Multistage multiproduct advertising budgeting

We propose and analyze an effective model for the Multistage Multiproduct Advertising Budgeting problem. This model optimizes the advertising investment for several products, by considering cross elasticities, different sales drivers and the whole planning horizon. We derive a simple procedure to compute the optimal advertising budget and its optimal allocation. The model was tested to plan a realistic advertising campaign. We observed that the multistage approach may significantly increase the advertising profit, compared to the successive application of the single stage approach.     

Real options in strategic investment games between two asymmetric firms

This paper examines strategic investment games between two firms that compete for optimal entry in a project that generates uncertain revenue flows. Under asymmetry on both the sunk cost of investment and revenue flows of the two competing firms, we investigate the value of real investment options and strategic interaction of investment decisions. Compared to earlier models that only allow for asymmetry on sunk cost, our model demonstrates a richer set of strategic interactions of entry decisions. We provide a complete characterization of pre-emptive, dominant and simultaneous equilibriums by analyzing the relative value of leader’s and follower’s optimal investment thresholds. In a duopoly market with negative externalities, a firm may reduce loss of real options value by selecting appropriate pre-emptive entry. When one firm has a dominant advantage over its competitor, both the dominant firm and dominated firm enter at their respective leader’s and follower’s optimal thresholds. When the pre-emptive thresholds of both firms happen to coincide, the two firms enter simultaneously. Under positive externalities, firms do not compete to lead.     

Optimal advertising pulsation policies: a dynamic programming approach

This study formulates and solves an advertising pulsation problem for a monopolistic firm using dynamic programming (DP). The firm aims at maximising profit through an optimal allocation of the advertising budget in terms of rectangular pulses over a finite planning horizon. Aggregate sales response to the advertising effort is assumed to be governed by a modified version of the Vidale–Wolfe model in continuous time proposed by Little. Using a numerical example in which a planning horizon of one year is divided into one, two through ten equal time periods, computing routines are developed to solve 150 DP problems. Computational results show among other findings that the performance yielded by the DP policy dominates the uniform advertising policy (constant spending) for a concave advertising response function and the advertising pulsing policy (turning advertising on and off) for a linear or convex response function.     

能源回购补偿机制下基于无限阶段折扣准则的最优生产与库存策略

世界经济的快速发展和工业化进程的推进促使各国电力需求激增,电力供需矛盾为能源回购项目的发展提供了条件。为能够实现错峰用电和缓解能源需求的紧张,能源回购项目在每个阶段出现能源短缺时,将根据短缺的不同程度为限产(或停产)企业提供了金额不同的资金补偿。因此,在该能源回购补偿机制下,企业需要确定每个阶段是否参加能源回购项目及其相应的生产库存策略,来实现其期望折扣成本的最小化。本文研究了能源回购补偿机制下企业以最小化期望折扣成本为目标的无限阶段最优生产/库存策略。引入启动成本和多个能源需求状态的资金补偿水平后,在合理的假设条件下,证明了每个阶段生产商的最优生产/库存策略在高峰状态为(sⁱ,S)策略,在非高峰状态为(s⁰,S,A)策略。     

Optimal policies for inventory systems with discretionary sales, random yield and lost sales

We determine replenishment and sales decisions jointly for an inventory system with random demand, lost sales and random yield. Demands in consecutive periods are independent random variables and their distributions are known. We incorporate discretionary sales, when inventory may be set aside to satisfy future demand even if some present demand may be lost. Our objective is to minimize the total discounted cost over the problem horizon by choosing an optimal replenishment and discretionary sales policy. We obtain the structure of the optimal replenishment and discretionary sales policy and show that the optimal policy for finite horizon problem converges to that of the infinite horizon problem. Moreover, we compare the optimal policy under random yield with that under certain yield, and show that the optimal order quantity （sales quantity） under random yield is more （less） than that under certain yield.     

A stochastic partially reversible investment problem on a finite time-horizon: Free-boundary analysis

We study a continuous-time, finite horizon, stochastic partially reversible investment problem for a firm producing a single good in a market with frictions. The production capacity is modeled as a one-dimensional, time-homogeneous, linear diffusion controlled by a bounded variation process which represents the cumulative investment–disinvestment strategy. We associate to the investment–disinvestment problem a zero-sum optimal stopping game and characterize its value function through a free-boundary problem with two moving boundaries. These are continuous, bounded and monotone curves that solve a system of non-linear integral equations of Volterra type. The optimal investment–disinvestment strategy is then shown to be a diffusion reflected at the two boundaries.     

Optimal policy for a dynamic,non-stationary,stochastic inventory problem with capacity commitment

This paper studies a single-product, dynamic, non-stationary, stochastic inventory problem with capacity commitment, in which a buyer purchases a fixed capacity from a supplier at the beginning of a planning horizon and the buyer’s total cumulative order quantity over the planning horizon is constrained with the capacity. The objective of the buyer is to choose the capacity at the beginning of the planning horizon and the order quantity in each period to minimize the expected total cost over the planning horizon. We characterize the structure of the minimum sum of the expected ordering, storage and shortage costs in a period and thereafter and the optimal ordering policy for a given capacity. Based on the structure, we identify conditions under which a myopic ordering policy is optimal and derive an equation for the optimal capacity commitment. We then use the optimal capacity and the myopic ordering policy to evaluate the effect of the various parameters on the minimum expected total cost over the planning horizon.     

不确定竞争市场投资决策

本文针对不确定的竞争市场 ,分析现在作一个数量为 I的不可逆投资 ,产生一个生产容量 k,以在将来不确定竞争市场中比潜在进入的竞争对手具有某种占先优势这样一个投资机会的策略投资行为和机会的价值。用博奕论方法分析和给出了基于现在投资可获得将来增长期权价值的决策方法。     

不确定需求下页岩气开发项目投资时机与钻井数量选择研究

基于工程化实现视角,结合规划产能与实际产出的不一致性和产量递减性,构建市场需求不确定情境下页岩气开发项目的投资时机与钻井数量决策模型,给出最佳投资时机和最优钻井数量的解析解,并探讨了不确定性、产量递减率、钻井成功率以及钻井成本变动对最佳投资时机和最优钻井数量的影响。结果表明最佳投资时机受单位有效钻井成本影响却与最优钻井数量多少无关,最优钻井数量受市场需求的期望增长率和波动影响而与市场需求量大小无关;不确定性、产量递减率或钻井成本的增大,将引起投资延迟和钻井数量增加,而钻井成功率的增大则会起到相反的作用;并发现投资阈值与最优钻井数量呈同向变动规律。     

Optimal production strategy under demand fluctuations: Technology versus capacity

This paper provides a comparative analysis of five possible production strategies for two kinds of flexibility investment, namely flexible technology and flexible capacity, under demand fluctuations. Each strategy is underpinned by a set of operations decisions on technology level, capacity amount, production quantity, and pricing. By evaluating each strategy, we show how market uncertainty, production cost structure, operations timing, and investment costing environment affect a firm’s strategic decisions. The results show that there is no sequential effect of the two flexibility investments. We also illustrate the different ways in which flexible technology and flexible capacity affect a firm’s profit under demand fluctuations. The results reveal that compared to no flexibility investment, flexible technology investment earns the same or a higher profit for a firm, whereas flexible capacity investment can be beneficial or harmful to a firm’s profit. Moreover, we prove that higher flexibility does not guarantee more profit. Depending on the situation, the optimal strategy can be any one of the five possible strategies. We also provide the optimality conditions for each strategy.     

Multi-resource investment strategies: Operational hedging under demand uncertainty

Consider a firm that markets multiple products, each manufactured using several resources representing various types of capital and labor, and a linear production technology. The firm faces uncertain product demand and has the option to dynamically readjust its resource investment levels, thereby changing the capacities of its linear manufacturing process. The cost to adjust a resource level either up or down is assumed to be linear. The model developed here explicitly incorporates both capacity investment decisions and production decisions, and is general enough to include reversible and irreversible investment. The product demand vectors for successive periods are assumed to be independent and identically distributed. The optimal investment strategy is determined with a multi-dimensional newsvendor model using demand distributions, a technology matrix, prices (product contribution margins), and marginal investment costs. Our analysis highlights an important conceptual distinction between deterministic and stochastic environments: the optimal investment strategy in our stochastic model typically involves some degree of capacity imbalance which can never be optimal when demand is known.     

The optimal resource portfolio under consumer choice and demand risk for vertically differentiated products

We study the optimal resource portfolio of a firm that sells two vertically differentiated products and utilizes resource flexibility and responsive pricing. We model this decision problem as a two-stage stochastic programming problem with recourse: In the first stage, the firm determines its resource mix and capacities so as to maximize the expected profit under demand uncertainty; in the second stage, uncertainty is resolved and the firm determines its production and pricing decision, constrained by its investment decision. We show that the objective function of this decision problem is not well-behaved (ie, it may have multiple local maxima). Using the concept of Pareto dominance, we reduce the feasible investment region, without loss of optimality, to one in which the objective function is well-behaved everywhere. This reduction allows us to derive the necessary and sufficient conditions for the optimal capacity decision and to gain insights.     

An analytical approach to the facility location and capacity acquisition problem under demand uncertainty

This article presents an analysis of facility location and capacity acquisition under demand uncertainty. A novel methodology is proposed, in which the focus is shifted from the precise representation of facility locations to the market areas they serve. This is an extension of the optimal market area approach in which market area size and facility capacity are determined to minimize the total cost associated with fixed facility opening, variable capacity acquisition, transportation, and shortage. The problem has two variants depending on whether the firm satisfies shortages by outsourcing or shortages become lost sales. The analytical approach simplifies the problem considerably and leads to intuitive and insightful models. Among several other results, it is shown that fewer facilities are set up under lost sales than under outsourcing. It is also shown that the total cost in both models is relatively insensitive to small deviations in optimal capacity choices and parameter estimations.     

A stochastic programming approach for planning horizons of infinite horizon capacity planning problems

Planning horizon is a key issue in production planning. Different from previous approaches based on Markov Decision Processes, we study the planning horizon of capacity planning problems within the framework of stochastic programming. We first consider an infinite horizon stochastic capacity planning model involving a single resource, linear cost structure, and discrete distributions for general stochastic cost and demand data (non-Markovian and non-stationary). We give sufficient conditions for the existence of an optimal solution. Furthermore, we study the monotonicity property of the finite horizon approximation of the original problem. We show that, the optimal objective value and solution of the finite horizon approximation problem will converge to the optimal objective value and solution of the infinite horizon problem, when the time horizon goes to infinity. These convergence results, together with the integrality of decision variables, imply the existence of a planning horizon. We also develop a useful formula to calculate an upper bound on the planning horizon. Then by decomposition, we show the existence of a planning horizon for a class of very general stochastic capacity planning problems, which have complicated decision structure.     

Diversity of firm’s life cycle adapted from the firm’s technology investment decision

The stylized model presented is an optimal control model of technology investment decision of a single product firm. The firm’s technology investment does not have only a long-run positive effect but also a short-run adverse effect on its sales volume. We examine the case of high adverse investment effects where the firm finally leaves the market but we have observed different life cycles till this happens. Depending on the firm’s initial technology stock and sales volume, we compute different firm’s life cycles, which are driven by a trade-off between two strategies: technology versus sales focus strategy. Indifference curves, where managers are indifferent to apply initially technology or sales focus strategies, separate founding conditions of the firm to various classes distinguishable because of the firm’s life cycle.     

Optimal pricing in a diffusion model with concave price-dependent market potential

Using optimal control theory, a diffusion model of new product acceptance is studied. We consider a profit-maximizing firm faced with the problem of determining its optimal pricing policy under the assumption that the total market potential is a concave decreasing function of price. For an infinite planning horizon it is shown by phase portrait analysis that the optimal price is steadily increasing and converging to a saddle point equilibrium.     

Compensation plan for competing salespersons under asymmetric information

We investigate the optimal compensation scheme involving one firm and two competing salespersons deployed in different territories under asymmetric information. The problem is analyzed using a two-stage game. In the first stage, the firm announces the compensation plans. The two salespersons, who are closer to customers, have superior market information and then simultaneously but independently decide which plans to sign. The firm decides the production quantity and the salespersons independently make effort decisions. In the second stage, sales volumes are realized and the associate payments are made.     

On optimal bidding and inventory control in sequential procurement auctions: the multi period case

We consider the problem of a firm that in each cycle of a planning horizon builds inventory of identical items that it acquires by participating in auctions in order to satisfy its own market demand. The firm’s objective is to have a procurement strategy that maximizes the expected present value of the profit for an infinite planning horizon of identical cycles. We formulate this problem as a Markov decision process. We establish monotonicity properties of the value function and of the optimal bidding rule.     

Resource allocation to defensive marketing and manufacturing strategies

In this paper, we investigate the coordinated use of marketing and manufacturing strategies to formulate a defensive business strategy for an incumbent firm upon market entry. While defensive marketing strategies have been researched extensively, little has been done on defensive manufacturing strategies and even less on coordinating these two. We propose a formulation for the joint formation of a defensive strategy by the marketing and production functions under budget constraint considerations. The incumbent firm may allocate its resourcesto advertising and distribution expenditures, as well as to manufacturing investments to reduce the unit production cost. The incumbent firm has also the option of simply allocating all of its resources purely to advertising and distribution. We show that while there exist cases where the optimal reaction to entry involves investments in manufacturing improvements, there are also cases where defense is in marketing strategies alone. We identify the market conditions that promote such reactions to entry and also provide sensitivity analysis.     

On upper bounds of sequential stochastic production planning problems

In this paper we consider a class of problems that determine production, inventory and work force levels for a firm in order to meet fluctuating demand requirements. A production planning problem arises because of the need to match, at the firm level, supply and demand efficiently. In practice, the two common approaches to counter demand uncertainties are (i) carrying a constant safety stock from period to period, and (ii) planning with a rolling horizon. Under the rolling horizon (or sequential) strategy the planning model is repeatedly solved, usually at the end of every time period, as new information becomes available and is used to update the model parameters. The costs associated with a rolling horizon strategy are hard to compute a priori because the solution of the model in any intermediate time period depends on the actual demands of the previous periods.In this paper we derive two a priori upper bounds on the costs for a class of production planning problems under the rolling horizon strategy. These upper bounds are derived by establishing correspondences between the rolling horizon problems and related deterministic programs. One of the upper bounds is obtained through Lagrangian relaxation of the service level constraint. We propose refinements to the non-Lagrangian bounds and present limited computational results. Extensions of the main results to the multiple item problems are also discussed. The results of this paper are intended to support production managers in estimating the production costs and value of demand information under a rolling horizon strategy.