Abstract
In current manufacturing systems, production processes and management are involved in many unexpected events and new requirements emerging constantly. This dynamic environment implies that operation rescheduling is usually indispensable. A wide variety of procedures and heuristics has been developed to improve the quality of rescheduling. However, most proposed approaches are derived usually with respect to simplified assumptions. As a consequence, these approaches might be inconsistent with the actual requirements in a real production environment, i.e., they are often unsuitable and inflexible to respond efficiently to the frequent changes. In this paper, a multi-objective job shop rescheduling problem (moJSRP) is formulated to improve the practical application of rescheduling. To solve the moJSRP model, an evolutionary algorithm is designed, in which a random key-based representation and interactive adaptive-weight (i-awEA) fitness assignment are embedded. To verify the effectiveness, the proposed algorithm has been compared with other apporaches and benchmarks on the robustness of moJRP optimziation. The comparison results show that iAWGA-A is better than weighted fitness method in terms of effectiveness and stability. Simlarly, iAWGA-A also outperforms other well stability approachessuch as non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm2 (SPEA2).
