Revenue management for operations with urgent orders (original) (raw)
Related papers
An Optimal Ordering Policy with Markov Decision Process
One of the most frequent decisions faced by operations managers is "how much" or "how many" items are they to make or buy in order to satisfy external or internal requirements for the item. Replenishment in many cases is made using the economic order quantity (EOQ) model. The model considers the tradeoff between ordering cost and storage cost in choosing the quantity to use in replenishing items in inventories. This paper demonstrates an approach to optimize the EOQ of an item under a periodic review inventory system with stochastic demand using value iteration. The objective is to determine in each period of the planning horizon, an optimal decision so that the long run costs are minimized and profits are maximized for the given state of demands. Using Markov decision process over a finite planning horizon with equal intervals, the decision of how much quantity to order or not to order is made. We use a numerical example with the aid of value iteration method to demonstrate the existence of an optimal decision policy.
Revenue Management in Order-Driven Production Systems
Decision Sciences, 2005
This article investigates the effectiveness of a tactical demand-capacity management policy to guide operational decisions in order-driven production systems. The policy is implemented via a heuristic that attempts to maximize revenue by selectively accepting or rejecting customer orders for multiple product classes when demand exceeds capacity constantly over the short term. The performance of the heuristic is evaluated in terms of its ability to generate a higher profit compared to a first-come-first-served (FCFS) policy. The policies are compared over a wide range of conditions characterized by variations in both internal (firm) and external (market) factors. The heuristic, when used with a Whole Lot order-processing approach, produces higher profit compared to FCFS when profit margins of products are substantially different from each other and demand exceeds capacity by a large amount. In other cases it is better to use the heuristic in conjunction with the Split Lot order-processing approach.
A Production-Inventory System With Both Patient and Impatient Demand Classes
IEEE Transactions on Automation Science and Engineering, 2012
We consider a production-inventory system with two customer classes, one patient and one impatient. Orders from the patient class can be backordered if needed while orders from the impatient class must be rejected if they cannot be fulfilled from on-hand inventory. Orders backordered incur a backorder cost while orders rejected incur a lost sales cost. The objective is to minimize the sum of inventory holding cost and the costs of backorders and lost sales. We formulate the problem as a Markov decision process and use this formulation to characterize the structure of the optimal policy. We show that the optimal policy can be described by two threshold functions that depend on the level of backorders from the patient class. These threshold functions specify (1) when it is optimal to produce, (2) how to allocate units produced to either increase inventory or reduce backorder, and (3) when to fulfill orders from on-hand inventory and when to backorder (in the case of the patient class) and when to reject them (in the case of the impatient class). We show that the priority in inventory allocation among the two classes is not static and instead depends on the backorder level from the class of patient customers. In particular, it is possible to start out fulfilling orders from the impatient class and backordering orders from the patient class and then to switch to fulfilling orders from the patient class and rejecting orders from the impatient class. In addition to characterizing the structure of the optimal policy, we also describe an effective heuristic that retains the essential features of the optimal policy but is significantly simpler to implement. This heuristic performs nearly as well as the optimal policy and significantly outperforms other plausible heuristics.
Dynamic order acceptance and capacity planning on a single bottleneck resource
Naval Research Logistics, 2007
We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for aggregate regular as well as non-regular capacity. The stream of incoming order arrivals is the main source of uncertainty in dynamic order acceptance and the company only has forecasts of the main properties of the future incoming projects. Project proposals arrive sequentially with deterministic interarrival times and a decision on order acceptance and capacity planning needs to be made each time a proposal arrives and its project characteristics are revealed. We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as to deterministically allocate a single bottleneck resource to the accepted projects, both with an eye on maximizing the expected revenues within the problem horizon. We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selection and aggregate capacity usage.
European Journal of Operational Research, 2006
This paper addresses the single-item, non-stationary stochastic demand inventory control problem under the nonstationary (R, S) policy. In non-stationary (R, S) policies two sets of control parameters-the review intervals, which are not necessarily equal, and the order-up-to-levels for replenishment periods-are fixed at the beginning of the planning horizon to minimize the expected total cost. It is assumed that the total cost is comprised of fixed ordering costs and proportional direct item, inventory holding and shortage costs. With the common assumption that the actual demand per period is a normally distributed random variable about some forecast value, a certainty equivalent mixed integer linear programming model is developed for computing policy parameters. The model is obtained by means of a piecewise linear approximation to the non-linear terms in the cost function. Numerical examples are provided. (S.A. Tarim). European Journal of Operational Research xxx (2005) xxx-xxx www.elsevier.com/locate/eor
Negotiating price/delivery date in a stochastic manufacturing environment
IIE Transactions, 1999
We study a make-to-order manufacturing system consisting of several processing centers that are subject to failures and repairs. Our objective is to build a model that can be used as a tool for negotiating the delivery date and the price of a certain upcoming order. The model takes into account the congestion level of the shop floor at the time the order is placed. Based on the workload of the processing centers, the model splits the order into lots and assigns them to the processing centers so as to determine the order completion time associated with the minimum operating cost. The efficiency of the solution method for the model allows real-time decision-making while negotiating the price and delivery date of the order to be placed. Since the decisions are made based on a snapshot of the congestion level at the shop floor, using this model will reduce the conflict between the marketing and the production activities in manufacturing organizations. ____________________________________________________________________________________________
Multiple items procurement under stochastic nonstationary demands
European Journal of Operational Research, 1995
In the consumer goods wholesaling and retailing industry, a large number of stock keeping units must be managed on a regular basis. The items are typically purchased in families, each family being associated with a specific external vendor, and usually there are some constraints on family order quantities and there are frequent opportunities to buy at a temporary low price. The demand for an item is often stochastic and not stationary. In this paper, by computing procurement plans over rolling planning horizons, we transform this difficult sequential decision problem into a multi-period static decision problem under risk. The problem is initially formulated as a stochastic program with simple recourse and a branch and bound algorithm is designed to solve an equivalent deterministic program. A piecewise concave approximation is proposed to reduce this program to a linear program with one 0-1 variable per planning period. The performances of the algorithms are studied in two simulation experiments. The simulations show that, when planning over a rolling horizon, the approach proposed yields excellent results.
Optimal Production Policies with Multistage Stochastic Demand Lead Times
Probability in the Engineering and Informational Sciences, 2009
We study the value of multistage advance demand information (MADI) in a production system in which customers place an order in advance of their actual need, and each order goes through multiple stages before it becomes due. Any order that is not immediately filled at its due date will be backordered. The producer must decide whether or not to produce based on real-time information regarding current and future orders. We formulate the problem as a Markov decision process and analyze the impact of the demand information on the production policy and the cost. We show that the optimal production policy is a state-dependent base-stock policy, and we show that it has certain monotonicity properties. We also introduce a simple heuristic policy that is significantly easier to compute and that inherits the structural properties of the optimal policy. In addition, we show that its base-stock levels bound those of the socially optimal policy. Numerical study identifies the conditions under whi...
Optimal control policies for an inventory system with commitment lead time
Optimal control policies for an inventory system with commitment lead time, 2019
We consider a firm which faces a Poisson customer demand and uses a base-stock policy to replenish its inventories from an outside supplier with a fixed lead time. The firm can use a preorder strategy which allows the customers to place their orders before their actual need. The time from a customer's order until the date a product is actually needed is called commitment lead time. The firm pays a commitment cost which is strictly increasing and convex in the length of the commitment lead time. For such a system, we prove the optimality of bang-bang and all-or-nothing policies for the commitment lead time and the base-stock policy, respectively. We study the case where the commitment cost is linear in the length of the commitment lead time in detail. We show that there exists a unit commitment cost threshold which dictates the optimality of either a buy-to-order (BTO) or a buy-to-stock strategy. The unit commitment cost threshold is increasing in the unit holding and backordering costs and decreasing in the mean lead time demand. We determine the conditions on the unit commitment cost for profitability of the BTO strategy and study the case with a compound Poisson customer demand. KEYWORDS advance demand information, commitment cost, inventory management, preorder strategy 1 INTRODUCTION The consequences of demand and supply uncertainties and eventual mismatch between demand and supply are well known to many companies. The need for designing company operations such that this mismatch is minimized or avoided has motivated many researchers and resulted in a rich literature on demand and supply management. Among the various methods, information sharing has received a lot of attention. The benefits of acquiring and providing information about future demand are undeniable. Having information on future customer demand helps companies in reducing their inventory levels without sacrificing high service levels. Customers, who provide information on the timing and quantity of their future demand get a high quality service. One form of advance demand information (ADI) is a pre-order strategy in which customers place orders ahead of their actual need. The preorder strategy is characterized by a commitment lead time which is defined as the time that elapses between the moment an order is communicated by the customer and the moment the order must be delivered to the customer. Although in today's competitive market firms cannot force their customers to place orders before their actual need, they can tempt them to follow the preorder strategy by giving a bonus. In order to make long commitment lead times acceptable and attractive, companies should propose bonuses which increase with the length of commitment lead times. The commitment lead time contracts reduce the companies' demand uncertainty risk and the customers' inventory unavailability risk (