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Optimal Computing Budget Allocation for Urban Regeneration: An Unprecedented Match Between Economic/Extra-Economic Evaluations and Urban Planning

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14112))

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Abstract

The path to creating livable cities passes through the transformation of underexploited urban areas and the revivification of neglected bits of urban fabrics within the contemporary and relevant theme of urban regeneration. However, this process is characterized by an operational misalignment between the urban administrative scale and the small scale of single neighborhoods. Due to their limited budget, public administrations refrain from attempting to perform capillary punctual regeneration interventions, as it would require higher economic investments. For this reason, local-scale actions often arise as bottom-up initiatives. These are sometimes effective, but their target context is hardly chosen by considering all the available possibilities through a higher-scale analysis.

A solution to this issue can be obtained by selecting the most suitable actions to implement according to a criterion of effectiveness and impact: this offers the ground for original contamination of OCBA (Optimal Computing Budget Allocation) methods and tools by using them in the field of urban planning. These methodologies are most frequently used in business management to determine the best use of limited resources: transferring them to urban planning involves finding criteria and parameters to quantify the impact of urban actions and compare alternatives. This paper describes the early reflections and articulations of this research work through a literature review of OCBA methods and their parameters and a tentative outline of suitable criteria for urban planning.

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References

  1. Derudder, B.: Network analysis of ‘urban systems’: potential, challenges, and pitfalls. Royal Dutch Geographical Society KNAG, 1–17 (2019)

    Google Scholar 

  2. Ahmed, N.O., El-Halafawy, A.M., Amin, A.M.: A critical review of urban livability. Eur. J. Sustain. Dev. 8(1), 165 (2019)

    Article  Google Scholar 

  3. Truszkowska, A., et al.: Urban determinants of COVID-19 spread: a comparative study across three cities in New York State. J. Urban Health 99(5), 909–921 (2022). https://doi.org/10.1007/s11524-022-00623-9

    Article  Google Scholar 

  4. Davies, C., Sanesi, G.: COVID-19 and the importance of urban green spaces. Urban for Urban Green 74, 127654 (2022). https://doi.org/10.1016/j.ufug.2022.127654

    Article  Google Scholar 

  5. Korpilo, S., et al.: Coping with crisis: green space use in helsinki before and during the COVID-19 pandemic. Front. Sustain. Cities 3, 713977 (2021)

    Article  Google Scholar 

  6. Burnett, H., Olsen, J.R., Mitchell, R.: Green space visits and barriers to visiting during the COVID-19 pandemic: a three-wave nationally representative cross-sectional study of UK adults. Land 11(4), 503 (2022). https://doi.org/10.3390/land11040503

    Article  Google Scholar 

  7. Lee, K.O., Mai, K.M., Park, S.: Green space accessibility helps buffer declined mental health during the COVID-19 pandemic: evidence from big data in the United Kingdom. Nat. Ment. Health 1, 124–134 (2023)

    Article  Google Scholar 

  8. Tiboni, M., Botticini, F., Sousa, S., Silva, N.J.: A systematic review for urban regeneration effects analysis in urban cores. Sustainability 12(21), 9296 (2020)

    Article  Google Scholar 

  9. D’Acci, L.: Simulating future societies in isobenefit cities: social isobenefit scenarios. Futures 54, 3–18 (2013)

    Article  Google Scholar 

  10. Moreno, C., et al.: Introducing the “15-Minute City”: sustainability, resilience and place identity in future post-pandemic cities. Smart Cities 4(1), 93–111 (2021)

    Article  MathSciNet  Google Scholar 

  11. Paris, the 15-minute city. https://tomorrow.city/a/paris-the-15-minute-city. last accessed 2023/04/11

  12. Website of the Municipality of Rome, “Città dei 15 minuti”. https://www.comune.roma.it/web/it/dipartimento-decentramento-servizi-delegati-e-citta-in-15-minuti-citta-dei-15-min.page. last accessed 2023/04/11

  13. The Plan, “The 15-minute city: Milan focuses on its suburbs for a polycentric future”. https://www.theplan.it/eng/whats_on/the-15%E2%80%93minute-city-milan-focuses-on-its-suburbs-for-a-polycentric-future. last accessed 2023/04/11

  14. Wu, H., Wang, L., Zhang, Z., Gao, J.: Analysis and optimization of 15-minute community life circle based on supply and demand matching: a case study of Shanghai. PLoS ONE 16(8), e0256904 (2021). https://doi.org/10.1371/journal.pone.0256904

    Article  Google Scholar 

  15. Allam, Z., Moreno, C., Chabaud, D., Pratlong, F.: In: The Palgrave Handbook of Global Sustainability, Brinkmann, S. (ed.). Palgrave Macmillan (2020)

    Google Scholar 

  16. Khavarian-Garmsir, A.R., Sharifi, A., Sadeghi, A.: The 15-minute city: urban planning and design efforts toward creating sustainable neighborhoods. Cities 132, 104101 (2023)

    Article  Google Scholar 

  17. Papas, T., Basbas, S., Campisi, T.: Urban mobility evolution and the 15-minute city model: from holistic to bottom-up approach. Transportation Research Procedia 69, 544–551 (2023)

    Article  Google Scholar 

  18. Chen, H.C., Chen, C.H., Dai, L., Yucesan, E.: New development of optimal computing budget allocation for discrete event simulation. In: Proceedings of the 1997 Winter Simulation Conference, pp. 334–341. Piscataway, NJ (1997)

    Google Scholar 

  19. Zheng, L., et al.: Time-of-day pricing for toll roads under traffic demand uncertainties: a distributionally robust simulation-based optimization method. Transp. Res. Part C Emerg. Technol. 144, 103894 (2022)

    Article  Google Scholar 

  20. Gong, X., Wang, X., Zhou, L., Geng, N.: Managing hospital inpatient beds under clustered overflow configuration. Comput. Oper. Res. 148, 1060 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  21. Liu, D., Geng, N.: Stochastic health examination scheduling problem based on genetic algorithm and simulation optimization. In: Proceedings of the 7th International Conference on Industrial Engineering and Applications (ICIEA), pp. 620–624. Bangkok, Thailand (2020)

    Google Scholar 

  22. Jiang, Y., et al.: Optimization for joint relocation of carsharing based on modular simulation. J. Southwest Jiaotong Univ. 58(1), 74–82 (2023)

    Google Scholar 

  23. Tian, Y., Ye, B., Estupiñá, M.S., Wan, L.: Stochastic simulation optimization for route selection strategy based on flight delay cost. Asia-Pacific J. Oper. Res. 35(06), 1850045 (2018). https://doi.org/10.1142/S0217595918500458

    Article  MATH  Google Scholar 

  24. Chen, C.H., Lin, J., Yucesan, E., Chick, S.E.: Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dyn. Syst. Theor. Appl. 10, 251–270 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  25. Fu, M.C., Hu, J.Q., Chen, C.H., Xiong, X.: Simulation allocation for determining the best design in the presence of correlated sampling. INFORMS J. Comput. 19(1), 101–111 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  26. Glynn, P., Juneja, S.: A large deviations perspective on ordinal optimization. In: Proceedings of the 2004 Winter Simulation Conference, pp. 577–585. Piscataway, NJ (2004)

    Google Scholar 

  27. Fu, M.C., Healy, K.J.: Techniques for simulation optimization: an experimental study on an (s, S) inventory system. IIE Trans. 29(3), 191–199 (1997)

    Article  Google Scholar 

  28. Lee, L.H., Chew, E.P., Teng, S.Y., Goldsman, D.: Optimal computing budget allocation for multi-objective simulation models. In: Proceedings of 2004 Winter Simulation Conference, pp. 586–594. Piscataway, NJ (2004)

    Google Scholar 

  29. Chick, S.E., Wu, Y.: Selection procedures with frequentist expected opportunity cost bounds. Oper. Res. 53(5), 889 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  30. Trailovic, L., Pao, L.Y.: Variance estimation and ranking of target tracking position errors modeled using Gaussian mixture distributions. Automatica 41(8), 1433–1438 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  31. Lin, J.T., Chiu, C.-C., Chang, Y.-H.: Simulation-based optimization approach for simultaneous scheduling of vehicles and machines with processing time uncertainty in FMS. Flex. Serv. Manuf. J. 31(1), 104–141 (2019). https://doi.org/10.1007/s10696-017-9302-x

    Article  Google Scholar 

  32. Ammeri, A., et al.: A comprehensive literature review of mono-objective simulation optimization methods. Adv. Prod. Eng. Manage. 6(4), 291–302 (2011)

    Google Scholar 

  33. Chen, H.C.: Optimal computing budget allocation in selecting the best design via discrete event simulation. Dissertations available from ProQuest (1998)

    Google Scholar 

  34. Pappas, I., et al.: Multiparametric programming in process systems engineering: recent development and path forward. Front. Chem. Eng. 2, 620168 (2020)

    Google Scholar 

  35. Moore, J.T., Bard, J.F.: The mixed integer linear bilevel programming problem. Oper. Res. 38, 911–921 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  36. Wen, U.P., Huang, A.D.: A simple tabu search method to solve the mixed755 integer linear bilevel programming problem. Eur. J. Oper. Res. 88, 563–571 (1996)

    Article  MATH  Google Scholar 

  37. Faisca, N.P., Dua, V., Rustem, B., Saraiva, P.M., Pistikopoulos, E.N.: Parametric global optimisation for bilevel programming. J. Global Optim. 38, 609–623 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  38. Caramia, M., Mari, R.: A decomposition approach to solve a bilevel capacitated facility location problem with equity constraints. Optim. Lett. 10(5), 997–1019 (2016). https://doi.org/10.1007/s11590-015-0918-z

    Article  MathSciNet  MATH  Google Scholar 

  39. Vicente, L., Savard, G., Judice, J.: Discrete linear bilevel programming 750 problem. J. Optim. Theory Appl. 89, 597–614 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  40. Dempe, S., Kalashnikov, D.V., Rios-Mercado, R.: Discrete bilevel programming: application to a natural gas cash-out problem 166, 469–488 (2005)

    Google Scholar 

  41. Handoko, S., Chuin, L., Gupta, A., Soon, O., Kim, H., Siew, T.: Solving multi-vehicle profitable tour problem via knowledge adoption in evolutionary bi-level programming. In: Proceedings of the 2015 IEEE Congress on Evolutionary Computation, pp. 2713–2720 (2015)

    Google Scholar 

  42. Bemporad, A., Morari, M., Dua, V., Pistikopoulos, E.: The explicit linear quadratic regulator for constrained systems. In: Proceedings of the American Control Conference, pp. 872–876. Chicago, IL (2000)

    Google Scholar 

  43. Tondel, P., Johansen, T.A., Bemporad, A.: An algorithm for multi-parametric quadratic programming and explicit MPC solutions. In: Proceedings of the 40th IEEE Conference on Decision and Control, pp. 1199–1205. Orlando, USA (2001)

    Google Scholar 

  44. Kunze, A., et al.: A conceptual participatory design framework for urban planning. In: Proceedings of the 29th eCAADe Conference “Respecting Fragile Places”. Ljubljana, Slovenia (2011)

    Google Scholar 

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Authors and Affiliations

  1. University of Enna “Kore”, Enna Cittadella Universitaria, 94100, Enna, Italy

    Giovanna Acampa & Alessio Pino

Authors
  1. Giovanna Acampa
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  2. Alessio Pino
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Correspondence to Giovanna Acampa .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Acampa, G., Pino, A. (2023). Optimal Computing Budget Allocation for Urban Regeneration: An Unprecedented Match Between Economic/Extra-Economic Evaluations and Urban Planning. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14112. Springer, Cham. https://doi.org/10.1007/978-3-031-37129-5_6

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  • DOI: https://doi.org/10.1007/978-3-031-37129-5_6

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