A Study of Priority Evaluation and Resource Allocation for Revitalization of Cultural Heritages in the Urban Development
Authors: Wann-Ming Wey, Yi-Chih Huang
Abstract:
Proper maintenance and preservation of significant cultural heritages or historic buildings is necessary. It can not only enhance environmental benefits and a sense of community, but also preserve a city's history and people’s memory. It allows the next generation to be able to get a glimpse of our past, and achieve the goal of sustainable preserved cultural assets. However, the management of maintenance work has not been appropriate for many designated heritages or historic buildings so far. The planning and implementation of the reuse has yet to have a breakthrough specification. It leads the heritages to a mere formality of being “reserved”, instead of the real meaning of “conservation”. For the restoration and preservation of cultural heritages study issues, it is very important due to the consideration of historical significance, symbolism, and economic benefits effects. However, the decision makers such as the officials from public sector they often encounter which heritage should be prioritized to be restored first under the available limited budgets. Only very few techniques are available today to determine the appropriately restoration priorities for the diverse historical heritages, perhaps because of a lack of systematized decision-making aids been proposed before. In the past, the discussions of management and maintenance towards cultural assets were limited to the selection of reuse alternatives instead of the allocation of resources. In view of this, this research will adopt some integrated research methods to solve the existing problems that decision-makers might encounter when allocating resources in the management and maintenance of heritages and historic buildings.
The purpose of this study is to develop a sustainable decision making model for local governments to resolve these problems. We propose an alternative decision support model to prioritize restoration needs within the limited budgets. The model is constructed based on fuzzy Delphi, fuzzy analysis network process (FANP) and goal programming (GP) methods. In order to avoid misallocate resources; this research proposes a precise procedure that can take multi-stakeholders views, limited costs and resources into consideration. Also, the combination of many factors and goals has been taken into account to find the highest priority and feasible solution results. To illustrate the approach we propose in this research, seven cultural heritages in Taipei city as one example has been used as an empirical study, and the results are in depth analyzed to explain the application of our proposed approach.
Keywords: Cultural Heritage, Historic Buildings, Priority Evaluation, Multi-Criteria Decision Making, Goal Programming, Fuzzy Analytic Network Process, Resource Allocation.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1088370
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2318References:
[1] Mishra, S., Deshmukh, S. G., & Vrat, P., 2002. Matching of technological forecasting technique to a technology. Technological Forecasting and Social Change, 69, 1–27.
[2] Torkkeli, M., & Tuominen, M., 2002. The contribution of technology selection to core competencies. International Journal of Production Economics, 77, 271–284.
[3] Reza K, Hossein A, Yvon G, 1988. An integrated approach to project evaluation and selection. IEEE Transactions on Engineering Management 35(4) 265-270.
[4] Lee S, 1972. Linear programming for decision analysis (Auerbach Publishers, Philadelphia, PA).
[5] Joyce E, 1988. Reusable software: passage to productive? Datamation 15 September, 97-102.
[6] Saaty T, 1996. The analytic network process (RWS Publications, Expert Choice, Inc.).
[7] Dantzig G, 1958. On integer and partial linear programming problems. The RAND Corporation, June, Paper P-1410.
[8] Iwasaki, S., & Tone, K., 1998. A search model with subjective judgments: Auditing of incorrect tax declarations. Omega-International Journal of Management Science, 26(2), 249–261.
[9] Kameya, N., Miyagi, H., Taira, N., & Yamashita, K., 2002. Multiplecriteria decision-making using sectional supermatrix. Paper presented at the Proceedings of the international technical conference on circuits/systems, computers and communications, Phuket, Thailand, pp. 838–840.
[10] Meade, L., 1998. Strategic analysis of logistics and supply chain management systems using the analytical network process. Transportation Research Part E-Logistics and Transportation Review, 34(3), 201–215.
[11] Raisinghani, M., 2001. A balanced analytic approach to strategic electronic commerce decisions: A framework of the evaluation method. In W. Van Grembergen (Ed.), Information technology evaluation methods and management (pp. 185–197). Hershey (PA): Idea Group Publishing.
[12] Kahalekai, L., & Phillips, L., 2002. Using analytic network process (ANP) methodology for the analysis, evaluation, and recommendation of courses of action (COA) based on economic, political, sociological, cultural, and psychological factors critical to operations other than war (OOTW). Paper presented at the Huntsville Simulation Conference, Huntsville, Alabama.
[13] Agarwal, A., & Shankar, R., 2002. Analyzing alternatives for improvement in supply chain performance. Work Study, 51(1), 32–37.
[14] Karsak E, Sozer S, Alptekin S, 2002. Product planning in quality function deployment using a combined ANP and GP approach. Computer & Industrial Engineering 44 171-190.
[15] Momoh, J. A., & Zhu, J., 2003. Optimal generation scheduling based on AHP/ANP. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 33(3), 531–535.
[16] Czajkowski A, Jones S, 1986. Selecting interrelated R&D projects in space technology planning. IEEE Transactions on Engineering management 33(1): 17-24.
[17] Weber R, Werners B, Zimmerman H, 1990. Planning models for research and development. European Journal of Operational Research 48 175-188
[18] King J, Scherem E, 1978. Cost-benefit analysis in information system development and operation. Comput. Survey 10(1): 20-34.
[19] Sanathanam R, Kyparisis G, 1996. A decision model for interdependent information system project selection. European Journal of Operational Research 89 380-399.
[20] Lee J, Kim S, 2000. Using ANP and GP for interdependent information system project selection. Computers & Operations Research 27 367-382.
[21] Meade L, Presley A, 2002. R&D project selection using the analytic network process. IEEE Transactions on Engineering Management 49(1) 59-66.
[22] Murray, T. J., Pipino, L. L., & Gigch, J. P. van (1985). A pilot study of fuzzy set modification of Delphi. Human Systems Management, 5, 76–80.
[23] Ringuest J, Graves S, 1989. The linear multi-objective R&D project selection problem. IEEE Transactions on Engineering Management 36(1) 54-57.
[24] Schniederjans M, Kim G, 1987. A Goal Programming Model to Optimize Departmental Preferences in Course Assignment. Computers and Operational Research 14(2) 87-96.
[25] Kaufmann A, Gupta M, 1988. Fuzzy Mathematical Models in Engineering and Management Science (North-Holland, Amsterdam).
[26] Saaty T, 1980. The analytic hierarchy process (McGraw-Hill, New York)
[27] Schniederjans M, Fowler K, 1989. Strategic Acquisition Analysis: A Multi-Objective Approach. Journal of the Operational Research Society 40(4) 333-345.
[28] Schniederjans M, Sanathanam R, 1993. A multi-objective constrained resource information system project selection method. European Journal of Operational Research 70 244-253.
[29] Schniederjans M. 1995. Goal programming: Methodology and applications (Norwell. Kluwer).
[30] Badri A, 1999. Combining the analytic hierarchy process and goal programming for global facility location–allocation problem. International Journal of Production Economics 62 237–248.
[31] Schniederjans M, Garvin T, 1997. Using the analytic hierarchy process and multi-objective programming for the selection of cost drivers in activity-based costing. European Journal of Operational Research 100 72-80.