Attractiveness-based airline network models with embedded spill and recapture

Purpose: In airline revenue management, the modeling of the spill and recapture effects is essential for an accurate estimation of the passenger flow and the revenue in a flight network. However, as most current approaches toward spill and recapture involve either non-linearity or a tremendous amount of additional variables, it is computationally intractable to apply those techniques to the classical network design and capacity planning models. Design/methodology: We present a new framework that incorporates the spill and recapture effects, where the spill from an itinerary is recaptured by other itineraries based on their attractiveness. The presented framework distributes the accepted demand of an itinerary according to the currently available itineraries, without adding extra variables for the recaptured spill. Due to its compactness, we integrate the framework with the classical capacity planning and network design models. Findings: Our preliminary computational study shows an increase of 1.07% in profitability anda better utilization of the network capacity, on a medium-size North American airline provided by Sabre Airline Solutions. Originality/value: Our investigation leads to a holistic model that tackles the network design and capacity planning simultaneously with an accurate modeling of the spill and re- capture effects.Furthermore, the presented framework for spill and recapture is versatile and can be easily applied to other disciplines such as the hospitality industry and product line design (PLD) problems.Peer Reviewe

Estimating Optimal Recommendation Set Sizes for Individual Consumers

Online consumers must burrow through vast piles of product information to find the best match to their preferences. This has boosted the popularity of recommendation agents promising to decrease consumers\u27 search costs. Most recent work has focused on refining methods to find the best products for a consumer. The question of how many of these products the consumer actually wants to see, however, is largely unanswered. This paper develops a novel procedure based on signal detection theory to estimate the number of recommendable products. We compare it to a utility exchange approach that has not been empirically examined so far. The signal detection approach showed very good predictive validity in two laboratory experiments, clearly outperforming the utility exchange approach. Our theoretical findings, supported by the experimental evidence, indicate conceptual inconsistencies in the utility exchange approach. Our research offers significant implications for both theory and practice of modeling consumer choice behavior

A Regret Minimization Approach in Product Portfolio Management with respect to Customers’ Price-sensitivity

In an uncertain and competitive environment, product portfolio management (PPM) becomes more challenging for manufacturers to decide what to make and establish the most beneficial product portfolio. In this paper, a novel approach in PPM is proposed in which the environment uncertainty, competitors’ behavior and customer’s satisfaction are simultaneously considered as the most important criteria in achieving a successful business plan. In terms of uncertainty, the competitors’ product portfolios are assumed as different scenarios with discrete occurrence probabilities. In order to consider various customer preferences, three different market segments are assumed in which the sensitivity of customers towards the products price are considered as high, medium and low and modeled by means of a modified utility functions. The best product portfolio with minimum risk of loss and maximum customer satisfaction is then established by means of a novel regret minimization index. The proposed index aims at finding the best product portfolio which minimizes the total possible loss and regret of the manufacturer, with respect to the other competitors of the market. To better illustrate the practicality of the approach, a numerical example is presented. The results show that the selected products in the suggested portfolio have the highest utility value in all market segments and also they are expected to achieve the highest expected payoff in each possible marketing scenario

An Integrated Framework For Configurable Product Assortment Planning

A manufacturer\u27s assortment is the set of products or product configurations that the company builds and offers to its customers. While the literature on assortment planning is growing in recent years, it is primarily aimed at non-durable retail and grocery products. In this study, we develop an integrated framework for strategic assortment planning of configurable products, with a focus on the highly complex automotive industry. The facts that automobiles are highly configurable (with the number of buildable configurations running into thousands, tens of thousands, and even millions) with relatively low sales volumes and the stock-out rates at individual dealerships (even with transshipments) are extremely high, pose significant challenges to traditional assortment planning models. This is particularly the case for markets such as the U.S. that mostly operate in a make-to-stock (MTS) environment. First, we study assortment planning models that account for exogenous demand models and stock-out based substitution while considering production and manufacturing complexity costs and economies-of-scale. We build a mathematical model that maximizes the expected profit for an Original Equipment Manufacturer (OEM) and is a mixed-integer nonlinear problem. We suggest using linear lower/upper bounds that will be solved through a Modified-Branch and Bound procedure and compare the results with commercial mixed-integer nonlinear solvers and show superiority of the proposed method in terms of solution quality as well as computational speed. We then build a modeling framework that identifies the optimal assortment for a manufacturer of automotive products under environmental considerations, in particular, Corporate Average Fuel Economy (CAFE) requirements as well as life-cycle Greenhouse Gas (GHG) emission constraints. We present a numerical experiment consisting of different vehicle propulsion technologies (conventional, Diesel, and hybrid) and study the optimal shares of different technologies for maximizing profitability under different target levels of CAFE requirements. Finally, we develop assortment planning formulations that can jointly identify optimal packages and stand-alone options over different series of the product model. Our numerical experiment reveals that product option packaging has a considerable effect on managing product complexity and profitability

Assortment Optimization under the Decision Forest Model

We study the problem of finding the optimal assortment that maximizes expected revenue under the decision forest model, a recently proposed nonparametric choice model that is capable of representing any discrete choice model and in particular, can be used to represent non-rational customer behavior. This problem is of practical importance because it allows a firm to tailor its product offerings to profitably exploit deviations from rational customer behavior, but at the same time is challenging due to the extremely general nature of the decision forest model. We approach this problem from a mixed-integer optimization perspective and propose two different formulations. We theoretically compare the two formulations in strength, and analyze when they are integral in the special case of a single tree. We further propose a methodology for solving the two formulations at a large-scale based on Benders decomposition, and show that the Benders subproblem can be solved efficiently by primal dual greedy algorithms when the master solution is fractional for one of the formulations, and in closed form when the master solution is binary for both formulations. Using synthetically generated instances, we demonstrate the practical tractability of our formulations and our Benders decomposition approach, and their edge over heuristic approaches. In a case study based on a real-world transaction data, we demonstrate that our proposed approach can factor the behavioral anomalies observed in consumer choice into assortment decision and create higher revenue

Optimization Of Strategic Planning Processes For Configurable Products: Considerations For Global Supply, Demand, And Sustainability Issues

The assortment planning problem is to decide on the set of products that a retailer or manufacturer will offer to its customers to maximize profitability. While assortment planning research has been expanding in recent years, the current models are inadequate for the needs of a configurable product manufacturer. In particular, we address assortment planning for an automobile manufacturer. We develop models to integrate assortment planning and supply chain management, designed for use by a large automaker in its strategic planning phase. Our model utilizes a multinomial logit model transformed into a mixed integer linear program through the Charnes-Cooper transformation. It is able to scale to problems that contain thousands of configurations to possibly be offered, a necessity given the number of possible configurations an automaker can build. In addition, most research in assortment planning contains simplified costs associated with product complexity. We model a full supply chain and give a rich treatment of the complexity associated with product complexity. We believe that our model can significantly aid automotive manufacturers to balance their product complexity with supply chain complexity, thus increasing profitability. In addition, we study the effect of packaging on the assortment and supply chain of an automaker. We develop a new model for mathematically expressing the effect that packaging has on the way in which customers choose products. Packaging significantly complicates the search space of the assortment planning problem. We introduce a heuristic method based on our packaging model that speeds up the solve times of the models while finding reasonably good solutions. Finally, we extend our initial model to study the effects of sustainability requirements on an automaker\u27s assortment and supply chain. We introduce constraints on the vehicle program average fuel economy, greenhouse gas emissions in the supply chain, and greenhouse gas emissions in the product use phase. We dive deep into each case to glean insights about how automakers can change their decision-making process to balance making their companies more sustainable with profit maximization. While all the examples discussed are from the automotive industry, the models developed can be adapted to address assortment planning for other types of configurable products (e.g., computers, printers, phones)

The Rank Pricing Problem with Ties

In the Rank Pricing Problem (RPP), a firm intends to maximize its profit through the pricing of a set of products to sell. Customers are interested in purchasing at most one product among a subset of products. To do so, they are endowed with a ranked list of preferences and a budget. Their choice rule consists in purchasing the highest-ranked product in their list and whose price is below their budget. In this paper, we consider an extension of RPP, the Rank Pricing Problem with Ties (RPPT), in which we allow for indifference between products in the list of preferences of the customers. Considering the bilevel structure of the problem, this generalization differs from the RPP in that it can lead to multiple optimal solutions for the second level problems associated to the customers. In such cases, we look for pessimistic optimal solutions of the bilevel problem : the customer selects the cheapest product. We present a new three-indexed integer formulation for RPPT and introduce two resolution approaches. In the first one, we project out the customer decision variables, obtaining a reduced formulation that we then strengthen with valid inequalities from the former formulation. Alternatively, we follow a Benders decomposition approach leveraging the separability of the problem into a master problem and several subproblems. The separation problems to include the valid inequalities to the master problem dynamically are shown to reduce to min-cost flow problems. We finally carry out extensive computational experiments to assess the performance of the resolution approaches