Solving the flexible job shop scheduling problem using an improved Jaya algorithm

Caldeira, Rylan H.; Gnanavelbabu, A.

doi:10.1016/j.cie.2019.106064

Cited by 64 publications

(31 citation statements)

References 49 publications

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“…The population size was chosen as 60 according to the parametric analysis carried out in Caldeira and Gnanavelbabu. 34…”

Section: Resultsmentioning

confidence: 99%

“…JA has gained popularity in solving optimization problems such as economic emission dispatch problems, 30 permutation flow shop problem, 31 urban traffic signal control problem, 32 flexible flow shop scheduling problem, 33 and FJSSP. 34 There have been limited applications of JA to address the SFJSSP, and hence this issue is addressed in this article.…”

Section: Introductionmentioning

confidence: 99%

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A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

Caldeira

Gnanavelbabu

2020

SIMULATION

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In this work, we address the flexible job shop scheduling problem (FJSSP), which is a classification of the well-known job shop scheduling problem. This problem can be encountered in real-life applications such as automobile assembly, aeronautical, textile, and semiconductor manufacturing industries. To represent inherent uncertainties in the production process, we consider stochastic flexible job shop scheduling problem (SFJSSP) with operation processing times represented by random variables following a known probability distribution. To solve this stochastic combinatorial optimization problem we propose a simulation-optimization approach to minimize the expected makespan. Our approach employs Monte Carlo simulation integrated into a Jaya algorithm framework. Due to the unavailability of standard benchmark instances in SFJSSP, our algorithm is evaluated on an extensive set of well-known FJSSP benchmark instances that are extended to SFJSSP instances. Computational results demonstrate the performance of the algorithm at different variability levels through the use of reliability-based methods.

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“…The population size was chosen as 60 according to the parametric analysis carried out in Caldeira and Gnanavelbabu. 34…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

Caldeira

Gnanavelbabu

2020

SIMULATION

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“…Rylan H. Caldeira i A. Gnanavelbabu su u radu [6] predložili Java algoritam za rešavanja problema fleksibilnog planiranja poslova. Rešavani problem je testiran kao varijanta sa vremenskim ograničenjem operacija i minimizaciji ukupnog vremena zakazivanja.…”

Section: Fleksibilni Problem Raspoređivanjaunclassified

“…Rylan H. Caldeira and A. Gnanavelbabu in the work [6] proposed Java problem-solving algorithflexible job planning. The problem that's been resolved is tested as a variant with a time limit operations and minimize the total appointment time.…”

Section: Flexible Job-shop Problem (Fjsp)mentioning

confidence: 99%

Metaheuristic algorithms for the flexible job-shop scheduling problem

Stanković¹,

Petrović²,

Marković³

et al. 2020

IMK-14 Istraživanje i razvoj

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The problem with flexible job planning (FJSP) is a modification o f the classic job booking problem. This paper deals with the problem o f flexible job deployment and processing o f operations on one machine from a set o f alternative machines. The problem o f deploying flexible jobs in real time is one o f a group o f difficult NP problems (Non-deterministic polynomial time). The motive o f this paper is to show the application o f meta-heuristic algorithms on the example of flexible job schedules and present the appropriate method to future studies. To solve this problem, two meta-heuristic algorithms were used: Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The aim o f the paper is to achieve the speed o f the conversion solution in a series o f iterations, minimizing the total time o f deployment of jobs on an alternative set of machines, as well as minimizing the total schedule time. The problem o f deploying flexible jobs has great application, and with this test we propose an algorithm for solving this problem.

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“…Rylan H. Caldeira and A. Gnanavelbabu [8] solved the FJSP by using an improved Jaya algorithm (JA). They intended to improve the algorithm to prevent getting trapped in the local optimum and needing specific tuning parameters to obtain an optimal solution.…”

Section: Introductionmentioning

confidence: 99%

The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem

Phu-ang

2021

ECTI-CIT

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This paper proposes a hybrid algorithm that applied the firefly algorithm (FA) with the new idea labeled as the fuzzy movement method for solving a complex scheduling problem called the flexible job shop scheduling. The step of the proposed algorithm is similar to the original FA, which is based on the concept of flashing behavior to attract the other fireflies. In order to improve the efficiency of the FA algorithm for the FJSP, the proposed algorithm introduces three new ideas. In the first idea, the genetic algorithm (GA) is used to generate the high quality of the initial population. Next, the self-adaptive roulette wheel selection which embedded in the GA introduced to increase the diversity of the machine selection process. Finally, the fuzzy movement method is presented to enhance the work balancing ability between the high workload machines and the low workload machines. The proposed algorithm has been evaluated with a benchmark data set and compared to the other algorithm. The experimental results demonstrate that the proposed algorithm can effectively solve the flexible job shop scheduling problem.

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Solving the flexible job shop scheduling problem using an improved Jaya algorithm

Cited by 64 publications

References 49 publications

A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

Metaheuristic algorithms for the flexible job-shop scheduling problem

The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem

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