بارگذاری بهینه از ماشین آلات در یک سیستم تولید انعطاف پذیر از مدل مخلوط برنامه ریزی خطی اعداد صحیح و الگوریتم ژنتیک

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.

A flexible manufacturing system (FMS) can be defined as an integrated configuration of numerical control (NC) machine tools, some auxiliary production equipment, and a material handling system designed to simultaneously manufacture a low to medium volumes of a wide variety of high quality products at low cost (Nagarjuna, Mahesh, & Rajagopal, 2006). An FMS can achieve the benefits of both flow shop and job shop factories, though its installation may be relatively complex because of the additional flexibility-related degrees of freedom. The benefits that can be accrued due to installation of an FMS are: increased machine utilization, fewer machines, reduction in required factory floor space, greater responsiveness to changes, reduced inventory requirements, lower manufacturing lead times, reduced direct labor requirement, higher labor productivity, opportunity for automated production, etc. (Groover, 2003).

This paper presents a mathematical programming model for solving machine loading problem in flexible manufacturing systems. The proposed model takes into account many practical parameters including capacity of machines, capacity of tool magazine, tool requirement of different operations, over-utilization and under-utilization costs of machines. An effective solution approach based on genetic algorithm is presented to solve the formulated model. Some benchmark problems adopted from the literature are solved by the proposed GA. Computational results indicate that the proposed model provides very promising solutions compared with those available in the literature.