Determining the optimal customization levels, lead times, and inventory positioning in vertical product differentiation

Tookanlou, Parisa Bagheri; Wong, Hartanto

doi:10.1016/j.ijpe.2019.08.014

Cited by 31 publications

(13 citation statements)

References 52 publications

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“…Heuristic algorithms aim at obtaining near optimal solutions in product family design, which is regarded as the best available strategy for large-scale cases. Belloni et al [45] classified several heuristic algorithms from three aspects: 1) methods operating in attribute space, such as, genetic algorithm [12] and simulated annealing [46]; 2) methods operating in product space, such as, greedy heuristic [47] and divide-and-conquer heuristic [48,49]; 3) methods evaluating partially formed products, including beam search heuristic [50] and dynamic programming heuristic [51,52]. Some scholars developed heuristic algorithms for new optimization problems.…”

Section: Analysis Of the Optimization Methodsmentioning

confidence: 99%

Service Family Design Optimization Considering a Multi-Server Queue

et al. 2021

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Service firms not only need to develop differentiated services to meet the requirements of customers with various preferences, but also have to improve service flexibility and the efficiency of the service system. A service family is a strategy by which different modules are configured, based on the service platform, to create a variety of differentiated services. This research considered both the effect of multi-server queues and the heterogeneous service processes in service family design problems to establish a framework of service modularization from three different perspectives-process, activity, and component. To optimize the service family design, a nonlinear integer-programming model was established to determine the optimal configurations of modules and prices for the service family and the optimal number of servers. The model is transformed into a linear form, and thus, can be solved using a commercial optimization software for smallscale problems. An improved genetic algorithm integrated with a neighborhood search was further developed to solve large-scale problems. The correctness of the linearized model and the effectiveness of the metaheuristic algorithm were demonstrated through case studies and numerical experiments.

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Section: Analysis Of the Optimization Methodsmentioning

confidence: 99%

Service Family Design Optimization Considering a Multi-Server Queue

et al. 2021

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“…Tookanlou and Wong [ 47 ] explained vertical product differentiation enabled customization with lead time versus customization as conflicting objectives.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing

James¹

2021

Neural Comput & Applic

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“…Mass customization, as a concept, is in a process of continuous revision and evolution, from a system based on modular elements to a system based on the co-design of products by consumers [ 73 , 79 , 83 , 84 , 85 ], so companies need new studies, methods, and tools to create personalized products with the costs and mass production efficiency.…”

Section: Initial Considerations and Main Synergies Of The Technolomentioning

confidence: 99%

Integration of Additive Manufacturing, Parametric Design, and Optimization of Parts Obtained by Fused Deposition Modeling (FDM). A Methodological Approach

2020

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The use of current computer tools in both manufacturing and design stages breaks with the traditional conception of productive process, including successive stages of projection, representation, and manufacturing. Designs can be programmed as problems to be solved by using computational tools based on complex algorithms to optimize and produce more effective solutions. Additive manufacturing technologies enhance these possibilities by providing great geometric freedom to the materialization phase. This work presents a design methodology for the optimization of parts produced by additive manufacturing and explores the synergies between additive manufacturing, parametric design, and optimization processes to guide their integration into the proposed methodology. By using Grasshopper, a visual programming application, a continuous data flow for parts optimization is defined. Parametric design tools support the structural optimization of the general geometry, the infill, and the shell structure to obtain lightweight designs. Thus, the final shapes are obtained as a result of the optimization process which starts from basic geometries, not from an initial design. The infill does not correspond to pre-established patterns, and its elements are sized in a non-uniform manner throughout the piece to respond to different local loads. Mass customization and Fused Deposition Modeling (FDM) systems represent contexts of special potential for this methodology.

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Determining the optimal customization levels, lead times, and inventory positioning in vertical product differentiation

Cited by 31 publications

References 52 publications

Service Family Design Optimization Considering a Multi-Server Queue

Service Family Design Optimization Considering a Multi-Server Queue

Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing

Integration of Additive Manufacturing, Parametric Design, and Optimization of Parts Obtained by Fused Deposition Modeling (FDM). A Methodological Approach

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