Genetic Algorithms with Feasible Operators for Solving Job Shop Scheduling Problem

Abstract

Job scheduling is one of the key activities performed in industries for manufacturing planning. In job scheduling, each job that contains various operations is allocated to one of the available machines for processing. Each job has a duration and each machine can handle only one operation at a time. An efficient allocation of jobs is mandatory for decreasing the makespan and idle time of the machines. In Job Shop Scheduling (JSS), the operations of the jobs are ordered. Genetic algorithm (GA) is a popular heuristic algorithm investigated to solve different scheduling problems. This paper presents feasibility preserving solution representation, initialization and operators for solving job shop scheduling problem. Proposed GA obtained best known results with good success rate for Lawrence (1984) datasets. Experiments show fast convergence of GA towards best solution. Hybridization of GA with local search or repair operator is required to obtain best solution with better success rate.Index Terms: Genetic algorithm, generalized order crossover, job shop scheduling problem, scheduling problem.

Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives

“…Several studies presented in the 1980s and 1990s approached the makespan minimization problem using optimization algorithms [14,[28][29][30]. However, this seminal JSP formulation still has relevance and continues receiving the interest of the research community working on metaheuristics [31,32].…”

Extended Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problem

“…These methods are called hybrid algorithms. Hybrid methods are frequently employed for solving JSSP for example, hybrid GA and heuristic rules [14], hybrid GA and local search [9], and GA with simulated annulling [12,13]. The hybrid algorithms perform better than its corresponding individual counterparts as with the hybridization convergence rate is usually high and it also helps in escaping local minima.…”