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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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Journal ArticleDOI
TL;DR: The paper investigates the authors' most recent branch and bound algorithm and discovers the following paradoxes and develops a very fast branch algorithm that handles instances with up to 500 jobs.
Abstract: The paper deals with the single-machine total tardiness problem. It investigates the authors' most recent branch and bound algorithm and discovers the following paradoxes. Deleting a lower bound drastically improves the performance of the algorithm, while adding a stronger component, like a better decomposition rule, negatively affects its performance. Guided by those paradoxes it develops a very fast branch algorithm that handles instances with up to 500 jobs. It also shows that the powerful recent result of Chang et al. (Operations Research Letters 1995; 17:221–229) can be further improved. Copyright © 2001 John Wiley & Sons, Ltd.

48 citations

Journal ArticleDOI
TL;DR: A sequential exchange approach utilizing a job exchange procedure and three previously established properties in common due date scheduling was developed and tested with a set of benchmark problems, generating results better than those of the existing dedicated heuristics but also in many cases those of meta-heuristic approaches.

47 citations

Journal ArticleDOI
TL;DR: A branch and bound scheme is proposed based on the definition of a master sequence, i.e. a sequence containing at least one optimal sequence to minimize the number of tardy (or late) jobs.
Abstract: This paper considers the problem of scheduling n jobs on a single machine to minimize the number of tardy (or late) jobs. Each job has a release date, a processing time and a due date. The general case with non-equal release dates and different due dates is considered. Using new and efficient lower bounds and several dominance rules, a branch and bound scheme is proposed based on the definition of a master sequence, i.e. a sequence containing at least one optimal sequence. With this procedure, 95% of 140-job instances are optimally solved in a maximum of one-hour CPU time.

47 citations

01 Jan 1996
TL;DR: A branch-and-bound algorithm for scheduling n independent jobs with release dates, due dates, and family setup times on a single machine to minimize the maximum lateness is presented in this paper.
Abstract: textWe address the NP-hard problem of scheduling n independent jobs with release dates, due dates, and family setup times on a single machine to minimize the maximum lateness. This problem arises from the constant tug-of-war going on in manufacturing between efficient production and delivery performance, between maximizing machine utilization by batching similar jobs and maximizing customers' satisfaction by completing jobs before their due dates. We develop a branch-and-bound algorithm, and our computational results show that it solves almost all instances with up to about 40 jobs to optimality. The main algorithmic contribution is out lower bounding strategy to deal with family setup times. The key idea is to see a setup time as a setup job with a specific processing time, release date, due date, and precedence relations. We develop several sufficient conditions to derive setup jobs. We specify their parameters and precedence relations such that the optimal solution value of the modified problem obtained by ignoring the setup times, not the setup jobs, is no larger than the optimal solution value of the original problem. One lower bound for the modified problem proceeds by allowing preemption. Due to the agreeable procedure structure, the preemptive problem is solvable in O(n log n) time.

47 citations

Journal ArticleDOI
TL;DR: Heuristic algorithms are presented by using the optimal permutations for the corresponding single machine scheduling problems with a general position-dependent learning effects to minimize one of the five regular performance criteria.
Abstract: A real industrial production phenomenon, referred to as learning effects, has drawn increasing attention. However, most research on this issue considers only single machine problems. Motivated by this limitation, this paper considers flow shop scheduling problems with a general position-dependent learning effects. By the general position-dependent learning effects, we mean that the actual processing time of a job is defined by a general non-increasing function of its scheduled position. The objective is to minimize one of the five regular performance criteria, namely, the total completion time, the makespan, the total weighted completion time, the total weighted discounted completion time, and the sum of the quadratic job completion times. We present heuristic algorithms by using the optimal permutations for the corresponding single machine scheduling problems. We also analyze the worst-case bound of our heuristic algorithms.

47 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202333
202270
202188
202083
201972
201889