Project scheduling under multiple resources constraints using a genetic algorithm

The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

A hybrid scatter search. Electromagnetism meta-heuristic for project scheduling.

In the last few decades, several effective algorithms for solving the resource-constrained project scheduling problem have been proposed. However, the challenging nature of this problem, summarised in its strongly NP-hard status, restricts the effectiveness of exact optimisation to relatively small instances. In this paper, we present a new meta-heuristic for this problem, able to provide near-optimal heuristic solutions. The procedure combines elements from scatter search, a generic population-based evolutionary search method, and a recently introduced heuristic method for the optimisation of unconstrained continuous functions based on an analogy with electromagnetism theory, hereafter referred to as the electromagnetism meta-heuristic. We present computational experiments on standard benchmark datasets, compare the results with current state-ofthe-art heuristics, and show that the procedure is capable of producing consistently good results for challenging instances of the resource-constrained project scheduling problem. We also demonstrate that the algorithm outperforms state-of-the-art existing heuristics.Algorithms; Effectiveness; Electromagnetism; Functions; Heuristic; Project scheduling; Scatter; Scatter search; Scheduling; Theory;

A random key based genetic algorithm for the resource constrained project scheduling problem

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

A scientometrics survey on project scheduling

In project management, a schedule is considered as a list a project's milestones, activities, and deliverables, normally with some start and finish time schedule, which are estimated by some information incorporated in the project schedule including resource allocation, budget, task duration, and linkages of dependencies and scheduled events. This paper presents a comprehensive review of the studies associated with project scheduling. The study uses Scopus database as a primary search engine and covers 3370 records over the period 1963-2019. The records are statistically analyzed and categorized in terms of different criteria. Based on the survey, "decision support systems" is the keyword which has carried the highest densities followed by heuristics methods. Among the most cited articles, papers published by researchers in Germany have received the highest citations (9084), followed by United States (7058) and Belgium with 4853 citations

Hybrid Variable Neighborhood and Simulated Annealing Heuristic Algorithm to Solve RCPSP

RESUMEN: En este artículo se presenta un algoritmo heurístico híbrido para resolver el Problema de Programación de Proyectos con Recursos Limitados (RCPSP). El algoritmo diseñado combina elementos de Recocido Simulado y Búsqueda en Múltiples Vecindarios. Adicionalmente, utiliza el método denominado Justificación, el cual es un método diseñado específicamente para el RCPSP. Para evaluar el desempeño del algoritmo se realizó un análisis estadístico para el ajuste de parámetros. Los resultados se comparan con los reportados en la literatura científica.ABSTRACT: This paper presents a hybrid heuristic algorithm for solving the Resource Constrained Project Scheduling Problem (RCPSP). The algorithm designed combines elements of Simulated Annealing and Variable Neighborhood Search. Additionally, it uses the method called Justification, which is a method designed specifically for the RCPSP. To evaluate the performance of the algorithm, a statistical analysis for tuning the parameters has done. The results were compared with those reported in the scientific literature