Optimal delivery time quotation to minimize total tardiness penalties with controllable processing times

Leyvand, Yaron; Shabtay, Dvir; Steiner, George

doi:10.1080/07408170903394322

Cited by 17 publications

(13 citation statements)

References 23 publications

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“…The outsourced portion of an order (of a job) can be considered as the compression of its original processing time. A large variety of applications of scheduling models with controllable processing times can be found in the literature (see, for example, Janiak 1987, Janiak and Kovalyov 1996, Shakhlevich and Strusevich 2005and Leyvand et al 2010. Scheduling models with controllable processing times were introduced by Vickson (1980) and have received considerable attention over the recent years.…”

Section: Controllable Processing Timesmentioning

confidence: 99%

“…For a single machine, a linear resource consumption function and a common due date assignment, Leyvand et al (2010) consider three types of problems: (i) to minimize n j =1 w j U j + γ nd subject to n j =1 v j u j ≤ V , where V is a bound on the total resource consumption cost; (ii) to minimize n j =1 v j u j subject to n j =1 w j U j + γ nd ≤ K, where K is a given upper bound on penalties for tardy jobs and the cost of due date assignment; (iii) to identify the set of Pareto-optimal solutions (V (S), Z(S)), where V (S) = n j =1 v j u j , Z(S) = n j =1 w j U j + γ nd and a schedule S is called Pareto-optimal (or efficient) if there does not exist another schedule S such that V (S ) ≤ V (S) and Z(S ) ≤ Z(S) with at least one of these inequalities being strict. The last problem will be denoted as 1|lin, CON|( n j =1 w j U j + γ nd, n j =1 v j u j ).…”

Section: Controllable Processing Timesmentioning

confidence: 99%

See 1 more Smart Citation

Scheduling with due date assignment under special conditions on job processing

Gordon¹,

Strusevich

Dolgui

2011

J Sched

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International audienceWe review the results on scheduling with due date assignment under such conditions on job processing as given precedence constraints, maintenance activity or various scenarios of processing time changing. The due date assignment and scheduling problems arise in production planning when the management is faced with setting realistic due dates for a number of jobs. Most research on scheduling with due date assignment is focused on optimal sequencing of independent jobs. However, it is often found in practice that some products are manufactured in a certain order implied, for example, by technological, marketing or assembly requirements and this can be modeled by imposing precedence constraints on the set of jobs. In classical deterministic scheduling models, the processing conditions, including job processing times, are usually viewed as given constants. In many real-life situations, however, the processing conditions may vary over time, thereby affecting actual durations of jobs. In the models with controllable processing times, the scheduler can speed up job execution times by allocating some additional resources to the jobs. In the models with deterioration or learning, the actual processing time can depend either on the position or on the start time of a job in the schedule. In scheduling with deterioration, the later a job starts, the longer it takes to process, while in scheduling with learning, the actual processing time of a job gets shorter, provided that the job is scheduled later. We consider also scheduling models with optional maintenance activity. In manufacturing processing, production scheduling with preventive maintenance planning is one of the most significant methods in preventing the machinery from failure or wear

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Section: Controllable Processing Timesmentioning

confidence: 99%

Section: Controllable Processing Timesmentioning

confidence: 99%

Scheduling with due date assignment under special conditions on job processing

Gordon¹,

Strusevich

Dolgui

2011

J Sched

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show abstract

“…Future research may extend the analysis to other due date assignment models and/or to problems with both due date assignment and controllable processing times. Recent publications on the unconstrained version of these problems include Leyvand et al (2010) and Shabtay and Steiner (2008).…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Minimizing the weighted number of tardy jobs with due date assignment and capacity-constrained deliveries

Steiner

Zhang

2011

Ann Oper Res

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We study a supply chain scheduling problem, where a common due date is assigned to all jobs and the number of jobs in delivery batches is constrained by the batch size. Our goal is to minimize the sum of the weighted number of tardy jobs, the due-dateassignment costs and the batch-delivery costs. We show that some well-known N P-hard problems reduce to our problem. Then we propose a pseudo-polynomial algorithm for the problem, establishing that it is N P-hard only in the ordinary sense. Finally, we convert the algorithm into an efficient fully polynomial time approximation scheme.

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“…As stated in Section 1, we solve one problem left open and improve the time complexity of the algorithm developed for another problem investigated in Cheng, Janiak, et al (1998). Leyvand, Shabtay, and Steiner (2010) investigate the bicriterion scheduling with common due date assignment, where the two criteria studied are the weighted number of tardy jobs plus the due date assignment cost, and the resource consumption cost. The authors show that the problem to minimize an integrated criterion is polynomially solvable, while the other three variants of the problem are

NP

‐hard, and develop an FPTAS for the three

NP

‐hard versions of the problem with the discrete type of resource.…”

Section: Literature Reviewmentioning

confidence: 99%

Two‐agent scheduling with linear resource‐dependent processing times

Wang

Qiu

et al. 2020

Naval Research Logistics

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This paper considers a two‐agent scheduling problem with linear resource‐dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is NP‐hard in the ordinary sense, and there does not exist a polynomial‐time approximation algorithm with performance ratio unless P=NP; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.

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Optimal delivery time quotation to minimize total tardiness penalties with controllable processing times

Cited by 17 publications

References 23 publications

Scheduling with due date assignment under special conditions on job processing

Scheduling with due date assignment under special conditions on job processing

Minimizing the weighted number of tardy jobs with due date assignment and capacity-constrained deliveries

Two‐agent scheduling with linear resource‐dependent processing times

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