A new approach for buffer sizing in critical chain scheduling

Generating a robust baseline schedule for the robust discrete time/resource trade-off problem under work content uncertainty

“…Newbold (1998) proposed another time buffering approach, named RSEM, which assumes that the buffer size is the square root of the sum of the squares of the differences between the low risk duration and the aggressive duration for each activity along the chain. Since then, quite some research has studied the buffer sizing approach (Tukel et al, 2006;Ashtiani et al, 2007;Bie et al, 2012;Jovanović et al, 2017) and the application of the buffering approach (Yeo & Ning, 2002;Umble & Umble, 2006;Yang, 2007;Kuo et al, 2009). However, most of the buffer approaches in the above-mentioned research could be viewed as a centralized buffer.…”

Buffer sizing in critical chain project management by network decomposition

“…We assume that the lognormal standard deviations of all task duration variables in a project have the same value σ. According to Ashtiani et al (2007), the lognormal standard deviation σ can be reasonably assigned to a value in (0, 0.5]. In our buffer sizing simulations, we vary the value of σ ∈ {0.1, 0.2, 0.3, 0.4, 0.5} and p ∈ {0.6, 0.7, 0.8, 0.9}.…”

“…A widely used alternative method is Root Square Error Method (RSEM), where the buffer size is the square root of the sum of the squared safety margins of tasks on the associated chain (Newbold, 1998;Leach, 1999;Herroelen and Leus, 2001). Ashtiani et al (2007) assume task durations follow lognormal distributions and use a shape parameter to estimate task safety margins, which are aggregated as in RSEM. Similarly, Fallah et al (2010) use the coefficient of variation, skewness and kurtosis of the assumed lognormal distribution to estimate buffer size.…”

An optimization model to solve the resource constrained project scheduling problem RCPSP in new product development projects•

“…According to the scientific literature review carried out in this research for the period 1996-2019, the solution techniques most frequently used to solve the RCPSP with random duration of activities were the Genetic Algorithms [5][6][7][8][9][10][11][12][13][14][15][16] and the Critical Chain Method [17][18][19][20][21][22][23][24][25][26][27][28]. The RCPSP with random duration of activities and stochastic tasks insertion was presented by [29].…”