Big Data optimization (Big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizin...
详细信息
Big Data optimization (Big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizing metaheuristics to improve the performance of optimization algorithms, is extended for multi-objective problems (MOSM), and then five configurations of it by combination of different search strategies are proposed to solve the EEG signal analysis problem which is a member of the big data optimization problems class. Experimental results demonstrate that the proposed configurations of MOSM are efficient in this kind of problems. The configurations are also compared with NSGA-III with uniform crossover and adaptive mutation operators (NSGA-III UCAM), which is a recently proposed method for Big-Opt problems. (C) 2019 Elsevier B.V. All rights reserved.
With the increase in awareness of energy conservation and emission reduction in the manufacturing sector, the energy efficiency optimization of the production process is considered an effective way to promote cleaner ...
详细信息
With the increase in awareness of energy conservation and emission reduction in the manufacturing sector, the energy efficiency optimization of the production process is considered an effective way to promote cleaner production and environmentally sustainable development. Moreover, the transportation process and its energy consumption are non-negligible during the entire workshop production process. In practice, cutting tool degradation during operation is inevitable, which causes the actual processing time of work to be extended, so it is necessary to consider the degradation effects and imperfect maintenance. To reduce the total energy consumption in the manufacturing workshop with degradation effects and imperfect maintenance, this paper constructs a novel integrated optimization model including the flexible job-shop scheduling problem (FJSP), forklift transportation and imperfect cutting tool maintenance. First, an imperfect cutting tool maintenance with degradation effect strategy is presented, which makes the actual processing time of each operation more closely approximate the real-world processing situation. Second, an integrated multi-objective optimization model is formulated to simultaneously minimize the makespan, total tardiness, total production cost and total energy consumption. Third, a hybrid multi-objective evolutionary algorithm with Pareto elite storage strategy (HMOEA/P) is proposed to address the model. More precisely, three improved operations are presented: hybrid dominance principle, local search algorithms and Pareto elite preservation strategy. The effectiveness and feasibility of the parameter setting, improved operations and the proposed algorithm are separately demonstrated by the comparative experiments. Last, the superiority of the integrated multiobjective optimization model is proven by comparing different maintenance strategies and energy consumption models. (C) 2020 Elsevier Ltd. All rights reserved.
A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of th...
详细信息
A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5MW wind-turbine blade. By hybridizing a multi-objectiveevolutionaryalgorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.
Production scheduling and maintenance planning are two of the most important tasks in the modern manufac-turing workshop. Meanwhile, due to the dynamic order arrival and real-time machine monitoring information updati...
详细信息
Production scheduling and maintenance planning are two of the most important tasks in the modern manufac-turing workshop. Meanwhile, due to the dynamic order arrival and real-time machine monitoring information updating, the integrated optimization of them becoming more complex and meaningful. Therefore, this study intends to address an adaptive flexible job-shop rescheduling problem with real-time order acceptance (ROA) and condition-based preventive maintenance (CBPM). More precisely, the main innovative works are described as follows: (1) a CBPM policy with both imperfect preventive maintenance (PM) and four inspection strategies is designed to find the optimal maintenance planning for each production machine;(2) a multi-objective optimization model is developed for the concerned problem;and (3) a hybrid multi-objective evolutionary algorithm (HMOEA) with hybrid initialization method, hybrid local search operators and adaptive rescheduling strategies is proposed. In the numerical simulation, the performance and competitiveness of the proposed CBPM policy are first demonstrated by comparing with other maintenance policies. Second, the effectiveness and superiority of parameter setting, order sorting rules, improved operators and overall performance of the proposed algorithm are verified by internal analysis of the algorithm. Third, an adaptive rescheduling strategy pool is constructed by running three rescheduling strategies on all rescheduling scenarios. Finally, a comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the adaptive rescheduling problem, and the results and comparisons show that the proposed HMOEA algorithm and order acceptance strategy have good robustness in most parameters.
Remanufacturing has been widely studied for its potential to achieve sustainable production in recent years. In the literature of remanufacturing research, process planning and scheduling are typically treated as two ...
详细信息
Remanufacturing has been widely studied for its potential to achieve sustainable production in recent years. In the literature of remanufacturing research, process planning and scheduling are typically treated as two independent parts. However, these two parts are in fact interrelated and often interact with each other. Doing process planning without considering scheduling related factors can easily introduce contradictions or even infeasible solutions. In this work, we propose a mathematical model of integrated process planning and scheduling for remanufacturing (IPPSR), which simultaneously considers the process planning and scheduling problems. An effective hybrid multi-objective evolutionary algorithm (HMEA) is presented to solve the proposed IPPSR. For the HMEA, a multidimensional encoding operator is designed to get a high-quality initial population. A multidimensional crossover operator and a multidimensional mutation operator are also proposed to improve the convergence speed of the algorithm and fully exploit the solution space. Finally, a specific legalising method is used to 'legalise' possible infeasible solutions generated by the initialisation method and mutation operator. Extensive computational experiments carried out to compare the HMEA with some well-known algorithms confirm that the proposed HMEA is able to obtain more and better Pareto solutions for IPPSR.
暂无评论