A hybrid TLBO-TS algorithm for integrated selection and scheduling of projects

Kumar, Manish; Mittal, Murari Lal; Soni, Gunjan; Joshi, Dheeraj

doi:10.1016/j.cie.2018.03.029

Cited by 50 publications

(13 citation statements)

References 39 publications

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“…Shariatmadari et al (2017) define a specific index to study the problem called integrated resource management, which considers simultaneous project selection and scheduling. All in all, authors of this cluster agree to consider both simultaneous selection and scheduling, as projects have a variety of interdependencies between them (Kumar et al, 2018). In their paper, Coelho and Vanhoucke (2011) define an approach that executes two steps (mode assignment and a single-mode project scheduling) in one run.…”

Section: Cluster Vii: the Resource-constrained Project Scheduling Problemmentioning

confidence: 99%

A clustering‐based review on project portfolio optimization methods

Saiz

Lostumbo

Juan

et al. 2021

Int Trans Operational Res

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Project portfolio management and optimization constitutes a critical activity for organizations in different industrial sectors and business. The scientific literature in this subject is extremely vast, which makes it difficult to understand the connections among the existing approaches and perspectives. This paper provides a clustering map of the existing work on the subject, thus identifying the main trends and approaches from different scientific communities. After analyzing each of the identified clusters, the paper provides insights and emerging trends that can be useful both for researchers and practitioners in the area.

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Section: Cluster Vii: the Resource-constrained Project Scheduling Problemmentioning

confidence: 99%

A clustering‐based review on project portfolio optimization methods

Saiz

Lostumbo

Juan

et al. 2021

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…premature convergence. TLBO algorithm has excellent exploration capability but lack in exploiting the solution space locally [40]. To enhance the exploitation capability of the algorithm, the TLBO algorithm is combined with SFLA, which is having a good local search ability [38].…”

Section: Hybrid Sfl-tlbo Algorithmmentioning

confidence: 99%

Multi‐objective optimal planning of FCSs and DGs in distribution system with future EV load enhancement

Battapothula

Yammani

Sydulu

2019

IET Electrical Systems in Transportation

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Current trends suggest that electrical vehicle (EV) is a promising technology for road transportation. There is a substantial increase in the number of EVs due to improved energy efficiency and reduction in environmental impact as compared with internal combustion engine vehicles. The improper planning of fast charging stations (FCSs) and distributed generations (DGs) hurts the distribution system. So the distribution system operator has a significant challenge to identify the optimal location and sizing of FCSs in the distribution power network. This study presents optimal planning of FCSs and DGs with the account of the present and future increase in EV population. A multi-objective optimisation problem is formulated for optimal planning of FCSs and DGs with the objective of minimising the voltage deviation, distribution network power loss, DGs cost and the energy consumption of EV users. This problem is solved for different levels of increase in EV population for different cases. A novel hybrid shuffled frog leap-teaching and learning based optimisation algorithm is proposed and implemented to solve the considered multi-objective problem. The performance of the proposed algorithm is compared with prior-art algorithms in the literature.

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“…91.88 as compared to other algorithms. Kumar et al have applied a hybrid TLBO-TS algorithm to deal with problem of simultaneous selection and scheduling of projects [37]. The proposed approach is tested on several datasets and compared with TLBO and TS algorithms.…”

Section: Related Workmentioning

confidence: 99%

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Kumar

Dahiya²,

Malik³

et al. 2019

IJCAI

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This paper presents a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems. The performance of the TLBO algorithm depends on coordination of teacher phase and learner phase. It is noticed that sometimes performance of TLBO algorithm is affected due to lack of diversity in teacher and learner phases. In this work, a new variant of TLBO algorithm is proposed based on genetic crossover and mutation strategies. These strategies are inculcated in TLBO algorithm for improving its search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find the good learner. The effectiveness of the proposed algorithm is tested on several bench mark test functions of CEC'14. From simulation results, it is stated that the proposed algorithm provides more optimized results in comparison to same class of algorithms.Povzetek: V prispevku je opisan nov globalni optimizacijski algoritem na osnovi učenja optimizacije (TLBO).

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A hybrid TLBO-TS algorithm for integrated selection and scheduling of projects

Cited by 50 publications

References 39 publications

A clustering‐based review on project portfolio optimization methods

A clustering‐based review on project portfolio optimization methods

Multi‐objective optimal planning of FCSs and DGs in distribution system with future EV load enhancement

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

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