Optimal integrated production and inventory control of an assemble-to-order system with multiple non-unitary demand classes

We study a pure assemble-to-order system subject to multiple demand classes where customer orders arrive according to a compound Poisson process. The finished product is assembled from m different components that are produced on m distinct production facilities in a make-to-stock fashion. We show that the optimal production policy of each component is a state-dependent base-stock policy and the optimal inventory allocation policy is a multi-level state-dependent rationing policy. Using numerical experimentation, we first study the system behavior as a function of order size variability and order size. We show that the optimal average cost rate is more sensitive to order size variability than to order size. We also compare the optimal policy to the first-come first-serve policy and show that there is great benefit to inventory rationing. We also propose two simple heuristics and show that these can effectively mimic the optimal policy which is generally much more difficult to determine and, especially, to implement.     

Robustness of capacity rationing policies

A recent paper discusses a capacity rationing policy that allows make-to-order manufacturing firms encountering expected total demand in excess of available capacity to discriminate between two classes of products, one yielding a higher profit contribution per unit of capacity allocated to it than the other. The result is a selective rejection of orders for the class with lower unit contribution, yielding an increase in total profit when compared to a base case that implements no capacity rationing. Implementation of the policy requires forecasts of demand parameters for both product classes. In this paper we test the sensitivity of the capacity rationing policy to forecast errors in these parameters. The results indicate that, on average, the rationing policy is quite robust in improving profit even when actual demands are approximately twenty percent different from forecast values.     

Inventory rationing for the News-Vendor problem with a drop-shipping option

We consider a single-period inventory model for a bricks-and-clicks business. Store inventory can be used to fulfill both store demand and internet demand. Drop-shipping is used as an additional option for internet sale. We analyze two rationing policies for store inventory: a threshold policy and a fixed-portion policy. We formulate the expected profit for both and prove concavity. There exists an optimal order quantity for store inventory and an optimal stock rationing level below which the manager starts to use drop-shipping for internet demand. Numerical examples show that considering the rationing problem for the single-period inventory model, which is ignored in some earlier works, can result in remarkable differences.     

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.     

Inventory rationing in an (<Emphasis Type="Italic">s,Q</Emphasis>) inventory model with lost sales and two demand classes

Whenever demand for a single item can be categorised into classes of different priority, an inventory rationing policy should be considered. In this paper we analyse a continuous review (s, Q) model with lost sales and two demand classes. A so-called critical level policy is applied to ration the inventory among the two demand classes. With this policy, low-priority demand is rejected in anticipation of future high-priority demand whenever the inventory level is at or below a prespecified critical level. For Poisson demand and deterministic lead times, we present an exact formulation of the average inventory cost. A simple optimisation procedure is presented, and in a numerical study we compare the optimal rationing policy with a policy where no distinction between the demand classes is made. The benefit of the rationing policy is investigated for various cases and the results show that significant cost reductions can be obtained.     

新产品定价决策的影响因素分析

新产品的成功销售取决于两个重要的因素:一是具有生产特性的工程变量,比如产品的可靠性水平;一是具有市场特征的影响因素,比如价格和保障机制。为了实现收益,制造商必须认真审视价格、产品可靠性和保障机制的选择。因此,本文将价格作为外生变量,将保障机制与可靠性作为决策变量,建立了以最大化为目标的收益模型,分析可靠性与保障机制的最优策略。另外,探讨当不同变量的敏感性参数发生变化时,最优保障机制与产品可靠性的变化规律。最后,通过算例分析收益函数的基本特性,结论显示消费者总是从产品保障机制的信号中判断产品的可靠性水平,这对新产品销售有一定的借鉴意义。     

Inventory rationing via drop-shipping in Internet retailing: A sensitivity analysis

In this paper, we study a threshold level inventory rationing policy that is of interest to e-tailers, operating in a business to consumer (B2C) environment and selling non-perishable, made-to-stock items such as books, CDs, consumer electronics, and body and bath products. A Monte Carlo simulation model is developed to examine this policy when the demand process is stochastic, lead-time is stochastic, and the e-tailer uses ‘drop-shipping’ as an order fulfillment option. The methodology presented, which includes computer simulation and a full factorial experimental design, permits understanding of the complexity of the decision-making environment and implications of different sources of uncertainty (e.g. demand variability and lead-time variability) on a profit-maximizing threshold level of inventory, a stock level below which low margin orders are drop-shipped directly from the e-tailer’s supplier rather than fulfilled from internal stock.     

Coordinated decisions for substitutable products in a common retailer supply chain

This paper studies coordination mechanisms in a supply chain which consists of two suppliers with capacity uncertainties selling differential yet substitutable products through a common retailer who faces price-sensitive random demand of these two products. We develop in a noncompetitive setting three coordination models – revenue sharing, return policy, and combination of revenue sharing and return policy – and contrast them with a basic and uncoordinated model. We are able to establish the ordinal relationship among the retailer’s ordering and pricing decisions and analytically compare the performances between certain models when two suppliers are identical. We find that the retailer’s ordering and pricing decisions in the model with return policy in the case of identical suppliers are independent of demand or supply uncertainty. Our numerical results reveal that the performances of coordination models in the case of nonidentical suppliers resemble those in the case of identical suppliers. We find that the retailer will place a larger order quantity in models where her average cost per unit sold is smaller. We also find that product substitutability and uncertainties have different effects on chain performances.     

Improved bid prices for choice-based network revenue management

One of the latest developments in network revenue management (RM) is the incorporation of customer purchase behavior via discrete choice models. Many authors presented control policies for the booking process that are expressed in terms of which combination of products to offer at a given point in time and given resource inventories. However, in many implemented RM systems—most notably in the hotel industry—bid price control is being used, and this entails the problem that the recommended combination of products as identified by these policies might not be representable through bid price control. If demand were independent from available product alternatives, an optimal choice of bid prices is to use the marginal value of capacity for each resource in the network. But under dependent demand, this is not necessarily the case. In fact, it seems that these bid prices are typically not restrictive enough and result in buy-down effects.We propose (1) a simple and fast heuristic that iteratively improves on an initial guess for the bid price vector; this first guess could be, for example, dynamic estimates of the marginal value of capacity. Moreover, (2) we demonstrate that using these dynamic marginal capacity values directly as bid prices can lead to significant revenue loss as compared to using our heuristic to improve them. Finally, (3) we investigate numerically how much revenue performance is lost due to the confinement to product combinations that can be represented by a bid price.The heuristic is not restricted to a particular choice model and can be combined with any method that provides us with estimates of the marginal values of capacity. In our numerical experiments, we test the heuristic on some popular networks examples taken from peer literature. We use a multinomial logit choice model which allows customers from different segments to have products in common that they consider to purchase. In most problem instances, our heuristic policy results in significant revenue gains over some currently available alternatives at low computational cost.     

Inventory control by different service levels

This paper examines the multiple period inventory control problem of a single product with multiple (two) prices, depending on service level, in which optimal pricing and ordering decisions are made in each period. Traditional inventory and pricing models consider only single products, single prices, and single service levels. However, this research paper finds that a seller can improve inventory control and revenue by offering multiple prices depending on service level. This research considers a single product with multiple (two) pricing policies corresponding to service level as follows: if the customer is willing to delay the shipment, he/she will be offered a lower regular price. Otherwise, the customer will pay the regular price plus extra charges for express service. In this paper, I show the following: (1) there is an optimal pricing and replenishment policy that can control inventory and (2) there exists a finite threshold for inventory levels such that if the inventory level at the beginning of each period is higher than the threshold, the customer will be offered the express service at the regular price, without any extra charge.     

Single vehicle routing problems with a predefined customer sequence, compartmentalized load and stochastic demands

We consider the problem of finding the optimal routing of a single vehicle that delivers K different products to N customers according to a particular customer order. The demands of the customers for each product are assumed to be random variables with known distributions. Each product type is stored in its dedicated compartment in the vehicle. Using a suitable dynamic programming algorithm we find the policy that satisfies the demands of the customers with the minimum total expected cost. We also prove that this policy has a specific threshold-type structure. Furthermore, we investigate a corresponding infinite-time horizon problem in which the service of the customers does not stop when the last customer has been serviced but it continues indefinitely with the same customer order. It is assumed that the demands of the customers at different tours have the same distributions. It is shown that the discounted-cost optimal policy and the average-cost optimal policy have the same threshold-type structure as the optimal policy in the original problem. The theoretical results are illustrated by numerical examples.     

Profit maximization in flexible serial queueing networks

We analyze the tradeoff between efficiency and service quality in tandem systems with flexible servers and finite buffers. We reward efficiency by assuming that a revenue is earned each time a job is completed, and penalize poor service quality by incorporating positive holding costs. We study the dynamic assignment of servers to tasks with the objective of maximizing the long-run average profit. For systems of arbitrary size, structured service rates, and linear or nonlinear holding costs, we determine the server assignment policy that maximizes the profit. For systems with two stations, two servers with arbitrary service rates, and linear holding costs, we show that the optimal server assignment policy is of threshold type and determine the value of this threshold as a function of the revenue and holding cost. The threshold can be interpreted as the best possible buffer size, and hence our results prove the equivalence of addressing service quality via a holding cost and via limiting the buffer size. Furthermore, we identify the optimal buffer size when each buffer space comes at a cost. We provide numerical results that suggest that the optimal policy also has a threshold structure for nonlinear holding costs. Finally, for larger systems with arbitrary service rates, we propose effective server assignment heuristics.     

A dynamic model for optimal design quality and return policies

A clearly explained and generous return policy has been established as a competitive weapon to enhance sales. From the firm’s point of view, a generous return policy will increase sales revenue, but will also increase cost due to increased likelihood of return. Design quality of the product has been used as a competitive weapon for a long time. This paper recognizes the relationship between design quality and price of the product, and the firm’s return policy. Quality level in the product would influence the amount of return directly. When the product quality is higher, the customer satisfaction rate will increase and the probability of return will decrease. We develop a profit-maximization model to jointly obtain optimal policies for the product quality level, price and the return policy over time. The model presented in this paper is dynamic in nature and considers the decisions as the product moves through the life cycle. We obtain a number of managerial guidelines for using marketing and operational strategy variables to obtain the maximum benefit from the market. We mention several future research possibilities.     

Assessing the benefits of remanufacturing option under one-way substitution

In this study, we consider a segmented market for a product that can either be manufactured or remanufactured. It is assumed that the remanufactured products can be substituted by the new ones. A steady-state profit model is constructed under certain environmental assumptions on capacity requirements of operations, and revenue and cost schemes. Exact steady-state probabilities of the Markovian model constructed are solved via matrix geometric techniques. An extensive computational study is performed to investigate the conditions under which the utilization of remanufacturing option and the use of one-way substitution policy increase the average expected profit.     

Incorporating one-way substitution policy into the newsboy problem with imprecise customer demand

This paper presents an approach for solving an inventory model for single-period products with maximizing its expected profit in a fuzzy environment, in which the retailer has the opportunity for substitution. Though various structures of substitution arise in real life, in this study we consider the fuzzy model for two-item with one-way substitution policy. This one-way substitutability is reasonable when the products can be stored according to certain attribute levels such as quality, brand or package size. Again, to describe uncertainty usually probability density functions are being used. However, there are many situations in real world that utilize knowledge-based information to describe the uncertainty. The objective of this study is to provide an analysis of single-period inventory model in a fuzzy environment that enables us to compute the expected resultant profit under substitution. An efficient numerical search procedure is provided to identify the optimal order quantities, in which the utilization of imprecise demand and the use of one-way substitution policy increase the average expected profit. The benefit of product substitution is illustrated through numerical example.     

基于随机动态规划的有限库存ATO系统优化控制

本文研究n维组件单一产品,有限库存的ATO系统。通过建立马尔可夫决策过程模型(MDP),构造优化算法,研究组件生产与库存的最优控制策略。最优策路可以表示为状态依赖型库存阈值,系统内任一组件的控制策略受其它组件库存状态的影响。利用最优控制理论动态规划方法和数值计算方法对最优控制策略的存在性、最优值的数值计算进行研究,建立更符合实际生产的ATO系统决策模型,进行相应的理论和实验验证,研究系统参数对最优策略的影响。     

易逝品价格连续下降情况下带有随机需求的供应链协调

本文研究了短生命周期产品价格随时间连续下降情况下单供应商和单销售商渠道中的供应链协调。分别建立了分散决策、集中决策和基于收入共享合同的最优订货策略模型,讨论了模型的相关性质,提出了一种基于收入共享合同的供应链协调策略,来实现整个供应链的协调。并指出该策略不仅适用于价格时变情况,能实现整个供应链的期望利润最大化,并且具有很好的适用性。最后,用实例仿真验证了本文所给出的模型和策略的有效性。     

On the convexity of the two-threshold policy for an M/G/1 queue with vacations

In this note, we consider a single server queueing system with server vacations of two types and a two-threshold policy. Under a cost and revenue structure the long-run average cost function is proven to be convex in the lower threshold for a fixed difference between the two thresholds.     

Product line pricing for services with capacity constraints and dynamic substitution

In this paper, we address a service provider’s product line pricing problem for substitutable products in services, such as concerts, sporting events, or online advertisements. For each product, a static price is selected from a pre-defined set such that the total revenue is maximised. The products are differentiated by some of their attributes, and their availability is restricted due to individual capacity constraints. Furthermore, they are simultaneously sold during a common selling period at the end of which the service is delivered. Consumers differ from one another with respect to their willingness to pay, and, hence, their reservation prices vary depending on the product. In the event of a purchase, they choose the product that maximises their consumer surplus.     

Heuristic and meta-heuristic approaches for multi-period shelf-space optimization: the case of motion picture retailing

The retail industry is in a highly competitive situation currently. The success of the industry depends upon the efficient allocation of products in the shelf space. Several previous authors have developed mathematical models for optimal shelf-space allocation. We extend the prior research in the direction of the multi-period problem and introduce more realistic characteristics, such as product demand perishability, pricing contract and cross-elasticity. The new characteristics help us address the case of the real-life movie allocation problem in multiplexes. We formulate a linear integer programming model to represent the problem. The proposed model shows a potential benefit of at least 11% increase in revenue for a multiplex theatre situation as compared to the existing methods. We also propose two greedy heuristics and a genetic algorithm to solve the same problem. A computational study shows that the genetic algorithm performs better than the existing method.