A TLBO and a Jaya heuristics for permutation flow shop scheduling to minimize the sum of inventory holding and batch delay costs

Mishra, Aseem K.; Shrivastava, Divya

doi:10.1016/j.cie.2018.07.049

Cited by 40 publications

(6 citation statements)

References 38 publications

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“…There are some requirements of PFS: preemption is not allowed; all jobs are independent and are available for processing at time zero; the machines are available continuously; the setup times of jobs on machines are sequence-independent and included in the processing times [86,113]. The no-wait performance measures of PFS and NPFS problems are categorized into three groups: completion time measures (C max , F ); due date measures (L, T ); measures based on inventory and utilization costs [35,38,89].…”

Section: The Permutation and Non-permutation Flexible Flow Shop Scheduling Problemsmentioning

confidence: 99%

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

2021

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Flow shop scheduling is a type of scheduling where sequence follows for each job on a set of machines for processing. In practice, jobs in flow shops can arrive at irregular times, and the no-wait constraint allows the changes in the job order to flexibly manage such irregularity. The flexible flow shop scheduling problems with no-wait have mainly addressed for flow optimization on the shop floor in manufacturing, processing, and allied industries. The scope of this paper is to identify the literature available on permutation and non-permutation flow shop scheduling with no-wait constraint. This paper organizes scheduling problems based on performance measures of variability and shop environments. The extended summary of two/three-machine and m-machine problems has been compiled, including their objectives, algorithms, parametric considerations, and their findings. A systematic appearance of both conceptual and analytical results summarizes various advances of the no-wait constraint. The paper includes independently investigated problems and suggestions for future research directions.

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Section: The Permutation and Non-permutation Flexible Flow Shop Scheduling Problemsmentioning

confidence: 99%

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

2021

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“…TLBO is an efficient optimization method which has been used for engineering problems among others [13], [28]- [30]. This method looks for a teacher (best individual) that will probably cause an influence on the learners (the rest of individuals) to improve their features.…”

Section: A the Tlbo Algorithmmentioning

confidence: 99%

Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

et al. 2019

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The utilization of optimization algorithms within engineering problems has had a major rise in recent years, which has led to the proliferation of a large number of new algorithms to solve optimization problems. In addition, the emergence of new parallelization techniques applicable to these algorithms to improve their convergence time has made it a subject of study by many authors. Recently, two optimization algorithms have been developed: Teaching-Learning Based Optimization and Jaya. One of the main advantages of both algorithms over other optimization methods is that the former do not need to adjust specific parameters for the particular problem to which they are applied. In this paper, the parallel implementations of Teaching-Learning Based Optimization and Jaya are compared. The parallelization of both algorithms is performed using manycore GPU techniques. Different scenarios will be created involving functions frequently applied to the evaluation of optimization algorithms. Results will make it possible to compare both parallel algorithms with regard to the number of iterations and the time needed to perform them so as to obtain a predefined error level. The GPU resources occupation in each case will also be analyzed.

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“…Yuan et al 17 combined simulated annealing, particle swarm optimization and genetic algorithm to solve the cost-benefit maximization problem, and verified the effectiveness of this method through examples. Based on simulated annealing particle swarm optimization, Mishra and Shrivastava 18 solved the comprehensive cost minimization problem in the scheduling process, which consists of the processing inventory carrying cost and penalty cost. For medium and large-scale problems, this method has more obvious advantage than the traditional optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

Chen

Zou

Wang

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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To solve the problems of low efficiency and insufficient dynamic response of job shop scheduling in the discrete manufacturing process, a multi-objective flexible job shop scheduling model for digital twin and its solution method are proposed. Firstly, a digital twin scheduling model with physical entity, virtual model and production plan is constructed, and four factors are taken as optimization goals. Then, a hybrid particle swarm optimization method is designed to increase the refined optimization ability, and the obtained Pareto optimal solution set is analyzed by grey relational analysis to obtain a satisfactory solution which coincides with the actual production. Finally, a three-dimensional model which is completely mapped with the real job shop scheduling is built by Plant Simulation software. The scheduling process is simulated and optimized by combining with the production data of an enterprise, which verifies the feasibility and applicability of this method, and will effectively guide the production practice.

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A TLBO and a Jaya heuristics for permutation flow shop scheduling to minimize the sum of inventory holding and batch delay costs

Cited by 40 publications

References 38 publications

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

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