Multi-skill project scheduling problem under time-of-use electricity tariffs and shift differential payments

Najafzad, Hamid; Davari-Ardakani, Hamed; Nemati-Lafmejani, Reza

doi:10.1016/j.energy.2018.11.070

Cited by 21 publications

(8 citation statements)

References 37 publications

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“…. , 16) represents the obtained Pareto solutions, and the final solutions (FS) are the union of all sets PA i with non-dominated sorting. Then, the relevance value (RV) of each parameter combination is calculated as RV(i) = |PA i ∩ FS|/|FS|.…”

Section: Parameter Settingmentioning

confidence: 99%

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Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Considering Flexible Resource Profiles

Luo,

Guo,

et al. 2024

Applied Sciences

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This paper addresses a novel multi-skill resource-constrained project scheduling problem with flexible resource profiles (F-MSRCPSP), in which the resource allocation of each activity consists of a certain number of discrete resources and is allowed to be adjusted over its duration. The F-MSRCPSP aims, therefore, to determine the flexible resource profile of each activity to minimize the make-span and total cost simultaneously. Then, a hybrid multi-objective fruit fly optimization algorithm is proposed to handle the concerned problem. In the proposed algorithm, two flexible parallel and serial schedule generation schemes are introduced, aiming to schedule activities and adjust allocated resource combinations. Additionally, two heuristic strategies are proposed to effectively select suitable resource combinations for activities. Moreover, a series of operators has been developed, including the rejoining operator, empirical re-arrangement operator, and empirical re-selection operator. These operators aim to accelerate the convergence speed and enhance the exploration of the proposed algorithm. Finally, the orthogonal test is used to select the optimal parameter combination, and comparative experiments based on tests with different scales are conducted, along with a t-test. The experimental results demonstrate that MOFOA-HS is effective in solving the F-MSRCPSP.

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Section: Parameter Settingmentioning

confidence: 99%

“…Hosseinian and Baradaran [15] proposed a MSRCPSP considering transfer times. Najafzad et al [16] addressed a MSRCPSP considering manpower under shift differential payments and equipment with time-of-use electricity tariffs. Ahmadpour and Ghezavati [17] studied a MSRCPSP considering project members' skill factor.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Considering Flexible Resource Profiles

Luo,

Guo,

et al. 2024

Applied Sciences

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“…In the process of looking for the optimal schedule, both priority constraint between operations and resource constraint have to be met. [5,13,36] was chosen because it is more practical than its general problem, RCPSP. MS-RCPSP uses the additional attribute, skill, in the problem formulation.…”

Section: Rcpsp Ms-rcpsp Is Just a Special Case Of Rcpspmentioning

confidence: 99%

“…e simulated dataset iMOPSE [5] has been used to investigate existing algorithms GreedyDO and GA [13,36]; therefore, we conduct simulations on iMOPSE dataset to fairly compare between DEM and those algorithms.…”

Section: Imopse Datasetmentioning

confidence: 99%

Intelligent Differential Evolution Scheme for Network Resources in IoT

Quoc

Doan²,

Thanh

et al. 2020

Scientific Programming

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Scheduling is a fundamental factor in managing the network resource of the Internet of things. For IoT systems such as production lines, to operate effectively, it is necessary to find an intelligent management scheme, i.e., schedule, for network resources. In this study, we focus on multiskill resource-constrained project scheduling problem (MS-RCPSP), a combinational optimization problem that has received extensive attention from the research community due to its advantages in network resource management. In recent years, many approaches have been utilized for solving this problem such as genetic algorithm and ant colony optimization. Although these approaches introduce various optimization techniques, premature convergence issue also occurs. Moreover, previous studies have only been verified on simulation data but not on real data of factories. This paper formulated the MS-RCPSP and then proposed a novel algorithm called DEM to solve it. The proposed algorithm was developed based on the differential evolution metaheuristic. Besides, we build the reallocate function to improve the solution quality so that the proposed algorithm converges rapidly to global extremum while also avoiding getting trapped in a local extremum. To evaluate the performance of the proposed algorithm, we conduct the experiment on iMOPSE, the simulated dataset used by previous authors in their research studies. In addition, DEM was verified on real dataset supported by a famous textile industry factory. Experimental results on both simulated data and real data show that the proposed algorithm not only finds a better schedule compared with related works but also can reduce the processing time of the production line currently used at the textile industry factory.

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“…Hosseinian and Baradaran [9] used an evaluation method to make plan decisions with MS-RCPSP. Nazafzad et al [10] employed a biobjective optimization model for the MS-RCPSP considering shift differential payments and time-of-use electricity tariffs. eir study tried to minimize the cost and the makespan of a given project.…”

Section: Introductionmentioning

confidence: 99%

Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem

Quoc

Lộc

Doan

et al. 2020

Journal of Advanced Transportation

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This paper defines and introduces the formulation of the Real-RCPSP (Real-Resource-Constrained Project Scheduling Problem), a new variant of the MS-RCPSP (Multiskill Resource-Constrained Project Scheduling Problem). Real-RCPSP is an optimization problem that has been attracting widespread interest from the research community in recent years. Real-RCPSP has become a critical issue in many fields such as resource allocation to perform tasks in Edge Computing or arranging robots at industrial production lines at factories and IoT systems. Compared to the MS-RCPSP, the Real-RCPSP is supplemented with assumptions about the execution time of the task, so it is more realistic. The previous algorithms for solving the MS-RCPSP have only been verified on simulation data, so their results are not completely convincing. In addition, those algorithms are designed only to solve the MS-RCPSP, so they are not completely suitable for solving the new Real-RCPSP. Inspired by the Cuckoo Search approach, this literature proposes an evolutionary algorithm that uses the function Reallocate for fast convergence to the global extremum. In order to verify the proposed algorithm, the experiments were conducted on two datasets: (i) the iMOPSE simulation dataset that previous studies had used and (ii) the actual TNG dataset collected from the textile company TNG. Experimental results on the iMOPSE simulation dataset show that the proposed algorithm achieves better solution quality than the existing algorithms, while the experimental results on the TNG dataset have proved that the proposed algorithm decreases the execution time of current production lines at the TNG company.

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Multi-skill project scheduling problem under time-of-use electricity tariffs and shift differential payments

Cited by 21 publications

References 37 publications

Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Considering Flexible Resource Profiles

Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Considering Flexible Resource Profiles

Intelligent Differential Evolution Scheme for Network Resources in IoT

Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem

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