A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems

Zhao, Fuqing; Hong, Yi; Yu, Dongmei; Yang, Yue; Zhang, Qiuyu

doi:10.1080/09511920903207472

Cited by 57 publications

(18 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To manage these dynamics several paradigms such as holonic [4], flexible [3], lean [5], reconfigurable [6], evolvable, self-organizing [7] and autonomous [8] assembly systems have been partly realised in the last decades. The flexibility and adaptability is realised by clustering the assembly system into subsystems and modules, which get a certain degree of autonomy and control themselves in a decentralized way [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Decision making logic for flexible assembly lines reconfiguration

Michalos

Sipsas

Makris

et al. 2016

Robotics and Computer-Integrated Manufacturing

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Decision making logic for flexible assembly lines reconfiguration

Michalos

Sipsas

Makris

et al. 2016

Robotics and Computer-Integrated Manufacturing

View full text Add to dashboard Cite

“…[14,15]). Scheduling based on MPP maintains the common organization between the process plan and scheduling department.…”

Section: Related Workmentioning

confidence: 97%

An Evolutionary Approach on Multi-objective Scheduling for Evolving Manufacturing Systems

Klöpperr¹,

Pater

Dangelmaier

2014

2014 47th Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

New paradigms for designing manufacturing systems, such as adaptive and service-oriented manufacturing systems or self-optimizing resources, introduce new degrees of freedom into manufacturing control. These paradigms lead to the research question how human decision makers can be enabled to exploit the high adaptability of the new manufacturing systems in accordance to the current requirements arising from quickly changing market requirements and demand for sustainable manufacturing. This contribution extends a scheduling model for self-optimizing and serviceoriented manufacturing systems through the consideration of alternative resources and introduces a multi-objective scheduling concept based on evolutionary algorithms in combination with a new optimized type of crossover operator.

show abstract

“…Baykasoglu et al [15] used a novel approach which made use of grammatical representation of generic process plans in a multiple objective tabu search framework in order to integrate process planning and scheduling effectively. Zhao et al [16] proposed a basic architecture of holonic manufacturing system from the viewpoint of the process planning and the scheduling system, and the hybrid particle swarm optimization with differential evolution was applied to balance the load for all machines. A new integration model and a modified genetic algorithm-based approach were developed to facilitate the integration and optimization of the two functions, and more efficient genetic representations and operator schemes were developed in order to improve the optimized performance of the modified genetic algorithm-based approach [3,[17][18][19][20].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of ant colony optimization algorithm in integrated process planning and scheduling

Liu

Zhang

Qiu

2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Optimization of integrated process planning and scheduling has important practical significance for balancing the load of the process resources, shortening production cycle, and reducing production costs. An optimization algorithm base on ant colony optimization (ACO) for integrated process planning and scheduling is proposed, which can handle the dynamic emergency situation. Firstly, the representation mechanisms of candidate operation and the scheduling scheme construction mechanism is proposed. Then, the process constraints and time cost functions are given; based on this, the mathematical model is constructed. The ACO algorithm has been developed to solve the proposed mathematical model of integrated process planning and scheduling. The optimization algorithm is divided into two stages: the scheduling scheme optimization algorithm and dynamic emergency situation handling mechanism. The scheduling scheme optimization algorithm is used to get feasible and optimize scheduling scheme, and the dynamic emergency situation handling mechanism is used to handle dynamic emergency situation, such as inserting new parts. An example is also provided to demonstrate the effectiveness of the algorithm, and the computing results show that the proposed algorithm performs well in searching the good scheduling scheme.

show abstract

A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems

Cited by 57 publications

References 39 publications

Decision making logic for flexible assembly lines reconfiguration

Decision making logic for flexible assembly lines reconfiguration

An Evolutionary Approach on Multi-objective Scheduling for Evolving Manufacturing Systems

Application of ant colony optimization algorithm in integrated process planning and scheduling

Contact Info

Product

Resources

About