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 sched...
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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-learningbasedoptimization (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.
In this paper, a new hybrid optimization algorithm, called "Interactive Search Algorithm (ISA)" is proposed for the solution of the optimization problems. This algorithm modifies and combines affirmative fea...
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In this paper, a new hybrid optimization algorithm, called "Interactive Search Algorithm (ISA)" is proposed for the solution of the optimization problems. This algorithm modifies and combines affirmative features of two developed metaheuristic methods called Integrated Particle Swarm optimization (iPSO) and teaching and learning based optimization (TLBO). ISA consists of two separate paradigms: (i) Tracking and (ii) Interacting. Tracking paradigm utilizes the information stored in the current agent's memory and two other important agents, the weighted and best agents, to guide the colony. On the other hand, interacting paradigm provides a pairwise interaction between agents to share their knowledge with each other. Each agent based on its tendency factor employs one of these two paradigms in each cycle of ISA to explore the search space. Additionally, rather than conventional penalty approach, ISA utilizes the improved fly-back approach to handle problem constraints. The search capability of the proposed method is tested on the number benchmark mathematical functions and constrained mechanical design problems as the real-world examples. Consequently, the achieved numerical results demonstrate that the proposed method is competitive with other well-established metaheuristic methods.
The widespread use of electric vehicles is a suitable solution to deal with the reduction of fossil fuel reserves and the increase in air pollution. Reducing pollution can be realized through optimal planning of charg...
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The widespread use of electric vehicles is a suitable solution to deal with the reduction of fossil fuel reserves and the increase in air pollution. Reducing pollution can be realized through optimal planning of charging and discharging of electric vehicles as consumers and storage of electric energy. Also, vehicles connected to the network increase the reliability of the network during power outages. However, the connection of electric vehicles to the grid can also bring limitations. The uncoordinated and unmanaged presence of electric vehicles as an additional load in the network can aggravate problems such as voltage drop, voltage stability and increase in network losses. Consequently, these problems will have economic costs. In this way, the management of charging and discharging of electric vehicles will be of special importance in the future of control and operation of distribution networks. In this article, electric vehicle charging and discharging management has been done in order to minimize the amount of active power loss, bus voltage deviation, increase the stability of the network voltage, and as a result, reduce the costs of the distribution company. For this purpose, a 33-bus distribution network that absorbed its required energy only through the connection point to the network was studied with different scenarios in which the objective function of the problem was different. The proposed algorithm was implemented to calculate the best state of charging and discharging of cars. In choosing these vehicles, it has been tried to examine all charging modes. The simulation results show the effectiveness of the proposed plan for planning the development of electric vehicles in the distribution network.
The machine remaining useful life (RUL), the job-machine release time and the correlation between the maintenance duration and the machine enlistment age are, in this paper, collectively emphasized at the parallel mac...
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The machine remaining useful life (RUL), the job-machine release time and the correlation between the maintenance duration and the machine enlistment age are, in this paper, collectively emphasized at the parallel machine scheduling problem. based on this, a corresponding mixed integer programming model is constructed to minimize the makespan and the processing loss beyond the machine RUL threshold, where a discrete teaching and learning based optimization algorithm is applied to solve this NP-hard problem, and a fault mode-assisted gated recurrent unit (FGRU) life prediction method is used to guide the predictive maintenance initiation time of all machines. In addition, this paper demonstrates that the FGRU method is more accurate than three common methods (Encoder-Decoder Recurrent Neural Network, Bidirectional Long Short-Term Memory and GRU) through two actual bearing degradation cases, and shows through three benchmark cases that the joint decision-making can effectively reduce the time cost of manufacturing enterprises.
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