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

Abstract

Resource constrained project scheduling problems (RCPSPs) are complex optimization problems that aim to minimize project completion time after considering limited resources and precedencerelated activities with known durations. However, due to the dynamic nature of real-world applications, activity durations are vulnerable to change. In addition, resource demands along with resource availability at any stage of a project can also vary due to disruptions, which compel practitioners to rethink existing scheduling methodologies. Consequently, to mitigate the conjoint effect of those uncertainties and/or disruptions, this paper proposes a real-time reactive scheduling approach. To deal with uncertain activity durations, a chance constrained based approach is followed, which is later solved by an advanced metaheuristic based approaches called IGFBIS and IGFBID. After solving an exhaustive list of stochastic RCPSP instances, this paper proves the efficacy of the proposed approaches, which is proven to be more useful to mitigate uncertainty or disruption while executing real-life projects. INDEX TERMS Scheduling Real-life projects, project scheduling with uncertainty and disruption, Iterated greedy algorithm, Chance constrained approach

An Effective Hybrid Algorithm Based on Particle Swarm Optimization with Migration Method for Solving the Multiskill Resource-Constrained Project Scheduling Problem

“…In recent years, researching good methods to solve the MS-RCPSP [7,12,14] problem has become important in deploying it into many domains. Since this is a combinatorial optimization problem belonging to the NP-Hard [11,15,16] classification, we cannot find an optimal solution in polynomial time, so the objective of methods is to find an approximate result based on metaheuristic techniques. Authors usually use evolutionary approaches as GA [17,18], PSO [19][20][21][22], Greedy, Min-Max, etc.…”

“…end if (15) end for (16) for i � 1 to size (P all ) do (17) if (f (P i ) < f (g best )) then (18) g best � P i (19) f (g best ) � f (P i ) (20) end if (21) if f(P i )! = f(P i-1 ) then (22) n f = 0 (23) else ( 24)…”

An Assets Maintenance-Workforce Planning Problem Under Uncertainty a Chance Constraints Assisted Simulation-Optimization Approach

“…Those methods can be coupled with optimization models and meta-heuristics algorithms for decision-making as reviewed by De Jonge and Scarf [36], where a significantly large number of decision combinations need to be tested and the optimal one to be chosen. As discussed in the previous section, the uncertainty in such models is commonly tackled by the Chance Constraints method [17]. However, this is rarely applied in the maintenance domain.…”

“…It is the time difference between two consecutive failures, and it is the reciprocal of the failure rate of an asset or system [16]. Even after PM planning, an asset may fail or break without any prior information [17,18] and therefore, MTBF is uncertain in reality. Thus, CM may take place between two consecutive MTBF.…”

“…The Chance Constrained (CC) programming (CCP) model is assumed to follow a certain probability distribution, and the motivation behind this selection is-(1) CC model is an effective tool that is used for solving complex decision-making problems with uncertainty in different areas, e.g., project scheduling problem [17,18], job-shop scheduling [25], assembly line balancing [26], power system optimization [27,28]. Like other areas, CC based approach has been developed for solving different maintenance planning problems too, e.g., railway maintenance planning [29,30].…”