scholarly journals Preference-Based Revenue Optimization for App-Based Lifestyle Membership Plans

2021 ◽  
Vol 20 (1) ◽  
pp. 21-31
Author(s):  
Fransiscus Rian Pratikto ◽  
Gerardus Daniel Julianto ◽  
Sani Susanto
Keyword(s):  
Optimization Model ◽  
Demand Function ◽  
Random Parameter ◽  
Mixed Integer ◽  
Revenue Optimization ◽  
First Choice ◽  
Simulation Scenario ◽  
Random Parameter Logit ◽  
Pricing Optimization ◽  
Better Than

The demand for a product is rooted in the consumers’ needs and preferences. Therefore, a pricing optimization model will be more valid if the demand function is represented under this basic notion. A preference-based revenue optimization model for an app-based lifestyle membership program is developed and solved in this research. The model considers competitor products and cannibalization effect from products in other fare-class, where both are incorporated using a preference-based demand function. The demand function was derived through a randomized first choice simulation that converts individual utility values into personal choices based on the random parameter logit model. Cannibalizing products are considered as competing products in the simulation scenario. In the pricing optimization, two and three fare classes based on the membership period are considered. The corresponding pricing optimization problem is a mixed-integer nonlinear programming problem with a solution-dependent objective function. Using enumeration, the three-fare-class optimal prices of Rp420,000, Rp300,000, and Rp60,000 for 12-month, 6-month, and 1-month membership, respectively, are better than those of the two-fare-class. Under this policy, the estimated total revenue is Rp30.56 billion, 41.74% greater than that of the current condition.

scholarly journals Incorporating Cannibalization into Pricing Optimization Using Choice Data: An Application to the Pricing of Mobile Broadband Services

2019 ◽  
Vol 21 (2) ◽  
pp. 57-68
Author(s):  
Fransiscus Rian Pratikto
Keyword(s):  
Demand Function ◽  
Hierarchical Bayes ◽  
Choice Data ◽  
Total Contribution ◽  
Mobile Broadband ◽  
Price Differentiation ◽  
First Choice ◽  
Broadband Services ◽  
Pricing Optimization

Price differentiation may not be as effective in increasing profitability due to imperfect segmentation, arbitrage, and, cannibalization. Cannibalization takes place when customer with higher willingness-to-pay buys lower-priced product. This research proposes an approach to incorporating cannibalization into pricing optimization using choice data. From choice data, individual level utilities are estimated using hierarchical Bayes and individual choice is predicted using randomized first choice simulation. Individual choices are then aggregated to obtain the demand function. The novelty of this research is in the way cannibalization is incorporated into the pricing optimization. Instead of integrating cannibalization into the demand function or representing it as a separate component in the optimization formulation, in this research, cannibalizing products are incorporated into the simulation scenario as competing products, based on which the demand functions used in the optimization are derived. This approach is more direct and realistic than those in the previous research. The approach was implemented in a case study of mobile broadband services in Indonesian price-sensitive market. The result shows that two-fare-class price differentiation incoporated with product differentiation increases total contribution of about 60% compared to single-fare-class policy. Furthermore, it is also shown from our case study that starting from a three-fare-class policy, through iterations, our approach suggests that policy with two-fare-class results in a not significantly different total contribution.

scholarly journals Taste Beats Reputation in New Food Products Choice: The Case of Ready-to-Eat Pomegranate among Young Consumers in Veneto Region (Italy)

Horticulturae ◽  
2021 ◽  
Vol 7 (7) ◽  
pp. 179
Author(s):  
Alice Stiletto ◽  
Erika Rozzanigo ◽  
Elisa Giampietri ◽  
Samuele Trestini
Keyword(s):  
Discrete Choice ◽  
Choice Experiment ◽  
Consumer Preferences ◽  
Random Parameter ◽  
Food Products ◽  
Sensory Features ◽  
Young Consumers ◽  
Random Parameter Logit ◽  
Italian Origin ◽  
Random Parameter Logit Model

This study investigates the preferences for ready-to-eat pomegranate arils in Italy through a discrete choice experiment (DCE) on 264 young consumers in Italy. The aim is to estimate consumers’ willingness to pay (WTP) for the reputational attributes of the product (e.g., the product origin and sales channel) and to discriminate the elicited preferences between tasting and non-tasting situations. To this purpose, a random parameter logit model was employed to assess the heterogeneity in consumer preferences. The results suggest that non-tasters attach a relevant value to the reputational attributes (e.g., +75% WTP for Italian origin). Moreover, considering the sensory features of the products, we found that consumers in this group discriminate against the proposed samples only through their visual characteristics: they prefer the sample with the largest size and red colored arils. In addition, we found that the tasting experience reduced the value attached to the reputational attributes (e.g., −50% WTP for local origin) for consumers, compared to non-tasting situation, thus shifting their preference to the samples that they appreciated the most (high liking). Specifically, we found that consumers in the tasting group preferred the product sample with the highest level of sweetness and the lowest level of sourness and astringency, showing a higher preference for sweetness. The findings contribute to the literature on consumers’ behavior on new food products (NFPs), showing that reputational attributes lose value after the tasting experience. In contrast, the sensory features of the NFPs can help tasters to reduce the information asymmetry, which traditionally represents a hurdle in purchases for new consumers. However, this depends on the individuals’ subjective preferences, as demonstrated by the significant effect of liking levels in discriminating consumers’ choices. To conclude, although these results cannot be extended to the general population, they may give some interesting insights about future trends of NFP demand.

scholarly journals The Effects of Life Course Events on Car Ownership and Sustainable Mobility Tools Adoption Decisions: Results of an Error Component Random Parameter Logit Model

2021 ◽  
Vol 13 (12) ◽  
pp. 6816
Author(s):  
Gaofeng Gu ◽  
Tao Feng ◽  
Chixing Zhong ◽  
Xiaoxi Cai ◽  
Jiang Li
Keyword(s):  
Life Course ◽  
Logit Model ◽  
Random Parameter ◽  
Error Component ◽  
Car Ownership ◽  
Sustainable Mobility ◽  
Car Sharing ◽  
Random Parameter Logit ◽  
Electric Bike ◽  
Random Parameter Logit Model

Life course events can change household travel demand dramatically. Recent studies of car ownership have examined the impacts of life course events on the purchasing, replacing, and disposing of cars. However, with the increasing diversification of mobility tools, changing the fleet size is not the only option to adapt to the change caused by life course events. People have various options with the development of sustainable mobility tools including electric car, electric bike, and car sharing. In order to determine the impacts of life course events on car ownership and the decision of mobility tool type, a stated choice experiment was conducted. The experiment also investigated how the attributes of mobility tools related to the acceptance of them. Based on existing literature, we identified the attributes of mobility tools and several life course events which are considered to be influential in car ownership decision and new types of mobility tools choice. The error component random parameter logit model was estimated. The heterogeneity across people on current car and specific mobility tools are considered. The results indicate people incline not to sell their current car when they choose an electric bike or shared car. Regarding the life course events, baby birth increases the probability to purchase an additional car, while it decreases the probability to purchase an electric bike or joining a car sharing scheme. Moreover, the estimation of error components implies that there is unobserved heterogeneity across respondents on the sustainable mobility tools choice and the decision on household’s current car.

Taking Peritoneal Dialysis beyond the Year 2000

1999 ◽  
Vol 19 (3_suppl) ◽  
pp. 35-42 ◽  
Author(s):  
Ram Gokal
Keyword(s):  
Peritoneal Dialysis ◽  
Peritoneal Membrane ◽  
Membrane Function ◽  
Fluid Removal ◽  
The Past ◽  
First Choice ◽  
Year 2000 ◽  
Economic Considerations ◽  
Appropriate Prescription ◽  
Better Than

Over the past 25 years, peritoneal dialysis (PD) has steadily improved so that now its outcomes, in the form of patient survival, are equivalent to, and at times better than, those for hemodialysis. We now have a better understanding of the pathophysiology of peritoneal membrane function and damage and the importance of appropriate prescription to meet agreed-upon targets of solute and fluid removal. In the next millennium, greater emphasis will be put on prescription setting and subsequent monitoring. This will entail an increase in automated PD, especially for lifestyle reasons as well as for patients with a hyperpermeable peritoneal membrane. To improve outcomes, dialysis should be started earlier than is currently the case. It is easy to do this with PD, where an incremental approach is made easier by the introduction of icodextrin for long-dwell PD. In the future, solutions will be tailored to be more biocompatible and to provide improved nutrition and better cardiovascular outcomes. Finally, economic considerations favor PD, which is cheaper than in-centre hemodialysis. Thus, for many, PD has become a first-choice therapy, and with further improvements this trend will continue.

Optimization design of drive system for industrial robots based on dynamic performance

2017 ◽  
Vol 44 (6) ◽  
pp. 765-775 ◽  
Author(s):  
LianZheng Ge ◽  
Jian Chen ◽  
Ruifeng Li ◽  
Peidong Liang
Keyword(s):  
Optimization Model ◽  
Optimization Problem ◽  
Industrial Robot ◽  
Dynamic Performance ◽  
System Optimization ◽  
Drive System ◽  
Industrial Robots ◽  
Mixed Integer ◽  
Light Weight ◽  
Content Type

Purpose The global performance of industrial robots partly depends on the properties of drive system consisting of motor inertia, gearbox inertia, etc. This paper aims to deal with the problem of optimization of global dynamic performance for robotic drive system selected from available components. Design/methodology/approach Considering the performance specifications of drive system, an optimization model whose objective function is composed of working efficiency and natural frequency of robots is proposed. Meanwhile, constraints including the rated and peak torque of motor, lifetime of gearbox and light-weight were taken into account. Furthermore, the mapping relationship between discrete optimal design variables and component properties of drive system were presented. The optimization problem with mixed integer variables was solved by a mixed integer-laplace crossover power mutation algorithm. Findings The optimization results show that our optimization model and methods are applicable, and the performances are also greatly promoted without sacrificing any constraints of drive system. Besides, the model fits the overall performance well with respect to light-weight ratio, safety, cost reduction and others. Practical implications The proposed drive system optimization method has been used for a 4-DOF palletizing robot, which has been largely manufactured in a factory. Originality/value This paper focuses on how the simulation-based optimization can be used for the purpose of generating trade-offs between cost, performance and lifetime when designing robotic drive system. An applicable optimization model and method are proposed to handle the dynamic performance optimization problem of a drive system for industrial robot.

scholarly journals Fishing location choice and risk preferences among small fishers – Implications for fisheries management policies

2020 ◽  
Vol 15 (2) ◽  
pp. 140-156
Author(s):  
Riad Sultan ◽  
Keyword(s):  
Fisheries Management ◽  
Risk Preferences ◽  
Random Parameter ◽  
Management Policy ◽  
Linear Utility ◽  
South West Coast ◽  
Random Parameter Logit ◽  
Management Policies ◽  
Expected Revenue ◽  
Random Parameter Logit Model

The study provides evidence for how risk preferences determine fishing location choices by artisanal fishers on the south-west coast of the island of Mauritius. Risk preference is modelled using a random linear utility framework defined over mean-standard deviation space. The study estimates expected revenue and revenue risk from the Just and Pope production function and applies the random parameter logit model to account for fisher-specific and location-specific characteristics. The findings are consistent with utility-maximising fishers, whereby the likelihood to choose a fishing location is positively associated with expected revenue and negatively related to revenue risk. Distance from fishing station to fishing grounds affects the choice of fishing location negatively. The estimated model allows heterogeneity in risk preferences and concludes that 51% of fishers can be classified as risk averse, 31% as risk seekers and the remaining as risk neutral. The study also estimates the degree of substitutability and complementarity between fishing locations based on the risk preferences of fishers and discusses the relevance of this for fisheries management policy.

scholarly journals Análisis econométrico de la heterogeneidad de las preferencias de los individuos: Aplicación a la valoración económica de la conservación del paisaje agrícola de montaña

2011 ◽  
Vol 8 (1) ◽  
pp. 103 ◽  
Author(s):  
Sergio Colombo ◽  
Nick Hanley
Keyword(s):  
Latent Class ◽  
Random Parameter ◽  
Choice Models ◽  
Latent Class Models ◽  
Preference Heterogeneity ◽  
Stated Choice ◽  
Empirical Application ◽  
Random Parameter Logit ◽  
Class Models

The need to account for respondents’ preference heterogeneity in stated choice models has motivated researchers to apply random parameter logit and latent class models. In this paper we compare these three alternative ways of incorporating preference heterogeneity in stated choice models and evaluate how the choice of model affects welfare estimates in a given empirical application. Finally, we discuss what criteria to follow to decide which approach is most appropriate.

Optimizing Disruption Tolerance for Rail Transit Networks Under Uncertainty

2021 ◽  
Author(s):  
Lei Xu ◽  
Tsan Sheng (Adam) Ng ◽  
Alberto Costa
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Optimization Problem ◽  
Mixed Integer ◽  
Rail Transit ◽  
Distributionally Robust Optimization ◽  
Performance Function ◽  
Transit Networks ◽  
Disruption Tolerance ◽  
Distributionally Robust

In this paper, we develop a distributionally robust optimization model for the design of rail transit tactical planning strategies and disruption tolerance enhancement under downtime uncertainty. First, a novel performance function evaluating the rail transit disruption tolerance is proposed. Specifically, the performance function maximizes the worst-case expected downtime that can be tolerated by rail transit networks over a family of probability distributions of random disruption events given a threshold commuter outflow. This tolerance function is then applied to an optimization problem for the planning design of platform downtime protection and bus-bridging services given budget constraints. In particular, our implementation of platform downtime protection strategy relaxes standard assumptions of robust protection made in network fortification and interdiction literature. The resulting optimization problem can be regarded as a special variation of a two-stage distributionally robust optimization model. In order to achieve computational tractability, optimality conditions of the model are identified. This allows us to obtain a linear mixed-integer reformulation that can be solved efficiently by solvers like CPLEX. Finally, we show some insightful results based on the core part of Singapore Mass Rapid Transit Network.

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