Evolving priority rules for resource constrained project scheduling problem with genetic programming

Đumić, Mateja; Šišejković, Dominik; Čorić, Rebeka; Jakobović, Domagoj

doi:10.1016/j.future.2018.04.029

Cited by 57 publications

(13 citation statements)

References 26 publications

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“…Scheduling: The Scheduling is a process that deals with the allocation of limited resources by serving for the given times. It has been utilized in many production and service industries [86]. The GP has been frequently used in many timing problems such as dynamic job shop scheduling(JSS) [8], [87] production scheduling [88], action scheduling [89] scheduling in heterogeneous network [90], [91] Environmental, natural disasters and agriculture: GP methods have used especially for data modeling and forecasting in many areas such as carbon emission [92], monitoring of volcanoes [93], earthquake prediction [94], atmosphere studies [95], airflow measurement [96], modeling rainwater quality [97], analysis of agricultural yield response [98], reservoir operations and irrigation [9].…”

Section: Artificial Neural Network (Ann) Design: a Corporation Of Artificial Neural Network (Ann)mentioning

confidence: 99%

A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

Ari

Alagöz

2021

Sakarya University Journal of Science

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Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems.

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Section: Artificial Neural Network (Ann) Design: a Corporation Of Artificial Neural Network (Ann)mentioning

confidence: 99%

A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

Ari

Alagöz

2021

Sakarya University Journal of Science

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“…U. and Kusiak [33]; Babiceanu et al [34], and Coban [35] deal with the RCPSP in a dynamic, real time way. To obtain particular schedules at some point, metaheuristics are the most used techniques for solving RCPSP, including Tabu search [36,37], Simulated Annealing [38,39], or Genetic Algorithm [40]. Swarm Intelligence metaheuristics [41] are also effective for solving related issues.…”

Section: Ms-rcpspmentioning

confidence: 99%

A Memetic Algorithm for Multiskill Resource‐Constrained Project Scheduling Problem under Linear Deterioration

Dai

2019

Mathematical Problems in Engineering

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This paper proposes a general variable neighborhood search-based memetic algorithm (GVNS-MA) for solving the multiskill resource-constrained project scheduling problem under linear deterioration. Integrating a solution recombination operator and a local optimization procedure, the proposed GVNS-MA is assessed on two sets of instances and achieves highly competitive results. One set of benchmark instances is commonly used in the literature where the capability of the proposed algorithm to find high quality solutions is demonstrated, compared with the state-of-the-art algorithms in the literature. The other set revises the former through incorporating the linear deterioration effect. Two key components of the proposed algorithm are investigated to confirm their critical role to the success of the proposed method.

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“…1,2 Since the work of Jackson 3 that introduced the dynamic scheduling problem in the 1950s for the first time, the dynamic scheduling problem has gained enormous popularity in the scheduling research community, and fruitful results have been reported in the literature, which can be classified into three main categories, namely, rescheduling, [4][5][6][7][8][9][10] robust scheduling, [11][12][13][14] and online scheduling. [15][16][17][18] Rescheduling is also known as pre-reaction scheduling, which implies to schedule again when new events occur. It consists of two stages, the first stage, the prescheduling stage, aims to generate a scheduling plan to be followed by actual production; and the second stage, also named as the rescheduling stage, adjusts some or all the original scheduling plans to accommodate the dynamic events happening at a certain moment during production.…”

Section: Introductionmentioning

confidence: 99%

Dynamic flexible job shop scheduling method based on improved gene expression programming

Zhang

Zhou

Peng

et al. 2020

Measurement and Control

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Dynamic scheduling is one of the most important key technologies in production and flexible job shop is widespread. Therefore, this paper considers a dynamic flexible job shop scheduling problem considering setup time and random job arrival. To solve this problem, a dynamic scheduling framework based on the improved gene expression programming algorithm is proposed to construct scheduling rules. In this framework, the variable neighborhood search using four efficient neighborhood structures is combined with gene expression programming algorithm. And, an adaptive method adjusting recombination rate and transposition rate in the evolutionary progress is proposed. The test results on 24 groups of instances with different scales show that the improved gene expression programming performs better than the standard gene expression programming, genetic programming, and scheduling rules.

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Evolving priority rules for resource constrained project scheduling problem with genetic programming

Cited by 57 publications

References 26 publications

A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

A Memetic Algorithm for Multiskill Resource‐Constrained Project Scheduling Problem under Linear Deterioration

Dynamic flexible job shop scheduling method based on improved gene expression programming

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