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This paper presents a discrete animal migration optimization (DAMO) to solve the dual-resource constrained energy-saving flexible job shop scheduling problem (DRCESFJSP), with the aim of minimizing the total energy consumption in the workshop. A job-resource-based two-vector encoding method is designed to represent the scheduling solution, and an energy-saving decoding approach is given based on the left-shift rule. To ensure the quality and diversity of initial scheduling solutions, a heuristic approach is employed for the resource assignment, and some dispatching rules are applied to acquire the operation permutation. In the proposed DAMO, based on the characteristics of the DRCESFJSP problem, the search operators of the basic AMO are discretized to adapt to the problem under study. An animal migration operator is presented based on six problem-based neighborhood structures, which dynamically changes the search scale of each animal according to its solution quality. An individual updating operator based on crossover operation is designed to obtain new individuals through the crossover operation between the current individual and the best individual or a random individual. To evaluate the performance of the proposed algorithm, the Taguchi design of experiment method is first applied to obtain the best combination of parameters. Numerical experiments are carried out based on 32 instances in the existing literature. Computational data and statistical comparisons indicate that both the left-shift decoding rule and population initialization strategy are effective in enhancing the quality of the scheduling solutions. It also demonstrate that the proposed DAMO has advantages against other compared algorithms in terms of the solving accuracy for solving the DRCESFJSP.

Manufacturing resources are shared and centrally managed on the cloud platform in cloud manufacturing, which is a new model of modern manufacturing. The production data are collected, which can be used to predict the manufacturing events. Based on those, disruption problems of scheduling should be researched from a new point of view. In this paper, new job arrivals were considered as the disruption event. The time of the occurrence of disruption was predictable in contrast to uncertainty. Alternative subcontractors chosen from the cloud platform were available for outsourcing with different processing prices and transporting distances. The objective of the original scheduling, the deviation between the new schedule and the old one, and the outsourcing cost were all considered. To express the problem, mathematical models and a three-field notation model were constructed. To solve the problem, a hybrid quantum-inspired chaotic group leader optimization algorithm was proposed, in which a hybrid encoding way was applied. To verify the algorithm, experiments were carried out. The results showed that the proposed algorithm performs well.