Nanfang Cui

Bio: Nanfang Cui is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Critical path method & Critical chain project management. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

A two-stage approach for the critical chain project rescheduling

Abstract: The fundamental principle of critical chain project management is to use the critical chain instead of a traditional critical path, to insert a project buffer at the end of the project and to insert feeding buffers wherever non-critical chains join the critical chain to protect a timely project completion. Due to the complexity of project, inserting feeding buffers may cause a conflict, such as precedence conflict or resource conflict, which can be solved by rescheduling. However, after rescheduling some new problems may arise: non-critical chain may start earlier than critical chain (non-critical chain overflow), or a gap may occur between activities on the critical chain (critical chain break-down). This paper is aiming to solve these new problems by a two-stage approach combined with feeding buffer for rescheduling. In the first stage, a first-stage rescheduling based on priority rules together with a backward-recursive procedure is proposed for rescheduling to solve resource and precedence conflicts, resulting in a critical chain break-down or a non-critical chain overflow. In the second stage, a second-stage rescheduling based on a heuristic algorithm is proposed to eliminate new problems and generate a better rescheduling scheme. Finally, we do simulations on the 110 Patterson instances set to verify the feasibility, effectiveness and applicability of our two-stage approach for rescheduling. Simulation results show that, it is an effective approach to generate reliable rescheduling schemes in most projects with excellent performances, i.e. the average project length, timely project completion probability and etc.

Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions

Abstract: In this digital era, data is new oil and artificial intelligence (AI) is new electricity, which is needed in different elements of operations management (OM) such as manufacturing, product development, services and supply chain. This study explores the feasibility of AI utilization within an organization on six factors such as job-fit, complexity, long-term consequences, affect towards use, social factors and facilitating conditions for different elements of OM by mining the collective intelligence of experts on Twitter and through academic literature. The study provides guidelines for managers for AI applications in different components of OM and concludes by presenting the limitations of the study along with future research directions.

Buffer sizing in critical chain project management by network decomposition

Abstract: Project management organizes about 30% of the world’s economy. Many recent projects apply critical chain project management (CCPM) methodology, which requires the design of project and feeding buffers. Accurate sizing of these buffers is essential, because too small buffers result in emergency procedures to prevent late delivery, whereas too large buffers result in uncompetitive bids and lost contracts. Previous buffer sizing research, focused predominantly on the critical chain, typically results in excessive buffers, and in critical chains being challenged by feeding buffers during planning. This work also performs inconsistently, for example in makespan estimation, at execution. We propose a new procedure for buffer sizing based on network decomposition, which offers logical advantages over previous ones. First, the size of a feeding buffer is determined from all associated noncritical chains. Second, the project buffer incorporates safety margins outside the critical chain by comparing feeding chains with their parallel critical counterparts. Computational testing on a case study of a real project and extensive simulated data shows that our procedure delivers much greater accuracy in estimating project makespan, and smaller feeding buffers. Furthermore, the resulting critical chain is never challenged. Additional benefits include delayed expenditure, and reductions in work-in-process, rework, and multitasking.

Project buffer sizing and dynamic buffer consumption algorithm in power generation construction

Abstract: The aim of this research is to propose a buffer sizing and buffer controlling algorithm (BSCA) as a heuristic algorithm for calculating project buffer and feeding buffers as well as dynamic controlling of buffer consumption in different phases of a wind power plant project in order to achieve a more realistic project duration.,The BSCA algorithm has two main phases of planning and buffer sizing and construction and buffer consumption. Project buffer and feeding buffers are determined in the planning and buffer sizing phase, and their consumption is controlled in the construction and buffer consumption phase. The heuristic algorithm was coded and run in MATLAB software. The sensitivity analysis was conducted to show the BSCA influence on project implementation. Then, to evaluate the BSCA algorithm, inputs from this project were run through several algorithms recently presented by researchers. Finally, the data of 20 projects previously accomplished by the company were applied to compare the proposed algorithm.,The results show that BSCA heuristic algorithm outperformed the other algorithms as it shortened the projects' durations. The average project completion time using the BSCA algorithm was reduced by about 15% compared to the previous average project completion time.,The proposed BSCA algorithm determines both the project buffer and feeding buffers and simultaneously controls their consumption in a dynamic way.