Optimization of production cost for integrating job shop scheduling with production resources

Abstract

New manufacturing technologies are emerging every day, pushing the bounds of possible and redefining the world around us. This is especially true in the world of computing where much work goes into the design and development of new planning systems, tools, and software packages. This led to the development of various process analysis and manufacturing software packages. Many of these packages use the heuristic methods for solving the problems. Optimization is a design technique in which the best design solution for a problem is seeded using multiple execution and comparison of analysis results. Optimization is carried out for one or more responses acted upon by various constraints. Job shop is an environment for the manufacture of large variety low volume products. In general, the integration of production functional areas with job shop scheduling problems is to be considered as too hard and complex problems. The Production functional areas are Material Requirement Planning, Production Resource Planning, Manufacturing Resource Planning, Employee Time tabling, Human Resource Planning and Lot Size etc. To minimize the loss due to resource allocation, integration of production function resources and job shop scheduling is encouraged. Production resources are resourcing the material, human labours and manufacturing machine tools. Manufacturing assumptions are deployed to found difficult integrated manufacturing systems. In this paper, a hierarchy mathematical modelling approach has been developed to integrate the production resources planning and job shop scheduling. In which, material requirement planning system for material resource arrangement, employee timetabling module for human resource allocation and manufacturing resource planning for machine allocations are to be considered. For solving the unique hierarchy model, a shuffled frog leaping heuristic algorithm (SFLA) is proposed and implemented for minimizing the overall production cost. To prove the optimized results, the integrated system has been tested with real time case studies.