2008
DOI: 10.1016/j.ejor.2006.06.074
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A genetic algorithm for the resource constrained multi-project scheduling problem

Abstract: This paper presents a genetic algorithm for the Resource Constrained Multi-Project Scheduling Problem (RCMPSP). The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.

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Cited by 265 publications
(137 citation statements)
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“…Quando se trata de múltiplos projetos, ou seja, de empresas que lidam com diversos projetos simultaneamente, o problema da alocação de recursos é apresentado de forma ampliada, pois se têm vários projetos disputando recursos limitados, tornando mais complexo o processo de busca pela distribuição ótima (GONÇALVES; MENDES; RESENDE, 2008).…”
Section: Introductionunclassified
“…Quando se trata de múltiplos projetos, ou seja, de empresas que lidam com diversos projetos simultaneamente, o problema da alocação de recursos é apresentado de forma ampliada, pois se têm vários projetos disputando recursos limitados, tornando mais complexo o processo de busca pela distribuição ótima (GONÇALVES; MENDES; RESENDE, 2008).…”
Section: Introductionunclassified
“…To resolve the resource-constrained multi-project scheduling problem, researchers have applied several different methodologies (Table 1), which can be loosely classified as either heuristic approaches or mathematical analyses. These include zero-one programming techniques, branch-and-bound dynamic programming, and genetic algorithms [11][12][13][41][42][43][44]. A new methodology has been proposed that combines genetic algorithms and heuristic techniques [14,41], but this type of approach suffers from a limited ability to optimize factory processes that involve clustered variables such as resources, equipment, people, and processes.…”
Section: Limitations Of Existing Research On Module Manufacturing Promentioning
confidence: 99%
“…Mohanty and Siddiq [11] Vercellis [12] Elsayed et al [47] Boctor [48] Advanced heuristic techniques Lawrence and Morton [13] Wiley et al [44] Kumanan et al [14] Goncalves et al [41] Simulation approaches Borrego [45] Liu [46] Mohamed et al [15] Taghaddos et al [49,50] In Borrego's model, a module manufacturing yard schedule is created that determines and simulates the order of priority based on priority dispatching rules and 3D visualization [45]. Here, the simulation model for each event is connected to a module manufacturing yard where a variety of processes occur simultaneously, and the interactions between these processes are analyzed.…”
Section: Mathematical and Heuristic Analysesmentioning
confidence: 99%
“…Experiments show that CGA outperforms many other priority-rule-based heuristics. Goncalves [4] presents a genetic algorithm based on random key representation, and a schedule generation creating parameterized active schedules. Other kinds of algorithms for solving RCMPSP include priority rules based methods [5][6][7][8] and typical scenarios in which each project has its own priority.…”
Section: Introductionmentioning
confidence: 99%