managers face numerous unpredictable risks in day-to-day operations, such as defects in the supplies of components or raw materials, or errors, failures, and wastage in the various production processes (Poon, Choy, & Lau, 2007). The unpredictable risks not only entail stringent requirements regarding the replenishment of materials but also increase the difficulty in preparing material stock. Therefore, it is essential to handle such risks effectively and efficiently in order to keep production going smoothly. Recently, researchers have been considering both machines and material handling equipment as constraints when addressing production material demand issues in production scheduling. Their researches considered only “off-line” scheduling problems, in which a schedule is generated within a time period and is not expected to involve any changes (Caumond, Lacomme, Moukrim, & Tchernev, 2009). However, these researches are incapable of solving stochastic production material demand problems. This is because the existing scheduling approaches solely focus on the allocation of production resources, such as machines and workers. The consideration of warehouse resources is in the form of fork lifts, but manpower is neglected. Warehouse resources are important for minimizing the risks as they are utilized to pick, transfer and store production materials between the warehouse and production lines when problems occur during the production process. Besides, the existing approaches can been seen as a process of allocation of equipment to production tasks before the production starts (Wong, Leung, Mak, & Fung, 2006). Such research does not take into consideration real-time equipment that is used to facilitate production. According to Poon et al. (2009), the consideration of real-time equipment not only helps improve the visibility of warehouse operations, but also enhances the productivity in the warehouse. Nevertheless, no attention was paid to the allocation of warehouse resources to facilitate production processes in the previous paper. The objective of this paper is to allocate warehouse resources effectively and efficiently for replenishing appropriate production materials between these two facilities, so that the production process can run smoothly. This paper proposes a real-time production operations decision support system (RPODS) for solving stochastic production material demand problems. Different RFID reading performance tests are first performed to evaluate the reading performance of all RFID equipment and to verify the most suitable location for the installation of the corresponding hardware. Thus, a reliable RFID technology implementation plan is formulated to capture real-time production and warehouse information simultaneously. The captured information is stored in a centralized database and data are selected and sent to a forklift allocation engine to generate a set of order sequences for solving stochastic production material demand production problems on the shop floor. The engine is supported by genetic algorithms which are able to provide reliable solutions for complex problems within a short period of time. In doing this, the objectives of reducing the effect of stochastic production demand problem and enhancing the productivity both on the shop floor and in the warehouse are achieved. The paper is divided into six sections. Section 1 is the introduction. Section 2 presents a literature review of related studies. The proposed real-time production operations decision support system is illustrated in Section 3. In Section 4 a case study is presented which reveals the improvement in productivity in the ABC Limited (ABC) as a result of implementing the real-time production operations decision support system. In Section 5, the results and a discussion on the findings are listed. Finally, a conclusion about the use of the real-time production operations decision support system is drawn in Section 6.

Nowadays, shop floor managers are facing numerous unpredictable risks in the actual manufacturing environment. These unpredictable risks entail stringent requirements regarding the replenishment of material and also increase the difficulty in preparing material stock. In this paper, a real-time production operations decision support system (RPODS) is proposed for solving stochastic production material demand problem. Three RFID reading performance tests are performed to evaluate the reading performance the RFID equipment. After doing this, a reliable RFID technology implementation plan is formulated to capture real-time production and warehouse information simultaneously. Also, a GA-based engine is proposed to generate a set of order sequences for solving stochastic production material demand production problems on the shop floor. The capabilities of RPODS are demonstrated in a mould manufacturing company. With the help of RPODS, the objectives of reducing the effect of stochastic production demand problems and enhancing the productivity both on the shop floor and in the warehouse are achieved.