Finding alternative stepwise improvement paths with different improvement effects

¹ Logistics and E-commerce College, Zhejiang Wanli University, Ningbo 315100, P.R. China
² The Key Research Center of Philosophy and Social Science of Zhejiang Province-Modern Port Service Industry and Creative Culture Research Center, Zhejiang Wanli University, Ningbo 315100, P.R. China
³ School of Economics and Management, Shanghai Maritime University, Shanghai 201306, P.R. China
⁴ Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, P.R. China
⁵ Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, NC 28372, USA
⁶ Department of Finance, NEOMA Business School, 1 Rue du Maréchal Juin, 76130 Mont-Saint-Aignan, France
⁷ Collaborative Innovation Center for Ecological Economics and Management, Anhui University of Finance and Economics, Bengbu 233030, P.R. China
⁸ Anhui Provincial Key Laboratory of Philosophy and Social Sciences for Low-Carbon Development and Carbon Finance, Anhui University of Finance and Economics, Bengbu 233030, P.R. China

^* Corresponding author: songmartin@yeah.net

Received: 26 October 2023
Accepted: 4 September 2024

Abstract

Targets on the data-envelopment-analysis (DEA) efficient frontier show the direction of inefficient decision-making units’ (DMUs) improvements. Some authors have correlated the improve- ment effects of targets with management needs. Nevertheless, targets are unattainable for DMUs with poor performance. Hence, stepwise improvement paths with different improvement effects should be provided to solve the problem of unachievable targets and flexibly meet decision-makers’ (DMs) di- verse management needs in performance improvement. For this purpose, we propose novel stepwise DEA benchmarking approaches and a search algorithm to find progress-improvement strategies with different improvement effects. The contributions of our approaches are as follows. (1) Several step- wise improvement paths with different improvement effects are found, which provides DMs with more abundant alternatives for performance optimization in various management situations. (2) The upper bound for efforts in all intermediate improvement steps can be effectively controlled, which overcomes the problem of unachievable targets and makes stepwise improvement paths more feasible. To illustrate our approaches, we conducted a case study on 18 ports in China. In this case study of Chinese ports, we further highlighted the advantages of our methods by comparing them with existing DEA mod- els. In sum, our approaches are suitable for poorly performing units that aspire to align performance improvements with management needs.