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

Robert, R.B. Jeen; Rajkumar, Ragunathan

doi:10.5755/j01.mech.23.4.15053

Cited by 6 publications

(8 citation statements)

References 14 publications

(21 reference statements)

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“…The assumptions and constraints of the FSSP are summarised as follows: All the jobs are no precedence constraints of each other. Each job is processed on only one machine while each machine can handle only one job at a time. The number of jobs and their execution on each machine time is known ahead and independent. Execution times include setup time, processing time and transportation time. Once a job starts to be processed on a machine, this process cannot be interrupted before its completion [13]. Each machine processes the job in the same sequence. …”

Section: Problem Descriptionmentioning

confidence: 99%

“…� Execution times include setup time, processing time and transportation time. � Once a job starts to be processed on a machine, this process cannot be interrupted before its completion [13]. � Each machine processes the job in the same sequence.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Once a job starts to be processed on a machine, this process cannot be interrupted before its completion [13].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In 2015, Sayoti and Essaid Ri [12] proposed an adaptation of a new approach called the golden ball algorithm which was employed for solving the FSSP. In 2017, Robert and Rajkumar [13] used the effective genetic algorithms (GAs) to optimally solve the FSSP. They found that the GAs performed better than the ant colony optimization (ACO) algorithm for solving the non‐permutation FSSP with the makespan criterion.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Q ‐learning algorithm for solving permutation flow shop scheduling problems

Wang²,

et al. 2021

IET Collab Intel Manufact

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Generally, scheduling problems refer to allocation of available shared resources and the sorting of production tasks, in order to satisfy the specified performance target within a certain time. The fundamental scheduling problem is that all jobs need to be processed on the same route, which is called flow shop scheduling problems (FSSP). The goal of FSSP, proven as an NP-hard problem, is to find a job sequence that minimizes the makespan. In this paper, an improved Q-learning algorithm is proposed for solving the FSSP. Firstly, a problem model based on the basic Q-learning algorithm is constructed. The makespan is used as the feedback signal, and the process of environmental state change is defined as the process of job selection. Q-learning gives the expected utility of taking a given action in a given state. Afterwards, combined with the NEH heuristic, the algorithm efficiency is enhanced by changing the job inserting mode. In order to validate the proposed method, several simulation experiments are carried out on a set of test problems having different scales. The obtained optimization results of the proposed algorithm are compared to the standard Q-learning algorithm and a hybrid algorithm. The discussion and analysis show that the proposed algorithm performs better than the others in solving the permutation FSSP. As a future direction, in order to shorten the running time, further improvements will be studied to increase the performance of the proposed algorithm and make it applicable and efficient for solving multi-objective optimization problems. K E Y W O R D S decision making, flow shop scheduling, learning (artificial intelligence), schedulingThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

“…Once a job starts to be processed on a machine, this process cannot be interrupted before its completion [13].…”

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Q ‐learning algorithm for solving permutation flow shop scheduling problems

Wang²,

et al. 2021

IET Collab Intel Manufact

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“…Nagar et al [11] developed a combined hybrid algorithm to solve PFSSP with better minimization of makespan and average total flow time. Rajkumar and Shahabudeen [9,16] described an EGA & IGA algorithm to solve the PFSP. Wang et al [22] proposed a hybrid harmony search algorithm for solving flow shop benchmark problems.…”

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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Considering the peak power consumption problem with learning and deterioration effect in flow shop scheduling

Lv,

Wang

2024

Computers & Industrial Engineering

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

Cited by 6 publications

References 14 publications

Improved Q ‐learning algorithm for solving permutation flow shop scheduling problems

Improved Q ‐learning algorithm for solving permutation flow shop scheduling problems

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

Considering the peak power consumption problem with learning and deterioration effect in flow shop scheduling

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