A Hybrid Algorithm for Minimizing Makespan in the Permutation Flow Shop Scheduling Environment

Robert, R.B. Jeen; Kumar, Rakesh

doi:10.5958/2249-7315.2016.00867.4

Cited by 3 publications

(3 citation statements)

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“…In early decades, many heuristic approaches has been developed for solving flow shop problems, e.g,, NEH heuristics [3],SPIRIT heuristics [4], Palmer heuristics [ 5], Hybrid Algorithm [6], Genetic Algorithm [7], Gupta heuristics [8]. Above all heuristics approach NEH heuristics is best constructive method for solving PFSP.…”

Section: Introductionmentioning

confidence: 99%

Testing the Performance of Bat-Algorithm for Permutation Flow Shop Scheduling Problems with Makespan Minimization

Robert¹,

Leela²,

Maharaj³

2022

Braz. arch. biol. technol.

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In this work, a BAT Algorithm is proposed to solve the permutation flow shop scheduling problem (PFSSP) with minimizing makespan criterion. In a PFSSP, there are n-jobs and m-machines with a proportional deterioration is considered in which all machines process the jobs in the same order, i.e., a permutation schedule. Every job comprises of a foreordained arrangement of assignment operations, each of which should be handled without intrusion for a given timeframe on a given machine. As of late, optimization algorithms such as ant colony optimization (ACO), simulated annealing (SA), artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimization (PSO) and tabu search (TS) have assumed a significant role in solving PFSSPs. The popular NEH algorithm is considered as the parent algorithm to find the initial solution, and the makespan is minimized in two stages of simulation. The proposed algorithm is tested on 12 flow shop scheduling bench mark problems from OR Library. The proposed algorithm is validated with a well-chosen set of benchmark problems in the literature. Computational results indicate that the proposed bat algorithm is more efficient than the TLBO & HPSO algorithm.

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Section: Introductionmentioning

confidence: 99%

Testing the Performance of Bat-Algorithm for Permutation Flow Shop Scheduling Problems with Makespan Minimization

Robert¹,

Leela²,

Maharaj³

2022

Braz. arch. biol. technol.

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“…For a limited-wait constraints, complex hybrid flow-shop scheduling problem was solved with discrete time exact arrangement approach by Gicquel et al [6], and a modified teaching-learning-based optimization algorithm has been used to solve bi-objective re-entrant hybrid flow shop scheduling by Shen et al [20]. Later, Jeen Robert and Rajkumar [8] proposed a hybrid algorithm for minimizing makespan in the PFSSP. Two-machine and three-machine flow shop scheduling problem is solved using branch-andbound (B&B) algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm for Multi-Objective Optimization of Minimizing Makespan and Total Flow Time in Permutation Flow Shop Scheduling Problems

Rajkumar¹,

Robert²

2019

ITC

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In this work, a hybrid algorithm has been proposed to solve bi-objective permutation flow shop scheduling problem. The primary concern of flow shop scheduling problem considered in this work is to obtain the best sequence, which minimizes the makespan and the total flow time of all jobs. Bi-objective issues are comprehended by doling out uniform weight to every objective function in view of its preference or determining every competent solutions. In the flow shop scheduling environment, many meta-heuristic algorithms have been used to find optimal or near-optimal solutions due to the computational cost of determining exact solutions. This work provides a hybridization of genetic algorithm and simulated annealing algorithm (HGASA) based multi-objective optimization algorithm for flow shop scheduling. The proposed HGASA algorithm is used to solve a bi-objective problem that minimizes the makespan and the total flow time. The performance of the proposed algorithm is demonstrated by applying it to benchmark problems available in the OR-Library. The test results show that the HGASA algorithm performed better in terms of searching quality and efficiency than other meta-heuristic algorithms.

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“…T'kindt, Gupta, and Billaut [5] presented mathematical programming designs, a branch-and-bound process, and a heuristic algorithm. Later, Jeen Robert et al [6] have given a hybrid algorithm for Minimizing Makespan in the flow shop Environment. Yeh [7] created a memetic algorithm to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

An Effective Genetic Algorithm for Flow Shop Scheduling Problems to Minimize Makespan

Robert¹,

Rajkumar²

2017

mech

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A Hybrid Algorithm for Minimizing Makespan in the Permutation Flow Shop Scheduling Environment

Cited by 3 publications

References 0 publications

Testing the Performance of Bat-Algorithm for Permutation Flow Shop Scheduling Problems with Makespan Minimization

Testing the Performance of Bat-Algorithm for Permutation Flow Shop Scheduling Problems with Makespan Minimization

A Hybrid Algorithm for Multi-Objective Optimization of Minimizing Makespan and Total Flow Time in Permutation Flow Shop Scheduling Problems

An Effective Genetic Algorithm for Flow Shop Scheduling Problems to Minimize Makespan

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