Single-machine rescheduling problems with learning effect under disruptions

Cheng, Mingming; Xiao, Shuxian; Liu, Guosheng

doi:10.3934/jimo.2017085

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Example 1. Let the demands for the first 10 periods be (8,6,9,29,5,6,5,15,5,6). Let the minimum order quantity S be 30.…”

Section: Property 4 An Optimal Solution Exists Such That: (mentioning

confidence: 99%

“…Another possible direction for future research on the subject is to incorporate multi-item, even though the analytical complexity will obviously increase. Fourth, learning in setup is an important phenomenon in practice [9], the inclusion of learning in setup can also provide opportunities for further analysis.…”

Section: Property 4 An Optimal Solution Exists Such That: (mentioning

confidence: 99%

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A dynamic lot sizing model with production-or-outsourcing decision under minimum production quantities

Tang¹,

Jing²,

Chao³

2020

Journal of Industrial &Amp; Management Optimization

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In the real-world production process, the firms need to determine the optimal production planning under minimum production quantity constraint in order to achieve economies of scale. However, the inventory cost will hugely increase when there is a very large amount of production in a period and also a large amount of total demands for the next few periods. This paper considers a single-item dynamic lot sizing problem with production-or-outsourcing decisions. In each period, the production level cannot be lower than a given quantity in order to make full use of resources, but the outsourcing is unrestricted. The demands in a period can be backlogged. The production and outsourcing costs are fixed-plus-linear, and the inventory and backlogging costs are linear. We establish a mathematical programming model according to the real problem in the firm. We explore some structural properties of the optimal solution and use them to develop a dynamic programming algorithm to solve the proposed problem. We further present a special case with stationary production and outsourcing costs which can be solved with reduced computational complexities. In the end, we use three numerical instances to show how to obtain the optimal solutions by using the dynamic programming algorithm. Furthermore, we show that the policy of backlogging or outsourcing can reduce the total cost.

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“…Example 1. Let the demands for the first 10 periods be (8,6,9,29,5,6,5,15,5,6). Let the minimum order quantity S be 30.…”

Section: Property 4 An Optimal Solution Exists Such That: (mentioning

confidence: 99%

Section: Property 4 An Optimal Solution Exists Such That: (mentioning

confidence: 99%

A dynamic lot sizing model with production-or-outsourcing decision under minimum production quantities

Tang¹,

Jing²,

Chao³

2020

Journal of Industrial &Amp; Management Optimization

View full text Add to dashboard Cite

show abstract

“…Jiang [9] studied a single machine scheduling problem with learning effect and the total processing time constraint, and the analysis of multiple targets proved that it is NP-hard. Cheng [7] studied the rescheduling problems with learning effect under disruption constraints to minimize multiple classical targets. For targets under single interference constraint and interference cost constraint, a polynomial time algorithm and a pseudo-polynomial time algorithm were proposed respectively.…”

Section: Introductionmentioning

confidence: 99%

A best possible algorithm for an online scheduling problem with position-based learning effect

Zhang

2022

JIMO

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<p style='text-indent:20px;'>In this paper, we focus on an online scheduling problem with position-based learning effect on a single machine, where the jobs are released online over time and preemption is not allowed. The information about each job <inline-formula><tex-math id="M1">\begin{document}$ J_j $\end{document}</tex-math></inline-formula>, including the basic processing time <inline-formula><tex-math id="M2">\begin{document}$ p_j $\end{document}</tex-math></inline-formula> and the release time <inline-formula><tex-math id="M3">\begin{document}$ r_j $\end{document}</tex-math></inline-formula>, is only available when it arrives. The actual processing time <inline-formula><tex-math id="M4">\begin{document}$ p_j' $\end{document}</tex-math></inline-formula> of each job <inline-formula><tex-math id="M5">\begin{document}$ J_j $\end{document}</tex-math></inline-formula> is defined as a function related to its position <inline-formula><tex-math id="M6">\begin{document}$ r $\end{document}</tex-math></inline-formula>, i.e., <inline-formula><tex-math id="M7">\begin{document}$ p_j' = p_j(\alpha-r\beta) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M8">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M9">\begin{document}$ \beta $\end{document}</tex-math></inline-formula> are both nonnegative learning index. Our goal is to minimize the sum of completion time of all jobs. For this problem, we design a deterministic polynomial time online algorithm <i>Delayed Shortest Basic Processing Time</i> (DSBPT). In order to facilitate the understanding of the online algorithm, we present a relatively common and simple example to describe the execution process of the algorithm, and then by competitive analysis, we show that online algorithm DSBPT is a best possible online algorithm with a competitive ratio of 2.</p>

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“…[9] proposed an inventory model to determine optimal lot-sizing and pricing strategies with trade credit and learning effect. Apart from those mentioned above, [19], [27], [26], [6], [21], etc. also devoted to the research of learning effect.…”

mentioning

confidence: 99%

Incentive contract design for supplier switching with considering learning effect

Qian¹,

Zhang²,

Sun³

2021

JIMO

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For minimizing purchase cost, a buying firm would switch to suppliers with providing more favorable prices. This paper investigates the optimal switching decision of a buyer that may switch to an entrant supplier with production learning ability (which is regarded as a private information) under a principal-agent framework. The results obtained show that the switching cost and the learning effect have significant impacts on the buyer's switching decision. Only when the fixed component of the switching cost is relatively low, the buyer can be better off from a partial switching strategy; otherwise, the buyer should take an all-or-nothing switching strategy or no switching strategy. As the learning ability of the entrant supplier increases, the buyer prefers to make more switching. Finally, a benefit-sharing contract is proposed to evaluate the performance of the principal-agent contract, and we demonstrate that the principal-agent contract almost completely dominates the benefit-sharing contract.

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Single-machine rescheduling problems with learning effect under disruptions

Cited by 6 publications

References 28 publications

A dynamic lot sizing model with production-or-outsourcing decision under minimum production quantities

A dynamic lot sizing model with production-or-outsourcing decision under minimum production quantities

A best possible algorithm for an online scheduling problem with position-based learning effect

Incentive contract design for supplier switching with considering learning effect

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