The Operational Data Analytics (ODA) for Service Speed Design

Published Online:https://doi.org/10.1287/mnsc.2023.00655

References

  • Akan M , Alagoz O , Ata B , Erenay FS , Said A (2012) A broader view of designing the liver allocation system. Oper. Res. 60(4):757–770.LinkGoogle Scholar
  • Aksin Z , Armony M , Mehrotra V (2007) The modern call center: A multi-disciplinary perspective on operations management research. Production Oper. Management 16(6):665–688.CrossrefGoogle Scholar
  • Allon G , Federgruen A (2007) Competition in service industries. Oper. Res. 55(1):37–55.LinkGoogle Scholar
  • Anand KS , Paç MF , Veeraraghavan S (2011) Quality–speed conundrum: Trade-offs in customer-intensive services. Management Sci. 57(1):40–56.LinkGoogle Scholar
  • Andrew LL , Wierman A , Tang A (2009) Optimal speed scaling under arbitrary power functions. Performance Evaluation Rev. 37(2):39–41.CrossrefGoogle Scholar
  • Armony M , Roels G , Song H (2021) Pooling queues with strategic servers: The effects of customer ownership. Oper. Res. 69(1):13–29.LinkGoogle Scholar
  • Ata B , Shneorson S (2006) Dynamic control of an M / M / 1 service system with adjustable arrival and service rates. Management Sci. 52(11):1778–1791.LinkGoogle Scholar
  • Bandi C , Bertsimas D , Youssef N (2015) Robust queueing theory. Oper. Res. 63(3):676–700.LinkGoogle Scholar
  • Baron O , Berman O , Krass D , Wang J (2017) Strategic idleness and dynamic scheduling in an open-shop service network: Case study and analysis. Manufacturing Service Oper. Management 19(1):52–71.LinkGoogle Scholar
  • Bassamboo A , Zeevi A (2009) On a data-driven method for staffing large call centers. Oper. Res. 57(3):714–726.LinkGoogle Scholar
  • Berger JO (2013) Statistical Decision Theory and Bayesian Analysis (Springer Science & Business Media, New York).Google Scholar
  • Bertsimas D , Gupta V , Kallus N (2017) Data-driven robust optimization. Math. Program. 167(2):235–292.CrossrefGoogle Scholar
  • Bimpikis K , Markakis MG (2019) Learning and hierarchies in service systems. Management Sci. 65(3):1268–1285.LinkGoogle Scholar
  • Borst S , Mandelbaum A , Reiman MI (2004) Dimensioning large call centers. Oper. Res. 52(1):17–34.LinkGoogle Scholar
  • Bren A , Saghafian S (2019) Data-driven percentile optimization for multiclass queueing systems with model ambiguity: Theory and application. INFORMS J. Optim. 1(4):267–287.LinkGoogle Scholar
  • Burnetas A (2022) Learning and data-driven optimization in queues with strategic customers. Queueing Systems 100:517–519.CrossrefGoogle Scholar
  • Buzacott JA , Shanthikumar JG (1993) Stochastic Models of Manufacturing Systems (Prentice Hall, Hoboken, NJ).Google Scholar
  • Byers RE , So KC (2007) Note-a mathematical model for evaluating cross-sales policies in telephone service centers. Manufacturing Service Oper. Management 9(1):1–8.LinkGoogle Scholar
  • Camargo M , Dumas M , González-Rojas O (2020) Automated discovery of business process simulation models from event logs. Decision Support Systems 134:113284.CrossrefGoogle Scholar
  • Chao X , Gong X , Shi C , Yang C , Zhang H , Zhou SX (2018) Approximation algorithms for capacitated perishable inventory systems with positive lead times. Management Sci. 64(11):5038–5061.LinkGoogle Scholar
  • Chen H , Frank M (2004) Monopoly pricing when customers queue. IIE Trans. 36(6):569–581.CrossrefGoogle Scholar
  • Chen N , Gurlek R , Lee DKK , Shen H (2023) Can customer arrival rates be modelled by sine waves? Service Sci., ePub ahead of print July 13, https://doi.org/10.1287/serv.2022.0045.Google Scholar
  • Chu L , Feng Q , Shanthikumar JG , Shen Z-JM , Wu J (2024) Solving the price-setting newsvendor problem with parametric operational data analytics (ODA). Management Sci. Forthcoming.LinkGoogle Scholar
  • Cruz FR , Smith JM , Medeiros RO (2005) An M / G / C / C state-dependent network simulation model. Comput. Oper. Res. 32(4):919–941.CrossrefGoogle Scholar
  • Dai JG , Shi P (2017) A two-time-scale approach to time-varying queues in hospital inpatient flow management. Oper. Res. 65(2):514–536.LinkGoogle Scholar
  • de Véricourt F , Perakis G (2020) Frontiers in service science: The management of data analytics services: New challenges and future directions. Service Sci. 12(4):121–129.LinkGoogle Scholar
  • Debo LG , Toktay LB , Van Wassenhove LN (2008) Queuing for expert services. Management Sci. 54(8):1497–1512.LinkGoogle Scholar
  • Diaconis P , Freedman D (1986) On the consistency of Bayes estimates. Ann. Statist. 14(1):1–26.CrossrefGoogle Scholar
  • Elmachtoub AN , Grigas P (2022) Smart “predict, then optimize.” Management Sci. 68(1):9–26.LinkGoogle Scholar
  • Elmachtoub AN , Lam H , Zhang H , Zhao Y (2023) Estimate-then-optimize versus integrated-estimation-optimization versus sample average approximation: A stochastic dominance perspective. Working paper, Columbia University, New York.Google Scholar
  • Feng Q , Shanthikumar JG (2022) Developing operations management data analytics. Production Oper. Management 31(12):4544–4557.CrossrefGoogle Scholar
  • Feng Q , Shanthikumar JG , Xue M (2022) Consumer choice models and estimation: A review and extension. Production Oper. Management 31(2):847–867.CrossrefGoogle Scholar
  • Geng X , Huh WT , Nagarajan M (2015) Fairness among servers when capacity decisions are endogenous. Production Oper. Management 24(6):961–974.CrossrefGoogle Scholar
  • Gilbert SM , Weng ZK (1998) Incentive effects favor nonconsolidating queues in a service system: The principal–agent perspective. Management Sci. 44(12-part-1):1662–1669.LinkGoogle Scholar
  • Gopalakrishnan R , Doroudi S , Ward AR , Wierman A (2016) Routing and staffing when servers are strategic. Oper. Res. 64(4):1033–1050.LinkGoogle Scholar
  • Guo P , Tang CS , Wang Y , Zhao M (2019) The impact of reimbursement policy on social welfare, revisit rate, and waiting time in a public healthcare system: Fee-for-service vs. bundled payment. Manufacturing Service Oper. Management 21(1):154–170.LinkGoogle Scholar
  • Gurvich I , Whitt W (2009) Queue-and-idleness-ratio controls in many-server service systems. Math. Oper. Res. 34(2):363–396.LinkGoogle Scholar
  • Hall J , Porteus E (2000) Customer service competition in capacitated systems. Manufacturing Service Oper. Management 2(2):144–165.LinkGoogle Scholar
  • Hassin R , Haviv M (2003) To Queue or Not To Queue: Equilibrium Behavior in Queueing Systems , vol. 59 (Springer Science & Business Media, New York).CrossrefGoogle Scholar
  • Hsu W-K , Xu J , Lin X , Bell MR (2022) Integrated online learning and adaptive control in queueing systems with uncertain payoffs. Oper. Res. 70(2):1166–1181.LinkGoogle Scholar
  • Hu Y , Kallus N , Mao X (2022) Fast rates for contextual linear optimization. Management Sci. 68(6):3975–4753.Google Scholar
  • Jiang Y , Mehrizi HA , Van Mieghem JA (2023) Geographic virtual pooling of hospital resources: Data-driven trade-off between waiting and traveling. Manufacturing Service Oper. Management 25(4):1527–1544.Google Scholar
  • Jónasson JO , Deo S , Gallien J (2017) Improving HIV early infant diagnosis supply chains in sub-Saharan Africa: Models and application to Mozambique. Oper. Res. 65(6):1479–1493.LinkGoogle Scholar
  • Kalai E , Kamien MI , Rubinovitch M (1992) Optimal service speeds in a competitive environment. Management Sci. 38(8):1154–1163.LinkGoogle Scholar
  • Kim S-H , Whitt W , Cha WC (2018) A data-driven model of an appointment-generated arrival process at an outpatient clinic. INFORMS J. Comput. 30(1):181–199.LinkGoogle Scholar
  • Kostami V , Rajagopalan S (2014) Speed–quality trade-offs in a dynamic model. Manufacturing Service Oper. Management 16(1):104–118.LinkGoogle Scholar
  • Krishnasamy S , Sen R , Johari R , Shakkottai S (2016) Regret of queueing bandits. Advances Neural Inform. Processing Systems , vol. 29 (Curran Associates, Inc., Red Hook, NY).Google Scholar
  • Kumar S , Randhawa RS (2010) Exploiting market size in service systems. Manufacturing Service Oper. Management 12(3):511–526.LinkGoogle Scholar
  • Lawton G (2023) 7 ways to collect customer data that keep you compliant. Accessed May 30, 2024, https://www.techtarget.com/searchcustomerexperience/tip/Ways-to-collect-customer-data-that-keep-you-compliant.Google Scholar
  • Lederer PJ , Li L (1997) Pricing, production, scheduling, and delivery-time competition. Oper. Res. 45(3):407–420.LinkGoogle Scholar
  • Li X , Guo P , Lian Z (2016) Quality-speed competition in customer-intensive services with boundedly rational customers. Production Oper. Management 25(11):1885–1901.CrossrefGoogle Scholar
  • Li L , Jiang L , Liu L (2012) Service and price competition when customers are naive. Production Oper. Management 21(4):747–760.CrossrefGoogle Scholar
  • Lin C-A , Shang K , Sun P (2023) Wait time–based pricing for queues with customer-chosen service times. Management Sci. 69(4):2127–2146.LinkGoogle Scholar
  • Liu N , van Jaarsveld W , Wang S , Xiao G (2023) Managing outpatient service with strategic walk-ins. Management Sci. 69(10):5904–5922.LinkGoogle Scholar
  • Lu LX , Van Mieghem JA , Savaskan RC (2009) Incentives for quality through endogenous routing. Manufacturing Service Oper. Management 11(2):254–273.LinkGoogle Scholar
  • Mandelbaum A , Momčilović P , Trichakis N , Kadish S , Leib R , Bunnell CA (2020) Data-driven appointment-scheduling under uncertainty: The case of an infusion unit in a cancer center. Management Sci. 66(1):243–270.LinkGoogle Scholar
  • Mehrbod N , Grilo A , Zutshi A (2018) Caller-agent pairing in call centers using machine learning techniques with imbalanced data. 2018 IEEE Internat. Conf. Engrg. Tech. Innovation (ICE/ITMC) (IEEE, Piscataway, NJ), 1–6.Google Scholar
  • Mehrotra V , Ozlük O , Saltzman R (2010) Intelligent procedures for intra-day updating of call center agent schedules. Production Oper. Management 19(3):353–367.CrossrefGoogle Scholar
  • Mesabbah M , Abo-Hamad W , McKeever S (2019) A hybrid process mining framework for automated simulation modelling for healthcare. 2019 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 1094–1102.Google Scholar
  • Miranda F (2021) How to forecast sales for new product launches. Accessed February 5, 2023, https://www.flieber.com/blog/sales-forecasts-for-new-product-launches.Google Scholar
  • Moallemi CC , Kumar S , Van Roy B (2008) Approximate and data-driven dynamic programming for queueing networks. Working paper, Stanford University, Stanford, CA.Google Scholar
  • Plambeck EL , Zenios SA (2003) Incentive efficient control of a make-to-stock production system. Oper. Res. 51(3):371–386.LinkGoogle Scholar
  • Rozinat A , Wynn MT , van der Aalst WM , ter Hofstede AH , Fidge CJ (2009) Workflow simulation for operational decision support. Data Knowledge Engrg. 68(9):834–850.CrossrefGoogle Scholar
  • Senderovich A , Rogge-Solti A , Gal A , Mendling J , Mandelbaum A , Kadish S , Bunnell CA (2015) Data-driven performance analysis of scheduled processes. Business Process Management (Springer, Cham, Switzerland), 35–52.Google Scholar
  • Shah V , Gulikers L , Massoulié L , Vojnović M (2020) Adaptive matching for expert systems with uncertain task types. Oper. Res. 68(5):1403–1424.LinkGoogle Scholar
  • Shanthikumar JG , Ding S , Zhang MT (2007) Queueing theory for semiconductor manufacturing systems: A survey and open problems. IEEE Trans. Automation Sci. Engrg. 4(4):513–522.CrossrefGoogle Scholar
  • Shi P , Helm JE , Deglise-Hawkinson J , Pan J (2021a) Timing it right: Balancing inpatient congestion vs. readmission risk at discharge. Oper. Res. 69(6):1842–1865.LinkGoogle Scholar
  • Shi P , Helm JE , Heese HS , Mitchell AM (2021b) An operational framework for the adoption and integration of new diagnostic tests. Production Oper. Management 30(2):330–354.CrossrefGoogle Scholar
  • Smith JM (2008) M / G / c / K performance models in manufacturing and service systems. Asia Pac. J. Oper. Res. 25(04):531–561.CrossrefGoogle Scholar
  • So KC (2000) Price and time competition for service delivery. Manufacturing Service Oper. Management 2(4):392–409.LinkGoogle Scholar
  • Stein CM (1981) Estimation of the mean of a multivariate normal distribution. Ann. Statist. 9:1135–1151.CrossrefGoogle Scholar
  • Su X , Zenios SA (2006) Recipient choice can address the efficiency-equity trade-off in kidney transplantation: A mechanism design model. Management Sci. 52(11):1647–1660.LinkGoogle Scholar
  • Taylor JW (2012) Density forecasting of intraday call center arrivals using models based on exponential smoothing. Management Sci. 58(3):534–549.LinkGoogle Scholar
  • Tijms H (1992) Heuristics for finite-buffer queues. Probab. Engrg. Inform. Sci. 6(3):277–285.CrossrefGoogle Scholar
  • van der Mei R , Bhulai S (2022) A data-driven approach to deriving closed-form approximations for queueing problems using genetic algorithms. Queueing Systems 100:1–3.CrossrefGoogle Scholar
  • Wang Z , Yang L , Cui S , Ülkü S , Zhou Y-P (2022) Pooling agents for customer-intensive services. Oper. Res. 71(3):860–875.LinkGoogle Scholar
  • Whitt W (2004) A diffusion approximation for the G / G I / n / m queue. Oper. Res. 52(6):922–941.LinkGoogle Scholar
  • Whitt W , You W (2019) Time-varying robust queueing. Oper. Res. 67(6):1766–1782.LinkGoogle Scholar
  • Xu K , Chan CW (2016) Using future information to reduce waiting times in the emergency department via diversion. Manufacturing Service Oper. Management 18(3):314–331.LinkGoogle Scholar
  • Yang L , De Vericourt F , Sun P (2014) Time-based competition with benchmark effects. Manufacturing Service Oper. Management 16(1):119–132.LinkGoogle Scholar
  • Yom-Tov GB , Mandelbaum A (2014) Erlang-R: A time-varying queue with reentrant customers, in support of healthcare staffing. Manufacturing Service Oper. Management 16(2):283–299.LinkGoogle Scholar
  • Zhou W , Chao X , Gong X (2014) Optimal uniform pricing strategy of a service firm when facing two classes of customers. Production Oper. Management 23(4):676–688.CrossrefGoogle Scholar
  • Zhu S , Wang H , Xie Y (2022) Data-driven optimization for Atlanta police-zone design. INFORMS J. Appl. Analytics 52(5):412–432.LinkGoogle Scholar
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