Product line optimization in the presence of preferences for compromise alternatives

Bechler, Georg; Steinhardt, Claudius; Mackert, Jochen; Klein, Robert

doi:10.1016/j.ejor.2020.06.029

Cited by 16 publications

(2 citation statements)

References 35 publications

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“…Gauss et al [37] classified studies on product family design in the past 20 years and reviewed the methods, algorithms, and technologies used. From the optimization method perspective, the literature on product family design can be divided into two groups: the first group applies exact algorithms and the second applies heuristic algorithms [38].…”

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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“…We follow Ghuge et al ( 2021) and generate the revenues r j uniformly over the interval [0, 1]; the revenue r 0 associated with the no-purchase option is set to 0. According to Bechler et al (2021), there exists a well-known relationship between product revenues and customer preferences-the so-called price compromise according to which customers tend to purchase neither the most expensive nor the cheapest alternative but prefer the mid-priced one whereby their preferences decrease when prices become higher or lower than average. To capture this real-world behavior, we sort both the preference weights and the revenues in descending order and then re-sort the revenues in a way such that the middle highest revenue corresponds to the highest preference weight, the second middle highest revenue corresponds to the second highest preference weight and so on.…”

Section: Products Groups Group-product Matrix Preference Weights and ...mentioning

confidence: 99%

Group-constrained assortment optimization under the multinomial logit model

Heger,

Klein

2024

J Revenue Pricing Manag

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We study an assortment problem under the multinomial logit model with two new types of group constraints that are motivated by a joint project with the German car manufacturer BMW. Under group constraints, products are either attributed to exactly one group or to several groups at once and there is either a bound on the number of products offered per group or on the number of groups from which products are offered. We formulate both optimization problems as binary fractional linear program and provide reformulations that can be solved using state-of-the-art solvers. Finally, we conduct a numerical study and find that all instances of the products-per-group constrained problem as well as small to medium size instances of the number-of-offered-groups constrained problem can be solved within fractions of a second, whereas large instances of the latter problem might take some seconds to be solved.

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