Proactive Resource Allocation Heuristics for Robust Project Scheduling

Abstract

The well-known deterministic resource-constrained project scheduling problem (RCPSP) involves the determination of a predictive schedule (baseline schedule or pre-schedule) of the project activities that satisfies the finish-start precedence relations and the renewable resource constraints under the objective of minimizing the project duration. This pre-schedule serves as a baseline for the execution of the project. During execution, however, the project can be subject to several types of disruptions that may disturb the baseline schedule. Management must then rely on a reactive scheduling procedure for revising or reoptimizing the pre-schedule.The objective of our research is to develop procedures for allocating resources to the activities of a given baseline schedule in order to maximize its stability in the presence of activity duration variability. We propose three integer programming based heuristics and one constructive procedure for resource allocation. We derive lower bounds for schedule stability and report on computational results obtained on a set of benchmark problems.

Heuristics for project scheduling with discounted cash flows optimisation

“…A resource flow network is composed of all pairs (i, j) of vertices (activities) for which non-zero resource flow occurs: f (i, j, k) > 0. The aggregate resources of a given type entering the vertex equals the aggregate resources of the type exiting the vertex and is denoted by r ik (for each resource type k) [30]. The aggregate of all resources of a given type exiting the initial activity 0 and the aggregate of all resources of the type entering the final activity n + 1 are both equal to a k (for each resource type k).…”

A Two-Phase Algorithm for a Resource Constrained Project Scheduling Problem with Discounted Cash Flows

“…The constraints for the resource allocation problem are formulated as follows (Deblaere et al, 2006): -The sum of all resources of a given type outflowing from the dummy start activity 0 equals the sum of those resources inflowing to the dummy finish activity n + 1 and amounts to a k (for each resource type k):…”

“…Research into the resource allocation problem covers the problem of robust resource allocation, which reduces to the minimisation of the number of additional arcs (Deblaere et al, 2006;Leus, 2003;Policella et al, 2004;Policella, 2005), as each additional arc in E R is a new precedence constraint, which decreases schedule robustness. With a view to such minimisation, the same resources are allocated to the execution of activities directly connected with each other in schedule ordering (Policella et al, 2004;Policella, 2005), aggregate flows between individual activities are maximised (Deblaere et al, 2006) and/or the effect of potential production interferences is minimised (Deblaere et al, 2006;Leus, 2003).…”

“…With a view to such minimisation, the same resources are allocated to the execution of activities directly connected with each other in schedule ordering (Policella et al, 2004;Policella, 2005), aggregate flows between individual activities are maximised (Deblaere et al, 2006) and/or the effect of potential production interferences is minimised (Deblaere et al, 2006;Leus, 2003). A review of resource allocation algorithms is included in the papers (Deblaere et al, 2006;Klimek & Łebkowski, 2011). In this paper, those procedures are described which have been used in the computational experiments.…”

“…Its operation is similar to that of ISH 2 , but here, the first launched procedure is the procedure of identifying unavoidable arcs (Deblaere et al, 2006). Each activity is first assigned to chains whose last activity is the direct predecessor of the given activity or is connected with it by an unavoidable arc.…”