Heterogeneous Customers Research Articles

Community Solar Value Generation and Distribution

Climate change mitigation efforts are taking place around the world, including efforts to decarbonize the electricity grid with renewable energy generation. In addition to small-scale rooftop and large-scale utility-scale solar, intermediate commercial-scale solar projects are gaining traction through an innovative business model known as community solar, in which residential customers pay a local solar project developer (via either an ongoing subscription fee or an upfront investment) for a share of solar capacity. The participating customers are compensated for the solar energy generated from their share of capacity by the utility, at a bill credit rate set by the regulator. The total cost of energy can be reduced if the bill credits are less than what the utility would otherwise pay for the conventional energy. In this paper, we study how the community solar business model generates value and how this value is distributed among all the players, using a model that endogenizes the adoption decisions of heterogeneous customers and the solar developer's pricing decisions in response to the bill credit rate. Our model captures the fundamental tradeoffs between the direct benefits of community solar participation and the indirect benefits from overall energy cost reduction, as well as between the solar developer's profit and the welfare improvement of the customers. Our analysis provides guidance to the policy makers on regulating community solar programs to achieve both high value generation and equitable value distribution.

The Sorting Process as a Tool for Promoting the Demand of Heterogeneous Customers

The present study concerns product diversification. The products differ in size, shape, flavor, fat content, etc., so that the producer can more specifically modify the particular product to the unique requirements of nonhomogeneous customers. The mathematical model assumes diversified demands of nonhomogeneous consumers for an initial unsorted item. The sorting process generates a better match between customer requirements and the actual supply of sorted products. Thus, the implementation of sorting costs allows for an increase in customer demands by adopting product characteristics that are closer to customer needs and tastes. The study also considers the pricing policy for diversified products in order to determine if price discrimination is preferable for attaining the manufacturer’s goal of profit maximization.

On the Market-Consistent Valuation of Participating Life Insurance Heterogeneous Contracts under Longevity Risk

The purpose of this paper is to conduct a market-consistent valuation of life insurance participating liabilities sold to a population of partially heterogeneous customers under the joint impact of biometric and financial risk. In particular, the heterogeneity between groups of policyholders stems from their offered minimum interest rate guarantees and contract maturities. We analyse the effects of these features on the company’s insolvency while embracing the insurer’s goal to achieve the same expected return for different cohorts of policyholders. Within our extensive numerical analyses, we determine the fair participation rates and other key figures, and discuss the implications for the stakeholders, taking account of various degrees of conservativeness of the insurer when pricing the contracts.

Asymmetric Information and Differentiated Durable Goods Monopoly: Intra-Period Versus Intertemporal Price Discrimination

A durable good monopolist faces a continuum of heterogeneous customers who make purchase decisions by comparing present and expected price-quality offers. The monopolist designs a sequence of price-quality menus to segment the market. We consider the Markov Perfect Equilibrium (MPE) of a game where the monopolist is unable to commit to future price-quality menus. We obtain the novel results that (a) under certain conditions, the monopolist covers the whole market in the first period (even when a static Mussa-Rosen monopolist would not cover the whole market), because this is a strategic means to convince customers that lower prices would not be offered in future periods, and that (b) this can happen only under the stage-wise Stackelberg leadership assumption (whereby consumers base their expectations on the value of the state variable at the end of the period). Conditions under which MPE necessarily involve sequentially trading are also derived.

Fair Scheduling of Heterogeneous Customer Populations

When managing congested service systems, it is common to use priority rules based on some operational criteria. In this paper, we consider the societal implications of such individual-focused priority policies, when individuals are considered as members of broader population groups. We find that optimal resource allocation policies such as the cμ-rule in scheduling can lead to significant inequity across different population groups. We propose policies that can mitigate this inequity and can even generate completely equitable outcomes across populations with little, or at times, even no additional system cost. Thus, we find that it can be possible to achieve more equitable outcomes while ensuring operational efficiency.

The Bhutan tourist satisfaction index

This paper develops an index, which is a single measure based on tourists' perspective for evaluating service attributes that will serve as a summative overall measure of tourist satisfaction in light of the 'new customer satisfaction index for evaluating transit service quality' propounded by Eboli and Mazzulla (2009). Precisely, both new indexes, heterogeneous tourist satisfaction index (HTSI) derived from heterogeneous customer satisfaction index (HCSI) and conventional index, tourist satisfaction index (TSI) derived from customer satisfaction index (CSI) are computed using eight service attributes and their corresponding importance scores. The data has been collected using a structured questionnaire from 3,062 international and regional tourists who visited Bhutan in 2017 covering all seasons. The indexes so computed would be useful both to destination managers and marketers of Bhutan as a broad framework for performance evaluation and reporting, and in particular to the Tourism Council of Bhutan for sound tourism policy formulations.

Object detection in factory based on deep learning approach

Manufacturing systems need to be highly adaptive and flexible to meet heterogeneous customer requirements and require fast and accurate object identification. This paper presents the implementation of deep learning for the object detection in a factory in which images of machines are used to train four models. For each model, three trials with different hyperparameter configurations are performed. After the training, evaluations based on the loss curves, mean average precision curves, and performance metrics are carried out. Finally, the trained models are tested with a video in order to select the model with the best detection performance and accuracy.

Benefiting from additive manufacturing for mass customization across the product life cycle

Additive manufacturing (AM) was initially designed for prototyping and product personalization, where high production quantities were not required. Now, it is also implemented for final part production to achieve cost-effective mass customization (MC). Thanks to its tool-less production and extreme flexibility, AM has the potential to address individual customer preferences with custom final parts. Nevertheless, despite its increased competitiveness, AM is not yet likely to replace traditional MC systems, but it can complement them, improving manufacturing efficiency. To broaden our understanding of how AM can complement traditional manufacturing systems, we develop an exploratory quantitative model. First, we leverage customer-centricity in a novel time-varying locational choice model of heterogeneous customers, coupling the Bass and the Hotelling–Lancaster models. Then, we investigate customer-centric marketing and operations decisions, exploring technology-switching scenarios that interchange AM with MC across the product life cycle (PLC). We formulate and solve an optimization problem by jointly deciding on technology-switching times, pricing, and product variety strategies to maximize a manufacturer’s profit and meet individual customers’ diverse and evolving needs. We use a validated Sample Average Approximation approach for the numerical solution of our non-convex optimization problem. Testing different pricing strategies, we show that decreasing and flexible trajectories are optimal. We derive analytical properties for the optimal pricing policy and demonstrate that a manufacturer can benefit from interchanging AM and MC across the PLC, in particular by adopting an AM-MC-AM scenario.

Stochastic analysis of an automated storage and retrieval system with multiple in‐the‐aisle pick positions

AbstractAn automated storage and retrieval system with multiple in‐the‐aisle pick positions (MIAPP‐AS/RS) is often used to support case‐picking occurring at floor and mezzanine levels. In this paper, the replenishment process is modeled as an M/G/1/N/N queueing problem in which the aisle‐captive AS/R machine is the server and pick positions requiring replenishments are customers. Four scenarios are considered: finite population with dedicated storage, finite population with random storage, infinite population with dedicated storage and infinite population with random storage. The corresponding probability density functions (PDFs) for travel time are derived using a Chebyshev travel metric. For the finite population case, PDFs are derived using a novel method based on contour lines; the contour‐line‐based method can be used to derive PDFs for similar problems. From queueing and simulation results, the robustness of the M/G/1/N/N queueing model is demonstrated, allowing it to be applied in analyzing an MIAPP‐AS/RS case‐picking operation when interarrival times of replenishment requests are not exponentially distributed and heterogeneous customers are present. Dedicated‐ and random‐storage policies for reserve storage are compared and conditions favoring each policy are provided. A case study demonstrates how queueing results can be applied in designing an MIAPP‐AS/RS.

The Value of Incumbency When Platforms Face Heterogeneous Customers

We study competition for the market in a dynamic model with network externalities, focusing on the efficiency of market outcomes. We propose a representation of the strategic advantages of incumbency and embed it in a dynamic framework with heterogeneous consumers. Then, we completely identify the conditions under which inefficient equilibria with several platforms emerge at equilibrium; explore the reasons why these inefficient equilibria arise; and compute the value of incumbency and analyze why static models generally exaggerate it. (JEL D43, D62, L13, L86)

The disadvantage of the Cμ‐rule when customers are strategic

AbstractThe question of priorities naturally arises when optimizing service systems with heterogeneous customers. If heterogeneity is with respect to time sensitivity and social welfare is of interest, it is well known that the Cμ‐rule is optimal in the sense that customers with high time sensitivity should be prioritized. This, for example, justifies prioritizing more acute patients in public health systems. Using a standard queueing model with strategic customers, we show that, as expected, if admission is centrally controlled, the Cμ‐rule remains optimal. However, if admission cannot be controlled, for example, in some public healthcare systems, the Cμ‐rule leads to an unusual equilibrium joining behavior. We show that this behavior may result in an inferior social welfare, compared to service disciplines that do not prioritize based on individual time sensitivity level, for example, first‐come first‐served (FCFS). In particular, the equilibrium strategy under the Cμ‐rule is not threshold‐based and all customers join with some (type‐specific) positive probability. The suboptimality of the Cμ‐rule is then demonstrated in a case where the potential arrival rate is high enough such that all the joining probabilities are also less than one. This implies that the resulting social welfare is zero while it is guaranteed to be positive under FCFS.

Dynamic Pricing (and Assortment) Under a Static Calendar

This work is motivated by our collaboration with a large consumer packaged goods (CPG) company. We have found that whereas the company appreciates the advantages of dynamic pricing, they deem it operationally much easier to plan out a static price calendar in advance. We investigate the efficacy of static control policies for revenue management problems whose optimal solution is inherently dynamic. In these problems, a firm has limited inventory to sell over a finite time horizon, over which heterogeneous customers stochastically arrive. We consider both pricing and assortment controls, and derive simple static policies in the form of a price calendar or a planned sequence of assortments, respectively. In the assortment planning problem, we also differentiate between the static vs. dynamic substitution models of customer demand. We show that our policies are within 1-1/e (approximately 0.63) of the optimum under stationary demand, and 1/2 of the optimum under nonstationary demand, with both guarantees approaching 1 if the starting inventories are large. We adapt the technique of prophet inequalities from optimal stopping theory to pricing and assortment problems, and our results are relative to the linear programming relaxation. Under the special case of stationary demand single-item pricing, our results improve the understanding of irregular and discrete demand curves, by showing that a static calendar can be (1-1/e)-approximate if the prices are sorted high-to-low. Finally, we demonstrate on both data from the CPG company and synthetic data from the literature that our simple price and assortment calendars are effective. This paper was accepted by Hamid Nazerzadeh, big data analytics.

Inventory rationing on a one-for-one inventory model for two priority customer classes with backorders and lost sales

In this study, we are primarily motivated by the research problem of recognizing heterogeneous customer behavior towards waiting for order fulfillment under the threshold rationing policy (also known as the critical level policy), and aim to find its effect on system stock levels and performance measures. We assume a continuous review one-for-one ordering policy with generally distributed lead times. In the first model, we consider the case in which the low-priority customer class exhibits zero patience for waiting if the demand is not satisfied immediately (a lost sale), whereas the demand of the high-priority customer class can be backordered. This is the first study in the literature to consider this model. We provide an exact analysis for the derivation of the steady-state probability distribution and the average infinite horizon cost per unit time. We then develop an efficient optimization procedure to minimize the average expected cost rate. We also determine the forms of the optimal solutions for the two service level optimization models that are common in practice. In the second model, we study the opposite case in which the high-priority customer class exhibits zero patience for waiting. We establish a theoretical basis for the rationale of using the Continuous-Time Markov Chain (CTMC) approach as an approximation. We show that under certain assumptions, the steady-state probabilities of the system with generally distributed lead times are identical to the steady-state probabilities of the CTMC system with the same mean. This result enables us to link the dynamics of the studied model to the CTMC model, which may open new doors for future research.

Choice data generation using usage scenarios and discounted cash flow analysis

Discrete choice analysis is a popular method of estimating heterogeneous customer preferences. Although model accuracy can be increased by including more choice data, this option is untenable when the obtaining of choice data from target customers is costly and time-consuming.. We thus propose a method for choice data generation for commercial products whose expected money value is a key factor in consumer choice (e.g., commercial vehicles and financial product). Using an individual usage scenario, we generate a discounted cash flow (DCF) model instead of a utility model to estimate the discount rates, rather than partworths, of individual consumers. The DCF model helps us generate synthetic choice data from choice sets consisting of various combinations of attribute levels. Using these data, we employ a hierarchical Bayesian (HB) discrete choice analysis. We conclude the study with a case study on the preference estimation of a hybrid courier truck conversion. The results reveal that the DCF-based HB estimation outperforms the traditional HB estimation.

Priority Multi-Server Queueing System with Heterogeneous Customers

In this paper, we analyze a multi-server queueing system with heterogeneous customers that arrive according to a marked Markovian arrival process. Customers of two types differ in priorities and parameters of phase type distribution of their service time. The queue under consideration can be used to model the processes of information transmission in telecommunication networks in which often the flow of information is the superposition of several types of flows with correlation of inter-arrival times within each flow and cross-correlation. We define the process of information transmission as the multi-dimensional Markov chain, derive the generator of this chain and compute its stationary distribution. Expressions for computation of various performance measures of the system, including the probabilities of loss of customers of different types, are presented. Output flow from the system is characterized. The presented numerical results confirm the high importance of account of correlation in the arrival process. The values of important performance measures for the systems with the correlated arrival process are essentially different from the corresponding values for the systems with the stationary Poisson arrival process. Measurements in many real world systems show poor approximation of real flows by such an arrival process. However, this process is still popular among the telecommunication engineers due to the evident existing gap between the needs of adequately modeling the real life systems and the current state of the theory of algorithmic methods of queueing theory.

Why do preferences for electricity services differ? Domestic appliance curtailment contracts in Ireland

Empirical evidence across several electricity markets reveals heterogeneous customer preferences for direct load control and other electricity service offerings but relatively little is understood concerning the drivers of this preference heterogeneity. Using a discrete choice experiment examining the potential role of domestic appliance curtailment contracts as a means of shifting load, this paper investigates potential drivers of preference heterogeneity with respect to electricity services. The research finds that electricity customers’ personal characteristics, including their environmental attitudes and behaviours, are associated with preferences for curtailment contract attributes though the scale of the relationship is more nuanced and muted than might have been anticipated. Age, family size, and environmental attitudes are the respondent characteristics with the strongest association with preferences, though the nature of the association varies substantially across attributes.

Dynamic Pricing and Matching for Two-Sided Queues

Motivated by diverse applications in sharing economy and online marketplaces, we consider optimal pricing and matching control in a two-sided queueing system. We assume that heterogeneous customers and servers arrive to the system with price-dependent arrival rates. The compatibility between servers and customers is specified by a bipartite graph. Once a pair of customer and server are matched, they depart from the system instantaneously. The objective is to maximize the long-run average profits of the system while minimizing average waiting time. We first propose a static pricing and max-weight matching policy, which achieves O(√ n) optimality rate when all of the arrival rates are scaled by n. We further show that a dynamic pricing and modified max-weight matching policy achieves an improved O(n1/3) optimality rate. In addition, we propose a constraint generation algorithm that solves value function approximation of the MDP and demonstrate strong numerical performance of this algorithm.

Proactive Handling of Flight Overbooking: How to Reduce Negative eWOM and the Costs of Bumping Customers

This research examines the extent to which proactivity in handling flight overbooking reduces negative electronic word-of-mouth (NeWOM) and the required costs of compensation, thus increasing firm profitability. It answers recent calls to use a multimethod approach (i.e., we include archival data, qualitative interviews, seven experiments, and a Monte Carlo simulation for a total of 10 studies) and to adapt recovery to specific contexts (i.e., airlines) and heterogeneous customers (i.e., voluntary/involuntary bumping or offloading). The preliminary studies indicate that overbooking and offloading are pervasive and that a proactive approach is both feasible and desirable. The experiments show that, compared to the default reactive approach (informing passengers at the gate), a proactive approach (informing them before they leave for the airport) substantially reduces NeWOM and the sought compensation. Further, a very reactive approach (informing them in the plane) significantly increases NeWOM and the sought compensation, especially when offloading occurs involuntarily. We also unveil the mechanism explaining the effects of proactivity on NeWOM, through the serial mediation of justice and betrayal. Finally, the results of a Monte Carlo simulation show that offering reduced compensation through a proactive approach allows more aggressive overbooking, higher capacity utilization, and increased net revenue of up to 1.3%.

Synchronizing pricing and replenishment to serve forward‐looking customers

AbstractWe incorporate strategic customer waiting behavior in the classical economic order quantity (EOQ) setting. The seller determines not only the timing and quantities of the inventory replenishment, but also the selling prices over time. While similar ideas of market segmentation and intertemporal price discrimination can be carried over from the travel industries to other industries, inventory replenishment considerations common to retail outlets and supermarkets introduce additional features to the optimal pricing scheme. Specifically, our study provides concrete managerial recommendations that are against the conventional wisdom on “everyday low price” (EDLP) versus “high‐low pricing” (Hi‐Lo). We show that in the presence of inventory costs and strategic customers, Hi‐Lo instead of EDLP is optimal when customers have homogeneous valuations. This result suggests that because of strategic customer behavior, the seller obtains a new source of flexibility—the ability to induce customers to wait—which always leads to a strictly positive increase of the seller's profit. Moreover, the optimal inventory policy may feature a dry period with zero inventory, but this period does not necessarily result in a loss of sales as customers strategically wait for the upcoming promotion. Furthermore, we derive the solution approach for the optimal policy under heterogeneous customer valuation setting. Under the optimal policy, the replenishments and price promotions are synchronized, and the seller adopts high selling prices when the inventory level is low and plans a discontinuous price discount at the replenishment point when inventory is the highest.

Dynamic Pricing and Matching for Two-Sided Queues

Motivated by diverse applications in sharing economy and online marketplaces, we consider optimal pricing and matching control in a two-sided queueing system. We assume that heterogeneous customers and servers arrive to the system with price-dependent arrival rates. The compatibility between servers and customers is specified by a bipartite graph. Once a pair of customer and server are matched, they depart from the system instantaneously. The objective is to maximize long-run average profits of the system while minimizing average waiting time. We first propose a static pricing and max-weight matching policy, which achieves $O(\sqrt{\eta})$ optimality rate when all of the arrival rates are scaled by $\eta$. We further show that a dynamic pricing and modified max-weight matching policy achieves an improved $O(\eta^{1/3})$ optimality rate. Under a broad class of pricing policies, we prove that any matching policy has an optimality rate that is lower bounded by $\Omega(\eta^{1/3})$. Thus, the dynamic pricing policy and modified max-weight matching policy achieves the optimal rate. In addition, we propose a constraint generation algorithm that solves an approximation of the MDP and demonstrate strong numerical performance of this algorithm.