Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a stochastic dynamic programming decomposition

Customer Relationship Management generally uses the value of customers to allocate marketing budget. But marketing interventions generally change the customer behavior, turning upside-down the customers ranking based on their initial valuations and making the budget allocation suboptimal. Rational Managers should allocate the marketing budget to maximize the expected net present value of future profits drawn from each customer, simultaneously planning mass marketing interventions and direct marketing effort on each individual. This is a large dimensional Stochastic Dynamic Program, which cannot be easily solved due to the curse of dimensionality. This paper propose a new decomposition algorithm to alleviate the curse of dimensionality in SDP problems, which allows forward-looking firms to allocate the marketing budget optimizing the CLV of their customer base, simultaneously using customized and mass marketing interventionsResearch funded by two research projects, S-0505/TIC-0230 by the Comunidad de Madrid and ECO20011-30198 by MICINN agency of Spanish Governmen

Real-time optimization of an integrated production-inventory-distribution problem.

In today\u27s competitive business environment, companies face enormous pressure and must continuously search for ways to design new products, manufacture and distribute them in an efficient and effective fashion. After years of focusing on reduction in production and operation costs, companies are beginning to look into distribution activities as the last frontier for cost reduction. In addition, an increasing number of companies, large and small, are focusing their efforts on their core competencies which are critical to survive. This results in a widespread practice in industry that companies outsource one or more than one logistics functions to third party logistics providers. By using such logistics expertise, they can obtain a competitive advantage both in cost and time efficiency, because the third party logistics companies already have the equipment, system and experience and are ready to help to their best efforts. In this dissertation, we developed an integrated optimization model of production, inventory and distribution with the goal to coordinate important and interrelated decisions related to production schedules, inventory policy and truckload allocation. Because outsourcing logistics functions to third party logistics providers is becoming critical for a company to remain competitive in the market place; we also included an important decision of selecting carriers with finite truckload and drivers for both inbound and outbound shipments in the model. The integrated model is solved by modified Benders decomposition which solves the master problem by a genetic algorithm. Computational results on test problems of various sizes are provided to show the effectiveness of the proposed solution methodology. We also apply this proposed algorithm on a real distribution problem faced by a large national manufacturer and distributor. It shows that such a complex distribution network with 22 plants, 7 distribution centers, 8 customer zones, 9 products, 16 inbound and 16 outbound shipment carriers in a 12-month planning period can be redesigned within 33 hours. In recent years, multi-agent simulation has been a preferred approach to solve logistics and distribution problems, since these problems are autonomous, distributive, complex, heterogeneous and decentralized in nature and they require extensive intelligent decision making. Another important part in this dissertation involved a development of an agent-based simulation model to cooperate with the optimal solution given by the optimization model. More specifically, the solution given by the optimization model can be inputted as the initial condition of the agent-based simulation model. The agent-based simulation model can incorporate many other factors to be considered in the real world, but optimization cannot handle these as needed. The agent-based simulation model can also incorporate some dynamics we may encounter in the real operations, and it can react to these dynamics in real time. Various types of entities in the entire distribution system can be modeled as intelligent agents, such as suppliers, carriers and customers. In order to build the simulation model more realistic, a sealed bid multiunit auction with an introduction of three parameters a, ß and y is well designed. With the help of these three parameters, each agent makes a better decision in a simple and fast manner, which is the key to realizing real-time decision making. After building such a multi-agent system with agent-based simulation approach, it supports more flexible and comprehensive modeling capabilities which are difficult to realize in a general optimization model. The simulation model is tested and validated on an industrial-sized problem. Numerical results of the agent-based simulation model suggest that with appropriate setting of three parameters the model can precisely represent the preference and interest of different decision makers

Non-convex Optimization for Resource Allocation in Wireless Device-to-Device Communications

Device-to-device (D2D) communication is considered one of the key frameworks to provide suitable solutions for the exponentially increasing data tra c in mobile telecommunications. In this PhD Thesis, we focus on the resource allocation for underlay D2D communications which often results in a non-convex optimization problem that is computationally demanding. We have also reviewed many of the works on D2D underlay communications and identi ed some of the limitations that were not handled previously, which has motivated our works in this Thesis. Our rst works focus on the joint power allocation and channel assignment problem in the D2D underlay communication scenario for a unicast single-input and single-output (SISO) cellular network in either uplink or downlink spectrums. These works also consider several degrees of uncertainty in the channel state information (CSI), and propose suitable measures to guarantee the quality of service (QoS) and reliability under those conditions. Moreover, we also present a few algorithms that can be used to jointly assign uplink and downlink spectrum to D2D pairs. We also provide methods to decentralize those algorithms with convergence guarantees and analyze their computational complexity. We also consider both cases with no interference among D2D pairs and cases with interference among D2D pairs. Additionally, we propose the formulation of an optimization objective function that combines the network rate with a penalty function that penalizes unfair channel allocations where most of the channels are assigned to only a few D2D pairs. The next contributions of this Thesis focus on extending the previous works to cellular networks with multiple-input and multiple-output (MIMO) capabilities and networks with D2D multicast groups. We also present several methods to accommodate various degrees of uncertainty in the CSI and also guarantee di erent measures of QoS and reliability. All our algorithms are evaluated extensively through extensive numerical experiments using the Matlab simulation environment. All of these results show favorable performance, as compared to the existing state-of-the-art alternatives.publishedVersio

Model-based approaches for large-scale optimization in business operations

Companies nowadays have to operate in an increasingly competitive and complex environment. Under these challenging conditions, it has become essential for them to optimize their business operations, i.e., the activities that they must conduct on a regular, often daily, basis. The nature of these business operations strongly varies between companies. For a pharmaceutical company, an important business operation is, for example, the scheduling of their research activities. With improved scheduling, new drugs are brought to markets earlier, which can lead to a decisive competitive advantage. For a telecommunications company, an important business operation is, for example, the promotion of new products and services to existing customers. Contacting the right customers for the right products may lead to an increase in sales and profitability of these products. Many business operations, including the two examples from above, can be improved by solving mathematical optimization problems with techniques from the field of Operations Research. An optimization problem consists of the decisions to be taken, the constraints that define the set of feasible decisions, and an objective that is either maximized (profit) or minimized (project duration). In the case of the telecommunications company, the decisions to be taken are which customers are contacted for which product on which day. An example of a constraint is an overall budget that cannot be exceeded, and an example of the objective is the maximization of the total expected profit that results from contacting the customers. A standard approach for solving such an optimization problem is first to express the problem as a mathematical model and then use standard optimization software, known as a solver, to find the best possible solution. A great advantage of this approach is that the mathematical model can easily be adjusted to changes in the underlying problem. This flexibility is required in a dynamic business environment where constraints or objectives may change over time. However, a major drawback of this standard approach is its limited scalability when applied to specific types of complex optimization problems. For these problems, the generic solvers fail to find the best or even a good solution in a reasonable running time. Specialized algorithms, so-called heuristics, are required instead. Heuristics apply problem-specific search strategies to derive a good solution to an optimization problem quickly. However, because these heuristics are designed for specific optimization problems, they are difficult to adapt if the constraints or the objective of the optimization problem change. A solution technique that has been shown to be both flexible and scalable for complex optimization problems are matheuristics. Matheuristics are model-based approaches that decompose an optimization problem into smaller subproblems and solve these subproblems using mathematical models. Essential for the performance of a matheuristic is how the problem is decomposed into subproblems, which is an important field of research in Operations Research. This thesis contributes to this field of research by introducing model-based approaches for large-scale optimization in business operations. It consists of three papers on three specific optimization problems in direct marketing, project management, and facility location. Real-world instances of all three of these problems involve a large number of customers, activities, or facilities and require the flexibility to incorporate practical constraints easily. To address these challenges, we developed three matheuristics. The matheuristics employ innovative problem decomposition strategies and outperform state-of-the-art approaches on large-scale instances. In the first paper, we study a customer assignment problem from a major telecommunications company. The telecommunications company runs different direct marketing campaigns to promote its products and services. The goal of the telecommunications company is to assign the customers to the direct marketing campaigns so that the total expected profit is maximized. Thereby, various business constraints, such as budgets and sales constraints, must be considered. Also, different customer-specific constraints ensure that each customer is not assigned to a direct marketing campaign too frequently. A particular challenge is the size of practical problem instances. These instances involve millions of customers and hundreds of direct marketing campaigns. The methodological contribution of this paper consists of decomposing the optimization problem into two subproblems that each can be solved efficiently. In the first subproblem, customers are assigned to campaigns based on their membership to a customer group. In the second subproblem, individual customers are assigned to campaigns based on the solution that was derived in the first subproblem. The unique feature of our decomposition strategy is that the customer-specific constraints are already considered in the first subproblem, even though the first subproblem deals with groups of customers and not individual customers. In an experimental analysis based on numerous generated and real-world instances, we can demonstrate that even though we decompose the problem, the resulting solutions are still of very high quality. The matheuristic has been deployed in the company and is now used daily. In a proof of benefit conducted by the company based on a selected campaign, they observed that using the matheuristic increased the number of sales by 90%, resulting in an improvement in the profitability of this campaign by 300%. The second paper deals with a project scheduling problem that often arises in the pharmaceutical industry, where research activities, e.g., clinical tests, can be executed at different locations, e.g., research labs. The problem consists of determining a start time for each activity, selecting a location for the execution of each activity, and assigning resource units, e.g., research staff or equipment, to the execution of the activities. Various practical constraints must be considered, such as transportation times that arise when, e.g., a resource unit must be transported from one location to another. With only a few activities involved, the number of possible schedules can already grow very large. We developed a mathematical model and, based on this model, a novel matheuristic for this problem. The main methodological contribution of the matheuristic is its problem decomposition strategy. Instead of dividing the project into subprojects, the model in the matheuristic is set up for all project activities. However, the solver makes some decisions only for a subset of the activities. To schedule an entire project, multiple iterations have to be performed, where in each iteration, another subset of activities is considered. This iterative decision process substantially reduces running times compared to when all decisions are conducted simultaneously. In a computational experiment, the novel model outperforms the leading model from the literature on small instances. The matheuristic outperforms the state-of-the-art heuristics on all considered performance metrics on larger instances. In the third paper, we consider the problem of locating obnoxious facilities. Obnoxious means that the facilities negatively affect their nearby environment and should thus be located far away from clients. Examples of obnoxious facilities are waste plants, oil refineries, and wind turbines. The problem consists of opening from a set of potential locations a given number of facilities such that the open facilities are far away from the clients. We further study an extension of this problem that includes practical constraints which limit the number of facilities that can be opened in certain regions of an instance. Our matheuristic starts from an initial solution and iteratively improves the solution by removing and adding facilities. The quality of the final solution (after the improvement iterations) strongly depends on the initial solution. When two very similar initial solutions are provided, the likelihood of finding very similar final solutions is high. One main methodological contribution is a procedure that we designed, which is guaranteed to generate initial solutions that are very different from each other. This diversification in the initial solutions increases the likelihood of finding high-quality final solutions. The matheuristic outperforms the state-of-the-art metaheuristics on instances including thousands of clients and potential locations for facilities. Even though we consider three specific optimization problems in this thesis, the contributions of the three papers can be generalized and applied to related problems and thus advance the state of knowledge in the field of large-scale optimization

Three essays on marketing dynamics

This thesis comprises three essays on marketing in a dynamic context. The firrst essay focuses on optimal dynamic marketing budget allocation problem and proposes a novel decomposition algorithm that allows forward-looking fi rms to optimize their customer lifetime value (CLV) using simultaneously customized and mass marketing interventions. In addition to showing the performance of the algorithm by using numerical simulations, we provide an empirical application for a manufacturer of kitchen appliances. The second essay deals with sales uncertainty problem from a strategic marketing point of view. We model and forecast time-varying retail sales and marketing mix volatility. In particular, we examine within-brand and between-brand e¤ects and trace the impact of marketing mix actions on sales growth volatility through volatility impulse-response functions. For the analysis, we use the data from six fast moving consumer product categories sold by Dominick s Finer Foods. The third essay centers on the analysis of trends in advertising media channels in the US. Using country-level annual time series data, we investigate whether there is a long-run equilibriumrelationship among ten different advertising media channels. The main contributions of this thesis can be summarized as follows: In the fi rst essay, the contributions of our study are three-fold: (i) we solve the high dimensional stochastic dynamic programming (SDP) problem for a large number of customers through our decomposition algorithm, (ii) our model accommodates cus- tomized and mass marketing interventions simultaneously, (iii) we treat the CLV as an output rather than a tool for optimal dynamic marketing budget allocation plan- ning. The simulation results as well as the empirical application of the model show that the proposed decomposition algorithm works well as the number of customers increases in the model. Thus, marketing managers can use the model to determine the level of the price, how much money they should spend on each customer and on general advertising to maximize their CLV even if they have a high number of customers. In the second essay, our contribution is that we investigate time varying volatility which has been ignored in the sales response literature. The focus in the marketing literature has been given on the expected sales, but on volatility which is not a desired outcome. Using multivariate time series methodology, we nd that lower price and promotional growth rates lead to less volatility in sales growth. Brand managers can use price and promotional actions as useful tools to curb sales volatility, and thus smoothing out the bullwhip effect at the retail level. Academic research on advertising at the country-level is less extensive compared to the company-level studies. In the third essay, we empirically investigate whether the entries of new advertising media (TV, yellow pages, cable and internet) affect the incumbents expenditure level in the form of creating fundamental change in the long-run evolution. We model the dynamic interrelationship among ten di¤erent advertising media channels in the U.S. by using multivariate time series econometrics. Our results show that internet and cable media cause substantive shift only on the evolution of newspapers and outdoor, respectively whereas TV and yellow pages entries create fundamental change in the spending levels of all incumbents,except for direct mail. We also nd that the long-run elasticity between total advertising expenditures and the GDP is negative implying that total advertising has counter- cyclical behavior. Furthermore, in the long-run, an increase in internet investment results in a decrease in newspapers as well as magazines investment.___________________________________________________________________________________________________________________________Esta tesis está compuesta por tres ensayos sobre marketing en contexto dinámico. El primero está enfocado en el problema de la asignación óptima y de forma dinámica del presupuesto en marketing. En él se propone un algoritmo novedoso que permite a las rmas con visión de futuro, optimizar su valor para el cliente de por vida (CLV) uti- lizando simultáneamente intervenciones personalizadas y masivas de marketing. Además de mostrar el desempeño de dicho algoritmo por medio de simulaciones numéricas, apor- tamos una aplicación empírica relativa a un productor de aparatos de cocina. El segundo ensayo trata el problema de la incertidumbre de las ventas desde el punto de vista del marketing estratégico. Desarrollamos un modelo que permite predecir en el tiempo las ventas al por menor y la volatilidad del marketing mix. En particular, examinamos los efectos en las marcas y entre marcas e indagamos el impacto de las acciones relacionadas al marketing mix en la volatilidad del crecimiento de las ventas a través de funciones de impulso-respuesta. Para el análisis usamos datos sobre seis categorías de productos de consumo envasados que se venden con rapidez y a relativamente bajo costo, en el de- tallista Dominick s Finer Foods. El tercer ensayo se centra en el análisis de las tendencias presentes en los medios de comunicación publicitaria en los Estados Unidos de América. Utilizando datos anuales, investigamos si existe una relación de equilibrio de largo plazo entre diez medios de comunicación publicitaria diferentes. Las contribuciones principales de la tesis pueden resumirse así: En el primer ensayo las contribuciones giran alrededor de tres ejes: (i) nuestro algoritmo resuelve un problema de gran dimensionalidad al considerar un elevado número de clientes en un contexto de programación dinámica estocástica, (ii) nue- stro modelo considera simultáneamente intervenciones masivas y personalizadas de marketing, (iii) tratamos el CLV como un resultado y no como una herramienta para la plani cación óptima y dinámica del presupuesto en marketing. Tanto los resultados de la simulación como la aplicación empírica del modelo demuestran que el algoritmo de descomposición propuesto funciona bien, incluso cuando el número de clientes aumenta. Con esta propuesta, los gerentes de marketing pueden usar el modelo para determi- nar el precio, cuánto dinero deberían gastar en cada cliente y cuánto en publicidad general para maximizar su CVL aún cuando el número de clientes es elevado. En el segundo ensayo, nuestra contribución se centra en el análisis de la volatil- idad, elemento ignorado en la literatura sobre la sensibilidad de las ventas a las variables de marketing. La literatura de marketing ha otorgado un papel impor- tante a las ventas esperadas pero no se ha centrado en el los efectos adversos de la volatilidad de las ventas. Por medio del análisis de series temporales multivari- ante encontramos que tasas de crecimiento de precio y de promoción más bajas conllevan menor volatilidad en el crecimiento de las ventas. Los gerentes de marca pueden usar el precio y las acciones promocionales como herramientas útiles para reducir la volatilidad de las ventas y, por tanto, suavizar el efecto látigo a nivel de los minoristas. Hay pocos trabajos de investigación en relación a la inversión de publicidad por países comparado con el numero de estudios a nivel de empresa. En el tercer tra- bajo de esta tesis, empíricamente investigamos si las entradas de nuevos medios de comunicación (TV, páginas amarillas, cable e internet) afecta el nivel de inversión de los canales de publicidad tradicional, de forma radical en su evolución a largo plazo. En este trabajo, considerados un modelo de interrelación dinámica entre 10 medios de comunicación diferentes en los EE.UU. mediante el uso de la econometría de series temporales multivariantes. Nuestros resultados muestran que los medios de comunicación de Internet y de la TV por cable ocasionan un cambio estructural en la evolución de la inversión en periódicos y en medios al aire libre, respectiva- mente, mientras que el comienzo de la TV y las Páginas Amarillas crean un cambio estructural en los niveles de gasto de todos los otros medios, a excepción de la Pub- 6 licidad Directa. También encontramos que la elasticidad de largo plazo entre los gastos de publicidad totales y el PIB es negativo lo que implica que la publicidad total tiene un comportamiento anticíclico. Por otra parte, en el largo plazo, un aumento en los resultados de la inversión en Internet implica en una disminución de inversión en periódicos, así como en revistas.The financial support from both the department of Business Administration at Universidad Carlos III de Madrid and the Ministry of Science and Innovation in Spain (research grant ECO2011-30198)

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods

Protocolo:revisión sistemática de literatura sobre los mecanismos de coordinación en los modelos de programación matemática para la toma de decisiones descentralizadas

[EN] The article presents the research protocol for a systematic literature review on the coordination mechanisms in the mathematical programming for decentralized decision making on the planning and scheduling, intra or inter companies from 2006 to 2016.[ES] El artículo presenta el protocolo de investigación para la realización de una revisión sistemática sobre los mecanismos de coordinación en los modelos de programación matemá- tica, para la toma de decisiones descentralizadas sobre la planificación y la programación de la producción, entre plantas de la misma empresa o entre plantas de diferentes empresas, en el periodo de 2006 a 2016.Rius-Sorolla, G.; Maheut, J.; Estelles-Miguel, S.; Garcia-Sabater, JP. (2017). Protocol: Systematic Literature Review on coordination mechanisms for the mathematical programming models in production planning with decentralized decision making. 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