A survey of single machine scheduling to minimize weighted number of tardy jobs

Adamu, Muminu O.; Adediran, Adewumi

doi:10.3934/jimo.2014.10.219

Cited by 44 publications

(29 citation statements)

References 119 publications

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“…We begin with two key observations that will be useful for formulating our MICP. The first can be considered by now as folklore (see, e.g., [1]), while the second follows from an easy pairwise interchange argument. Lemma 1.…”

Section: Roadmap and Techniquesmentioning

confidence: 99%

“…Accordingly, our problem can be solved by solving formulation Π where we need to minimize the objective in (3) subject to the set of linear constraints in (1) and (2).…”

Section: Roadmap and Techniquesmentioning

confidence: 99%

“…Again, we add the k constraints of type (1) to ensure that the pairs x i , y i sum up appropriately. Moreover, we add k new non-negative non-integer variables z 1 , .…”

Section: Lemmamentioning

confidence: 99%

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New algorithms for minimizing the weighted number of tardy jobs on a single machine

et al. 2018

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In this paper we study the classical single machine scheduling problem where the objective is to minimize the weighted number of tardy jobs. Our analysis focuses on the case where one or more of three natural parameters is either constant or is taken as a parameter in the sense of parameterized complexity. These three parameters are the number of different due dates, processing times, and weights in our set of input jobs. We show that the problem belongs to the class of fixed parameter tractable (FPT) problems when combining any two of these three parameters. We also show that the problem is polynomial-time solvable when the latter two parameters are constant, complementing Karp's result who showed that the problem is NP-hard already for a single due date. ⋆

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Section: Roadmap and Techniquesmentioning

confidence: 99%

“…Accordingly, our problem can be solved by solving formulation Π where we need to minimize the objective in (3) subject to the set of linear constraints in (1) and (2).…”

Section: Roadmap and Techniquesmentioning

confidence: 99%

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New algorithms for minimizing the weighted number of tardy jobs on a single machine

et al. 2018

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“…Multi-machine scheduling is a fundamental scheduling problem modeling a range of important real-life problems from scheduling tasks on a CPU to optimizing manufacturing processes [12,Ch.1]. Such problems have shown to be computationally very challenging and are an active area of research [1]. Recently, singlemachine scheduling problems have been solved more quickly by lower bounds produced by relaxed decision diagrams [8,10].…”

Section: Introductionmentioning

confidence: 99%

Multi-machine scheduling lower bounds using decision diagrams

Bogaerdt

Weerdt

2018

Operations Research Letters

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We consider parallel multi-machine scheduling with due times, where a partition of jobs is given where jobs in the same partition have a common release time, possibly precedence constraints, and cannot overlap. A formulation of decision diagrams for this problem greatly improves upon a more natural extension of the state-of-the-art for single-machine scheduling, and can provide decent lower bounds, outperforming existing solvers given the same short runtime limit, for problem instances with large time scales and tight due times.

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“…Several categorizations of scheduling problems are found in the literature [1,2]. However, of particular interest in this study is the problem of Just-in-Time (JIT) scheduling [3,4]. JIT scheduling, as described by Taiichi Ohno (commonly referred to as the father of JIT) is, "in a flow process, the right parts needed in assembly reach the assembly line at the time they are needed and only in the amount needed" [5].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem

Chetty

Adediran

2015

Discrete Dynamics in Nature and Society

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The Just-In-Time (JIT) scheduling problem is an important subject of study. It essentially constitutes the problem of scheduling critical business resources in an attempt to optimize given business objectives. This problem is NP-Hard in nature, hence requiring efficient solution techniques. To solve the JIT scheduling problem presented in this study, a new local search metaheuristic algorithm, namely, the enhanced Best Performance Algorithm (eBPA), is introduced. This is part of the initial study of the algorithm for scheduling problems. The current problem setting is the allocation of a large number of jobs required to be scheduled on multiple and identical machines which run in parallel. The due date of a job is characterized by a window frame of time, rather than a specific point in time. The performance of the eBPA is compared against Tabu Search (TS) and Simulated Annealing (SA). SA and TS are well-known local search metaheuristic algorithms. The results show the potential of the eBPA as a metaheuristic algorithm.

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A survey of single machine scheduling to minimize weighted number of tardy jobs

Cited by 44 publications

References 119 publications

New algorithms for minimizing the weighted number of tardy jobs on a single machine

New algorithms for minimizing the weighted number of tardy jobs on a single machine

Multi-machine scheduling lower bounds using decision diagrams

A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem

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