Allocation of flexible and indivisible resources with decision postponement and demand learning

We consider a firm that uses two perishable resources to satisfy two demand types. Resources are flexible such that each resource can be used to satisfy either demand type. Resources are also indivisible such that the entire resource must be allocated to the same demand type. This type of resource flexibility can be found in different applications such as movie theater complexes, cruise lines, and airlines. In our model, customers arrive according to independent Poisson processes, but the arrival rates are uncertain. Thus, the manager can learn about customer arrival rates from earlier demand figures and potentially increase the sales by postponing the resource allocation decision. We consider two settings, and derive the optimal resource allocation policy for one setting and develop a heuristic policy for the other. Our analysis provides managerial insights into the effectiveness of different resource allocation mechanisms for flexible and indivisible resources.     

随机需求下考虑半成品库存的多周期生产决策优化

为了更好地应对需求的不确定性,在需求实现之前,企业既可以生产成品直接满足需求,亦可生产部分半成品,在观察到实际需求之后短时间内迅速完成剩余生产环节以满足需求。未加工的半成品和未售出的成品可用于满足后续周期的需求。作为一种提高生产灵活性的手段,分阶段生产的方式会产生更高的成本。企业需要在成本和灵活性之间作出权衡,优化生产决策。模型通过动态规划的方法,研究需求不确定情况下考虑半成品库存的多周期生产决策问题,通过分析目标函数以及最优值函数的结构性质,推导出最优的多周期生产策略为修正的目标库存策略,并且分析了不同参数对最优策略的影响。     

A Semi-Markov decision problem for proactive and reactive transshipments between multiple warehouses

Lateral transshipments are an effective strategy to pool inventories. We present a Semi-Markov decision problem formulation for proactive and reactive transshipments in a multi-location continuous review distribution inventory system with Poisson demand and one-for-one replenishment policy. For a two-location model we state the monotonicity of an optimal policy. In a numerical study, we compare the benefits of proactive and different reactive transshipment rules. The benefits of proactive transshipments are the largest for networks with intermediate opportunities of demand pooling and the difference between alternative reactive transshipment rules is negligible.     

Inventory control based on advanced probability theory,an application

Whenever stock is placed as a buffer between consumption and supply the decision when to replenish the stock is based on uncertain values of future demand and supply variables. Uncertainty exists about the replenishment lead time, about the number of demands and the quantities demanded during this period. We develop a new analytical expression for the reorder point, which is based on the desired service level and three distributions: the distribution of the quantity of single demands during lead time, the distribution of the lengths of time intervals between successive demands, and the distribution of the lead time itself. The distribution of lead time demand is derived from the distributions of individual demand quantities and not from the demand per period. It is not surprising that the resulting formulae for the mean and variance are different from those currently used. The theory developed is also applicable to periodic review systems. The system has been implemented at CERN and enables a significant enhancement of the service level, while reducing the average stock.     

Managing an integrated production inventory system with information on the production and demand status and multiple non-unitary demand classes

In this paper, we study a system consisting of a manufacturer or supplier serving several retailers or clients. The manufacturer produces a standard product in a make-to-stock fashion in anticipation of orders emanating from n retailers with different contractual agreements hence ranked/prioritized according to their importance. Orders from the retailers are non-unitary and have sizes that follow a discrete distribution. The total production time is assumed to follow a k₀-Erlang distribution. Order inter-arrival time for class l demand is assumed to follow a k_l-Erlang distribution. Work-in-process as well as the finished product incur a, per unit per unit of time, carrying cost. Unsatisfied units from an order from a particular demand class are assumed lost and incur a class specific lost sale cost. The objective is to determine the optimal production and inventory allocation policies so as to minimize the expected total (discounted or average) cost. We formulate the problem as a Markov decision process and show that the optimal production policy is of the base-stock type with base-stock levels non-decreasing in the demand stages. We also show that the optimal inventory allocation policy is a rationing policy with rationing levels non-decreasing in the demand stages. We also study several important special cases and provide, through numerical experiments, managerial insights including the effect of the different sources of variability on the operating cost and the benefits of such contracts as Vendor Managed Inventory or Collaborative Planning, Forecasting, and Replenishment. Also, we show that a heuristic that ignores the dependence of the base-stock and rationing levels on the demands stages can perform very poorly compared to the optimal policy.     

基于回购契约的供应链鲁棒协调模型

研究了需求不确定条件下供应链回购契约协调问题。针对未知需求具体分布形式的两级供应链系统,建立了基于回购契约的供应链鲁棒协调模型。给出了仅知需求均值和方差信息条件下,集成供应链系统的鲁棒订货策略和分散供应链系统的鲁棒契约协调策略。分析了不同契约参数条件下的供应链及其成员利润情况。最后,进行了数值计算,验证了当获得需求的真实分布形式时．供应链鲁棒回购契约协调策略的有效性。结果表明,供应链鲁棒回购契约协调策略能够有效减少需求不确定性对系统及其成员利润的影响。     

Managing the supply and demand uncertainty in workforce recruitment: planned or just-in-time acquisition

Managing knowledge workers is highly complex because of the need to balance the costs associated with their training and holding costs against the need to meet market demand as quickly as possible. Unlike previous approaches to this problem in the workforce management literature, this paper develops a stochastic optimization model to examine the impact of not only uncertainty of the demand of knowledge services but also that of the supply of knowledge workers on a recruiting strategy. Hypotheses on optimal recruiting decisions that this paper suggests include: (1) high holding and training costs decrease recruitment of apprentices; (2) high mobility of skilled workers decreases recruitment of apprentices; (3) high elasticity of the supply of skilled workers decreases recruitment of apprentices; (4) high volatility of the demand of knowledge services decreases recruitment of apprentices; and (5) in high seasonal effect on the demand, the decision policy based on decision thresholds proportional to seasonal demands outperforms the decision policy based on a constant decision threshold. Suggested hypotheses are supported by a simulation of the model. Model parameters in the simulation are estimated based on the survey of information security consulting service companies in South Korea.     

“酒店+OTA”双渠道供应链超订策略研究

为解决市场需求不确定环境下,酒店和在线旅行网站(Online Travel Agency,OTA)合作时的能力超订量与在线房间预留量的决策问题,建立了基于佣金合作模式的数学模型,给出了实现酒店整体期望收益最大化的在线房间预留量与能力超订量。借助数值分析,进一步研究了佣金率与需求不确定性对最优决策的影响。结果表明,当佣金率与门店需求不确定性较小时,酒店采取双渠道策略并且实施超订;当佣金率与门店需求不确定性很大时,酒店采取门店单渠道策略但不实施超订。另外,在线房间预留量随着佣金率、门店需求不确定性的增大而减小。     

Retail price markup commitment in decentralized supply chains

We investigate the operational decisions and resulting profits for a supply chain facing price-dependent demand under a policy where there is an ex-ante commitment made on the retail price markup. We obtain closed-form solutions for this policy under the assumption of a multiplicative demand function and we analytically compare its performance with that of a traditional price-only policy. We compare these results to results obtained when demand follows a linear additive form. These formulations are shown to be qualitatively different as the manufacturer’s wholesale pricing decision is independent of the retail price markup commitment in the multiplicative case, but not when demand is linear additive. We demonstrate that the ex-ante commitment can lead to Pareto-improving solutions under linear additive demand, but not under the multiplicative demand function. We also consider the effect of pricing power in the supply chain by varying who determines the retail price markup.     

Fuzzy bicriteria multi-index transportation problems for coal allocation planning of Taipower

Taipower, the official electricity authority of Taiwan, encounters several difficulties in planning annual coal purchase and allocation schedule, e.g., with multiple sources, multiple destinations, multiple coal types, different shipping vessels, and even in uncertain demand and supply. In this study, these concerns are formulated as a fuzzy bicriteria multi-index transportation problem. Furthermore, an effective and interactive algorithm is proposed which combines reducing index method and interactive fuzzy multi-objective linear programming technique to cope with a complicated problem which may be prevalent in other industries. Results obtained in this study clearly demonstrate that this model can not only satisfy more of the actual requirements of the integral system but also offer more information to the decision makers (DMs) for reference in favor of exalting decision making quality.     

配送量不定的供应链协调

本文研究供应链管理中配送量不确定时，制造商和分销商之间的协调问题。通过建立惩罚和奖励机制模型来协调供应链各部分的运作，使得分散系统就如一个整体。同时分析供应链的利润分配问题，分别得出各个机制下分销商的最优决策。     

非对称信息和模糊需求下的闭环供应链协调决策研究

在模糊需求和非对称销售价格信息下,研究了由单一制造商和零售商组成的两级闭环供应链系统的协调问题。通过建立不确定环境下的集中决策和收益共享-费用共担契约决策模型,分别得到了不同模型下闭环供应链系统的最优策略。并进一步通过算例分析了契约下供应链各方收益随模糊需求和价格的变化情况,以及该契约对整个系统决策效率的影响。结果表明:收益共享-费用共担契约能有效实现模糊闭环供应链系统的协调,实现闭环供应链系统决策的最优化;其次,需求和零售价格的不确定性程度越高,对零售商的最优订购量、供应链成员及系统利润的影响也越大。     

Managing capacity flexibility in make-to-order production environments

This paper addresses the problem of managing flexible production capacity in a make-to-order (MTO) manufacturing environment. We present a multi-period capacity management model where we distinguish between process flexibility (the ability to produce multiple products on multiple production lines) and operational flexibility (the ability to dynamically change capacity allocations among different product families over time). For operational flexibility, we consider two polices: a fixed allocation policy where the capacity allocations are fixed throughout the planning horizon and a dynamic allocation policy where the capacity allocations change from period to period. The former approach is modeled as a single-stage stochastic program and solved using a cutting-plane method. The latter approach is modeled as a multi-stage stochastic program and a sampling-based decomposition method is presented to identify a feasible policy and assess the quality of that policy. A computational experiment quantifies the benefits of operational flexibility and demonstrates that it is most beneficial when the demand and capacity are well-balanced and the demand variability is high. Additionally, our results reveal that myopic operating policies may lead a firm to adopt more process flexibility and form denser flexibility configuration chains. That is, process flexibility may be over-valued in the literature since it is assumed that a firm will operate optimally after the process flexibility decision. We also show that the value of process flexibility increases with the number of periods in the planning horizon if an optimal operating policy is employed. This result is reversed if a myopic allocation policy is adopted instead.     

A stochastic programming approach for multi-site aggregate production planning

Production planning problems play a vital role in the supply chain management area, by which decision makers can determine the production loading plan—consisting of the quantity of production and the workforce level at each production plant—to fulfil market demand. This paper addresses the production planning problem with additional constraints, such as production plant preference selection. To deal with the uncertain demand data, a stochastic programming approach is proposed to determine optimal medium-term production loading plans under an uncertain environment. A set of data from a multinational lingerie company in Hong Kong is used to demonstrate the robustness and effectiveness of the proposed model. An analysis of the probability distribution of economic demand assumptions is performed. The impact of unit shortage costs on the total cost is also analysed.     

Pricing and strategy selection in a closed-loop supply chain under demand and return rate uncertainty

Closed-loop supply chain (CLSC) decision-making involves many uncertainties, which makes the decision-making process more complex and diversified. This study considered a two-stage CLSC consisting of an original manufacturer and a third-party recycler. Without any government policy support, considering the effects of market demand, product return rate, and consumer perceived value, a CLSC decision model based on market demand with a [0,1] distribution was established. The model analyzes three situations—a manufacturer monopoly, the Cournot duopoly game, and the Stackelberg competition game—and solves them. The optimal values of decision variables such as optimal pricing, market demand, and all parties’ profits in the CLSC are obtained, and a strict mathematical proof is given. Through the model-solving process, the effects of product return rate and consumer perceived value on decision variables are analyzed; then, the profit allocation between the original manufacturer and the third-party recycler under different cooperation modes is analyzed. In addition, the four combinations of competition and cooperation are analyzed based on game theory. The Nash equilibrium solution and Pareto optimal solution of the four modes are analyzed by drawing a bimatrix Nash equilibrium table. The results indicate that the cooperation–cooperation mode is difficult to produce automatically, and government policy guidance and support are often needed to achieve Pareto optimality. Finally, a numerical example is given to validate the proposed model. In this way, the proposed model provides reliable theoretical support for the decision-making of both sides in a CLSC.

     

Sourcing decision under disruption risk with supply and demand uncertainty: A newsvendor approach

Multi-sourcing is considered as a common practice to hedge against supply disruption risk. In this context, this paper proposes two models for optimal order allocation in newsvendor setting, where both supply and demand are uncertain. The first model considers a risk neutral decision maker who maximizes the total expected profit under disruption risk. The second one is for a risk averse decision maker who does so under service level constraints. Analytical closed form solutions for both the models are derived. To overcome the computational complexity of the exact optimal solution, two algorithms are developed to generate optimal order quantity and the corresponding set of suppliers. The solutions with exact optimization algorithms and the proposed ones are illustrated and compared with numerical examples. The results show that the proposed algorithms give the exact optimal solution while being tractable. Finally, a case study is used to illustrate the applicability of the proposed model.     

考虑风险偏好的动态生产库存问题的鲁棒优化模型

不同阶段需求不确定情况下,决策者的风险偏好和生产过程中的废品处理影响着供应链生产库存管理和供应链整体效益。本文考虑决策者风险偏好下,构建了包含I个生产者企业,一个库存点和一个废物处理基地的T阶段动态供应链生产库存框架,建立了椭球型需求不确定集下,以追求整体收益最大化为目标的不确定优化模型,并应用鲁棒优化理论得到了数据确定性线性鲁棒对应模型,讨论了模型解的可靠性和有效性。最后的算例表明,只有当决策者风险偏好参数在一定范围内时,才会存在满足条件且具有较高可靠性的鲁棒决策,验证了该鲁棒优化模型的合理性。     

Revenue impacts of fare input and demand forecast accuracy in airline yield management

Most airline yield management seat allocation models require inputs of the expected demand by fare class, the variance of this demand, and a revenue value associated with the bookings expected in each class. In this paper, we examine the impacts of errors in the demand forecasts and fare estimates on the revenue performance of some commonly used seat allocation heuristic decision rules. Through simulation analysis of scenarios in which the fare or demand inputs used by the models differ from the ‘actual’ values simulated in the flight booking process, we examine the effects of unexpected variability in the actual fare values, misestimation of the mean fare values of the different booking classes, and forecasting errors in the expected demand for each class. Our findings confirm previous studies that found the accuracy of the demand forecasts to be of greatest importance, but we also uncover some instances where misestimation of the mean demands and/or mean fare values used as inputs to the decision models can actually be beneficial. At the same time, we conclude that the variability of actual fare values around the mean fare values used as inputs does not have a significant impact, given the mathematical characteristics of existing EMSR seat allocation methods.     

A traffic shaping model for optimizing network operations

The management of technology in multi-service computer networks, such as university networks, has become a challenge with the explosive growth of entertainment oriented peer-to-peer (P2P) traffic. Traffic shaping is one of the tools used to manage bandwidth to improve system performance by allocating bandwidth between P2P and non-peer-to-peer (NP2P) traffic. We present a model for traffic shaping and bandwidth management that considers the trade-offs from allocating different amounts of bandwidths for different application categories and use data from a university network. The current policy allocates varying bandwidths over the day to P2P and NP2P traffic to reflect the importance of not letting entertainment based traffic choke the network during the day time at the expense of the more important traffic, such as Web traffic. We highlight the difficulties in obtaining data in the form required for analysis, and the need to estimate demand for allocations not covered by current policy. We present a goal programming model for this estimation task. We also model the traffic shaping problem as a Markov decision process and develop an algorithm for determining the optimal bandwidth allocation to maximize the utility of all users. Finally we use a numerical example to illustrate our approach.     

Optimal inventory policy with supply uncertainty and demand cancellation

We consider a periodic review model where the firm manages its inventory under supply uncertainty and demand cancellation. We show that because of supply uncertainty, the optimal inventory policy has the structure of re-order point type. That is, we order if the initial inventory falls below this re-order point, otherwise we do not order. This is in contrast to the work of Yuan and Cheung (2003) who prove the optimality of an order up to policy in the absence of supply uncertainty. We also investigate the impact of supply uncertainty and demand cancellation on the performance of the supply chain. Using our model, we are able to quantify the importance of reducing the variance of either the distribution of yield or the distribution of demand cancellation. The single, multiple periods and the infinite horizon models are studied.