Design of single assembly line for the delayed differentiation of product variants

AlGeddawy, Tarek; ElMaraghy, Hoda A.

doi:10.1007/s10696-011-9074-7

Cited by 39 publications

(5 citation statements)

References 25 publications

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“…The proliferation of product varieties is mandated by competition and customer demands and is clearly evident in the automotive industry (AlGeddawy and ElMaraghy 2010). As also shown by AlGeddawy and ElMaraghy (2010), this is well demonstrated in the example of car engine accessories where families of products that exhibit wide variety exist; yet they have many common functions, components, and assembly processes.…”

Section: Introductionmentioning

confidence: 82%

Benefits of robust multiobjective optimization for flexible automotive assembly line balancing

2018

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Changing conditions and variations in the demand are frequent in real industrial environments. Decision makers have to take into account this uncertainty and manage it properly. One clear example is the automotive industry where manufacturers have to assume an uncertain and heterogeneous demand. For instance, automotive manufacturers must adapt their decisions when balancing the assembly line by considering different flexible solutions. Our proposal is using robust multiobjective optimization and simulation techniques to provide managers with a set of robust and equally-preferred solutions for assembly line balancing. We study a Nissan case where the demand of each product family is uncertain. The problem is addressed by considering a robust multiobjective model for assembly line balancing based on a high number of production plans. After the selection of six different assembly line configurations, we study the implications of robustness metrics based on workstations' overload. We show that the adverse managerial effects of not having flexible line configuration when demand changes are alleviated. For the real Nissan automotive case, our analysis and conclusions show the managerial and industrial benefits of using robust assembly lines. We also encourage decision makers to use robust multiobjective optimization methods for selecting the most flexible decisions.

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Section: Introductionmentioning

confidence: 82%

Benefits of robust multiobjective optimization for flexible automotive assembly line balancing

2018

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“…For the delayed production mode in process production enterprises, particularly the production of multiple products and different processes, some studies have also considered the impact of PDP in addition to CODP and inventory management. Vanteddu et al [ 35 ] and AlGeddawy et al [ 36 ] studied the location problem of PDP by building models in different scenarios, but mainly for the delayed production of discrete production enterprises.…”

Section: Data Mining To Analyze the Influencing Factors Of Delayed Pr...mentioning

confidence: 99%

A study on optimization of delayed production mode of iron and steel enterprises based on data mining

Shi

2023

PLoS ONE

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Delayed production mode has been adopted by an increasing number of process production enterprises as a method to realize mass customization of multi-products. This paper used the convolutional neural network-long short-term memory artificial neural network algorithm (C-LSTM) in data mining technology to analyze and determine factors that have an impact on delayed production mode in the internal and external production and operation of enterprises. Combined with the actual production situation of iron and steel enterprises, a quantitative model of the delayed production was constructed. Lastly, data from a large iron and steel enterprise with good operation was used to verify the validity of the proposed model and analyze key influencing factors. According to the research, in scenarios of considering PDP alone, considering CODP alone, considering both PDP and CODP, considering PDP and CODP and using data mining technology to model, the matching degree of these methods with the actual situation of the enterprise is 31.8%, 61.4%, 71.6% and 86.6%, respectively. The numerical analysis results of the model based on data mining technology show that in delayed production, when customer service level improves or the delay penalty coefficient increases, the optimal locations of the product differentiation point (PDP) and customer order decoupling point (CODP) move toward the end of production, and the total cost increases gradually. When the difference in production cost or benefit of early delivery between the candidate locations of PDP and CODP is small, optimal locations of PDP and CODP are close to the beginning of the general and dedicated production processes. With an increase of cost difference or early delivery benefit, the optimal locations of PDP and CODP jumped to the end stage of the general and dedicated production processes, and the total cost begins to decrease.

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“…Part of the literature focused on the positioning of the point of differentiation (POD). In different scenarios, Vanteddu and Chinnam [30], AlGeddawy and EIMaraghy [31], Hsu and Wang [32], Grag and Tang [33], and Lee and Tang [34] studied the positioning of POD or product differentiation point (PDP) in delayed production by building a model. These studies showed that the POD and the PDP are similar in definition and can be collectively referred to as "product differentiation point", which is the starting point of all products in the enterprise or all products in a product category to create a differentiation.…”

Section: Studies On Postponementmentioning

confidence: 99%

A Study on Optimal Location Selection and Semi-Finished Product Inventory Allocation in the Steel Industry

Shi

Bohács

et al. 2022

Sustainability

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With the development of industrial production technologies and market economy, Postponement is increasingly being adopted by continuous production enterprise as a method that enables multi-product, mass customization production. In order to make use of the Postponement in manufacturing production enterprises need to achieve customized production and reduce enterprise costs. A modeling is conducted in the production process of Postponement in iron and steel enterprises in this paper. With the goal of minimizing total costs on a specific customer service level, mixed integer nonlinear programming and particle swarm algorithms are adopted for modeling and solving, to determine the optimal location and optimal semi-finished product inventory of PDP and CODP. Finally, taking an iron and steel enterprise as an example, the feasibility and effectiveness of the model and algorithm are verified. The study shows that the change of the optimal locations of PDP and CODP is affected by the change of customer service level and delay penalty coefficient, but the speed of change of the optimal location of PDP shows longer delays. In addition, the size of the capacity of semi-finished product inventory corresponding to CODP has a direct influence on whether the semi-finished product inventory corresponding to PDP participates in production, which in turn affects the optimal semi-finished product inventory on all levels. Through the analysis, it is found that the model constructed in this paper can better describe the overall situation and the influence relationship of the Postponement, and the study supplements the deficiency of the research on Postponement in the continuous manufacturing enterprises and enriches the content of the quantitative research on the Postponement.

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Design of single assembly line for the delayed differentiation of product variants

Cited by 39 publications

References 25 publications

Benefits of robust multiobjective optimization for flexible automotive assembly line balancing

Benefits of robust multiobjective optimization for flexible automotive assembly line balancing

A study on optimization of delayed production mode of iron and steel enterprises based on data mining

A Study on Optimal Location Selection and Semi-Finished Product Inventory Allocation in the Steel Industry

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