Journal of Advanced Manufacturing Systems Vol. 10, No. 2 (2011) 223–240 c  World Scientific Publishing Company DOI: 10.1142/S0219686711002235 A GENETIC ALGORITHM-BASED APPROACH FOR OPTIMIZATION OF SCHEDULING IN JOB SHOP ENVIRONMENT KUMAR RITWIK and SANKHA DEB * Department of Mechanical Engineering Indian Institute of Technology Kharagpur Kharagpur-721302, India * sankha.deb@mech.iitkgp.ernet.in The present work aims to develop a genetic algorithm-based approach to solve the scheduling optimization problem in the Job Shop manufacturing environment. A new encoding scheme for chromosome representation has been developed for this purpose that denotes a priority sequence of operations, from which a schedule can be generated if the precedence constraints are known. The successful implementation of the proposed encoding scheme has been presented and its performance has been compared with the existing operation-based scheme found in literatures across different test cases by varying the number of jobs and machines in the shop floor. Keywords : Job shop; scheduling; optimization; genetic algorithm. 1. Introduction and Background Scheduling is an optimization process intended to make the best possible use of the limited resources by making suitable allotment of the said resources over a period of time. Such problems are combinatorial optimization problems, where the set of fea- sible solutions is discrete or can be reduced to a discrete one, and the goal is to find the best possible solution. One important application of scheduling is the problem of machine scheduling. Machine scheduling finds application in production planning for developing processing schedules, workforce scheduling, etc. Machine scheduling falls in the NP-hard class of combinatorial optimization problems. Development of job processing schedules in a manufacturing environment is a complex task. The number of feasible schedules increases exponentially with the increase in the number of jobs and associated operations. This exponential growth makes it almost impos- sible to use mathematical programming and/or exhaustive search-based approaches to find the global optimum schedules. The aim of the present work is to implement genetic algorithms to optimize the machine loading schedules in machine shops and in manufacturing systems, especially in relation to Job Shop/Open Shop Scheduling and scheduling in Flexible Manufacturing Systems. The machine loading problem can be defined as “given a 223