Distributed job-shop rescheduling problem considering reconfigurability of machines: a self-adaptive hybrid equilibrium optimiser

Mahmoodjanloo, Mehdi; Tavakkoli–Moghaddam, Reza; Baboli, Armand; Bozorgi-Amiri, Ali

doi:10.1080/00207543.2021.1946193

Cited by 19 publications

(4 citation statements)

References 34 publications

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“…For example, equilibrium optimizer (EO) [90] is one of these novel and successful algorithms that gained the attention of researchers, and it was used in many papers such as [116][117][118][119][120].…”

Section: Neural Computing and Applicationsmentioning

confidence: 99%

“…For fractional-order proportional-integral-derivative plus derivative (FOPIDD) controller [118], a self-adaptive hybrid equilibrium optimizer (SAHEO) for distributed jobshop rescheduling problem [119], enhanced equilibrium optimization (E. 2 O) to application engineering [116], stock market prediction [120], a modified EO for optimal allocation of PVs in radial distribution systems [117] According to the no free lunch (NFL) theorem, there is no perfect algorithm for solving all optimization problems. Consequently, novel and more efficient algorithms are needed.…”

Section: Neural Computing and Applicationsmentioning

confidence: 99%

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A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

Mohammadi

Nazarpour

Beiraghi

2023

Neural Comput & Applic

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In this modern world, we are encountered with numerous complex and emerging problems. The metaheuristic optimization science plays a key role in many fields from medicine to engineering, design, etc. Metaheuristic algorithms inspired by nature are among the most effective and fastest optimization methods utilized to optimize different objective functions to minimize or maximize one or more specific objectives. The use of metaheuristic algorithms and their modified versions is expanding every day. However, due to the abundance and complexity of various problems in the real world, it is always necessary to select the most proper metaheuristic method; hence, there is a strong need to create new algorithms to achieve our desired goal. In this paper, a new and powerful metaheuristic algorithm, called the coronavirus metamorphosis optimization algorithm (CMOA), is proposed based on metabolism and transformation under various conditions. The proposed CMOA algorithm has been tested and implemented on the comprehensive and complex CEC2014 benchmark functions, which are functions based on real-world problems. The results of the experiments in a comparative study under the same conditions show that the CMOA is superior to the newly-developed metaheuristic algorithms including AIDO,

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Section: Neural Computing and Applicationsmentioning

confidence: 99%

Section: Neural Computing and Applicationsmentioning

confidence: 99%

A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

Mohammadi

Nazarpour

Beiraghi

2023

Neural Comput & Applic

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“…They developed two MILP models and a Self-Adaptive Differential Evolution Algorithm (SADEA) to solve the problem considering the makespan as the objective function. Later, Mahmoodjanloo et al [42] devised a self-adaptive hybrid Equilibrium Optimizer (EO) algorithm for a dynamic and multi-factory extension of this problem with the objective of the total weighted lateness. Fan et al [43] also studied the scheduling problem of a reconfigurable flexible job shop with the objective of total weighted tardiness.…”

Section: Literature Reviewmentioning

confidence: 99%

Workforce planning and production scheduling in a reconfigurable manufacturing system facing the COVID-19 pandemic

Vahedi-Nouri¹,

Tavakkoli–Moghaddam²,

Hanzálek³

et al. 2022

Journal of Manufacturing Systems

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“…Rescheduling studies have involved various manufacturing systems, including single machine system, [27][28][29] parallel machine system, [30][31][32] flow shop, [33][34][35] job shop, [36][37][38] and flexible manufacturing systems. 39,40 As far as the authors know, few studies have dealt with the rescheduling problem of assembly job shop.…”

Section: Introductionmentioning

confidence: 99%

Self-reconfiguration and rescheduling of aero-engine assembly shop with rework disruption in knowledgeable manufacturing environment

Yan

Wan

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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An aero-engine has to be assembled twice or more to satisfy stringent technical standards. Substandard engines are required to re-enter for additional reassembly, which may trigger rework disruption. Work groups with flexible skills are responsible for the operations and can be reassigned when rescheduling triggers. An integrated problem of rescheduling of engines and reconfiguration of work groups with rework disruption is formulated here. A heuristic is proposed to solve the problem. In self-reconfiguration optimization, work groups are reassigned in accordance with the workload balance criterion without violating the assembly skills constraints. In rescheduling optimization, locally optimal sequencing propositions of operations are derived and applied to generate a feasible schedule. Three neighborhood structures are defined, and a variable neighborhood search is implemented to optimize the assignment and the schedule of operations on parallel work groups. The effectiveness of the proposed algorithm is tested by several numerical experiments. The results show that the proposed algorithm promises apparently lower total cost and more desirable assignment of work groups by scheduling.

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Distributed job-shop rescheduling problem considering reconfigurability of machines: a self-adaptive hybrid equilibrium optimiser

Cited by 19 publications

References 34 publications

A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

Workforce planning and production scheduling in a reconfigurable manufacturing system facing the COVID-19 pandemic

Self-reconfiguration and rescheduling of aero-engine assembly shop with rework disruption in knowledgeable manufacturing environment

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