Optimization of Resource Allocation and Leveling Using Genetic Algorithms

TLDR

Resource allocation and leveling are major challenges in project management, often treated separately and solved with heuristics that lack optimal guarantees. The study proposes GA-based improvements to jointly optimize resource allocation and leveling. The authors introduce random task priorities into heuristics and use a GA to search for an optimal priority set that shortens project duration and balances resources, implemented via a macro in commercial software. The GA approach is easily integrated into commercial tools and, as shown in a case study, yields shorter durations and better resource leveling, demonstrating its multiobjective benefits.

Abstract

Resource allocation and leveling are among the top challenges in project management. Due to the complexity of projects, resource allocation and leveling have been dealt with as two distinct subproblems solved mainly using heuristic procedures that cannot guarantee optimum solutions. In this paper, improvements are proposed to resource allocation and leveling heuristics, and the Genetic Algorithms (GAs) technique is used to search for near-optimum solution, considering both aspects simultaneously. In the improved heuristics, random priorities are introduced into selected tasks and their impact on the schedule is monitored. The GA procedure then searches for an optimum set of tasks' priorities that produces shorter project duration and better-leveled resource profiles. One major advantage of the procedure is its simple applicability within commercial project management software systems to improve their performance. With a widely used system as an example, a macro program is written to automate the GA procedure. A case study is presented and several experiments conducted to demonstrate the multiobjective benefit of the procedure and outline future extensions.