An effective genetic algorithm for the resource levelling problem with generalised precedence relations

Kaynak dengeleme problemi (KDP) sezgisel, modern sezgisel ve matematiksel yöntemlerle çözülmektedir. Fakat belirtilen yöntemler özellikle büyük boyutlu problemler için kesin çözümü garanti edememektedir. Bu çalışmada KDP'nin aktiviteler arasındaki bağımlılık ilişkilerini ihlal etmeden ve proje süresinde uzamaya neden olmayacak şekilde bolluğu olan aktivitelerin ertelenmesi ile elde edilebilecek birbirinden farklı kaç iş programı oluşturulabileceği hesaplanmıştır. Arama uzayı olarak tanımlanan tüm uygulanabilir iş programlarının tamamının denenmesi ile garantili biçimde KDP'nin en iyi çözümü elde edilerek mevcut yöntemlerden farklı biçimde KDP'nin çözülmesi sağlanmıştır. Aktivite sayısı ile arama uzayı arasında seri bağlı aktiviteler için üstel bağıntı formülü türetilerek büyük projelerin tek işlemci ile çözümünün makul sürede gerçekleşemeyeceği belirlenmiştir. Problemin paralel programlama ile tüm işlemcilere eşit sayıda şebeke çözümü düşecek şekilde paylaştırılması sağlanmıştır. Bu çalışmada en büyüğü 36 aktiviteli olan 4 KDP arama uzayının tamamı taranıp makul sürede çözülerek geliştirilen yöntemin uygulanabilir olduğu kanıtlanmıştır. Bu yöntem ile daha küçük parçalara ayırmak sureti ile daha büyük kaynak dengeleme problemlerinin kesin çözümü elde edilebilecektir.

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“…(2013) ve Li vd. (2017) tarafından da kullanılmıştır [15][16][17][18][19]. Parçacık Sürü Optimizasyonu KDP'nin çözümü için Qi vd.…”

Section: Kdp'nin çöZümünde Uygulanan Yöntemlerunclassified

Exact Solution of Resource Leveling Problem by Exhaustive Enumeration with Parallel Programming

Bettemir¹,

Erzurum

2021

Teknik Dergi

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“…In recent years, the meta heuristic algorithm using intelligent search strategy (based on population) or evaluating a single feasible solution (based on neighborhood) has been widely studied by researchers, which provides strong support for solving scheduling optimization problems such as RLP [19] [20]. Genetic algorithms, ant colony algorithms, and other meta heuristics have been used by researchers to solve the RLP and some have achieved good results [21] [22]. Among many meta heuristics, quantum-behaved particle swarm optimization (QPSO) has been successfully used to solve a large number of engineering problems, such as power system load scheduling [23], parameter identification [24], etc.…”

Section: Introductionmentioning

confidence: 99%

Float-Based Resource Leveling Optimization of Linear Projects

Wang

Tang

2020

IEEE Access

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In recent years, the linear scheduling method (LSM) approach to the linear project scheduling optimization problem has been widely studied by researchers. In this paper, the problem of resource leveling of linear projects under the framework of LSM is studied. Firstly, the concept of a third type of float is proposed, and the advantages of this third type of float to the optimization of linear project resource leveling are analyzed. On this basis, a two-stage optimization process of the linear project resource leveling problem based on the third type of float is proposed. The process takes into account new constraints such as total resource constraint, resource utilization constraint, and construction mileage constraint, and it aims to maximize the space-time flexibility of construction activities and realize the depth optimization of linear project resource leveling. According to the integer nonlinear characteristic of the model, a quantum particle swarm optimization algorithm with a constraint processing mechanism and particle search range restriction is designed. Finally, the effectiveness of the proposed model is verified by case analysis and comparison of the results in previous studies.

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“…The well-known resource-constrained project scheduling problem (RCPSP) involves the determination of a schedule of the project activities, considering precedence relations and limited resource capacities while minimizing the project makespan (Delgoshaei et al, 2015(Delgoshaei et al, , 2016(Delgoshaei et al, , 2017Chen et al, 2018;Vega-Velázquez et al, 2018). The RCPSP has been widely applied to production environments, such as make-to-order (MTO) and engineering-to-order systems, where highly customized products are required to be designed and delivered according to customer's orders and specifications (Li et al, 2018). In these cases, each order can be organized as a project and a master production schedule needs to be generated with a specified production time table.…”

Section: Introductionmentioning

confidence: 99%

A bi-objective robust resource allocation model for the RCPSP considering resource transfer costs

Wang

Demeulemeester

et al. 2019

International Journal of Production Research

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Resource allocation is one of the core issues in project scheduling to ensure the effective use of scare renewable resource, and has been regularly encountered in production systems in the manufacturing and service industries. The transfers of renewable resources between activities generally incur certain scheduling costs and affect the robustness of a certain schedule in an uncertain environment. To address this issue, a bi-objective optimization model is proposed to make the resource transfer decisions, which aims to minimize the transfer cost and maximize solution robustness in the presence of activity duration variability. The proposed model employs a novel resourceoriented flow formulation that is different from those of the previous literature. A NSGA-II and a Pareto simulated annealing (PSA) algorithm have been applied as the solution methodologies. Besides, the effectiveness of the metaheuristics are evaluated in comparison with a  -constraint method. In detail, the algorithms are carried out on a set of benchmarks and are compared to test their efficiencies based on four performance metrics: number of nondominated solutions, general distance, hypervolume and spacing. Finally, a case study of a real project further indicates that the suggested model and algorithms are applicable and beneficial to the problem in practice.

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An effective genetic algorithm for the resource levelling problem with generalised precedence relations

Cited by 40 publications

References 60 publications

Exact Solution of Resource Leveling Problem by Exhaustive Enumeration with Parallel Programming

Exact Solution of Resource Leveling Problem by Exhaustive Enumeration with Parallel Programming

Float-Based Resource Leveling Optimization of Linear Projects

A bi-objective robust resource allocation model for the RCPSP considering resource transfer costs

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