Author : Lakshmi Kameswari Aripirala and K.Sreenivasa Rao

A main characteristic of a job shop production system is that various products are manufactured on same machines; the processing times will also vary. The manufacturing lead time is mainly defined by the waiting times in front of the work centers and the processing times at the machines. Due to the strong varying routings and occupation times, the number of orders arriving at the units strongly varies per time unit (day or week). So when it is chosen to measure the number of produced parts in a period, it will be difficult to define a standard or desired level of production and also it will be difficult to compare the performed productivity between periods. This is because each order has different processing times and thus a different total lead time in comparison with other orders. The incoming orders do vary in amount, design, urgency and processing time, which result in a very complex material flow control. It is very hard to define how the different orders will be distributed among the machines in next periods. Machine utilization and variation of the orders will generally lead to long waiting times for orders on the floor. A difficulty is the production speed of a work centre is dependent of another unit. In fact, the amount of work to be done by a unit varies a lot per period. This unknown distribution and machine utilization leads to a difficult determination of productivity standards and goals for the coming periods and the control on productivity over time. Hence an attempt is made in the following paper to estimate the production functions of jobshop scheduling problem applicable to glassware manufacturing unit and production functions with operating characteristics are presented. The near optimal solution to JSSP is obtained by genetic algorithm and productivity is estimated and presented.

Keywords: Job shop production system, Genetic Algorithm etc.

Article published in International Journal of Current Engineering and Technology, Vol.5, No.6 (Dec-2015)