Cyclic hybrid flow shop scheduling problem with limited buffers and machine eligibility constraints

Soltani, Sima; Karimi, Behrooz

doi:10.1007/s00170-014-6343-0

Cited by 17 publications

(5 citation statements)

References 46 publications

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“…Researchers have found in the research domain operator that the particle swarm algorithm can significantly improve the performance of the algorithm after adding this method, and can prevent particles from falling into local extreme values. When Suganthan joins the variable domain operator, the random walk of the particle swarm algorithm and the gradual adjustment of the inertia weight allow the particles to adapt to a better search for the optimal value [16][17].…”

Section: Main Improvements Of Particle Swarm Algorithmmentioning

confidence: 99%

Production Planning and Scheduling Assembly Simulation Based on VR Technology

2022

IJMC

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The operating mode of the traditional manufacturing industry is "design-manufacture-test-operate". With the development of virtual reality technology and 3D modeling technology, 3D modeling software can be used to build 1:1 models before traditional assembly operations. Combine VR technology to simulate the assembly process of physical objects. By applying collision detection technology to predict the feasibility of the assembly path in the virtual assembly process, experts can exchange professional knowledge more immersively and intuitively in the virtual environment, and predict design defects. This has become a hot spot for enterprises and research institutes. Based on this, this paper studies the ship block assembly simulation based on VR technology. This article uses modeling software to construct the 3D model required for assembly simulation, and imports the model into the virtual reality development software to build a virtual manufacturing scene; then the digital prototype ship model constructed by professional ship design software is delivered in segments The details are optimized in the 3D modeling software to turn it into a realistic simulation model and imported into the virtual reality development software. This paper establishes the model of work shift formation and assignment model of work shift group. Then, the genetic algorithm that is often used to solve the problem of shop scheduling is improved, and a solution method based on the adaptive simulated annealing genetic algorithm is proposed for the fixed object pipeline model. In this paper, the Petri net model is used to study the problem of the scheduling machine failure of the hybrid flow shop, and the Petri net-based simulation model is established by using the dynamic simulation characteristics of the Petri net. Experimental research shows that the maximum completion time obtained by using the improved accelerated particle swarm algorithm is 20 hours. The improved accelerated particle swarm algorithm proposed in this paper has better convergence than the standard particle swarm algorithm and accelerated particle swarm algorithm.

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Section: Main Improvements Of Particle Swarm Algorithmmentioning

confidence: 99%

Production Planning and Scheduling Assembly Simulation Based on VR Technology

2022

IJMC

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“…Shao and Pi [21] constructed a distribution model with an adaptive scaling factor, counted the dominant individuals in population evolution, and changed the selection mode of self-suitability, thereby avoiding the local optimum trap. Based on probability model, Soltani and Karimi [22] used the distribution estimation algorithm to keep the differential evolution algorithm away from the local optimum trap, and solve the mixed zero-idle permutation FSP.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Objective Hybrid Differential Optimization Algorithm for Flow-Shop Scheduling Problem

Pei¹,

Shan²

2019

Int. j. simul. model.

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This paper puts forward a multi-objective hybrid difference optimization algorithm to solve multiobjective flow-shop scheduling problem (FSP). The hybrid algorithm inherits the merits of differential evolution vector operation, and makes dynamic adjustments to the search direction based on historical data. However, the basic differential evolution algorithm is prone to the local optimum trap, due to the low population diversity in the later stage of evolution. To solve the problem, a hybrid sampling strategy was introduced obtain the distribution information of solution sets and to design the mutation operator of differential evolution, thus improving the convergence of the hybrid algorithm. Finally, our algorithm was applied to solve FSPs through simulation. The simulation results show that our algorithm greatly outperformed the basic multi-objective evolutionary algorithm in convergence and distribution performance.

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“…These oversimplified schemes often cannot perfectly solve such scheduling problems in the actual environment. In real-world manufacturing situations, some special constraints or uncertainties must usually be considered and handled, such as sequence-dependent setup times [1][2][3], the kinds of parallel machines under study [4,5], machine eligibility constraints [6][7][8][9], resource constraints [10,11], and fuzzy stochastic 2 of 24 demand [12,13]. Considerations of these additional constraints and uncertainties make the developed scheduling models closer to real production scenarios, but also increase their scheduling complexity.…”

Section: Introductionmentioning

confidence: 99%

“…parallel machines under study [4,5], machine eligibility constraints [6][7][8][9], resource constraints [10,11], and fuzzy stochastic demand [12,13]. Considerations of these additional constraints and uncertainties make the developed scheduling models closer to real production scenarios, but also increase their scheduling complexity.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization for Multi-Objective Nonidentical Parallel Machining Line Scheduling with a Jumping Process Operation Constraint

Guan

Yue

et al. 2021

Symmetry

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This paper investigates the nonidentical parallel production line scheduling problem derived from an axle housing machining workshop of an axle manufacturer. The characteristics of axle housing machining lines are analyzed, and a nonidentical parallel line scheduling model with a jumping process operation (NPPLS-JP), which considers mixed model production, machine eligibility constraints, and fuzzy due dates, is established so as to minimize the makespan and earliness/tardiness penalty cost. While the physical structures of the parallel lines in the NPPLS-JP model are symmetric, the production capacities and process capabilities are asymmetric for different models. Different from the general parallel line scheduling problem, NPPLS-JP allows for a job to transfer to another production line to complete the subsequent operations (i.e., jumping process operations), and the transfer is unidirectional. The significance of the NPPLS-JP model is that it meets the demands of multivariety mixed model production and makes full use of the capacities of parallel production lines. Aiming to solve the NPPLS-JP problem, we propose a hybrid algorithm named the multi-objective grey wolf optimizer based on decomposition (MOGWO/D). This new algorithm combines the GWO with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to balance the exploration and exploitation abilities of the original MOEA/D. Furthermore, coding and decoding rules are developed according to the features of the NPPLS-JP problem. To evaluate the effectiveness of the proposed MOGWO/D algorithm, a set of instances with different job scales, job types, and production scenarios is designed, and the results are compared with those of three other famous multi-objective optimization algorithms. The experimental results show that the proposed MOGWO/D algorithm exhibits superiority in most instances.

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Cyclic hybrid flow shop scheduling problem with limited buffers and machine eligibility constraints

Cited by 17 publications

References 46 publications

Production Planning and Scheduling Assembly Simulation Based on VR Technology

Production Planning and Scheduling Assembly Simulation Based on VR Technology

A Multi-Objective Hybrid Differential Optimization Algorithm for Flow-Shop Scheduling Problem

Modeling and Optimization for Multi-Objective Nonidentical Parallel Machining Line Scheduling with a Jumping Process Operation Constraint

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