Overbooking as a Means to Manage Restaurant No-Shows and Cancellations: A Novel Model Extension

Newsvendor models have been developed to determine the optimal overbooking level of hotel rooms to manage no-shows and cancellations. This research note extends the newsvendor model to a restaurant context by taking into account the “stretched capacity” of restaurants to determine the optimal overbooking levels for restaurant seats. Data were collected from a restaurant in Taiwan to illustrate the model. The percentage of no-shows per day in this restaurant ranged from 11% to 16%. Utilizing its stretched capacity, the restaurant can overbook up to five more seats than the estimated number of no-shows. The extended model will be most suitable for restaurants that largely depend on reservations (rather than walk-ins), such as luxury or fine-dining restaurants. Directions for future research on restaurant overbooking are provided.

Maximization of container slot booking profits for carriers in the liner shipping industry

In the liner shipping industry, if a shipper wants to transport its cargo by container ships, it first needs to contact a carrier to book container slots based on the estimated transportation demand. However, one problem in the booking process is that the actual demand is uncertain, resulting in mismatch between the required demand and the booked quantity. To address this issue, this study develops a Newsvendor model to find the optimal order quantity of container slots for the shipper. In addition, uncertainties in the quantity of container slots booking made by the shipper might cause revenue loss to the carrier and low utilization of ship capacity in the daily operations of liner shipping services. Therefore, this study suggests that the shipper should pay reservation fee when booking container slots. This study also aims to find the maximum profit for the carrier under the optimal order quantity of the shipper. In sensitive analysis, how different prices per container slot offered by the carrier would influence the reservation fee, the optimal order quantity of the shipper, and the expected profit of the carrier are explored and discussed. This study can help to manage and promote the online container booking systems in the liner shipping industry.

"Extra!" Using the Newsvendor Model to Optimize War Reserve Storage

The United States Marine Corps (USMC) Installation and Logistics Command requested a study for determining appropriate inventory levels of war reserve materiel to meet future operational needs under surge demands in uncertain environments. This study sought to explore a potential approach by using the common newsvendor model, but modified for a military scenario. The authors’ novel version of this core concept considers the purchase and storage costs of an item and proposes an intangible cost function to capture the consequences of a shortage. Further, they show a sample application of the model using a ubiquitous military item—the BA-5590/U battery. The output of the model provides USMC with a new tool to optimize inventory levels of a given item of interest, depending on scenario inputs.

“Extra!” Using the Newsvendor Model to Optimize War Reserve Storage

The United States Marine Corps (USMC) Installation and Logistics Command requested a study for determining appropriate inventory levels of war reserve materiel to meet future operational needs under surge demands in uncertain environments. This study sought to explore a potential approach by using the common newsvendor model, but modified for a military scenario. The authors’ novel version of this core concept considers the purchase and storage costs of an item and proposes an intangible cost function to capture the consequences of a shortage. Further, they show a sample application of the model using a ubiquitous military item—the BA-5590/U battery. The output of the model provides USMC with a new tool to optimize inventory levels of a given item of interest, depending on scenario inputs.

A Risk-Averse Newsvendor Model under Stochastic Market Price

The optimal inventory control is closely related to an enterprise’s operational efficiency, survival, and development. Market price uncertainty is introduced into the newsvendor model and the uncertainty’s impact on the firm's optimal stocking quantity is discussed. The results show that the impact of stochastic market price on the optimal stocking quantity under a given condition mainly depends on the magnitude of inventory cost. When the inventory cost is low, the market price’s uncertainty leads the firm to increase the stocking quantity. In contrast, when the inventory cost is high, market price uncertainty leads the firm to decrease inventory. Besides, the risk-averse behaviour leads the firm to reduce its stocking quantity.

Single-Period Capacity and Demand Allocation Decision Making under Uncertainty

The newsvendor model deals with a single-period capacity allocation problem under uncertainty. The real world examples include perishable products (e.g., fish, vegetable), holiday-related products (e.g., Easter, Christmas, Halloween), seasonal products (e.g., fashion), and promotional products. This section addresses three newsvendor models: traditional newsvendor, inverse newsvendor, and sequential newsvendor models. The main decision under the traditional newsvendor setting is capacity allocation (i.e., how much to order), whereas the main decision under the inverse newsvendor setting is demand allocation (i.e., how many customers to be served) under the fixed capacity. This section demonstrates how to compare profit maximization approach to customer-oriented approach under the traditional newsvendor. The inverse newsvendor applies to revenue management for the hospitality industry. The sequential newsvendor model determines the optimal sequence when the number of customers to be served (determined by the inverse newsvendor model) is given. Normal distribution is considered for analytical solution and numerical studies. In addition, a discrete distribution is considered for numerical studies.

Green Supply Chain Newsvendor Model and Analysis under Multiplicative Random Demand

In this paper, we investigate the performance of green supply chain newsvendor model under multiplicative random demand. In our model, the green supply chain consists of one manufacturer and one retailer. The manufacturer acts as a leader, which decides the variables: production quantity, wholesale price and green-level; the retailer acts as a follower, decides on the retail price. Centralized and decentralized optimal solutions are obtained to optimize the manufacturer's production, stocking and green-level decision while anticipating the retailer's price optimization decision. Through research, it is found that the stocking factor decreases with the increasing price elasticity and is not affected by the green-level elasticity; Lower price elasticity will increase the optimal selling price of the green product, while green-level elasticity has no effect on the price; Larger price elasticity or green-level elasticity will increase the optimal green-level of the green product; Lower price elasticity or higher green-level elasticity will increase the overall profitability of the supply chain; Profit sharing between manufacturer and retailer under the decentralized model only depends on price elasticity and green-level elasticity; The greater price elasticity, the more beneficial to the retailer, the greater the green-level, the more beneficial to the manufacturer.