An Efficient Approach to Job Shop Scheduling Problem using Simulated Annealing

Chakraborty, Shouvik; Bhowmik, S.

doi:10.14257/ijhit.2015.8.11.23

Cited by 48 publications

(16 citation statements)

References 9 publications

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“…It is a kind of hybrid approach that exploits the facility of both the techniques. Several metaheuristic optimization algorithms [15]- [20] and hybrid metaheuristics [21] are fused with these methods to enhance the efficiency. In this work, different methods will be studied.…”

Section: Methods To Provide Security To the Biomedical Imagementioning

confidence: 99%

A Study on the Applications of the Biomedical Image Encryption Methods for Secured Computer Aided Diagnostics

Roy

Mali

Chatterjee

et al. 2019

2019 Amity International Conference on Artificial Intelligence (AICAI)

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Computer aided diagnostic is one of the most active research area and has huge impact on the health care industry. With the advent of intelligent methods, biomedical data processing becomes easier and less error prone. Moreover, remote health care is also possible using the IoT infrastructure. However, data security over the network is always considered as a challenge. Biomedical data are generally sensitive to external disturbances and small manipulation in the data may cause huge difference in the ultimate result. Wrong diagnosis can be life threatening in some scenarios or can be severe in almost every instance. Therefore, biomedical data security is one of the major challenge and necessary for remote health care. In this work, some application of cryptographic methods have been discussed which are used to protect biomedical images from unwanted modifications and access. This work can be highly beneficial in future research in the field of biomedical image protection as well as multimedia data security.

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Section: Methods To Provide Security To the Biomedical Imagementioning

confidence: 99%

A Study on the Applications of the Biomedical Image Encryption Methods for Secured Computer Aided Diagnostics

Roy

Mali

Chatterjee

et al. 2019

2019 Amity International Conference on Artificial Intelligence (AICAI)

Self Cite

View full text Add to dashboard Cite

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“…Concerning the optimization techniques on the multiobjective job shop scheduling problem, many approaches that imitate nature, social behaviors, etc., have been widely applied in job shop scheduling, such as ant colony optimization [34], particle swarm optimization [35], evolutionary algorithm [36], tabu search [37], simulated annealing [38], migrating birds optimization algorithm [39]. Although there exist some researchers attempting to solve the multi-objective job shop problem using machine learning methods, such as Wang and Tang proposed a machine-learning-based multiobjective memetic algorithm (ML-MOMA) for the discrete permutation flow shop scheduling problem [40], Zhang et al introduced particle swarm optimization (PSO) and neural network (NN) to solve the job-shop scheduling problem (JSP) [34].…”

Section: Literature Reviewmentioning

confidence: 99%

Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

Wei

Quan

et al. 2021

IEEE Access

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In recent years, people have paid more and more attention to traditional manufacturing's environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and makespan into a typical production scheduling model-the job shop scheduling problem, based on a machine status switching framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic algorithm combined with the MinMax (MM) and Nawaz-Enscore-Ham (NEH) algorithms) population initialization method is used to solve the problem. The multi-objective optimization algorithm can generate a Pareto set of solutions so that production managers can flexibly select a schedule from these non-dominated schedules based on their priorities. Three sets of numerical experiments have been carried out on the extended Taillard benchmark to verify this three-objective model's effectiveness and the multi-objective optimization algorithm. The results show that U-NSGA-III has obtained better Pareto solutions in most test problem instances than NSGA-II and NSGA-III. Furthermore, the non-processing energy consumption is reduced by 46%-69%, which is 13-83% of the total energy consumption.INDEX TERMS Job shop scheduling, energy efficiency, unified multi-objective genetic algorithm, machine status switching.

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“…The Job Shop Scheduling problem (JSP) has been solved using the technique of simulated annealing [5][6][7]. A quantum annealing solution has first been introduced in [8] where the JSP has been modelled as cost or energy function to be minimized (QUBO, see below).…”

Section: Related Workmentioning

confidence: 99%

Solving the Extended Job Shop Scheduling Problem with AGVs -- Classical and Quantum Approaches

Geitz¹,

Grozea²,

Steigerwald³

et al. 2021

Preprint

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The subject of Job Scheduling Optimisation (JSO) deals with the scheduling of jobs in an organization, so that the single working steps are optimally organized regarding the postulated targets. In this paper a use case is provided which deals with a sub-aspect of JSO, the Job Shop Scheduling Problem (JSSP or JSP). As many optimization problems JSSP is NP-complete, which means the complexity increases with every node in the system exponentially. The goal of the use case is to show how to create an optimized duty rooster for certain workpieces in a flexible organized machinery, combined with an Autonomous Ground Vehicle (AGV), using Constraint Programming (CP) and Quantum Computing (QC) alternatively. The results of a classical solution based on CP and on a Quantum Annealing model are presented and discussed. All presented results have been elaborated in the research project PlanQK.

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An Efficient Approach to Job Shop Scheduling Problem using Simulated Annealing

Abstract: The Job-Shop Scheduling Problem (JSSP)

Cited by 48 publications

References 9 publications

A Study on the Applications of the Biomedical Image Encryption Methods for Secured Computer Aided Diagnostics

A Study on the Applications of the Biomedical Image Encryption Methods for Secured Computer Aided Diagnostics

Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

Solving the Extended Job Shop Scheduling Problem with AGVs -- Classical and Quantum Approaches

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