Reconfiguration of Virtual Cellular Manufacturing Systems via Improved Imperialist Competitive Approach

Liu, Chunfeng; Wang, Jufeng; Zhou, MengChu

doi:10.1109/tase.2018.2878653

Cited by 20 publications

(9 citation statements)

References 46 publications

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“…They used randomly generated datasets to test their model. Liu et al [26] configured a virtual cellular manufacturing system which involves resource constraint, time cost and energy consumption level.…”

Section: 1mentioning

confidence: 99%

“…The imperialist competitive algorithm (ICA) is a sociopolitical meta-heuristic, inspired by historical colonization process and competition among imperialists, to capture more colonies. Each country in the population represents a point in the search space or a potential solution of the optimization problem [26]. In fact, those countries are divided into two main groups called imperialists and colonies.…”

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confidence: 99%

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A revised imperialist competition algorithm for cellular manufacturing optimization based on product line design

Liu

Wang

2023

JIMO

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<p style='text-indent:20px;'>Due to the fierce market competition, enterprises try to satisfy customers' requirements for personalized products in order to maximize profit or market share of their products. This not only needs to determine the product variants through product line design, but also needs to pay attention to resource allocation in the manufacturing process. This paper proposes a cellular manufacturing optimization model that considers the market and production. If the company excessively pursues the satisfaction of customers' personalized needs, the manufacturing time and cost may increase accordingly. Of course, with the restriction of production capacity in manufacturing cells and the expectation of reducing cost, managers cannot design attributes' levels of a product line casually, which may result in its unstable marketing share and profit. Therefore, the product demand influenced by customers' preferences could be a key factor to link market and production. The objective of propose model is to maximize product profit which consists of revenue and miscellaneous costs (material, processing, transportation, final assembly and fixed costs). A revised imperialist competitive algorithm (RICA) is developed to optimize the discrete problem. Extensive numerical experiments and t-test are carried out to verify the effect of this method. The results demonstrate the proficiency of RICA over another imperialist competitive algorithm based method and genetic algorithm in terms of solution quality.</p>

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Section: 1mentioning

confidence: 99%

mentioning

confidence: 99%

A revised imperialist competition algorithm for cellular manufacturing optimization based on product line design

Liu

Wang

2023

JIMO

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“…Imperialist Competitive Algorithm (ICA) is classified into population-based methods, like genetic algorithm. ICA is a useful method in optimization because of its reasonable runtime and effectiveness [44,45]. Moreover, ICA is a new solution approach, which has not been applied to product line design optimization problems.…”

Section: Discrete Imperialist Competitive Algorithmmentioning

confidence: 99%

Optimization of product line considering compatibility and reliabilityviadiscrete imperialist competitive algorithm

2021

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In the era of mass customization, designing optimal products is one of the most critical decision-making for a company to stay competitive. More and more customers like customized products, which will bring challenges to the product line design and the production. If a company adopts consumers' favorite levels, this may lead to lower product reliability, or incompatibility among the components that make up the product. Moreover, it is worth outsourcing certain attribute levels so as to reduce production cost, but customers may dislike these levels because of their delivery delay. If managers consider the compatibility issue, the quality issue, outsource determination, and the delivery due date in the product design and production stages, it will avoid unreasonable product configuration and many unnecessary expenses, thereby bringing benefits to the company. To solve this complicated problem, we establish a nonlinear programming model to maximize a metric about profit, termed as Per-capita-contribution Margin considering Reliability Penalty (PMRP). Since the integrated product line design and production problem is NP-hard, we propose an improved Discrete Imperialist Competitive Algorithm (DICA) that can find a most powerful imperialist (i.e., solution) by the competition among all countries in the world. The proposed DICA is compared with genetic algorithm (GA) and simulated annealing (SA) through extensive numerical experiment, and the results show that DICA has more attractive performance than GA and SA.

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“…For example, a parametric GA is proposed to find the optimal spatial distribution of renewable units in [21]. To minimize the total operation cost of material handling, a novel discrete ICA with a priority rule based heuristic is designed in [22]. While in [23], the ABC is utilized to effectively generate a set of Pareto solutions for a dual-objective disassembly optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Generative Adversarial Network

Zhao

Liao

Wang

et al. 2020

Journal of Modern Power Systems and Clean Energy

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The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution network. In this paper, a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network (IGAN). Firstly, both real and predicted data are used to train the IGAN consisting of a discriminator and a generator. The noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario reduction. Then, a new improved wolf pack algorithm (IWPA) is presented to solve the formulated robust voltage control model, since the accuracy of the solutions obtained by traditional methods is limited. The simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads, which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional methods. Furthermore, IWPA has a better performance than traditional methods in terms of convergence speed, accuracy, and stability for robust voltage control.

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Reconfiguration of Virtual Cellular Manufacturing Systems via Improved Imperialist Competitive Approach

Cited by 20 publications

References 46 publications

A revised imperialist competition algorithm for cellular manufacturing optimization based on product line design

A revised imperialist competition algorithm for cellular manufacturing optimization based on product line design

Optimization of product line considering compatibility and reliabilityviadiscrete imperialist competitive algorithm

Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Generative Adversarial Network

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