Topic
Single-machine scheduling
About: Single-machine scheduling is a research topic. Over the lifetime, 2473 publications have been published within this topic receiving 56288 citations.
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TL;DR: The scheduling problems arising when several agents, each owning a set of nonpreemptive jobs, compete to perform their respective jobs on one shared processing resource are considered.
Abstract: We consider the scheduling problems arising when several agents, each owning a set of nonpreemptive jobs, compete to perform their respective jobs on one shared processing resource. Each agent wants to minimize a certain cost function, which depends on the completion times of its jobs only. The cost functions we consider in this paper are maximum of regular functions (associated with each job), number of late jobs and total weighted completion time. The different combinations of the cost functions of each agent lead to various problems, whose computational complexity is analysed in this paper. In particular, we investigate the problem of finding schedules whose cost for each agent does not exceed a given bound for each agent.
158 citations
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TL;DR: Three heuristic algorithms are developed to solve large sized problems and Computational tests indicate that the proposed algorithms are both computationally efficient and effective even for instances up to 300 orders.
154 citations
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TL;DR: The approach used in the paper is fully based on the eliminative properties of a block of jobs and in contradiction to the existing algorithms, it does not require any heuristic method to generate a current solution in a search tree.
151 citations
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TL;DR: A mathematical model to minimize energy consumption and reduce total completion time of a single machine is proposed, and a multiobjective genetic algorithm is utilized to obtain an approximate set of nondominated alternatives.
Abstract: Energy is an expensive resource that is becoming more scarce with increasing population and demand. In this paper, a mathematical model to minimize energy consumption and reduce total completion time of a single machine is proposed, and a multiobjective genetic algorithm is utilized to obtain an approximate set of nondominated alternatives. Furthermore, dominance rules and a heuristic are proposed to increase the speed of the proposed genetic algorithm. Finally, the analytical hierarchical process is utilized to select a solution with some additional criteria.
151 citations
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TL;DR: The problem of sequencing jobs on a single machine to minimize total cost is considered, and it is shown that the dynamic programming formulation can be relaxed by mapping the state-space onto a smaller state- space and performing the recursion on this smallerstate-space, thereby giving a lower bound.
Abstract: The problem of sequencing jobs on a single machine to minimize total cost is considered. Machine capacity constraints require that, at any time, at most one job is processed. Also, no machine idle-time between processing jobs is allowed. In contrast to most research, it is not assumed that the cost is a non-decreasing function of completion time. A dynamic programming formulation of the problem is presented. Since the number of states required by this formulation is prohibitively large, the possibilities for branch and bound algorithms are explored. It is shown that the dynamic programming formulation can be relaxed by mapping the state-space onto a smaller state-space and performing the recursion on this smaller state-space, thereby giving a lower bound. Techniques for improving this lower bound through the use of penalties and through the use of state-space modifiers are discussed. Computational results are presented for the problem in which each job has a due date, and the objective is to minimize the sum of holding costs for jobs completed before their due date and tardiness costs for jobs completed after their due date.
146 citations