Achieving High Individual Service Levels Without Safety Stock? Optimal Rationing Policy of Pooled Resources

Jiashuo, Jiang,; Wang, Shixin; Zhang, Jiawei

doi:10.1287/opre.2022.2386

Cited by 14 publications

(14 citation statements)

References 37 publications

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“…The main difference between our paper and Jiang et al. (2022) is in the characterization of uncertainty and risk. While Jiang et al.…”

Section: Literature Reviewmentioning

confidence: 68%

“…There are many studies of the resource pooling problem with Type I service-level constraints (Alptekinoglu et al, 2013;Swaminathan & Srinivasan, 1999) or Type II servicelevel constraints (Lyu et al, 2019;Zhong et al, 2018). Recently, Jiang et al (2022) considered a framework for a very general resource pooling problem with service-level constraints. The main difference between our paper and Jiang et al ( 2022) is in the characterization of uncertainty and risk.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The fulfillment policy defines how many demands are satisfied and how resources are allocated and consumed to fulfill the demand. We denote s j as the satisfied demand of customer j and s = (s 1 , … , s J ) ∈ (x, d), where (x, d) is the feasible set of fulfilled demand for given x and d. We follow the unified formulation given by Jiang et al (2022) and define…”

Section: Determine the Fulfillment Policymentioning

confidence: 99%

“…While Jiang et al (2022) use the conventional service measure functions and assume that the probability distribution of uncertain demand is given exactly, we use the RS framework and assume that the knowledge of the probability distribution is limited. Zhong et al (2018) studied a resource pooling system with heterogeneous service-level requirements of different customers.…”

Section: Production and Operations Managementmentioning

confidence: 99%

“…Recently, resource pooling problems focusing on improving service levels have received considerable attention from both academics and practitioners (Jiang et al., 2022; Zhong et al., 2018). Different types of service‐level constraints have been proposed in the operations management literature.…”

Section: Introductionmentioning

confidence: 99%

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Target‐based resource pooling problem

Cui

Ding

Xie

et al. 2023

Production and Operations Management

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We study a two-stage resource pooling problem with multiple resources and customers. The central decision-maker decides the capacity level of the resources within a total budget before the realization of uncertain demand. Then, the fulfillment policy is determined by individual service-level requirements. We use a robust satisficing framework to formulate the problem and allow ambiguity in the distribution of demand. Moreover, we introduce a new utility-based probability distance allowing the model to be solved exactly using a column and constraint generation algorithm. We also provide a more efficient solution method for some special cases. Finally, we conduct experiments for a process flexibility problem, which is an example of the resource pooling problem. We show the advantage of our model over some benchmark approaches, and show the impact of the flexibility structure and correlation between demands.

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“…The main difference between our paper and Jiang et al. (2022) is in the characterization of uncertainty and risk. While Jiang et al.…”

Section: Literature Reviewmentioning

confidence: 68%

Section: Literature Reviewmentioning

confidence: 99%

Section: Determine the Fulfillment Policymentioning

confidence: 99%

Section: Production and Operations Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Target‐based resource pooling problem

Cui

Ding

Xie

et al. 2023

Production and Operations Management

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Benefits of Collaboration on Capacity Investment and Allocation

Ahn,

Çetinkaya,

Duenyas

et al. 2024

Production and Operations Management

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This paper studies how capacity collaboration can benefit two competing firms. We consider a two-stage model where capacity decisions are made in the first stage when there are significant uncertainties about market conditions, and then production decisions are made in the second stage after most of these uncertainties are resolved. We vary the degree of collaboration between the two firms in their capacity and production decisions, examining multiple models and comparing the outcomes. We find that a firm can benefit from collaboration even with its competitors. Interestingly, the firms do not have to make production decisions jointly to realize the benefits of collaboration. Additionally, while collaborative capacity investment proves beneficial, collaborating on production with existing capacity can often yield greater benefits. We find that the advantages of collaboration are most pronounced when competition intensifies, demand fluctuates significantly, and investment costs are high.

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Online Advertisement Allocation Under Customer Choices and Algorithmic Fairness

Li,

Rong,

Zhang

et al. 2024

Management Science

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Advertising is a crucial revenue source for e-commerce platforms and a vital online marketing tool for their sellers. In this paper, we explore dynamic ad allocation with limited slots upon each customer’s arrival for an e-commerce platform, where customers follow a choice model when clicking the ads. Motivated by the recent advocacy for the algorithmic fairness of online ad delivery, we adjust the value from advertising by a general fairness metric evaluated with the click-throughs of different ads and customer types. The original online ad-allocation problem is intractable, so we propose a novel stochastic program framework (called two-stage target-debt) that first decides the click-through targets and then devises an ad-allocation policy to satisfy these targets in the second stage. We show the asymptotic equivalence between the original problem, the relaxed click-through target optimization, and the fluid-approximation ( Fluid ) convex program. We also design a debt-weighted offer-set algorithm and demonstrate that, as long as the problem size scales to infinity, this algorithm is (asymptotically) optimal under the optimal first-stage click-through target. Compared with the Fluid heuristic and its resolving variants, our approach has better scalability and can deplete the ad budgets more smoothly throughout the horizon, which is highly desirable for the online advertising business in practice. Finally, our proposed model and algorithm help substantially improve the fairness of ad allocation for an online e-commerce platform without significantly compromising efficiency. This paper was accepted by Jeannette Song, operations management. Funding: Y. Rong is supported by the National Natural Science Foundation of China [Grants 72025201, 72331006, and 72221001]. R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grants 14502722 and 14504123] and the National Natural Science Foundation of China [Grants 72293560 and 72293565]. H. Zheng is supported by the National Natural Science Foundation of China [Grants 72231003, 72325003, and 72221001]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04091 .

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Achieving High Individual Service Levels Without Safety Stock? Optimal Rationing Policy of Pooled Resources

Cited by 14 publications

References 37 publications

Target‐based resource pooling problem

Target‐based resource pooling problem

Benefits of Collaboration on Capacity Investment and Allocation

Online Advertisement Allocation Under Customer Choices and Algorithmic Fairness

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