A Framework for Assessment of Logistics Enterprises’ Safety Standardization Performance Based on Prospect Theory

Authors

DOI:

https://doi.org/10.31181/jopi21202418

Keywords:

Safety standardization, Logistics enterprise, Performance evaluation, Prospect theory, Choquet integral

Abstract

To evaluate the performance of the logistics safety standard system, we propose an evaluation framework based on the performance evaluation theory. First, we construct the performance evaluation indicator of the safety standard system for logistics enterprises. It is based on the existing performance evaluation indicator and combined with the construction goal of the logistics safety standard system. Second, we combine the triangular fuzzy number and prospect theory to determine the indicator state according to the characteristics of performance evaluation indicators. Then, we use the Choquet integral, fuzzy method, and Shapley value methods to evaluate the information, which considers the interaction of indicators. Third, we use the entropy and fuzzy analytic hierarchy process to determine the expert weight. The performance evaluation information of the logistic enterprise’s safety standard system is aggregated to obtain the assessment results. Finally, the proposed framework is validated by an example analysis. The results show that the proposed framework can be used to evaluate the performance of logistics enterprise safety standard systems.

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Author Biographies

  • Cao Yushuo, School of Economics and Management, Anhui Normal University, Wuhu, 241002, Anhui, China

    .

  • Ding Ling, School of Economics and Management, Anhui Normal University, Wuhu, 241002, Anhui, China

    .

References

Choi, T.-M. (2021). Risk analysis in logistics systems: A research agenda during and after the COVID-19 pandemic. Transportation Research Part E: Logistics and Transportation Review, 145. https://doi.org/10.1016/j.tre.2020.102190

Tian, G.& Lu, W., Zhang, X., Zhan, M., Dulebenets, M. A., Aleksandrov, A., & Ivanov, M. (2023). A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems. Environmental Science and Pollution Research, 30(20), 57279-57301. https://doi.org/10.1007/s11356-023-26577-2

Li, L., Gong, Y., Wang, Z., & Liu, S. (2023). Big data and big disaster: a mechanism of supply chain risk management in global logistics industry. International Journal of Operations & Production Management, 43(2), 274-307. https://doi.org/https://10.1108/ijopm-04-2022-0266

Sergi, I., Montanaro, T., Benvenuto, F. L., & Patrono, L. (2021). A Smart and Secure Logistics System Based on IoT and Cloud Technologies. Sensors, 21(6). https://doi.org/10.3390/s21062231

Acheampong, T., & Kemp, A. G. (2022). Health, safety and environmental (HSE) regulation and outcomes in the offshore oil and gas industry: Performance review of trends in the United Kingdom Continental Shelf. Safety Science, 30, 57279–57301. https://doi.org/https://10.1016/j.ssci.2021.105634

Spyropoulos, N., Mousteri, V., Regan, L., O' Sullivan, M., Thompson, K., Juniper, B., & Davies, L. (2021). Developing a novel composite index for monitoring occupational health and wellbeing: A case study in the rail sector in Great Britain. Safety Science, 144. https://doi.org/10.1016/j.ssci.2021.105446

Phinias, R. N. (2023). Benefits and challenges relating to the implementation of health and safety leading indicators in the construction industry: A systematic review. Safety Science, 163. https://doi.org/10.1016/j.ssci.2023.106131

Liu, P., & Li, Y. (2021). An improved failure mode and effect analysis method for multi-criteria group decision-making in green logistics risk assessment. Reliability Engineering & System Safety, 215. https://doi.org/10.1016/j.ress.2021.107826

Singh, A., & Misra, S. C. (2021). Safety performance & evaluation framework in Indian construction industry. Safety Science, 134. https://doi.org/10.1016/j.ssci.2020.105023

Ortiz-Barrios, M., Silvera-Natera, E., Petrillo, A., Gul, M., & Yucesan, M. (2022). A multicriteria approach to integrating occupational safety & health performance and industry systems productivity in the context of aging workforce: A case study. Safety Science, 152. https://doi.org/10.1016/j.ssci.2022.105764

Frankish, E. J., Phan-Thien, K.-Y., Ross, T., McConchie, R., Luning, P. A., & Bozkurt, H. (2022). Performance assessment of food safety management systems in Australian apple packhouses in view of microbial control. Food Control, 133. https://doi.org/10.1016/j.foodcont.2021.108642

Golabchi, H., Abellanosa, A. D., Lefsrud, L., Pereira, E., & Mohamed, Y. (2024). A comprehensive systematic review of safety leading indicators in construction. Safety Science, 172. https://doi.org/10.1016/j.ssci.2024.106433

Yuan, S., Yang, M., Reniers, G., Chen, C., & Wu, J. (2022). Safety barriers in the chemical process industries: A state-of-the-art review on their classification, assessment, and management. Safety Science, 148. https://doi.org/10.1016/j.ssci.2021.105647

Hayes, J., Slotsvik, T. N., Macrae, C., & Gould, K. A. P. (2023). Tracking the right path: Safety performance indicators as boundary objects in air ambulance services. Safety Science, 163. https://doi.org/10.1016/j.ssci.2023.106139

Hammond, D. M., King, A. L., Joe, M., & Miller, J. R. (2023). Understanding the relationship between safety culture and safety performance indicators in U.S. nuclear waste cleanup operations. Safety Science, 166. https://doi.org/10.1016/j.ssci.2023.106241

Sangiorgio, V., Mangini, A. M., & Precchiazzi, I. (2020). A new index to evaluate the safety performance level of railway transportation systems. Safety Science, 131. https://doi.org/10.1016/j.ssci.2020.104921

Tonka, Ş. K., & Ekmekci, I. (2022). A Model Proposal for Occupational Health and Safety Performance Measurement in Geothermal Drilling Areas. Sustainability, 14(23). https://doi.org/10.3390/su142315669

Qi, H., Zhou, Z., Li, N., & Zhang, C. (2022). Construction safety performance evaluation based on data envelopment analysis (DEA) from a hybrid perspective of cross-sectional and longitudinal. Safety Science, 146. https://doi.org/10.1016/j.ssci.2021.105532

Jahanvand, B., Bagher Mortazavi, S., Asilian Mahabadi, H., & Ahmadi, O. (2023). Determining essential criteria for selection of risk assessment techniques in occupational health and safety: A hybrid framework of fuzzy Delphi method. Safety Science, 167. https://doi.org/10.1016/j.ssci.2023.106253

Fang, S., Zhou, P., Dinçer, H., & Yüksel, S. (2021). Assessment of safety management system on energy investment risk using house of quality based on hybrid stochastic interval-valued intuitionistic fuzzy decision-making approach. Safety Science, 141. https://doi.org/10.1016/j.ssci.2021.105333

Sadeghi, H., Zhang, X., & Mohandes, S. R. (2023). Developing an ensemble risk analysis framework for improving the safety of tower crane operations under coupled Fuzzy-based environment. Safety Science, 158. https://doi.org/10.1016/j.ssci.2022.105957

Chen, J., Liu, C., Meng, Y., & Zhong, M. (2021). Multi-Dimensional evacuation risk evaluation in standard subway station. Safety Science, 142. https://doi.org/10.1016/j.ssci.2021.105392

Riascos, C. E. M., Ensslin, S. R., & Merino, E. A. D. (2021). Development of performance indicators for Occupational Health and Safety: a constructivist multicriteria approach for PPE. Production, 31, https://doi.org/10.1590/0103-6513.20200106

Küçükarslan, A. B., Köksal, M., & Ekmekci, I. (2023). A Model Proposal for Measuring Performance in Occupational Health and Safety in Forest Fires. Sustainability, 15(20). https://doi.org/10.3390/su152014729

Tan, J., Liu, Y., Senapati, T., Garg, H., & Rong, Y. (2022). An extended MABAC method based on prospect theory with unknown weight information under Fermatean fuzzy environment for risk investment assessment in B&R. Journal of Ambient Intelligence and Humanized Computing, 14(10), 13067-13096. https://doi.org/10.1007/s12652-022-03769-1

Wang, W., Han, X., Ding, W., Wu, Q., Chen, X., & Deveci, M. (2023). A Fermatean fuzzy Fine-Kinney for occupational risk evaluation using extensible MARCOS with prospect theory. Engineering Applications of Artificial Intelligence, 117, 105518. https://doi.org/https://DOI:10.1016/j.engappai.2022.105518

Li, Y., Liu, P., & Wu, X. (2023). Failure mode and effect analysis approach considering risk attitude of dynamic reference point cumulative prospect theory in uncertainty contexts. Artificial Intelligence Review, 56(12), 14557-14604. https://doi.org/10.1007/s10462-023-10501-8

Yao, X., Guo, H.-X., Zhu, J., & Shi, Y. (2022). Dynamic selection of emergency plans of geological disaster based on case-based reasoning and prospect theory. Natural Hazards, 110(3), 2249-2275. https://doi.org/10.1007/s11069-021-05036-6

Amir-Heidari, P., Maknoon, R., Taheri, B., & Bazyari, M. (2017). A new framework for HSE performance measurement and monitoring. Safety Science, 100, 157-167. https://doi.org/10.1016/j.ssci.2016.11.001

Gao, D., Xie, W., Cao, R., Weng, J., & Ming Lee, E. W. (2023). The performance of cumulative prospect theory's functional forms in decision-making behavior during building evacuation. International Journal of Disaster Risk Reduction, 99, 104132. https://doi.org/https://doi.org/10.1016/j.ijdrr.2023.104132

Meng, F., Zhang, Q., & Cheng, H. (2013). Approaches to multiple-criteria group decision making based on interval-valued intuitionistic fuzzy Choquet integral with respect to the generalized λ-Shapley index. Knowledge-Based Systems, 37, 237-249. https://doi.org/https://DOI:10.1016/j.knosys.2012.08.007

Meng, F., Tan, C., & Zhang, Q. (2013). The induced generalized interval-valued intuitionistic fuzzy hybrid Shapley averaging operator and its application in decision making. Knowledge-Based Systems, 42, 9-19. https://doi.org/https://DOI:10.1016/j.knosys.2012.12.006

Wang, C., Chu, S., Ying, Y., Wang, A., Chen, R., Xu, H., & Zhu, B. (2023). Underfrequency Load Shedding Scheme for Islanded Microgrids Considering Objective and Subjective Weight of Loads. IEEE Transactions on Smart Grid, 14(2), 899-913. https://doi.org/10.1109/TSG.2022.3203172

Chang, K.-H. (2023). Integrating Subjective–Objective Weights Consideration and a Combined Compromise Solution Method for Handling Supplier Selection Issues. Systems, 11(2), 74. https://www.mdpi.com/2079-8954/11/2/74

Ben Rabia, M. A., & Bellabdaoui, A. (2023). Collaborative intuitionistic fuzzy-AHP to evaluate simulation-based analytics for freight transport. Expert Systems with Applications, 225, 120116. https://doi.org/https://doi.org/10.1016/j.eswa.2023.120116

Antão, P., Calderón, M., Puig, M., Michail, A., Wooldridge, C., & Darbra, R. M. (2016). Identification of Occupational Health, Safety, Security (OHSS) and Environmental Performance Indicators in port areas. Safety Science, 85, 266-275. https://doi.org/https://doi.org/10.1016/j.ssci.2015.12.031

Published

2024-02-14

How to Cite

Yushuo, C., & Ling, D. (2024). A Framework for Assessment of Logistics Enterprises’ Safety Standardization Performance Based on Prospect Theory. Journal of Operations Intelligence, 2(1), 153-166. https://doi.org/10.31181/jopi21202418