اکتشافی بر اساس روش برنامه نویسی چند مرحله ای برای ماشین-بارگذاری مشکل در سیستم تولید انعطاف پذیر

Manufacturing industries are rapidly changing from economies of scale to economies of scope, characterized by short product life cycles and increased product varieties. This implies a need to improve the efficiency of job shops while still maintaining their flexibility. These objectives are achieved by Flexible manufacturing systems (FMS). The basic aim of FMS is to bring together the productivity of flow lines and the flexibility of job shops. This duality of objectives makes the management of an FMS complex. In this article, the loading problem in random type FMS, which is viewed as selecting a subset of jobs from the job pool and allocating them among available machines, is considered. A heuristic based on multi-stage programming approach is proposed to solve this problem. The objective considered is to minimize the system unbalance while satisfying the technological constraints such as availability of machining time and tool slots. The performance of the proposed heuristic is tested on 10 sample problems available in FMS literature and compared with existing solution methods. It has been found that the proposed heuristic gives good results.

A flexible manufacturing system (FMS) can be defined as an integrated computer-controlled configuration of numerical control (NC) machine tools, other auxiliary production equipment, and a material handling system (MHS) designed to simultaneously manufacture a low to medium volumes of a wide variety of high quality products at low cost [1]. FMS is a production system based on economies of scope wherein several part types are simultaneously resident in the system. The subsystems are versatile computer-controlled equipment such as NC machines, automated guided vehicles (AGV), coordinate measuring machines and robots. The setup times are small and the number of buffer spaces provided in the system is minimal. Thus the system follows a flow-type work-piece movement so that the manufacturing lead time almost equals the processing time and at the same time flexibility of job shop is retained. The material flow integration is achieved through a computer-controlled MHS. 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 change, reduced inventory requirements, lower manufacturing lead times, reduced direct labor requirements, higher labor productivity, opportunity for unattended production, etc. [2].

The main contribution of this research is to develop an efficient heuristic based on multi-stage programming approach for solving machine-loading problem of random FMS. Most of the previous studies generate job sequence based on either predetermined sequencing rules such as shortest processing time (SPT) rule or employing meta-heuristics such as simulated annealing (SA), Genetic algorithm (GA) etc. and operation allocation problem is considered next. In this research, an attempt has been made to treat job sequencing and operation allocation problem concurrently. Based on multi-stage programming approach, the given problem is divided into number of stages. The maximum number of stages is equal to the number of jobs to be loaded. At each stage partial sequences are generated and evaluated. Only those partial sequences, which are feasible and perform better in achieving the objective function, are retained and new partial sequences are generated from these and evaluated. This process is repeated iteratively until a best feasible job sequence with operation allocation on machines is obtained. Exhaustive computations have been carried out to assess the effectiveness of the heuristic and performance is compared with the previous studies. From this comparative study, it has been observed that the proposed heuristic offers better results for majority of the test problems.