Measuring schedule uncertainty for a stochastic resource-constrained project using scenario-based approach with utility-entropy decision model

Abstract The aim of this study is to propose a scenario-based approach with utility-entropy decision model to measure the uncertainty related to the evolution of a resource-constrained project scheduling problem with uncertain activity durations (a stochastic RCPSP). The approach consists of two stages. The first is to apply the proposal proposed by Tseng and Ko to convert a stochastic RCPSP into a full scenario tree. In stage two, we introduce the Expected Utility–Entropy (EU-E) decision model, a weighted linear average of expected utility and entropy, to establish an EU-E criterion. Then we apply the criterion to prune the worse branch(es) to lead a reduced scenario tree. Based on an illustrated example, it has been concluded that the reduced scenario tree by the EU-E criterion with larger trade-off coefficient λ has less number of possible paths, less uncertainty, and lengthier expected project duration than that with smaller trade-off coefficient λ. Thus, this has demonstrated that not only can the whole scenario during the course of a project be obtained, but also the uncertainty related to the evolution of a project can be measured.

[1]  Ching-Chih Tseng,et al.  An approach for identifying the most likely activity sequence in a resource-constrained stochastic network , 2010 .

[2]  Douglas D. Gemmill,et al.  Using tabu search to schedule activities of stochastic resource-constrained projects , 1998, Eur. J. Oper. Res..

[3]  A. Csébfalvi,et al.  A UNIFIED MODEL FOR RESOURCE-CONSTRAINED PROJECT SCHEDULING PROBLEM WITH UNCERTAIN ACTIVITY DURATIONS , 2012 .

[4]  I. Segal.  A Note on the Concept of Entropy , 1960 .

[5]  Dimitri Golenko-Ginzburg,et al.  A heuristic for network project scheduling with random activity durations depending on the resource allocation , 1998 .

[6]  Willy Herroelen,et al.  Project scheduling under uncertainty: Survey and research potentials , 2005, Eur. J. Oper. Res..

[7]  Franz Josef Radermacher,et al.  Preselective strategies for the optimization of stochastic project networks under resource constraints , 1983, Networks.

[8]  Giovanni Mummolo.  PERT-path network technique: a new approach to project planning , 1994 .

[9]  Francisco Ballestín,et al.  When it is worthwhile to work with the stochastic RCPSP? , 2007, J. Sched..

[10]  Sergey Bushuyev,et al.  Entropy measurement as a project control tool , 1999 .

[11]  Bruce M. Woodworth,et al.  Identifying the critical sequence in a resource constrained project , 1988 .

[12]  Franz Josef Radermacher,et al.  Algorithmic approaches to preselective strategies for stochastic scheduling problems , 1983, Networks.

[13]  Vinod Kumar,et al.  Entropic measures of manufacturing flexibility , 1987 .

[14]  Jan Karel Lenstra,et al.  Scheduling subject to resource constraints: classification and complexity , 1983, Discret. Appl. Math..

[15]  J. Bowers.  Identifying critical activities in stochastic resource constrained networks , 1996 .

[16]  R. Kolisch,et al.  Heuristic algorithms for the resource-constrained project scheduling problem: classification and computational analysis , 1999 .

[17]  Dimitri Golenko-Ginzburg,et al.  Stochastic network project scheduling with non-consumable limited resources , 1997 .

[18]  W. Cosgrove.  Entropy as a Measure of Uncertainty for PERT Network Completion Time Distributions and Critical Path Probabilities , 2010 .

[19]  Wanhua Qiu,et al.  A measure of risk and a decision-making model based on expected utility and entropy , 2005, Eur. J. Oper. Res..

[20]  Terry Williams.  Criticality in Stochastic Networks , 1992 .

[21]  Konstantin Aksyonov,et al.  Multiagent genetic optimisation to solve the project scheduling problem under uncertainty , 2014 .

[22]  Roel Leus,et al.  New competitive results for the stochastic resource-constrained project scheduling problem: exploring the benefits of pre-processing , 2011, J. Sched..

[23]  Eleni Hadjiconstantinou,et al.  A decomposition-based stochastic programming approach for the project scheduling problem under time/cost trade-off settings and uncertain durations , 2010, Comput. Oper. Res..

[24]  Krešimir Fertalj,et al.  Resource Constrained Project Scheduling under Uncertainty: A Survey , 2012 .

[25]  Nadia Bhuiyan,et al.  Production, Manufacturing and Logistics Entropy as a measure of operational flexibility , 2005 .

[26]  Roel Leus,et al.  Resource‐Constrained Project Scheduling for Timely Project Completion with Stochastic Activity Durations , 2007 .

[27]  César Martínez-Olvera,et al.  Entropy as an assessment tool of supply chain information sharing , 2008, Eur. J. Oper. Res..

[28]  David D. Yao,et al.  Material and information flows in flexible manufacturing systems , 1985 .

[29]  Pedro Godinho,et al.  Adaptive policies for multi-mode project scheduling under uncertainty , 2012, Eur. J. Oper. Res..

[30]  Giovanni Mummolo.  Measuring uncertainty and criticality in network planning by PERT-path technique , 1997 .

[31]  Patrizia Beraldi,et al.  A heuristic approach for resource constrained project scheduling with uncertain activity durations , 2011, Comput. Oper. Res..