A Hybrid Algorithm for Multi-Objective Optimization of Minimizing Makespan and Total Flow Time in Permutation Flow Shop Scheduling Problems.
R. Rajkumar,R.B. Jeen Robert +1 more
TL;DR: A hybridization of genetic algorithm and simulated annealing algorithm (HGASA) based multi-objective optimization algorithm for flow shop scheduling is considered and shows that the HGASA algorithm performed better in terms of searching quality and efficiency than other meta-heuristic algorithms.
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Abstract: This paper proposes, a multi-objective optimization of minimizing makespan and total flow time of jobs for permutation flow shop scheduling is considered. 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 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. HGASA could be a simple and proficient algorithm that is utilized to determine for every single and multi-objective problem in flow shop scheduling shop environment. This algorithm can works simply for realistic manufacturing system applications. The proposed hybrid algorithm searches the optimal solution for objectives by considering the makespan and total flow time. The performance of the proposed HGASA was tested on standard flow shop benchmark problems to calculate its performance. 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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Citations
Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems
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Optimal Foraging Algorithm That Incorporates Fuzzy Relative Entropy for Solving Many-Objective Permutation Flow Shop Scheduling Problems
TL;DR: A membership function is used to connect the function values of many-objective optimization problems with a fuzzy membership degree and an approach for mapping function values to fuzzy sets is presented, demonstrating that the fuzzy relative entropy (FRE) can be employed in many-Objective PFSPs.
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A new approach for solving the flow‐shop scheduling problem using a parallel optimization algorithm
TL;DR: A novel method is provided to decrease the makespan and completion time of the permutation flow-shop using a parallel ant colony optimization algorithm to solve the mentioned problem.
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Whale Optimization Algorithms for Multi-Objective Flowshop Scheduling Problems
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An effective approach for total completion time minimization subject to makespan constraint in permutation flowshops
E. Pastore,A. Alfieri +1 more
- 07 Feb 2024
TL;DR: This study proposes two local search algorithms to minimize total completion time in permutation flowshops subject to a makespan constraint, achieving a good trade-off between work-in-process and system utilization through extensive computational testing on literature benchmark instances.
1
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Application of the Branch and Bound Technique to Some Flow-Shop Scheduling Problems
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