A multi-population co-evolutionary genetic programming approach for optimal mass customisation production

Yu, Biao; Zhao, Han; Xue, Deyi

doi:10.1080/00207543.2016.1194538

Cited by 17 publications

(5 citation statements)

References 25 publications

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“…attributes and their corresponding levels (Kuzmanovic et al 2012 andYu et al 2017). In line with these arguments, we have adopted attribute-level demand forecasting within this study.…”

Section: )mentioning

confidence: 99%

“…For a typical digital camera, various product attribute could be “charging time”, “battery life”, “optical zoom” and several others. Extant research studies have modeled the product sales (within a market segment) to be a function of associated product attributes and their corresponding levels (Kuzmanovic et al , 2012; Yu et al , 2017). In line with these arguments, we have adopted attribute-level demand forecasting within this study.…”

Section: Introductionmentioning

confidence: 99%

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Mitigating demand risk of durable goods in online retailing

Ghadge¹,

Bag²,

Goswami³

et al. 2020

IJRDM

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PurposeAn uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost.Design/methodology/approachFour e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer.FindingsMixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction.Research limitations/implicationsLimited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss.Practical implicationsThe analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner.Originality/valueThe study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information.

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“…attributes and their corresponding levels (Kuzmanovic et al 2012 andYu et al 2017). In line with these arguments, we have adopted attribute-level demand forecasting within this study.…”

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mitigating demand risk of durable goods in online retailing

Ghadge¹,

Bag²,

Goswami³

et al. 2020

IJRDM

View full text Add to dashboard Cite

show abstract

“…Lei et al [34] proposed an enhanced multi-objective co-evolutionary algorithm. Yu et al [35] proposed a multi-population co-evolutionary genetic programming approach to identify the optimal design. Goran and Tihana [36] proposed a co-evolutionary multi-population genetic program.…”

Section: Related Workmentioning

confidence: 99%

Study on an Adaptive Co-Evolutionary ACO Algorithm for Complex Optimization Problems

2018

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The ant colony optimization (ACO) algorithm has the characteristics of positive feedback, essential parallelism, and global convergence, but it has the shortcomings of premature convergence and slow convergence speed. The co-evolutionary algorithm (CEA) emphasizes the existing interaction among different sub-populations, but it is overly formal, and does not form a very strict and unified definition. Therefore, a new adaptive co-evolutionary ant colony optimization (SCEACO) algorithm based on the complementary advantages and hybrid mechanism is proposed in this paper. Firstly, the pheromone update formula is improved and the pheromone range of the ACO algorithm is limited in order to achieve the adaptive update of the pheromone. The elitist strategy and co-evolutionary idea are used for reference, the symbiotic mechanism and hybrid mechanism are introduced to better utilize the advantages of the CEA and ACO. Then the multi-objective optimization problem is divided into several sub-problems, each sub-problem corresponds to one population. Each ant colony is divided into multiple sub-populations in a common search space, and each sub-population performs the search activity and pheromone updating strategy. The elitist strategy is used to retain the elitist individuals within the population and the min-max ant strategy is used to set pheromone concentration for each path. Next, the selection, crossover, and mutation operations of individuals are introduced to adaptively adjust the parameters and implement the information sharing of the population and the co-evolution. Finally, the gate assignment problem of a hub airport is selected to verify the optimization performance of the SCEACO algorithm. The experiment results show that the SCEACO algorithm can effectively solve the gate assignment problem of a hub airport and obtain the effective assignment result. The SCEACO algorithm improves the convergence speed, and enhances the local search ability and global search capability.

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“…As a term, it was coined by Prof. Wolfgang Wahlster in Germany [1,2]. There has been a paradigm shift from mass production to mass customization, resulting from increasingly rapid changes in consumer tastes, rapid changes in demand, and the emergence of new competitors [3][4][5][6]. To survive and succeed in such competitive conditions, manufacturers should manage product varieties effectively [7].…”

Section: Introductionmentioning

confidence: 99%

AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times

Szajna

Kostrzewski

2022

Sustainability

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The world faces the continuously increasing issue of a lack of skilled employees, staff migration, and turnover. It is strengthened by unexpected situations such as wars, pandemics, and other civilization crises. Solutions are sought and researched in various branches of industry and academia, including engineering, social sciences, management, and political and computer sciences. From the viewpoint of this paper, this is a side topic of Industry 4.0 and, more specifically, sustainability in working environments, and the issue is related to production employees who perform manual operations. Some of the tasks cannot be carried out under robotization or automation; therefore, novel human-work support tools are expected. This paper presents such highly demanded support tools related to augmented reality (AR) and artificial intelligence (AI). First, a panoramic literature review is given. Secondly, the authors explain the main objective of the presented contribution. Then the authors’ achievements are described—the R&D focus on such solutions and the introduction of the developed tools that are based on AR and AI. Benefits connected to the AR-AI technology applications are presented in terms of both time savings with the tool usage and job simplification, enabling inexperienced, unskilled, or less skilled employees to perform the work in the selected manual production processes.

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A multi-population co-evolutionary genetic programming approach for optimal mass customisation production

Cited by 17 publications

References 25 publications

Mitigating demand risk of durable goods in online retailing

Mitigating demand risk of durable goods in online retailing

Study on an Adaptive Co-Evolutionary ACO Algorithm for Complex Optimization Problems

AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times

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