We consider heuristic algorithms for the resource-constrained project scheduling problem. Starting with a literature survey, we summarize the basic components of heuristic approaches. We briefly describe so-called X -pass methods which are based on priority rules as well as metaheuristic algorithms. Subsequently, we present the results of our in-depth computational study. Here, we evaluate the performance of several state-of-the-art heuristics from the literature on the basis of a standard set of test instances and point out to the most promising procedures. Moreover, we analyze the behavior of the heuristics with respect to their components such as priority rules and metaheuristic strategy. Finally, we examine the impact of problem characteristics such as project size and resource scarceness on the performance.

Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem

Project scheduling under uncertainty survey and research potentials

A review of analytical models, approaches and decision support tools in project monitoring and control

https://isiarticles.com/bundles/Article/pre/pdf/42388.pdf

Construction and evaluation framework for a real-life project database

https://biblio.ugent.be/publication/5793774/file/5808440.pdf

Support Vector Machine Regression for project control forecasting

A Taxonomic Review of Metaheuristic Algorithms for Solving the Vehicle Routing Problem and Its Variants

Impact of sensitivity information on the prediction of project's duration using earned schedule method

Robust scheduling is aiming at constructing proactive schedules capable of dealing with multiple disruptions during project execution. Insertion a time buffer, before an activity start time, is a method to improve the robustness (stability) of a baseline schedule. In this paper, we introduce new heuristics for inserting time buffers in a given baseline schedule while the project due date is predefined and stochastic activity duration is considered. Computational results obtained from a set of benchmark projects show that the proposed heuristics capable of generating proactive schedules with acceptable quality and solution robustness.

New Proactive Time Buffer Heuristics for Robust Project Scheduling

Due to the combinatorial nature of the resource-constrained project scheduling problem (RCPSP), there is a lot of artificial intelligence methods proposed to solve it. The Genetic Algorithm (GA), one of these methods, is considered to be a valuable search algorithm capable of finding a reasonable solution in a short computational time. The primary objective of this paper is to build a genetic algorithm for solving RCPSP problem aiming at minimizing project’s makespan. Based on a comprehensive review of different GAs and a full factorial experiment, a proposed GA has been presented. The proposed algorithm has been tested on a well-known benchmark (PSPLIB). The computation results show that the proposed GA outperforms many published algorithms and on average performs as well as other algorithms.  Also, the performance of the algorithm improves in solving large scale problems

https://jaes.journals.ekb.eg/article_19298_647396ffdd95c9090894ad78cf6b4dea.pdf

Raafat Elshaer

Papers

A Taxonomic Review of Metaheuristic Algorithms for Solving the Vehicle Routing Problem and Its Variants

Impact of sensitivity information on the prediction of project's duration using earned schedule method

New Proactive Time Buffer Heuristics for Robust Project Scheduling

Solving resource-constrained project scheduling problem using genetic algorithm

The impact of project network topology and resource restrictions on the performance of schedule generation schemes: A comparative study