Prescriptive Analytics for Flexible Capacity Management

Notz, Pascal M.; Pibernik, Richard

doi:10.2139/ssrn.3387866

Cited by 4 publications

(17 citation statements)

References 35 publications

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“…framework based on a weighted adaption of SAA (wSAA); Notz and Pibernik (2021), who compare a kERM approach to wSAA; and Notz et al (2020), who apply wSAA and kERM to a queuing capacity planning problem and propose an additional approach they term Optimization Prediction (OP) approach. 43 A more detailed review of this stream of literature can be found in Notz and Pibernik (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first problem is a capacity planning problem of a mail logistics provider that plans the (staff) capacity for three service lines under demand uncertainty with an upgrading option after demand has been realized (Bassok et al 1999, Netessine et al 2002, Notz and Pibernik 2021. The second problem is the multi-shift staffing problem (MSSP) of a maintenance service provider in the aviation industry that plans daily staff capacities for two shifts while facing uncertain hourly demand arrivals (Notz et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this section we present the two-shift staffing problem originally studied by Notz et al (2020) and use the results of Theorem 4.1 to derive a subgradient for the STB approach.…”

Section: Capacity Planning Problemmentioning

confidence: 99%

“…Unfulfilled demand backlogged between the shifts incurs a cost c 3 per item, and leftover demand at the end of the second shift incurs an overtime cost of c 2 per item. See Notz et al (2020) for additional details and the derivation of the following problem statement: (4.25) where M t is the length of the queue at the end of time period t. Following Notz et al (2020), the corresponding linearized loss function can be defined as:…”

Section: Capacity Planning Problemmentioning

confidence: 99%

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Prescriptive Analytics for Data-Driven Capacity Management

Notz

2023

Lecture Notes in Operations Research

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First and foremost, I would like to thank my doctoral advisor, Prof. Dr. Richard Pibernik, for his excellent supervision. I am extremely grateful for his continuous support and guidance along the journey that led to this dissertation. His insightful suggestions and constructive criticism in numerous deep and fruitful discussions enriched my work and enabled me to develop early ideas and findings into solid results.I would also like to thank my second advisor, Prof. Dr. Christoph Flath, for the many insightful comments, useful hints, and challenging questions that not only opened up new perspectives but oftentimes led to a new avenue for further investigation that helped to improve my research.Furthermore, I would like to thank all colleagues at the Chair of Logistics and Quantitative Methods in Business Administration and the Chair of Information Systems and Business Analytics for the stimulating discussions and the valuable feedback during our OPIM seminars, and in particular Peter Wolf for the great collaboration on our research article. Thanks for all the chats at the coffee machine, for all the fun during our retreats and the legendary Christmas celebrations, and for making this journey such a great experience.Finally, I would like to express my gratitude to my family and especially to my wife, who always encouraged and supported me in a loving way. I am deeply grateful for her endless patience and loving care throughout this journey.vContents 4.4.3 Mail Sorting Capacity-A Two-Stage Capacity Planning Problem .

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…In this section we present the two-shift staffing problem originally studied by Notz et al (2020) and use the results of Theorem 4.1 to derive a subgradient for the STB approach.…”

Section: Capacity Planning Problemmentioning

confidence: 99%

Section: Capacity Planning Problemmentioning

confidence: 99%

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Prescriptive Analytics for Data-Driven Capacity Management

Notz

2023

Lecture Notes in Operations Research

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“…Ultimately, the goal is to produce a high quality prediction model that leads to a good decisions when implemented, such as a position that leads to a large return or a route that induces a small realized travel time. There has been a fair amount of recent work examining this paradigm and other closely related problems in data-driven decision making, such as the works of Bertsimas and Kallus [2020], Donti et al [2017], Elmachtoub and Grigas [2021], Kao et al [2009], Estes and Richard [2019], Ho and Hanasusanto [2019], Notz and Pibernik [2019], Kotary et al [2021], the references therein, and others.…”

Section: Introductionmentioning

confidence: 99%

Risk Bounds and Calibration for a Smart Predict-then-Optimize Method

Liu¹,

Grigas²

2021

Preprint

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The predict-then-optimize framework is fundamental in practical stochastic decision-making problems: first predict unknown parameters of an optimization model, then solve the problem using the predicted values. A natural loss function in this setting is defined by measuring the decision error induced by the predicted parameters, which was named the Smart Predict-then-Optimize (SPO) loss by Elmachtoub and Grigas [2021]. Since the SPO loss is typically nonconvex and possibly discontinuous, Elmachtoub and Grigas [2021] introduced a convex surrogate, called the SPO+ loss, that importantly accounts for the underlying structure of the optimization model. In this paper, we greatly expand upon the consistency results for the SPO+ loss provided by Elmachtoub and Grigas [2021]. We develop risk bounds and uniform calibration results for the SPO+ loss relative to the SPO loss, which provide a quantitative way to transfer the excess surrogate risk to excess true risk. By combining our risk bounds with generalization bounds, we show that the empirical minimizer of the SPO+ loss achieves low excess true risk with high probability. We first demonstrate these results in the case when the feasible region of the underlying optimization problem is a polyhedron, and then we show that the results can be strengthened substantially when the feasible region is a level set of a strongly convex function. We perform experiments to empirically demonstrate the strength of the SPO+ surrogate, as compared to standard 1 and squared 2 prediction error losses, on portfolio allocation and cost-sensitive multi-class classification problems.

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Explainable Subgradient Tree Boosting for Prescriptive Analytics in Operations Management

Notz

2020

SSRN Journal

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Prescriptive Analytics for Flexible Capacity Management

Cited by 4 publications

References 35 publications

Prescriptive Analytics for Data-Driven Capacity Management

Prescriptive Analytics for Data-Driven Capacity Management

Risk Bounds and Calibration for a Smart Predict-then-Optimize Method

Explainable Subgradient Tree Boosting for Prescriptive Analytics in Operations Management

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