A Comparison of Genetic Algorithms for the Dynamic Job Shop Scheduling Problem

The majority of the research using evolutionary algorithms for the Job Shop Scheduling Problem (JSSP) has studied only the static JSSP. Few evolutionary algorithms have been applied to the Dynamic Job Shop Scheduling Problem (DJSSP) which is more similar to real-world applications. We implement a hybrid genetic algorithm for solving the dynamic job shop problem. A direct chromosome representation, containing the schedule itself, is used. Order-based operators are combined with techniques that produce active and non-delay schedules. We refer to our algorithm as the Order-Based Gifer and Thompson (OBGT) Genetic Algorithm. OBGT is compared in terms of the quality of solutions against published solutions for benchmark problems. OBGT consistently nds better solutions on larger problems compared to several other evolutionary algorithms, including Temporal Horizon GA (THX) and Heuristically guided GA (HGA).

• Type: Conference
• Year: 2000
• Where: GECCO
• Authors: Manuel Vázquez, L. Darrell Whitley
• Keywords: Evolutionary Algorithms, GECCO 2000, Job Shop, Optimization, Shop Scheduling Problem