Firms are being increasingly pressured under the current marketing environment, the uncertainty and risks has greatly influenced the Supply Chain Management project implementation. In this paper, we present a new buffer sizing method that takes into consideration the activity duration risk as well as the multi-resource constraint risk under uncertainty. First of all, the key risk factors are identified and the SCM activity duration risk is effectively assessed by using the Bayesian Network. Secondly, the resource constraint risk is appropriately determined by using the resource flow network. The, a reasonable buffer sizing method is developed for the construction of a stable critical chain scheduling plan. A numerical example and comparative study validate the effectiveness and practicability of the proposed method, which is capable of maintaining the schedule stability with a short project makespan, while ensuring a high on-time completion rate.
HerroelenW. and LeusR., Project scheduling under uncertainty: Surveyand research potentials, Eur J Oper Res165(2) (2005), 289–306.
2.
GoldrattE.M., Critical Chain. The North River Press, New York, 1997.
3.
HuX., CuiN. and DemeulemeesterE., Effective expediting to improve project due date and cost performance through buffer management [J], Int J Prod Res53(5) (2015), 1460–1471.
4.
HuX., CuiN., DemeulemeesterE. and BieL., Incorporation of activity sensitivity measures into buffer management to manage project schedule risk, Eur J Oper Res (2015). doi: 10.1016/j.ejor.2015.08.066.
5.
ZhangJ.W. and LiR.N., Review of critical chain project scheduling method, Cont & Dec28(9) (2013), 1281–1286.
6.
CuiN.F., ZhaoY. and HuX.J., Buffer management in robust project scheduling, Cont & Dec29(2) (2014), 368–372.
7.
MaG.F., YanY. and YouJ.X., A study on robust optimization of critical chain project scheduling, J Sys Manage23(5) (2014), 704–710.
8.
MaG., WangA. and LiN., Improved critical chain project management framework for scheduling construction projects, J Cons Eng Man140(12) (2014), 1–12.
9.
HuC., XuZ. and YuZ.J., Calculation method of buffer size on critical chain with duration distribution and multi-resource constraints, J Sys Man24(2) (2015), 237–242.
10.
BieL. and CuiN.F., Research on dynamic buffer monitoring in critical chain project management, Chin J Manage Sci18(6) (2010), 97–103.
11.
NewboldR.C., Project Management in the Fast Lane -Applying the Theory of Constraints. The St Lucie Press, Boca Raton, 1998.
12.
TukelO.I., RomW.O. and EksiogluS.D., An investigation of buffer sizing techniques in critical chain scheduling [J], Eur J Oper Res172(2) (2006), 401–416.
13.
BieL., CuiN.F. and ZhangX.M., Buffer sizing approach with dependence assumption between activities in critical chain scheduling, Int J Prod Res50(24) (2012), 7343–7356.
14.
ZhangJ., SongX. and DiazE., Project buffer sizing of a critical chain based on comprehensive resource tightness, Eur J Oper Res248(1) (2016), 174–182.
15.
ZhangJ., SongX. and DiazE., Critical chain project buffer sizing based on resource constraints, Int J Prod Res (2016). doi: 10.1080/00207543.2016.1200151
16.
ChuC.C., Buffer sizing and critical chain project management, Com Inte Manu Sys14(5) (2008), 1029–1035.
17.
CreemersS., DemeulemeesterE. and Van de VonderS., Anew approach for quantitative risk analysis, Ann Oper Res213(1) (2014), 27–65.
18.
JiangG.P. and ChenY.W., Schedule risk management method of software project based on critical chain, Com App25(1) (2005), 56–57.
19.
ZhangL., WuX. and DingL., Decision support analysis for safety control in complex project environments based on Bayesian Networks, Ex Sys App40(11) (2013), 4273–4282.
20.
ArtiguesC., MichelonP. and ReusserA., Insertion techniques for static and dynamic resource-constrained project scheduling, Eur J Oper Res149(2) (2003), 249–267.
21.
DeblaereF., DemeulemeesterE. and HerroelenW., Robust resource allocation decisions in resource-constrained projects, Dec Sci38(1) (2007), 5–37.
22.
DemeulemeesterE. and HerroelenW., A branch and-bound procedure for the multiple resource-constrained project scheduling problem, Manage Sci38(12) (1992), 1803–1818.
23.
CuiN.F., ZhaoY. and TianW.D., Bi-objective robust project scheduling based on intelligent algorithms, J Sys Manage24(3) (2015), 379–388.
24.
LiuZ., XiaoL. and TianJ., An activity-list-based nested partitions algorithm for resource-constrained project scheduling, Int J Prod Res16 (2016), 3450–3454.
25.
EshraghiA., A new approach for solving resource constrained project scheduling problems using differential evolution algorithm, Int J Ind Eng Com7(2) (2016), 205–216.
26.
BrzezińskiD.W., Accuracy problems of numerical calculation of fractional order derivatives and integrals applying the riemann-liouville/caputo formulas, Applied Mathematics and Nonlinear Sciences1(1) (2016), 23–44.
27.
BalibreaF., On problems of Topological Dynamics in non-autonomous discrete systems, Applied Mathematics and Nonlinear Sciences1(2) (2016), 391–404.
