A Combination of Palmer Algorithm and Gupta Algorithm for Scheduling Problem in Apparel Industry

Nugraheni, Cecilia Esti; Abednego, Luciana; Widyarini, Maria; Nugraheni, Luciana Abednego Cecilia E.

doi:10.5121/ijfls.2021.11101

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Data from each group are transmitted through a specific interface signal to the DUT. There are two main operators which are used in most implementations of genetic algorithms in the industry, namely, crossover and mutation [28,29].…”

Section: Operation Of Genetic Algorithmsmentioning

confidence: 99%

Coverage Fulfillment Automation in Hardware Functional Verification Using Genetic Algorithms

Danciu

Dinu

2022

Applied Sciences

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The functional verification process is one of the most expensive steps in integrated circuit manufacturing. Functional coverage is the most important metric in the entire verification process. By running multiple simulations, different situations of DUT functionality can be encountered, and in this way, functional coverage fulfillment can be improved. However, in many cases it is difficult to reach specific functional situations because it is not easy to correlate the required input stimuli with the expected behavior of the digital design. Therefore, both industry and academia seek solutions to automate the generation of stimuli to reach all the functionalities of interest with less human effort and in less time. In this paper, several approaches inspired by genetic algorithms were developed and tested using three different designs. In all situations, the percentage of stimulus sets generated using well-performing genetic algorithms approaches was higher than the values that resulted when random simulations were employed. In addition, in most cases the genetic algorithm approach reached a higher coverage value per test compared to the random simulation outcome. The results confirmed that in many cases genetic algorithms can outperform constrained random generation of stimuli, that is employed in the classical way of doing verification, considering coverage fulfillment level per verification test.

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Section: Operation Of Genetic Algorithmsmentioning

confidence: 99%

Coverage Fulfillment Automation in Hardware Functional Verification Using Genetic Algorithms

Danciu

Dinu

2022

Applied Sciences

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“…Scheduling of jobs in FSS is an NP-hard problem that is generally solved using heuristic and metaheuristic algorithms [1]- [3]. These heuristics include dispatching rules such as FCFS (First Come First Serve) and SPT (Shortest Processing Time), NEH algorithm, Gupta algorithm, Palmer algorithm, and other algorithms [4]- [6]. Another heuristics group is metaheuristics, such as genetic algorithms, simulated annealing, and particle swarm optimization [1]- [3], [8]- [13].…”

Section: Introductionmentioning

confidence: 99%

A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem

Halim

Nugraheni

2021

JOIV : Int. J. Inform. Visualization

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Flow Shop Scheduling (FSS) is scheduled to involve n jobs and m machines in the same process sequence, where each machine processes precisely one job in a certain period. In FSS, when a machine is doing work, other machines cannot do the same job simultaneously. The solution to this problem is the job sequence with minimal total processing time. Many algorithms can be used to determine the order in which the job is performed. In this paper, the algorithm used to solve the flow shop scheduling problem is the bee colony algorithm. The bee colony algorithm is an algorithm that applies the metaheuristic method and performs optimization according to the workings of the bee colony. To measure the performance of this algorithm, we conducted some experiments by using Taillard's Benchmark as problem instances. Based on experiments that have been carried out by changing the existing parameter values, the size of the bee population, the number of iterations, and the limit number of bees can affect the candidate solutions obtained. The limit is a control parameter for a bee when looking for new food sources. The more the number of bees, the more iterations, and the limit used, the better the final time of the sequence of work. The bee colony algorithm can reach the upper limit of the Taillard case in some cases in the number of machines 5 and 20 jobs. The more the number of machines and jobs to optimize, the worse the total processing time.

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A Combination of Palmer Algorithm and Gupta Algorithm for Scheduling Problem in Apparel Industry

Cited by 2 publications

References 8 publications

Coverage Fulfillment Automation in Hardware Functional Verification Using Genetic Algorithms

Coverage Fulfillment Automation in Hardware Functional Verification Using Genetic Algorithms

A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem

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