Critical chain project buffer sizing based on resource constraints

Project scheduling is a complex process involving many types of resources and activities that require optimisation. The resource-constrained project scheduling problem is one of the well-known problematic issues when project activities have to be scheduled to minimise the project duration. Consequently, several methods have been proposed for adjusting the buffer size but none of these traditional methods consider buffer sizing accuracy based on resource constraints. The purpose of this paper is to develop a buffer sizing method based on a fuzzy resource-constrained project scheduling problem in order to obtain an appropriate proportionality between the activity duration and the buffer size. Specifically, a comprehensive resource-constrained method that considers both the general average resource constraints (GARC) and the highest peak of resource constraints (HPRC) is proposed in order to obtain a new buffer sizing method. This paper contributes to the research by considering several different aspects. First, this paper adopts a fuzzy method to calculate and obtain the threshold amount. Second, this paper discusses the resource levelling problem and proposes the HPRC method. Third, the proposed method uses a fuzzy quantitative model to calculate the resource requirement. The findings indicate that the project achieved higher efficiency, providing effective protection and an appropriate buffer size.

[1]  Walter O. Rom,et al.  An investigation of buffer sizing techniques in critical chain scheduling , 2006, Eur. J. Oper. Res..

[2]  Maurizio Bevilacqua,et al.  Critical chain and risk analysis applied to high-risk industry maintenance: A case study , 2009 .

[3]  Junguang Zhang,et al.  Project buffer sizing of a critical chain based on comprehensive resource tightness , 2016, Eur. J. Oper. Res..

[4]  Simone Göttlich,et al.  A continuous buffer allocation model using stochastic processes , 2015, Eur. J. Oper. Res..

[5]  Xiaoming Zhang,et al.  Buffer sizing approach with dependence assumption between activities in critical chain scheduling , 2012 .

[6]  Nebojsa J. Bojovic,et al.  Optimal allocation of buffer times to increase train schedule robustness , 2017, Eur. J. Oper. Res..

[7]  Junguang Zhang,et al.  Buffer sizing of critical chain based on attribute optimization , 2014, Concurr. Eng. Res. Appl..

[8]  J. Sanderson.  Risk, uncertainty and governance in megaprojects: A critical discussion of alternative explanations , 2012 .

[9]  George Ellis.  Project Management in Product Development: Leadership Skills and Management Techniques to Deliver Great Products , 2015 .

[10]  L. M. Naeni,et al.  A fuzzy approach for the earned value management , 2011 .

[11]  Eliyahu M. Goldratt,et al.  Critical Chain: A Business Novel , 1997 .

[12]  David Cleden,et al.  Managing Project Uncertainty , 2012 .

[13]  Frederik Ahlemann,et al.  A process framework for theoretically grounded prescriptive research in the project management field , 2013 .

[14]  Mohammad Fallah,et al.  CRITICAL CHAIN PROJECT SCHEDULING: UTILIZING UNCERTAINTY FOR BUFFER SIZING , 2010 .

[15]  Herman Steyn.  Project management applications of the theory of constraints beyond critical chain scheduling , 2002 .

[16]  Kim Wikström,et al.  Defining uncertainty in projects – a new perspective , 2008 .

[17]  Zheng Zheng,et al.  A critical chains based distributed multi-project scheduling approach , 2014, Neurocomputing.

[18]  Graham K. Rand.  Critical chain: the theory of constraints applied to project management , 2000 .

[19]  Wu Hai.  A Confidence DEA Model for LR-Type Fuzzy Numbers , 2003 .

[20]  Mario Vanhoucke,et al.  A comparison of the performance of various project control methods using earned value management systems , 2015, Expert Syst. Appl..

[21]  Yue Liu,et al.  Evaluating Transit Operator Efficiency: An Enhanced DEA Model with Constrained Fuzzy-AHP Cones , 2016 .

[22]  George Ellis.  Critical Chain Project Management (CCPM) , 2016 .

[23]  Margarita Knyazeva,et al.  Resource-constrained Project Scheduling Approach Under Fuzzy Conditions , 2015 .

[24]  Jian Zuo,et al.  Application of Innovative Critical Chain Method for Project Planning and Control under Resource Constraints and Uncertainty , 2010 .

[25]  Junguang Zhang,et al.  Optimisation of critical chain sequencing based on activities’ information flow interactions , 2015 .

[26]  Erik Demeulemeester,et al.  Incorporation of activity sensitivity measures into buffer management to manage project schedule risk , 2016, Eur. J. Oper. Res..

[27]  Rainer Kolisch,et al.  PSPLIB - A project scheduling problem library: OR Software - ORSEP Operations Research Software Exchange Program , 1997 .

[28]  Yao-Chen Kuo,et al.  Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects , 2013 .

[29]  Min Huang,et al.  A critical chain project scheduling method based on a differential evolution algorithm , 2014 .

[30]  Ario Ohsato,et al.  Fuzzy critical chain method for project scheduling under resource constraints and uncertainty , 2008 .

[31]  Robert C. Newbold,et al.  Project Management in the Fast Lane: Applying the Theory of Constraints , 1998 .

[32]  Lei Fu,et al.  Critical chain and evidence reasoning applied to multi-project resource schedule in automobile R&D process , 2014 .

[33]  Andrew Lim,et al.  New concepts for activity float in resource-constrained project management , 2011, Comput. Oper. Res..

[34]  Willy Herroelen,et al.  On the merits and pitfalls of critical chain scheduling , 2000 .

[35]  B. Asady.  Trapezoidal Approximation of a Fuzzy Number Preserving the Expected Interval and Including the Core , 2013 .

[36]  Andrew W Gale,et al.  Conceptualising uncertainty in safety-critical projects: A practitioner perspective , 2015 .