Benchmarks for fuzzy job shop problems

Palacios, Juan José; Puente, Jorge de Hoyos; Vela, Camino R.; González-Rodríguez, Inés

doi:10.1016/j.ins.2015.09.042

Cited by 21 publications

(23 citation statements)

References 55 publications

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“…For all algorithms in this chapter, the size of the population Ns = 100, and the maximum number of iterations is set to max_ite = 1000. According to the literatures [28,38] and experiments, the parameter for the IHKA is set as N ξ = 10, α = 0.9, β = 5 and Bsi ze = Nd 5 + 0.5 . The parameter for the PSO is set as c 1 = 2.8, c 2 = 1.3 and w = 0.729 [33].…”

Section: Resultsmentioning

confidence: 99%

“…Particularly deeply explored is the field of Planning and Scheduling, from service activities [42] to production systems [37]. The authors implement Artificial Intelligence algorithms to benchmark examples [28], as well as to realworld examples [33]. The high efficiency of modern Artificial Intelligence methods are reflected in the implementation of the Kalman Filter approach for the purpose of multiprocessor evolutionary computation and obtaining estimated solutions [21].…”

Section: Literature Reviewmentioning

confidence: 99%

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Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

Zhang

Buchmeister

Liu

et al. 2019

Springer Series in Advanced Manufacturing

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At the time of Industry 4.0 and the emergence of collaborative workplaces based on the cooperation of robots (machines) and humans, the number of human workplaces in the Industry 4.0 production system is crucial. In this chapter, we present the use of the evolutionary computation methods that use the input data of a real production system and transfer it through the five-stage Cyber-Physical System architecture into the simulation environment in order to determine the optimal number of workers. By using these methods, we confirm the hypothesis of the importance of correctly determining the number of workers in the manufacturing process in Industry 4.0. Number of workers' determination has a key influence on the product flow time, machine utilization and cost-effectiveness of a production system. Research results show the importance and effectiveness of combining evolutionary computation methods and simulation modelling for the purpose of implementing the advanced approaches of Industry 4.0. The demonstrated approach of combining evolutionary computing, simulation environments and methods of Industry 4.0 can be used from mass customization to mass production systems for the purpose of single-criteria or multi-criteria optimization.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

Zhang

Buchmeister

Liu

et al. 2019

Springer Series in Advanced Manufacturing

View full text Add to dashboard Cite

show abstract

“…The authors present their research work in two separate groups. Some researchers perform experiments on benchmark datasets (Palacios et al, 2016), while others perform their optimization algorithms in applied cases. In doing so, there is a problem of mutual evaluation and comparison, since, in some cases, it is impossible to transfer parallels.…”

Section: Issues and Open Questionsmentioning

confidence: 99%

Multi-objective optimization of production scheduling with evolutionary computation: A review

Ojsteršek¹,

Brezočnik²,

Buchmeister³

2020

10.5267/j.ijiec

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Multi-Objective (MO) optimization is a well-known research field with respect to the complexity of production planning and scheduling. In recent years, many different Evolutionary Computation (EC) methods have been applied successfully to MO production planning and scheduling. This paper is focused on making a review of MO production scheduling methods, starting from production scheduling presentation, notation and classification. The research field of EC methods is presented, then EC algorithms` classification is introduced for the purpose of production scheduling optimization. As a main goal, MO optimization is focused on hybrid EC methods, and presenting their advantages and limitations. Finally, a survey of five scientific databases is presented, with the analysis of the scientific publications the terminology development of the scientific field is presented. Using the citation analysis of the scientific publications, the application for the MO optimization in manufacturing scheduling is discussed.

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“…Palacios et al 24 proposed benchmarks for fuzzy job shop problems, and had played out to fill the gap around by investigating existing benchmarks and moreover proposing new benchmark issues. In the light of this examination, they had proposed another gathering of all troublesome benchmark issues and gave cut down points of confinement to the typical makes of the dish of each event, and likewise, reference makespan values procured with a mimetic computation from the composition.…”

Section: Literature Reviewmentioning

confidence: 99%

A vibrant crossbreed social spider optimization with genetic algorithm tactic for flexible job shop scheduling problem

Kavitha

Venkumar

2020

Measurement and Control

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Job shop scheduling is one of the major issues in which the scheduling process is associated with the real-time manufacturing industry. A flexible job shop scheduling problem is one of the most important issues among the hardest combinatorial advancement issues. Flexible job shop scheduling is extremely a nondeterministic polynomial combinatorial problem. In this paper, it is proposed that a mixture of improvement demonstrates to make makespan minimization in the flexible job shop scheduling problem issue. This paper includes the hybridization of social spider optimization and genetic algorithm that is effectively controlled by the calculation via optimization techniques. Most of the part in this method is given as the scavenging methodology of social insects, which use the vibrations spread over the bug-catching network to decide the position of the target. These hybridization approaches after arachnid upgrading process hereditary calculation chromosomes are chosen to produce new arrangements nearer to the minimum makespan time. The main objective of this paper is to minimize the makespan time of ''n'' jobs and ''m'' machines. The proposed algorithms have effectively investigated many benchmark problems and the computational results were compared with existing metaheuristic, including progressive calculations and algorithms for the swarm intelligence in the flexible job shop scheduling problem.

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Benchmarks for fuzzy job shop problems

Cited by 21 publications

References 55 publications

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

Multi-objective optimization of production scheduling with evolutionary computation: A review

A vibrant crossbreed social spider optimization with genetic algorithm tactic for flexible job shop scheduling problem

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