A pplication of Fuzzy Inference Systems and Genetic A lgorithms … International Journal of The Computer, The Internet and Management, Vol. 10, No2, 2002, p 81 - 96 81 Application of Fuzzy Inference Systems and Genetic Algorithms in Integrated Process Planning and Scheduling Zalinda Othman, Khairanum Subari and Norhashimah Morad Division of Quality Control and Instrumentation School of Industrial Technology University Sains Malaysia 11800 Minden Penang Email: ispsamin@hotmail.com , khairanu@usm.my and nhashima@usm.my Abstract. This paper proposes a fuzzy inference system in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The MTTF values are input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalized based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, the genetic algorithms have been used to balance the load for all the machines. A n example of simulation problem of a 4 jobs 3 machines environment is presented. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling. Keyword: genetic algorithms, integrated process planning and scheduling, fuzzy inference system, load balancing and machine reliability. 1. Introduction Process planning and scheduling play important roles in manufacturing systems. Their roles are to ensure the availability of manufacturing resources (such as material, machines and labour) needed to accomplish production tasks result from a demand forecast. These two functions are highly related; in process planning, each machining operation is assigned to a certain machine tool and in scheduling the assignment of machine tool over time to different machine is performed. Traditionally, these two functions are accomplished in two different stages. Production scheduling will get its input from the complete process planning. This results in conflicting objectives and the inability to communicate the dynamic