Topic
Job shop scheduling
About: Job shop scheduling is a research topic. Over the lifetime, 35258 publications have been published within this topic receiving 658347 citations.
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TL;DR: A Genetic Algorithm is developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem and the performance of the algorithm is compared with that of a naive Neighbourhood Search technique and with a proven Simulated Annealing algorithm.
849 citations
TL;DR: In this paper, a branch and bound method for solving the job-shop problem is proposed, which is based on one-machine scheduling problems and is made more efficient by several propositions which limit the search tree by using immediate selections.
Abstract: In this paper, we propose a branch and bound method for solving the job-shop problem. It is based on one-machine scheduling problems and is made more efficient by several propositions which limit the search tree by using immediate selections.
It solved for the first time the famous 10 × 10 job-shop problem proposed by Muth and Thompson in 1963.
836 citations
10 Aug 1998
TL;DR: A model of dynamically variable voltage processors and basic theorems for power-delay optimization and a static voltage scheduling problem is proposed and formulated as an integer linear programming (ILP) problem.
Abstract: This paper presents a model of dynamically variable voltage processors and basic theorems for power-delay optimization. A static voltage scheduling problem is also proposed and formulated as an integer linear programming (ILP) problem. In the problem, we assume that a core processor can vary its supply voltage dynamically, but can use only a single voltage level at a time. For a given application program and a dynamically variable voltage processor, a voltage scheduling which minimizes energy consumption under an execution time constraint can be found.
826 citations
TL;DR: This work improves the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles and simultaneously scheduling all DER schedules, to investigate the potential consumer value added by coordinated DER scheduling.
Abstract: We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.
824 citations