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

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 develop-
ment 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 func-
tion 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 dif-
ficult 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, mate-
rial requirement planning system for material resource arrangement, employee timetabling module for

human resource allocation and manufacturing resource planning for machine allocations are to be con-
sidered. For solving the unique hierarchy model, a shuffled frog leaping heuristic algorithm (SFLA) is pro-posed and implemented for minimizing the overall production cost. To prove the optimized results, theintegrated system has been tested with real time case studies. 2020 Elsevier Ltd. All rights reserved.Selection and peer-review under sponsibility of the scientific committee of the ternational Confer-ence on Newer Trends and Innovation in Mechanical Engineering: Materials Science.