On the optimality of output inventory decisions of a firm under imperfect stochastic markets

A class of suboptimal strategies in price, output and inventories is proposed for a firm in imperfectly competitive stochastic markets. When the demand faced is incompletely known and inventories are held partly as a buffer against fluctuations in demand, and partly as active decision variables, the solution is characterized by maximizing expected profits over two periods with an additive error in the demand function having a known prior density. Simulations of the solution profiles show that for the two-period moving horizon model proposed here, the variance of profits and revenues are sharply reduced, a feature that is most attractive for a risk averse firm.     

A leader-follower game in competitive facility location

We address the problem of locating new facilities of a firm or franchise that enters a market where a competitor operates existing facilities. The goal of the new entrant firm is to decide the location and attractiveness of its new facilities that maximize its profit. The competitor can react by opening new facilities, closing existing ones, and adjusting the attractiveness levels of its existing facilities, with the aim of maximizing its own profit. The demand is assumed to be aggregated at certain points in the plane and the new facilities of both the firm and the competitor can be located at predetermined candidate sites. We employ the gravity-based rule in modeling the behavior of the customers where the probability that a customer visits a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. We formulate a bilevel mixed-integer nonlinear programming model where the firm entering the market is the leader and the competitor is the follower. We propose heuristics that combine tabu search with exact solution methods.     

Randomized local search for the discrete competitive facility location problem

Consider a finite set of consumers that two competing companies are willing to service. The companies open facilities one by one. The set of locations available to open facilities is finite. The problem is to find a facility location for the first company that maximizes its profit given that the second company also makes its decision by maximizing the profit. We propose a randomized local search scheme that employs an internal local search procedure to estimate the solutions being enumerated. Numerical experiments with random input data show that the scheme is able to find high quality approximate solutions for examples with dimension that has not been amenable to previously known algorithms.     

A GIS-Based Optimization Framework for Competitive Multi-Facility Location-Routing Problem

In a dynamic market setting, firms need to quickly respond to shifting demographics and economic conditions. In this paper, we investigate the problem of determining the optimum set of locations for a firm, which operates a chain of facilities under competition. We consider the objective of maximizing profit, defined as gross profit margin minus logistics costs. We propose a location-routing model where revenue is realized according to probabilistic patronization of customers and routing costs are incurred due to vehicles serving the open facilities from a central depot. We propose a hybrid heuristic optimization methodology for solving this model. The optimal locations are searched for by a Genetic Algorithm while an integrated Tabu Search algorithm is employed for solving the underlying vehicle routing problem. The solution approach is tested on a real dataset of a supermarket chain. The results show that the location decisions made by the proposed methodology lead to increased market share and profit margin, while keeping logistics costs virtually unchanged. Finally, we present a GIS-based framework that can be used to store, analyze and visualize all data as well as model solutions in geographic format.     

The vendor location problem

The vendor location problem is the problem of locating a given number of vendors and determining the number of vehicles and the service zones necessary for each vendor to achieve at least a given profit. We consider two versions of the problem with different objectives: maximizing the total profit and maximizing the demand covered. The demand and profit generated by a demand point are functions of the distance to the vendor. We propose integer programming models for both versions of the vendor location problem. We then prove that both are strongly NP-hard and we derive several families of valid inequalities to strengthen our formulations. We report the outcomes of a computational study where we investigate the effect of valid inequalities in reducing the duality gaps and the solution times for the vendor location problem.     

Matching office firms types and location characteristics: An exploratory analysis using Bayesian classifier networks

While most models of location decisions of firms are based on the principle of utility maximizing behavior, the present study assumes that location decisions are just part of business cycle models, in which location is considered along other business decisions. The business model results in a series of location requirements and these are matched against location characteristics. Given this theoretical perspective, the modeling challenge then becomes how to find the match between firm types and the set of location characteristics using observations of the spatial distribution of firms. In this paper, several Bayesian classifier networks are compared in terms of their performance, using a large data set collected for the Netherlands. Results demonstrate that by taking relationships between predictor variables into account the Bayesian classifiers can improve prediction accuracy compared to commonly used decision tree. From a substantive point of view, our results indicate that different sets of urban characteristics and accessibility requirements are relevant to different office types as reflected in the spatial distribution of these office firms.     

A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price prediction in deregulated electricity market

This paper examines electricity price time series from dynamical system perspective and proposes a hybrid model which employs a synergistic combination of Recurrent Neural Network (RNN) and coupled excitable system for prediction of future prices in deregulated electricity markets. Driven by profit maximizing decisions taken by various agents, these markets belong to the class of financial systems. However presence of intermittent spikes and complex dynamic nonlinearities in electricity price time series render the prediction task extremely challenging. The approximation ability of Recurrent Neural Networks to map dynamic functions together with sharp jumping attribute of coupled excitable systems allows close approximation of spiky time series. The developed hybrid model was applied for point and interval forecasting in various markets worldwide over different seasons for testing its adaptability in different environments. Satisfactory prediction results were obtained in all the markets, in stable as well as spiking regions of the time series.     

Pricing and production lot-size/scheduling with finite capacity for a deteriorating item over a finite horizon

Although the lately evolved manufacturing technologies such as enterprise resource planning (ERP) provide a unified platform for managing and integrating core business processes within a firm, the decision-making between marketing and production planning still remains rather disjoint. It is due in large parts to the inherent weaknesses of ERP such as the fixed and static parameter settings and uncapacitated assumption. To rectify these drawbacks, we propose a decision model that solves optimally the production lot-size/scheduling problem taking into account the dynamic aspects of customer's demand as well as the restriction of finite capacity in a plant. More specifically, we consider a single product that is subject to continuous decay, faces a price-dependent and time-varying demand, and time-varying deteriorating rate, production rate, and variable production cost, with the objective of maximizing the profit stream over multi-period planning horizon. We propose both coordinated and decentralized decision-making policies that drive the solution of the multivariate maximization problem. Both policies are formulated as dynamic programming models and solved by numerical search techniques. In our numerical experiments, the solution procedure is demonstrated, comparative study is conducted, and sensitivity analysis is carried out with respect to major parameters. The numerical result shows that the solution generated by the coordinated policy outperforms that by the decentralized policy in maximizing net profit and many other quantifiable measures such as minimizing inventory investment and storage capacity.Scope and purposeWe consider a manufacturing firm who produces and sells a single product that is subjected to continuous decay over a lifetime, faces a price-dependent and time-varying demand function, shortages are allowed and a completely backlogged, and has the objective of determining price and production lot-size/scheduling so as to maximize the total profit stream over multi-period planning horizon. We develop a tactical-level decision model that solves the production scheduling problem taking into account the dynamic nature of customer's demand which is partially controllable through pricing schemes. As analogous to the sales and operations planning, the proposed scheme can be used as a coordination center of the APS system within a generic enterprise resource planning framework which integrates and coordinates distinct functions within a firm.This paper differs from the existing works in several ways. First, we propose a dynamic version of the joint pricing and lot-size/scheduling problem taking into account the capacitated constraint. Second, several key factors being considered in the model, such as the demand rate, deteriorating rate, production rate, and variable production cost are assumed time-varying that reflect the dynamic nature of the market and the learning effect of the production system. A third difference between the past research and ours is that the price can be adjusted upward or downward in our model, making the proposed pricing policy more responsive to the structural change in demand or supply.     

Innovators and Imitators in Novelty‐intensive Markets: A Research Agenda

The phenomenon of demand for novelty is defined and explored as a unique and under‐examined aspect of certain markets. Demand for novelty is the portion of demand not explained by practical utility or marketing effects – it is the demand for the new and unique. We explore markets characterized by high demand for novelty and how they differ from typical markets. Primarily, this involves the central role of novelty in the product or service value proposition as well as rapid growth rates and product or service obsolescence. Within this context, we consider the dynamics of innovating and imitating and suggest several ways that first mover competition is unique in markets with high demand for novelty. From the perspective of the knowledge‐based theory of the firm, we consider the implications of organizational learning and knowledge and decision making as they relate to new product development routines, improvisation, and top management team decision making. We conclude by considering several avenues for future empirical research.     

Reaction Function Based Dynamic Location Modeling in Stackelberg–Nash–Cournot Competition

We formulate a dynamic facility location model for a firm locating on a discrete network. It is assumed that this locating firm will act as the leader firm in an industry characterized by Stackelberg leader–follower competition. The firm’s I competitors are assumed to act as Cournot firms and are each assumed to operate under the assumption of zero conjectural variation with respect to their I–1 Cournot competitors. Using sensitivity analysis of variational inequalities within a hierachical mathematical programming approach, we develop reaction function based dynamic models to optimize the Stackelberg firm’s location decision. In the second half of this paper, we use these models to illustrate through a numerical example the insights yielded by our approach.     

Maple语言在确立实现企业生产规模中的应用

Maple是目前广泛使用的数学计算工具之一.本文通过对企业生产规模的考察分析,确定实现利润最大化的条件,由条件出发设计了实现此目的的算法,并用实例说明了此算法的正确性,其结果对现实中企业生产规模的确定具有一定的指导意义.     

Fuzzy stochastic dynamic programming for marketing decision support

One of the most important goals in marketing is to realize the highest profit by applying appropriate means to optimize the process of acquiring customers. To assist the marketer in making marketing decisions, this paper introduces a stochastic dynamic programming model for the process of acquiring customers. It is actually a stochastic multistage decision process, whose state space consists of granularized information on customers and whose transitions are controlled by marketing actions. Then it shows how to control this process using fuzzy constraints and how to characterize the goal of maximizing profit by a fuzzy set. After an overview of approaches in dynamic programming under fuzziness given by Bellman and Zadeh, this paper further presents a new model of fuzzy stochastic dynamic programming to solve the decision problem for a stochastic system with implicitly defined termination time. It is argued that this study can facilitate research and development of both financial engineering and e‐commerce. © 2000 John Wiley & Sons, Inc.     

The Cournot game under a fuzzy decision environment

The conventional precision-based decision analysis methodology is invalid for business decision analysis when precise assessment data seldom exist. This paper considers the Cournot game with fuzzy demand and fuzzy costs that are assumed to be triangular fuzzy numbers. Our model utilizes the weighted center of gravity (WCoG) method to defuzzify the fuzzy profit function into a crisp value. We derive the equilibrium Cournot quantity of each firm by simultaneously solving the first-order condition of each firm. Our model explicitly derives the necessary condition to avoid an unreasonable outcome of negative equilibrium quantities and lack of flexibility for modification of the ranking method. In addition, we examine the standard deviation of the fuzzy profit resulting from the fuzziness of each firm’s cost and market demand functions. We conduct sensitivity analysis to investigate the effect of parameter perturbations on firms’ outcomes. The results indicate that the center of parameter plays an important role in sensitivity analysis and dominates over variations in equilibrium quantity due to a perturbation of fuzzy parameters.     

Model Selection in an Information Economy: Choosing What to Learn

As online markets for the exchange of goods and services become more common, the study of markets composed, at least in part, of autonomous agents has taken on increasing importance. In contrast to traditional complete–information economic scenarios, agents that are operating in an electronic marketplace often do so under considerable uncertainty. In order to reduce their uncertainty, these agents must learn about the world around them. When an agent producer is engaged in a learning task in which data collection is costly, such as learning the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the agent has a limited number of chances to experiment, it must explicitly consider the cost of learning (in terms of foregone profit) against the value of the information acquired. Information goods add an additional dimension to this problem; due to their flexibility, they can be bundled and priced according to a number of different price schedules. An optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule. In this paper, we demonstrate the tradeoff between complexity and profitability for a number of common price schedules. We begin with a one–shot decision as to which schedule to learn. Schedules with moderate complexity are preferred in the short and medium term, as they are learned quickly, yet extract a significant fraction of the available profit. We then turn to the repeated version of this one–shot decision and show that moderate complexity schedules, in particular two–part tariff, perform well when the producer must adapt to nonstationarity in the consumer population. When a producer can dynamically change schedules as it learns, it can use an explicit decision–theoretic formulation to greedily select the schedule which appears to yield the greatest profit in the next period. By explicitly considering both the learnability and the profit extracted by different price schedules, a producer can extract more profit as it learns than if it naively chose models that are accurate once learned.     

考虑定向能力的竞争性企业优惠券定向投放与定价策略

企业定向投放优惠券的精准性对优惠券面值、企业利润等均有较大的影响.针对企业定向优惠券的精准投放问题,运用博弈论研究双寡头竞争环境下,企业定向投放能力对优惠券面值和企业利润的影响,并进一步将定向能力作为决策变量引入模型进行拓展研究.研究结果表明:当定向能力达到一定阈值时,企业会在“忠诚市场”实行高价策略,而在“竞争市场”实行低价策略;定向能力对企业利润呈双向调节作用,即在阈值范围内,定向能力的提升会带来更高的企业利润,但超过阈值时,定向能力的提升将会降低企业利润;定向能力的投资成本系数较大时,拥有较多忠诚客户的企业倾向于提高定向能力;反之,竞争双方都将提高定向能力以增加企业利润.     

Analyses of sensitivity and competition of an EOQ model

Min (1992) proposed a symmetric Cournot-like oligopoly model consisting of sellers who are profit maximizing EOQ-based decision makers. In this paper Min's 1992 model is extended by means of sensitivity and equilibrium analyses. The primary objective of such extensions is to enhance the general understanding of relationships between critical quantities in inventory theory (such as the order quantity) and in microeconomic theory (such as the number of competing sellers). In particular, for EOQ-based decision making sellers, the effects on each seller's profitability caused by the entry of an additional competitor, as well as the maximum number of competitors the entire market can sustain, are examined.     

Genetic algorithm based model for optimizing bank lending decisions

To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in different financial research areas, especially in banking decisions’ optimization. However, choosing optimum bank lending decisions that maximize the bank profit in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank profit and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12%–50%. Moreover, it greatly increases the bank profit by a range of 3.9%–8.1%.     

Competitive Delivered Pricing by Mail-Order and Internet Retailers

Mail-order and internet sellers must decide how customers pay shipping charges. Typically, these sellers choose between two pricing policies: either “uniform pricing,” where the firm delivers to any customer at a fixed delivery charge (that may be volume dependent), or “mill pricing,” where the firm bills the customer a distance-related shipping charge. This paper studies price competition between a mail-order (or internet) seller and local retailers, and the mail-order firm’s choice of pricing policy. The price policy choice is studied when retailers do not change price in reaction to the mail-order firm’s policy choice, and when they do. In the second case, a two-stage non-cooperative game is used and it is found that for low customer willingness to pay, mill pricing is favored but as willingness to pay rises, uniform pricing becomes more attractive. These results are generalized showing that larger markets, higher transportation rates, higher unit production cost, and greater competition between retailers all increase profit under mill pricing relative to uniform pricing (and vice versa). On the other hand, cost asymmetries that favor the mail-order firm will tend to induce uniform rather than mill pricing. Some empirical data on retail and mail-order sales that confirm these results are presented.     

Managing digital piracy under consumer valuation uncertainty: The roles of product demonstrations and antipiracy measures

Product demonstrations (e.g., software or video game trials) are commonly used in digital-good markets to resolve prospective consumers’ valuation uncertainty. We theoretically investigate how a monopolistic digital-good firm may utilize the tool of product demonstration, together with technical and legal antipiracy measures which increase the difficulty or cost of pirating, to optimally manage the impact of piracy on its new product’s profitability. We unexpectedly find that, in equilibrium, the firm’s profit may strictly decrease with the difficulty of pirating when the true product quality is not especially high, such that the firm chooses not to offer product demonstrations. This is because, with higher piracy costs, the firm has more incentive to offer product demonstrations and, as a result, rational customers’ quality belief conditional on non-demonstration is lower, which leads to a lower profit for the firm. We extend our model to incorporate consumers’ fit uncertainty about the product due to idiosyncratic tastes. Related managerial implications for public policy and legislation regarding piracy and copyright are discussed.     

Computation of Multi-facility Location Nash Equilibria on a Network Under Quantity Competition

We deal with the location-quantity problem for competing firms when they locate multiple facilities and offer the same type of product. Competition is performed under delivered quantities that are sent from the facilities to the customers. This problem is reduced to a location game when the competing firms deliver the Cournot equilibrium quantities. While existence conditions for a Nash equilibrium of the location game have been discussed in many contributions in the literature, computing an equilibrium on a network when multiple facilities are to be located by each firm is a problem not previously addressed. We propose an integer linear programming formulation to fill this gap. The formulation solves the profit maximization problem for a firm, assuming that the other firms have fixed their facility locations. This allows us to compute location Nash equilibria by the best response procedure. A study with data of Spanish municipalities under different scenarios is presented and conclusions are drawn from a sensitivity analysis.