Mitigation of Make Span Time in Job Shop Scheduling Problem Using Gannet Optimization Algorithm

Anil Kumar K. R; Edwin Raja Dhas J.

doi:10.4108/eetsis.2913

Authors

Anil Kumar K. R Research Scholar ,Mechanical Engineering Department ,Noorul Islam Centre for Higher Education, Kumarakovil Tamil Nadu, India.
Edwin Raja Dhas J. Department of Automobile Engineering, Noorul Islam Centre for Higher Education, Kumarakovil Tamil Nadu, India.

DOI:

https://doi.org/10.4108/eetsis.2913

Keywords:

job shop scheduling problem, make span, gannet optimization algorithm, machine and jobs

Abstract

INTRODUCTION: Effective (JSS) was required in the manufacturing industry to satisfy demand productivity, reduce production costs, and increase competitiveness in a market that was becoming more active and demanding of a variety of goods.

OBJECTIVES: The (JSSP) has gained importance in recent years as a result of rising customer demand across a variety of categories, shifting markets due to increased global competition, and the quick development of new technology. The proper scheduling and sequencing of jobs on machines was one of the fundamental and important issues that a shop or factory management faces.

METHODS: Different machines can be found in a shop, and depending on the task, one or more of these equipment may need to be used in a particular order. The aim in correcting this issue might be to reduce the make span. For each machine, the jobs sequencing must be done once the make span had been reduced.

RESULTS: To solve these issues, (GOA) was used to reduce make span time. Both jobs and machines were fed as an input to the proposed optimization and to found optimal job scheduling with low make span time. The outcome of the proposed work was compared the outcomes of various optimization strategies in JSSP in order to minimize the make span time. CONCLUSION: The objective of optimization was to reduce the total amount of time or duration required to complete a task. A proposed gannet optimization method was employed to reduce the make span time in various sectors to resolve the job shop scheduling problem.

References

Viana MS, Contreras RC, Morandin Junior O. A New Frequency Analysis Operator for Population Improvement in Genetic Algorithms to Solve the Job Shop Scheduling Problem. Sens. 2022 Jun 17; 22(12):4561.

Pourghaffari A, Barari M, Sedighian Kashi S. An efficient method for allocating resources in a cloud computing environment with a load balancing approach. Concurr. Comput. Pract. Exp. 2019 Sep 10; 31(17):e5285.

Zhang M, Tao F, Nee AY. Digital twin enhanced dynamic job-shop scheduling. J. Manuf. Syst. 2021 Jan 1; 58:146-56.

Gong X, De Pessemier T, Martens L, Joseph W. Energy-and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation. J. Clean. Prod. 2019 Feb 1; 209:1078-94.

Park J, Chun J, Kim SH, Kim Y, Park J. Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning. Int. J. Prod. Res. 2021 Jun 3; 59(11):3360-77.

Tafakkori K, Tavakkoli-Moghaddam R, Siadat A. Sustainable negotiation-based nesting and scheduling in additive manufacturing systems: A case study and multi-objective meta-heuristic algorithms. Eng. Appl. Artif. Intell. 2022 Jun 1; 112:104836.

Anuar NI, Fauadi MH. A Study on Multi-Objective Particle Swarm Optimization in Solving Job-Shop Scheduling Problems. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 2021; 13:051-61.

Frausto-Solis J, Hernández-Ramírez L, Castilla-Valdez G, González-Barbosa JJ, Sánchez-Hernández JP. Chaotic multi-objective simulated annealing and threshold accepting for job shop scheduling problem. Math. Comput. Appl. 2021 Jan 12; 26(1):8.

Zolpakar NA, Lodhi SS, Pathak S, Sharma MA. Application of multi-objective genetic algorithm (MOGA) optimization in machining processes. InOptimization of manufacturing processes 2020 (pp. 185-199). Springer, Cham.

Park JS, Ng HY, Chua TJ, Ng YT, Kim JW. Unified genetic algorithm approach for solving flexible job-shop scheduling problem. Appl. Sci. 2021 Jul 13; 11(14):6454.

Anil Kumar KR, Das ER. Genetic Algorithm and Particle Swarm Optimization in Minimizing MakeSpan Time in Job Shop Scheduling. InProceedings of ICDMC 2019 2020 (pp. 421-432). Springer, Singapore.

Anil A, Venkateswarlu K, Srinivasan M, Kumar S. Solving Job Shop Scheduling Problem With the Aid of Evolution of Cub to Predator (ECP). Int. j. adv. res. sci. eng. technol. 2020;11(11).

Kress D, Müller D. Mathematical models for a flexible job shop scheduling problem with machine operator constraints. IFAC-PapersOnLine. 2019 Jan 1; 52(13):94-9.

Zhang F, Mei Y, Nguyen S, Zhang M. Evolving scheduling heuristics via genetic programming with feature selection in dynamic flexible job-shop scheduling. IEEE Trans Cybern. 2020 Oct 20; 51(4):1797-811.

Wang Y, Zhu Q. A hybrid genetic algorithm for flexible job shop scheduling problem with sequence-dependent setup times and job lag times. IEEE Access. 2021 Jul 9; 9:104864-73.

Juvin C, Houssin L, Lopez P. Logic-Based Benders Decomposition for the Preemptive Flexible Job-Shop Scheduling Problem. Available at SSRN 4068164.

Dehghan-Sanej K, Eghbali-Zarch M, Tavakkoli-Moghaddam R, Sajadi SM, Sadjadi SJ. Solving a new robust reverse job shop scheduling problem by meta-heuristic algorithms. Eng. Appl. Artif. Intell. 2021 May 1; 101:104207.

Dai M, Tang D, Giret A, Salido MA. Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robot. Comput.-Integr. Manuf. 2019 Oct 1; 59:143-57.

Meng L, Zhang C, Shao X, Ren Y. MILP models for energy-aware flexible job shop scheduling problem. J. Clean. Prod. 2019 Feb 10; 210:710-23.

Mihoubi B, Bouzouia B, Gaham M. Reactive scheduling approach for solving a realistic flexible job shop scheduling problem. Int. J. Prod. Res. 2021 Oct 2; 59(19):5790-808.

Caldeira RH, Gnanavelbabu A. Solving the flexible job shop scheduling problem using an improved Jaya algorithm. Comput Ind Eng. 2019 Nov 1; 137:106064.

Li JQ, Deng JW, Li CY, Han YY, Tian J, Zhang B, Wang CG. An improved Jaya algorithm for solving the flexible job shop scheduling problem with transportation and setup times. Knowl Based Syst. 2020 Jul 20; 200:106032.