برنامه ریزی رباتیک کنترل مواد در سیستم های تولید انعطاف پذیر برای مشتری گرایی انبوه

The scheduling problem of robotic material handlers in flexible manufacturing systems (FMSs) is NP-hard. This paper proposes a state-dependent algorithm for the FMS robot scheduling problem in make-to-order (MTO) environments for mass customization (MC). A mathematical model of the problem is formulated. A computational study of the proposed algorithm is performed. The algorithm is compared to an effective FMS robot scheduling rule, the shortest remaining processing time first (SRPF) rule. The results reveal the effectiveness of the algorithm in increasing the productivity-based measures of the FMS. Practical application insights are discussed. Further research is also provided.

Enterprises place more efforts on customer requests to obtain competitive advantage in global competition. Mass customization (MC) is aimed to give customers exactly what they want with low-cost, high-quality in ever changing environments [1]. Customer co-design and customer specified manufacturing are often performed in MC systems and make-to-order (MTO) or build-to-order (BTO) processes are often utilized [2], [3] and [4]. A flexible manufacturing system (FMS) is an integrated, computer-controlled complex of automated material handling devices and numerically controlled machine tools that can simultaneously process medium-sized volumes of a variety of part types [5]. FMSs can produce product components within its wide envelope of variety and can be used to automate MC [1]. The design and operation of FMSs involve the problems that can be formulated as quantitative problems and approached through operations research methods. The problems are categorized as design, planning, scheduling, and control problems [6].

This paper proposes a state-dependent algorithm for the FMS robot scheduling problem in MTO environments for MC. A mathematical model of the problem is formulated. The strategy of dynamic workload balancing and the SRPF rule are applied to the development of the applicable and effective algorithm. The performance of the algorithm is compared to SRPF, an effective FMS robot scheduling rule. The results indicate that the proposed algorithm is effective in increasing the productivity-based measures of the FMS. The investigation also reveals the effectiveness of dynamic workload balancing in improving the productivity-based measures for the problem. The proposed algorithm provides practical application insights for FMS management. As customer requirements increase in importance, enterprises have to make quick response to customer orders and apply corresponding strategies and approaches. Many enterprises approach MC. FMS managers may face today the challenging situation of MTO environments. They have to respond to customer orders dynamically in the environments. The proposed algorithm could be applied to robot scheduling of the FMSs approaching MC.