Increased carbon dioxide emissions and energy consumption increase interest in energy efficient scheduling problems. In this study, speed scaling method is discussed in order to provide energy saving in multiobjective...
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Increased carbon dioxide emissions and energy consumption increase interest in energy efficient scheduling problems. In this study, speed scaling method is discussed in order to provide energy saving in multiobjective energy efficient single machine scheduling problem. In the literature with the speed scaling method, the energy consumption rate increases when the machine is operated at high speed, but the job is completed in a shorter time. When the machine is operate at low speed, the energy consumption rate decreases but the completion time of the jobs are prolonged. In the study, the objective functions are minimization of the total amount of energy consuption and the number of tardy jobs. The problem is defined by the scheduling problem in a manufacturing firm that produces plastic parts. Jobs have a sequence dependent setup times, and the setup time of a job depends on the job completed before it. A mathematical model is proposed for the problem. The epsilon-constraint method is used for the solution of the problem. All Pareto efficient solutions for small size problems are obtained by using the proposed heuristic method. However, as a result of the problem not being solved in polynomial time, a multi-objective heuristic algorithm is proposed for solving large-scale problems. The success of the proposed algorithm is shown by comparing with Non- dominated Sorting Genetic algorithm II.
To decide which orders to be accepted for an IC design house, besides wafers and capacity, several factors also need to be considered, including product flexibility, minimal cost path, order delay cost, and the fairne...
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To decide which orders to be accepted for an IC design house, besides wafers and capacity, several factors also need to be considered, including product flexibility, minimal cost path, order delay cost, and the fairness of order fulfillment rate between customers. This research focuses on a multi-objective order promising problem for the hybrid MTS-MTO outsourcing supply network of an IC design house. The objectives are to maximize total profit, minimize total delay cost of accepted orders, and minimize the discrepancy of order fulfillment rate between customers. The multi-stage and multi-site outsourcing supply network, technical capacity constraints, product flexibility and multi-chip module are considered. This problem is modeled with a multi-objective mixed integer programming model. A heuristicalgorithm, called the fairness-based multi-objective order promising algorithm (FMOPA) with four modes is developed to solve the problem. Moreover, experimental design is conducted to evaluate the efficiency of the proposed model with various factors and levels. Analysis of variance (ANOVA) is used to evaluate the factors in accordance to the objectives. The optimal algorithm mode under thirty-two combinations of different environment factors are also evaluated. This can assist decision makers to use the optimal algorithm mode to generate the best favorable result under different environment factors.
In deployed wireless sensor networks (WSNs), how to efficiently transmit information collected by sensor nodes with limited energy is a challenging problem. An appropriate cluster head selection strategy can efficient...
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In deployed wireless sensor networks (WSNs), how to efficiently transmit information collected by sensor nodes with limited energy is a challenging problem. An appropriate cluster head selection strategy can efficiently solve this problem, but there are many factors to be considered, such as energy consumption, the coverage of cluster head nodes, and the number of cluster head nodes. Each factor has a profound impact on the performance of wireless sensor networks, and there are conflicts among them. In order to solve the conflict of multiple factors and obtain the optimal selection strategy of the cluster head node, this paper proposes a Binary multi-objective Adaptive Fish Migration Optimization (BMAFMO) algorithm. The algorithm introduces the Pareto optimal solution storage strategy to improve the global search ability of the optimization algorithm and transform the continuous solution into a binary solution according to the sigmoid transformation function to solve the problem of cluster head node selection. The new algorithm was comprehensively tested using eight test problems and four test metrics. At the same time, the reliability of the algorithm is tested by rank sum test. The test results show that the BMAFMO algorithm obtained the best results in 78.13% test problems compared with other algorithms. Finally, the BAMFMO algorithm is applied to solve the cluster head selection problem of WSN and the simulation results show the novel algorithm has better optimization ability than other heuristicalgorithms.
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