Efficient scheduling benefits productivity promotion, energy savings and the customer's satisfaction. In recent years, with a growing concern about the energy saving and environmental impact, energy oriented scheduling is going to be a hot issue for sustainable manufacturing. In this study, we investigate an energy-oriented scheduling problem deriving from the hybrid flow shop with unrelated parallel machine. First, we formulate the scheduling problem with a mixed integer linear programming (MILP) model, which considers two objectives including minimizing the completion time and energy consumption. Second, a hybrid multi-objective teaching-learning based optimization (HMOTLBO) algorithm based on decomposition is proposed. In the proposed HMOTLBO, a new solution presentation and five decoding rules are designed for mining the optimal solution. To reduce the standby energy consumption and turning on/off energy consumption, a greedy shifting algorithm is developed without changing the completion time of a scheduling. To improve the converge speed of the algorithm, a weight matching strategy is designed to avoid randomly matching weight vectors with students. To enhance the exploration and exploitation capacities of the algorithm, A teaching operator based on crossover and a self-learning operator based on a variable neighborhood search(VNS) are proposed. Finally, fourth different experiments are performed on 15 cases, the comparison result verified the effectiveness and the superiority of the proposed algorithm.INDEX TERMS Hybrid flow shop scheduling, teaching and learning based optimization, multi-objective, makespan, energy consumption.
Batch processing machine (BPM) scheduling problem is a NP hard problem for it includes machine allocation, job grouping, and batch scheduling. In this paper, to address the BPM scheduling problem with unrelated parallel machine, a multiobjective algorithm based on multipopulation coevolution is proposed to minimize the total energy consumption and the completion time simultaneously. Firstly, the mixed integer programming model of the problem is established, and four heuristic decoding rules are proposed. Secondly, to improve the diversity and convergence of the algorithm, the population is divided into two populations: each of the populations evolves independently by using different decoding rules, and the two populations will communicate through a common external archive set every certain number of generations. Thirdly, an initialization strategy and a variable neighborhood search algorithm (VNS) are proposed to improve the overall performance of the algorithm. Finally, in order to evaluate the proposed algorithm, a large number of comparative experiments with the state-of-the-art multiobjective algorithms are carried out, and the experimental results proved the effectiveness of the proposed algorithm.
Abstract.Researched the uncertain Job-Shop Scheduling, on the basis of the original triangular fuzzy number to describe fuzzy processing time, structured the fuzzy Job-Shop Scheduling model. Algorithm using the concept of "big valley" topology represent solution space, using strong swap mutations in early immune genetic algorithm, and implanting vaccines in three styles, rapidly improved the ability of search "mountain"; After immune selection using taboo search's "climb" idea improve the local search ability of the algorithm, so as to choose the individual with maximum satisfaction in the "big valley" quickly and efficiently. And through Matlab2012a software simulation examples verify the effectiveness of the immune genetic and taboo hybrid intelligent algorithm.
In this paper, a hybrid particle swarm algorithm is proposed to minimize the makespan of job-shop scheduling problem which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. The new algorithm is based on the principle of particle swarm optimization (PSO). PSO as an evolutionary algorithm, it combines coarse global search capability (by neighboring experience) and local search ability. Simulated annealing (SA) as a neighborhood search algorithms, it has strong local search ability and can employ certain probability and can to avoid becoming trapped in a local optimum. Three neighborhood SA algorithms is designed and combined with PSO(called HPSO), for each best solution that particle find, SA is performed on it to find it's best neighbor solution. The effectiveness and efficiency of HPSO are demonstrated by applying it to 43 benchmark job-shop scheduling problems. Comparison with other researcher's results indicates that HPSO is a viable and effective approach for the job-shop scheduling problem.
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