Dynamic resource levelling in projects under uncertainty

作     者：Li, Hongbo Zhang, Xianchao Sun, Jinshuai Dong, Xuebing 

作者机构：Shanghai Univ Sch Management Shangda Rd 99 Shanghai 200114 Peoples R China China Acad Elect & Informat Technol Beijing Peoples R China Tianjin Univ Commerce Sch Accountancy Tianjin Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH》 (国际生产研究杂志)

年 卷 期：2023年第61卷第1期

页      面：198-218页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0802[工学-机械工程] 

基　　金：National Natural Science Foundation of China [71602106  71702097  71801013] 

主　　题：Approximate dynamic programming uncertain project scheduling resource levelling activity overlaps resource breakdowns 

摘      要：In the resource levelling problem (RLP) under uncertainty, existing studies focus on obtaining an open-loop Bellman activity list that is not updated during project execution. In project management practice, it is also necessary to address more situations, such as activity overlaps and resource breakdowns. In this paper, we extend the uncertain RLP by proposing a resource levelling problem with multiple uncertainties (RLP-MU) that simultaneously considers uncertainties in activity durations, activity overlaps and resource availabilities. We formulate the RLP-MU as a Markov decision process model. Aimed at levelling resource usage by dynamically scheduling activities at each decision point based on the observed information, we develop a hybrid open-closed-loop approximate dynamic programming algorithm (HOC-ADP). In the HOC-ADP, we devise a closed-loop rollout policy to approximate the cost-to-go function and use the concept of the average project to avoid time-consuming simulation. A greedy-decoding-based estimation of distributed algorithm is also devised to construct an open-loop policy that is embedded in the HOC-ADP to further improve it. We additionally develop a simulation algorithm to evaluate the resource levelling performance of the HOC-ADP. Computational experiments on a benchmark dataset consisting of 540 problem instances are conducted to analyze the performance of the HOC-ADP, and the impact of various factors on resource levelling are investigated. The comparison experimental results indicate that our HOC-ADP outperforms the state-of-the-art meta-heuristics.