The newsboy problem with multiple demand classes

We consider the single item newsboy problem, where the item can be sold to different demand classes at different prices. The demands are realized sequentially over time. That is, the newsboy purchases newspapers at the beginning of the day and sells them in the morning and in the afternoon with different prices. We analyze two cases where the prices are either decreasing or increasing; the former case applies, for example, to fashion goods retailing, while the latter to airlines and hotels. In the decreasing price case, we find the optimal order quantity to maximize the expected profit with independent multiple demands. We show numerically that aggregating the multiple demands with a single average price or applying the single demand newsboy model separately to multiple demand classes may lead to large sub-optimality. In the increasing price case, we analyze a two demand class model in which a fraction of the unsatisfied lower fare demand diverts to the high fare class, thus causing dependent sales. We follow a policy of protecting the sales in the higher fare class by limiting the sales in the lower fare class. We derive both the fare allocation limit and the initial capacity, and discuss managerial implications. For both models, we give bounds on the optimal order quantity.     

Stochastic optimization model for order acceptance with multiple demand classes and uncertain demand/supply

This article considers an order acceptance problem in a make-to-stock manufacturing system with multiple demand classes in a finite time horizon. Demands in different periods are random variables and are independent of one another, and replenishments of inventory deviate from the scheduled quantities. The objective of this work is to maximize the expected net profit over the planning horizon by deciding the fraction of the demand that is going to be fulfilled. This article presents a stochastic order acceptance optimization model and analyses the existence of the optimal promising policies. An example of a discrete problem is used to illustrate the policies by applying the dynamic programming method. In order to solve the continuous problems, a heuristic algorithm based on stochastic approximation (HASA) is developed. Finally, the computational results of a case example illustrate the effectiveness and efficiency of the HASA approach, and make the application of the proposed model readily acceptable.     

Dynamic pricing for multiple class deterministic demand fulfillment

We consider how a firm should allocate inventory to multiple customer classes that differ based on the price they pay and their willingness to incur delay in fulfillment of their demand. The problem is set in a deterministic demand, economic-order-quantity-like environment with holding, backorder, lost demand and setup costs. The firm either fulfills demand or offers a price discount to induce the demand to wait for fulfillment from the next reorder. We determine the optimal policy and discuss how changes in various parameters affect profitability, customer service, and operational measures such as order frequency and base stock levels. We compare the results to a policy that only rations inventory without dynamic discounting and to a policy that only provides discounts. Through the comparison, we observe that dynamic pricing can be seen as a combination of a pricing mechanism which determines demand and an allocation mechanism that differentiates between customer classes, serving each ones needs. We show that if lower-value customers are distinguished by accepting reduced service, it is possible that both high and low-value customer classes see better levels of service under the optimal policy than under a discounting only policy. In addition we demonstrate the applicability of the results to a stochastic version of the problem.     

Use of advance demand information in multi-stage production-inventory systems with multiple demand classes

This paper considers three-stage production-inventory systems serving two customer classes, where only one class provides advance demand information and early order fulfilment is acceptable. We propose a new approach for production replenishment and order fulfilment in such systems that uses advance demand information for performance improvement. The approach combines the benefits of early fulfilment and Kanban-based pull systems. Simulation is used to establish the performance of the resulting policy vs. two existing policies, for a variety of production scenarios and cost structures. A lower bound on total cost is also established using a simulation-based procedure. In general, early fulfilment with one-for-one replenishment is shown to provide greater benefit than making items to order. Neither of the existing policies, however, was found to use advance demand information as effectively as the proposed approach, which outperformed the existing policies in every case considered. Additionally, the proposed policy has the advantage of both retaining its benefit at high levels of system utilisation and increasing benefit up to the maximum level of advance demand information provided.     

Multi-echelon service models for inventory systems under different rationing policies

In this paper we model the service performance of two-echelon divergent production networks consisting of one central stock-point feeding a number of end stock-points where the external demand is realized. The systems we consider allow all stock-points to hold inventory and use periodic review ordering policies at all levels. Whenever the lower echelon has insufficient inventory to cover all upper echelon requisitions some policy to ration the available material is needed. Concentrating on one push and one pull rationing policy, we explore the system dynamics and develop models for three popular measures of the system service performance. All models make use of non-dimensional ratios which drastically reduce the number of parameters involved. Although these models are only approximate, numerical and simulation results demonstrate the adequacy of the approximations involved.     

Multilevel rationing policy for spare parts when demand is state dependent

OR Spectrum - The multilevel rationing (MR) policy is the optimal inventory control policy for single-item M / M / 1 make-to-stock queues serving different...     

Dynamic response to demand variability for precast production rescheduling with multiple lines

Production scheduling plays a crucial role in the prefabricated construction productivity and on-time delivery of precast components (PCs). However, previous studies mainly focused on the static scheduling of single production line without considering the demand variability in practice. To achieve dynamic production planning, a Two-level Rescheduling Model for Precast Production with multiple production lines is developed to minimise the rescheduling costs based on genetic algorithm, from the two levels of (1) selection of production line and (2) rescheduling of jobs based on PCs’ priority. Further, two scenarios of different and shared mould types are investigated to represent real-world production environments. Finally, a real case study is conducted to test the validity of proposed rescheduling model. 58.1 and 48.5% cost savings are achieved by comparison to no response to changes and heuristic rescheduling methods, respectively. This research contributes to the precast production theory by expanding the insight into dynamic rescheduling with multiple production lines. The methodology will promote the on-time delivery of PCs and enhance the dynamic precast production management.     

Spare parts inventory management with demand lead times and rationing

We study an inventory system that consists of two demand classes. The orders in the first class need to be satisfied immediately, whereas the orders in the second class are to be filled in a given demand lead time. The two classes are also of different criticality. For this system, we propose a policy that rations the non-critical orders. Under a one-for-one replenishment policy with backordering and for Poisson demand arrivals for both classes, we first derive expressions for the service levels of both classes. The service level for the critical class is an approximation, whereas the service level for the non-critical class is exact. We then conduct a computational study to show that our approximation works reasonably, the benefits of rationing can be substantial, and the incorporation of demand lead time provides more value when the demand class with demand lead time is the critical class. The research is motivated by the spare parts service system of a major capital equipment manufacturer that faces two types of demand. For this company, the critical down orders need to be satisfied immediately, while the less critical maintenance orders can be satisfied after a fixed demand lead time. We conduct a case study with 64 representative parts and show that significant savings (as much as 14% on inventory on hand) are possible through incorporation of demand lead times and rationing.     

Optimal ordering decisions under two returns policies

To avoid stockouts and maintain product availability, retailers typically carry excess units and subsequently incur higher cost. In case of style/fashion goods, demand forecasting is extremely difficult due to short selling cycles. The purpose of this study was to minimise the cost of excess stocking without compromising product availability. To achieve these conflicting objectives, our study includes two ordering instances and two returns policies. The time between orders subsequently helps resolve demand uncertainty. Existing studies consider only one type of returns policy, that is, returns on the entire purchase quantity; whereas our study considers two types of returns policies: returns on the first order size and returns on the entire purchase quantity. This study also includes models for the retailer and the supply chain system. Analytical and numerical insights into our study enable the retailer to select his appropriate returns policies to maximise his as well as system’s expected profits. We also show that perfect coordination of partners will help them improve their profits considerably.     

On deficiencies of common ordering policies for multi-level inventory control

We consider two common types of ordering policies for multi-level inventory control: installation stock (R,Q)-policies and echelon stock (R,Q)-policies. The batch quantities are assumed to be given, but each policy is optimized with respect to its reorder point R. We demonstrate that there is no bound for the worst case performance ratio of these policies when applied to distribution inventory systems with a central warehouse and a number of retailers.     

Optimal batch ordering policies for assembly systems with guaranteed service

For a supply chain modelled as a multi-echelon inventory system, effective management of its inventory at each stock is critical to reduce inventory costs while assuring a given service level to customers. In our previous work, we used the guaranteed-service approach (GSA) to design optimal echelon batch ordering policies for continuous-review serial systems with Poisson customer demand and fixed order costs. The approach assumes that the final customer demand is bounded and each stock has a guaranteed service time in the sense that the demand of its downstream stock can always be satisfied in the service time. This paper extends this work by considering more general assembly systems. We first derive an analytical expression for the total cost of the system in the long run. The problem of finding optimal echelon batch ordering policies for the system can then be decomposed into two independent sub-problems: order size decision sub-problem and reorder point decision sub-problem. We develop efficient dynamic programming algorithms for the two sub-problems. Numerical experiments on randomly generated instances show the effectiveness of the proposed approach.     

Economic ordering model for deteriorating items with random demand and deterioration

In modern marketplaces, competitive products frequently face volatile demand. In particular, the majority of deteriorating items are frequently subject to dual uncertainty, simultaneously suffering from demand and deterioration. Accordingly, this study attempts to develop an economic ordering model for a newsboy-style deteriorating item with lognormal demand and Poisson-type deterioration. Finally, an effective and practical economic ordering model involving deteriorating inventory is developed for optimising the order quantity of a given deteriorating item during an upcoming selling period to maximise expected profits of retailers. Numerical examples demonstrate that, as expected, the proposed economic ordering model can obtain an optimal solution for the inventory problem involving deteriorating items under uncertain and random demand and deterioration.     

Joint economic production allocation and ordering policies in a supply chain consisting of multiple plants and a single retailer

This article discusses the production and ordering policies in a supply chain consisting of a single manufacturer and a single retailer. The retailer places orders based on the EOQ-like policy, and the manufacturer purchases raw materials and allocates them to the multiple plants in parallel to produce demand requirement from the retailer. The model is mathematically formulated, and the solution procedure is developed to determine the production cycle length, ordering quantity and frequency, and production allocation ratios for multiple plants. The closed forms of the production cycle length, ordering quantity, and frequency in terms of the production allocation ratios are obtained. It is also shown that the production allocation problem is NP-hard. An efficient and effective heuristic algorithm is proposed to determine the near-optimal production allocation ratios. A numerical experiment demonstrates that the proposed algorithm performs quite satisfactorily.     

Optimal inventory policies for uniform replenishment systems with time-dependent demand

This study develops an analysis of lot size inventory systems where the replenishment rate is uniform and demand follows a power demand pattern. Shortages are not allowed. Holding cost, replenishing cost and purchasing cost are considered in inventory system control. The objective of the study is to find the economic production quantity that minimises total inventory cost per unit of time. We conclude that optimal inventory policies depend on the demand pattern index chosen to represent customer demand. Theoretical results are illustrated with a business case study. A sensitivity analysis is proposed to describe the optimal policy behaviour.     

Methods for customer and demand response policies selection in new electricity markets

Different methodologies are available for clustering and classification purposes. The objective of the research is to prove the capability of self-organising maps (SOMs) to classify customers and their response potential from distributor, commercialiser, or customer electrical demand databases, with the help of load response modelling methodologies as support tools. The search for customer suitability is restricted to day-ahead and real-time products, in which interest is growing in developed countries. Therefore customer demand and response (demand and distributed generation policies) have been tested and compared with price curves. Both steps have been performed through SOMs. The results clearly show the capability of this approach to improve data management and easily to find coherent policies to accomplish cleared-demand offers in different prices scenarios.     

Optimal policies for aggregate production and capacity planning under rapidly changing demand conditions

This paper presents an optimal control approach to continuous-time aggregate production planning problems. The proposed approach describes the production and capacity expansion processes in the form of differential equations with regular production, overtime and capacity expansion rates controllable on one hierarchical level. In this way, the traditional disadvantages of the two-level problem consideration (one level for strategic capacity planning and the other for production smoothing) are avoided and optimal control policies can be obtained. This approach is of special importance when taking into account highly changing demand profiles and nonlinear cost functions. Study of the maximum principle results in analytical properties for the optimal production and capacity expansion policies and conditions for their changeover. Based on these results, insight into the optimal behaviour of the production system is gained and a fast numeral method is developed to identify and sequence the optimal policies for arbitrary demand profiles. A computational example illustrates the effectiveness of the approach.     

Optimal ordering policy for a price-setting newsvendor with option contracts under demand uncertainty

In this article, we investigate the newsvendor problem in a joint ordering and pricing setting in the presence of option contracts under demand uncertainty. At the beginning of a single selling season, the newsvendor who faces additive stochastic demand can obtain goods through two ways: ordering from a firm or purchasing and exercising call options. Single ordering (ordering from a firm only or purchasing and exercising call options only) and mixed ordering (ordering from a firm and purchasing and exercising call options simultaneously) cases are investigated. We find that the newsvendor’s optimal pricing and ordering strategies exist and are unique for both cases, respectively. In addition, when both cases are available, mixed ordering is the newsvendor’s optimal ordering policy. If only single ordering is available, the newsvendor prefers ordering from a firm when demand risk is low, while enjoys purchasing and exercising call options when demand risk is high. We also find that with option contracts, the newsvendor’s optimal order quantity and maximum expected profit are all decreasing in the option price and exercise price of product, while the optimal retail price in terms of option price and exercise price of product are intricate. Moreover, we show that, mixed ordering is more capable to deal with supply price volatility risk.     

Incorporating a delay mechanism in ordering policies into multi-echelon distribution systems

In this paper, we devise a framework for obtaining the optimal ordering policy in a single location, continuous-review inventory system with arbitrary inter-demand times. We show that it is optimal to order at demand arrival epochs only if the inter-demand time has a constant or decreasing failure rate. When the inter-demand time has an increasing failure rate, we show that the optimal policy is to delay the order. We then extend this policy to multi-echelon distribution systems consisting of one supplier and many retailers. Both decentralized and centralized systems are considered. We derive expressions and procedures for the evaluation of the total cost and the computation of optimal delay in all the considered settings. More importantly, we study the impact of our delay policy in all the settings. The numerical results indicate that for the single-location model, the optimal delay can significantly reduce the total cost. Results from the single-location model can be applied to the decentralized multi-echelon system, where the upstream supplier acts as a single-location system. The supplier order delay can also have a significant impact (either positive or negative) on the retailers' total cost as well as the system's total costs. Finally, the impact of supplier order delay is minimal in the centralized multi-echelon setting. We offer an intuitive explanation for this observation.     

Ordering policies under one time only discount and price sensitive demand

Purchase price reductions, even on a one-time-only basis, allow retailers the opportunity to lower their own selling prices, albeit temporarily, in an effort to increase sales. This results in not only additional revenues but also in extra inventory costs. The model presented in this paper examines such a cost/revenue tradeoff, through simultaneous determination of the most profitable: (i) stock level to be purchased by the retailer; (ii) discount level to be passed on to the customers; (iii) stock level qualifying for the retailer's discount; and (iv) time to initiate the discount. Numerical examples are included throughout to illustrate the main features of the model.     

Robust policies for a multi-stage production/inventory problem with switching costs and uncertain demand

In this paper, we seek robust policies for a multi-stage production/inventory problem to minimise total costs, including switching, production, inventory or shortage costs. While minimising switching costs often leads to non-convexity in the model, 0–1 variables are introduced to linearise the objective function. Considering the impossibility of obtaining the exact distribution of uncertain demand, we study the production/inventory problem under worst cases to resist uncertainty. In contrast to traditional inventory problems, unexpected yields in production are considered. Robust support vector regression is developed to approximate the yields of each unit. A mixed-integer linear programming is proposed, employing the duality theory to address the min–max model. A practical case study from cold rolling is considered. Experiments on the actual steel production data are reported to illustrate the validity of the proposed approach.