Important classes of reactions for the proactive and reactive resource-constrained project scheduling problem

Abstract

The proactive and reactive resource-constrained project scheduling problem (PR-RCPSP), that has been introduced recently (Davari and Demeulemeester, 2017), deals with activity duration uncertainty in a very unique way. The optimal solution to an instance of the PR-RCPSP is a proactive and reactive policy (PR-policy) that is a combination of a baseline schedule and a set of required transitions (reactions). In this research, we introduce two interesting classes of reactions, namely the class of selection-based reactions and the class of buffer-based reactions, the latter in fact being a subset of the class of selection-based reactions. We also discuss the theoretical relevance of these two classes of reactions. We run some computational results and report the contributions of the selection-based reactions and the buffer-based reactions in the optimal solution. The results suggest that although both selection-based reactions and buffer-based reactions contribute largely in the construction of the optimal PR-policy, the contribution of the buffer-based reactions is of much greater importance. These results also indicate that the contributions of non-selectionbased reactions (reactions that are not selection-based) and selection-but-not-buffer-based reactions (selection-based reactions that are not buffer-based) are very limited. * This is a draft of an article accepted for publication in Annals of Operations Research.

Managing Uncertainty and Disruptions in Resource Constrained Project Scheduling Problems: A Real-Time Reactive Approach

“…In computation, most often these uncertainties are quantified by using selected probability distribution functions, which help to convert stochastic values to deterministic ones [24]. Since activity durations are unknown, solutions for SRCPSPs are more dynamic policies than they are schedules [30], which dictate certain rescheduling at certain decision points. Among the many policies that have been proposed, the most notable are: early-start policy [31], pre-selective policies [32], resource-based policies [33], activity-based policies [32], and pre-processor policies [34].…”

An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing

“…where µ The outputs of the ANN are formulated in Output (31), which means the possibilities of completion status as Equations (24)- (26).…”

“…The network inside the ANN-based approach is constructed in PyTorch, and its main parameters are listed in Table 4. Before starting to train the ANN model, we need to sample the data that is consistent with the style of Input (29) and Output (31). The quality of an ANN model can be judged by its cross-entropy loss function in Equation (38) during training, as depicted in Figure 9.…”

“…Researchers have also proposed two reactive scheduling strategies based on the selection and buffering to solve the random resource constrained project scheduling problem. They found that the buffer-based reaction rules combined with the baseline scheduling turned out to be an effective solution [31].…”

Robust Predictive-Reactive Scheduling: An Information-Based Decision Tree Model

“…Recently [3] remarked that in this series of approaches, the proactive and the reactive phases were rather treated separately while they should mutually influence each other. So in [3,4] the authors propose an integrated proactive reactive approach where they aim to find the best policy, which is in their case a robust initial schedule and a set of reactions giving transitions from a schedule to another schedule in response to a disruption, given a certain reaction cost. In a pioneering work, [6] proposed the so-called Just In Case approach, in which they compute a multiple contingent schedule, where transitions from a baseline schedule to alternative schedules were anticipated at some events having a high probability of break.…”