Previous production scheduling models often set optimization objectives from the perspective of manufacturers, such as makespan, tardiness and energy consumption. However, none of the objectives can reflect the extent...
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Previous production scheduling models often set optimization objectives from the perspective of manufacturers, such as makespan, tardiness and energy consumption. However, none of the objectives can reflect the extent to which the scheduling plan affects the demand side. In fact, the delivery time of orders will directly affect the equipment utilization or project schedule on the demand side. In this paper, we focus on a new objective named total operational utility of all distributed equipment from the demand side, and integrate it into an energy-efficient production scheduling model based on the distributed parallel machine environment, in which the total energy consumption of manufacture side including processing energy consumption and transportation energy consumption is another objective. The orders are the spare parts used to replace the deteriorated components of distributed equipment based on forecasting information. Based on the scheduled delivery time fed back from the scheduling plan, the relationship among operating speed, deterioration rate and operating efficiency is used, and an optimal speed adjustment strategy is developed for each equipment to improve the operational utility. A memetic algorithm (NMA) based on the structure of NSGA-II is presented for the model. A list scheduling heuristic and a problem-dependent heuristic are designed to generate initial population. Two problem-dependent local search operators are developed to enhance the searching ability. By performing extensive experiments and comparing NMA with some well-known algorithms, the effectiveness and superiority of NMA are demonstrated.
This article examines an alliance of heterogeneous factories operating as a production network, in which jobs can be divided into several sub-jobs and independently processed in distributed factories. This problem is ...
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This article examines an alliance of heterogeneous factories operating as a production network, in which jobs can be divided into several sub-jobs and independently processed in distributed factories. This problem is considered as a distributed unrelated parallelmachine scheduling with splitting jobs (DUPMSP/S). A mathematical model and an effective multi-stage evolutionary algorithm (EMSEA) are proposed, aiming to minimize the total tardiness and total cost of production and transportation. In the EMSEA, the optimization process is divided into three stages according to the population in each generation, and four problem-based initial methods and three knowledge-based local exploitation strategies are embedded to improve its performance. Extensive experiments are conducted to compare the EMSEA with four other algorithms and to compare the scheduling model with no splitting jobs. The results demonstrate that EMSEA is the most promising method in solving the DUPMSP/S, and the job splitting mode is effective.
We present a simulation-based performance model to analyze a parallel sparse LU factorization algorithm on modern cached-based, high-end parallel architectures. We consider supernodal right-looking parallel factorizat...
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We present a simulation-based performance model to analyze a parallel sparse LU factorization algorithm on modern cached-based, high-end parallel architectures. We consider supernodal right-looking parallel factorization on a bi-dimensional grid of processors, that uses static pivoting. Our model characterizes the algorithmic behavior by taking into account the underlying processor speed, memory system performance, as well as the interconnect speed. The model is validated using the implementation in the SuperLU_DIST linear system solver, the sparse matrices from real application, and an IBM POWER3 parallelmachine. Our modeling methodology can be adapted to study performance of other types of sparse factorizations, such as Cholesky or QR, and on different parallelmachines.
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