A novel teaching-learning-based optimization algorithm for energy-efficient scheduling in hybrid flow shop

Lei, Deming; Gao, Liang; Zheng, Yewei

doi:10.1109/tem.2017.2774281

Cited by 128 publications

(61 citation statements)

References 35 publications

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“…To minimize total energy consumption and workload balance they propose a shuffled frog-leaping algorithm. In a later work Lei et al (2018) present a novel teaching-learning algorithm to minimize total energy consumption and total tardiness in a hybrid flow shop scheduling problem. The problem is basically similar to the one we are investigating here.…”

Section: (F) Variable Production Speedmentioning

confidence: 99%

Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices

2020

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Energy costs play an important role in industrial production and are closely related to environmental concerns. As sustainability aspects are coming into focus in recent years, energy-oriented objectives are increasingly being taken into account in scheduling. At the same time, requirements for punctual delivery become more and more important in times of just-in-time delivery and highly networked supply chains. In this paper, a hybrid flow shop scheduling problem with variable discrete production speed levels is considered with the aim of minimizing both energy costs and total tardiness. Although lower speeds can reduce energy consumption, they also increase processing times, which counteract the objective of punctual delivery. Two new model formulations additionally taking time-of-use energy prices into account are presented and compared. The influence of variable discrete production speed levels on energy costs, energy consumption and punctual delivery as well as the interdependencies between these objectives are analysed in a numerical case study.

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Section: (F) Variable Production Speedmentioning

confidence: 99%

Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices

2020

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“…The TLBO algorithm was developed by Rao et al 66 and has been widely accepted as an excellent optimization algorithm for number of problems such as optimal design of spur-gears, composite test functions, multi-objective unconstrained and constrained test functions, design of pressure vessel, compression/tension spring design, speed reducer design, etc. 49,[67][68][69][70][71] This algorithm has also been successfully employed on unconstrained problems as well. 72,73 The problem of Pareto Front (PF) has been solved by implementing modified version of TLBO and it is known as elitist TLBO (ETLBO).…”

Section: Comparison Of Mtlbo With Tlbo and Samentioning

confidence: 99%

Efficient content retrieval in fog zone using Nano‐Caches

Singh

Kumar

Sharma

2019

Concurrency and Computation

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Summary It is always desired to improve the response time from cloud servers, which deliver contents without buffering. As the penetration of mobile/fog devices is increasing, the limits of cellular ranges come under question. This question arises in spite of the fact that the current Internet Service Providers and data operators are adding cellular towers frequently to reduce delay and enhance performance. This performance can be improved by increasing Nano‐Cache(s) at the edges of the network for forwarding interrelated contents to remote corner of the earth. In this research work, Nano‐Caches are integrated for delivering contents efficiently, using search‐based optimization techniques, which are energy and response aware in nature. An algorithm, namely, Modified Teaching Learning‐Based Optimization(MTLBO), is devised and implemented in fog zone to find efficient route for forwarding contents using Nano‐Caches and subsequently to improve content retrieval time. Mathematical distribution model of traffic is used for simulation process. MTLBO is compared with existing algorithms, namely, Teaching Learning‐Based Optimization (TLBO) Algorithm and Simulated Annealing (SA) Algorithm. The design of experiments (DOE) was carried out to observe number of iterations, learning rate, and by changing the network size. Java library was used for observing values of memory and execution time. The results show that Modified Teaching Learning‐Based Optimization (MTLBO) approach is better than Teaching Learning‐Based Optimization (TLBO) approach as it has less overheads in terms of memory (considering number of fog caches) and network size for delivering contents at remote areas. In comparison to the Simulated Annealing (SA) algorithm, MTLBO performs better in terms of execution time, overhead in terms of memory, and scalability as function of network size.

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“…It became clear that the utilized GA model is quite effective to assess the scheme selection and, thus lowering the makespan and the EC to the least-possible value. Lei et al [12] presented a novel teaching-learning-based optimization model for energy-efficient scheduling in hybrid flow shops. The computational results showed that the teachers' teaching-learning-based optimization method is a competitive algorithm for the considered hybrid flow shop scheduling problem.…”

Section: Literature Review and Research Backgroundmentioning

confidence: 99%

“…In the first scenario, which was performed using the FCFS method, the processing of the jobs coming to the job-shop environment on the basis of their order has been performed. The considered flow of the jobs for each work station is shown to be as follows: (1) Work station 2 : J 1 (8, 6), J 2 (8, 10), J 3 (8,15), J 4 (9.5, 14), J 5 (12,16), J 6 (16, 18), J 7 (22, 16), J 8 (0, 11.2), J 9 (0, 24), J 10 (0, 23).…”

Section: Analysis Of the Sequencing Of The Jobsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Operation and Scheduling of Parallel Machines in Jobshop Environments

Faccio¹,

Nedaei²

2018

Management of Sustainable Development

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Scheduling of machines together with the optimization of the energy consumption (EC) play a crucial role in every manufacturing system. The current paper aims to present a multi-objective optimization approach for planning and scheduling of a manufacturing system in a job-shop environment. First, an inclusive review and comparison of the latest advancements in this field was performed. Then, a manufacturing system in a job-shop environment, which consists of six designed machines with zero buffer capacity, and 53 jobs, was simulated. Four heuristics for the purpose of sequencing are adopted during the analysis process. A comparison of the optimum results suggested the most efficient values for the EC, make-span, and tardiness. The EC variations and processing time of the machines were studied under sequencing rules to let the designers exploring more efficient solutions, and planning strategies towards enhancing the performance and reliability of studied machines. Next step of this study was to estimate the optimum completion time of each process in the considered manufacturing system and with consideration of the proposed scheduling methods.

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A novel teaching-learning-based optimization algorithm for energy-efficient scheduling in hybrid flow shop

Cited by 128 publications

References 35 publications

Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices

Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices

Efficient content retrieval in fog zone using Nano‐Caches

Optimal Operation and Scheduling of Parallel Machines in Jobshop Environments

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