Journals / İşletme Araştırmaları Dergisi / 2021 / Cilt: 13 - Sayı: 2

Evaluating Predictive Models in the Orienteering Problem with Stochastic Profits:A Simulation Study

Pages
1067–1081
Publication Date
2022-11-06
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Findings – The findings of this paper include experimental results showing a deficiency of about 6.5units of classical selection measures relative to a decision-based selection measure for the Tsiligiridesorienteering benchmark instances.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Purpose The aim of this paper is to test the effectiveness of statistical model selection measures interms of decision quality for the orienteering problem with stochastic profits using simulation.
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Key Findings & Data:Design/methodology/approach – This paper is based on a quantitative numerical approach wherevarious model selection measures are evaluated using computational experiments including model based computer-generated random data.
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Conclusion & Impact:Findings – The findings of this paper include experimental results showing a deficiency of about 6.5units of classical selection measures relative to a decision-based selection measure for the Tsiligiridesorienteering benchmark instances.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Purpose The aim of this paper is to test the effectiveness of statistical model selection measures interms of decision quality for the orienteering problem with stochastic profits using simulation. Design/methodology/approach – This paper is based on a quantitative numerical approach wherevarious model selection measures are evaluated using computational experiments including model based computer-generated random data. Findings – The findings of this paper include experimental results showing a deficiency of about 6.5units of classical selection measures relative to a decision-based selection measure for the Tsiligiridesorienteering benchmark instances. Discussion – While classical model selection measures are suitable for accuracy reasons, misspecifiedmodels sometimes do lead to better decision outcomes. From a practical perspective, in order to carryout prescriptive analytics for orienteering problems, having access to a reasonable decision algorithmat the prediction stage of data-analysis can be beneficial for downstream realized profit.

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Cite This Article

ÇAKIR, F. (2022). Evaluating Predictive Models in the Orienteering Problem with Stochastic Profits:A Simulation Study. İşletme Araştırmaları Dergisi, Cilt: 13 - Sayı: 2, 1067–1081. https://ajindex.com/articles/652145

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References (12)

  1. Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In Selected papers of hirotugu akaike, pages 199–213.
  2. Springer. Arlot, S., Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics surveys, 4:40– 79. Bastani, H. and Bayati, M. (2015). Online decision-making with high-dimensional covariates. Forthcoming in Operations Research. Besbes, O., Phillips, R., and Zeevi, A. (2010). Testing the validity of a demand model: An operations perspective. Manufacturing & Service Operations Management, 12(1):162–183.
  3. Campbell, A. M., Gendreau, M., and Thomas, B. W. (2011a). The orienteering problem with stochastic travel and service times. Annals of Operations Research, 186(1):61–81.
  4. Campbell, A. M., Gendreau, M., and Thomas, B. W. (2011b). The orienteering problem with stochastic travel and service times. Annals of Operations Research, 186(1):61–81.
  5. Chen, W., Liu, T.-Y., Lan, Y., Ma, Z.-M., and Li, H. (2009). Ranking measures and loss functions in learning to rank. Advances in Neural Information Processing Systems, 22:315–323.
  6. Claeskens, G., Hjort, N. L. (2008). Model selection and model averaging. Cambridge Books. den Boer, A. V. and Sierag, D. D. (2020). Decision-based model selection. European Journal of Operational Research. 00006. Evers, L., Glorie, K., van der Ster, S., Barros, A. I., and Monsuur, H. (2014). A two-stage approach to the orienteering problem with stochastic weights. Computers & Operations Research, 43:248–260.
  7. Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American statistical Association, 70(350):320–328.
  8. Golden, B. L., Levy, L., and Vohra, R. (1987). The orienteering problem. Naval Research Logistics (NRL), 34(3):307–318.
  9. Ilhan, T., Iravani, S. M. R., and Daskin, M. S. (2008). The orienteering problem with stochastic profits. IIE Transactions, 40(4):406–421. 00018. Liyanage, L. H. and Shanthikumar, J. G. (2005). A practical inventory control policy using operational statistics. Operations Research Letters, 33(4):341–348.
  10. Ramamurthy, V., George Shanthikumar, J., and Shen, Z.-J. M. (2012). Inventory policy with parametric demand: Operational statistics, linear correction, and regression. Production and Operations Management, 21(2):291–308.
  11. Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14(5):465–471.
  12. Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 6(2):461–464. Tsiligirides,