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The purpose of this paper is to study the impact of differentiation price which has been utilized to segment demand, but results in imperfect segmentation. The use of a differentiation price is among the most widely used Revenue Management (RM) techniques to segment a firm’s demand to augment profitability.

Mathematical models are developed for a firm’s RM which use a differentiation price to categorize its market demand into two segments. Three distinct demand situations are considered: price-dependent deterministic demand, price-dependent stochastic demand whose distribution is known and price-dependent stochastic demand whose distribution is unknown. Models are analyzed to determine optimal joint control of a firm’s pricing and inventory decisions for each market segment.

The analysis of the firm’s RM model has shown that revenue is jointly concave in pricing and order quantity. In most demand situations, closed-form mathematical expressions for optimal pricing and inventory are obtained.

In RM models developed in this paper, a firm only selects a differentiation price. Thus, an optimal selection of the differentiation price along with the pricing and inventory decisions may lead to an additional profitability which has not been explored in this research.

The findings reported are relevant to RM managers and practitioners and help them to calibrate their optimal revenues by segmenting markets using a differentiation price.

This paper provides a quantitative perspective of a firm’s decision on the use of the differentiation price and the market response.

The paper provides a firm’s optimal decision on pricing and inventory when it experiences demand leakage due to categorizing its market demand into two segments using a differentiation price.

This paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.

A stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.

Compared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.

The results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.

Extending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.

A novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.

The purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.

In total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.

There are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.

Only peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.

Stress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.

The current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.

Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors.

This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering.

As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands.

This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.

The increasing popularity of ERP solutions has provided dietary supplement manufacturing companies with modules to manage pricing and inventory. However, the decisions made by these modules are often independent and rely on deterministic forecasts. This paper studies a multi-product dietary supplement manufacturing system under stochastic demands. The purpose is to maximize the long-run expected profit by jointly considering pricing and inventory strategies.

The authors investigate both the general cases and three special cases including stable demand, negligible backlog and instantaneous replenishment. A two-stage algorithm named PAS is proposed. In the strategy construction stage, the constructed objective bounds are combined to provide estimates which then help to derive the optimal product prices. In the system operation stage, replenishment decisions are further made based on the prices generated from the previous stage.

It is proved that base-stock policy is optimal for the studied system, and the optimal based-stock level is provided. The global optimal strategies are obtained for three important special cases. For the general case, theoretical objective bounds are established. These bounds provide quick and reliable performance estimates for practical applications.

Very few studies have jointly considered pricing and inventory strategies with uncertainty demands in the dietary supplement industry. The PAS algorithm developed integrates these decisions and consistently generates high-quality solutions even under highly varying demands. Such algorithm could be a valuable add-on to the pricing and inventory management modules in ERP systems.

The transition from a traditional purchasing system to a JIT purchasing system can be a slow process or even unattainable, because of unreliable suppliers. The purchaser tries to co‐operate with the vendor, with the goal of receiving smaller, more frequent deliveries, on time, with the quality and quantity required. Often the vendor is ready to co‐operate, but is unable to fulfil these requirements. Provides simple models and methods to aid purchasers in this transition state. Gives simple approximate formulas for the minimum safety stock necessary to ensure the required service level of supply. Considers the case of random delays in shipments, random yield and uncertain demand, which are typical characteristics during the transition period. This safety stock depends on the order quantity and the number of shipments. Provides a simple method to find the order quantity, the number of shipments and safety stock, which minimize the joint total cost of the vendor and purchaser and ensure the required level of supply. Analyzes the savings provided by this method and the sensitivity of the models, in detail.

This paper aims to develop efficient decision support tools for a firm’s environment protection by using greening effort while yet improving profitability by utilizing pricing and inventory decisions with discount consideration.

This study proposed a mathematical model for price- and greening effort-dependent demand rate with discount considerations. Later, the mathematical model is extended to the situation in which the demand rate is also dependent on the stock level, in addition to the price and greening effort. Efficient solution methodologies are developed for finding the optimal solution to the proposed models.

Simple yet elegant models are proposed to mimic real-life applications. Structural properties of the models are explored to outline efficient algorithms with quantity discounts.

The paper considers monopoly and assumes deterministic demand. Only a more commonly observed all-units discount scheme is studied.

The models provide decision support tools for firms in pursuit of joint profit maximization and environment consciousness goals.

The study develops environment-friendly approaches for inventory management and improving the profitability alike.

This study is among the first to consider environmental protection with an investment in greening effort along with inventory management and pricing decision. The study also explored the effect of all-unit quantity discounts.

To promote the development of renewable energy, the Chinese Government adopts the policy of Feed-in Tariff and subsidy. However, the high purchase price and the intermittence limit the development of renewable energy source electricity (RES-E). The purpose of this paper is to discuss the pricing strategy for system operators to stimulate the development of the RES-E industry under the scenario of uncertain supply and demand.

The authors establish a two-echelon supply chain investment model led by a power grid operator considering the uncertainties in both demand and supply, and study the impact of the power purchase price designed by a system operator using Stackelberg’s model.

There is an optimal capacity for RES-E generators, that is, independent of the market demand. Besides, the optimal order of grid operators is independent of the uncertain RES-E supply and the purchase price of fossil fuel. By properly setting the purchase prices, the system operator can stimulate the capacity investment in renewable energy. Finally, increasing the punishment in power shortage can stimulate the capacity investment in RES-E under certain conditions.

The result of this paper can mitigate the phenomenon of power abandonment in the RES-E industry and promote the grid integration of RES-E.

Both uncertain demand and supply are considered in this paper. A heuristic algorithm is provided to compute the optimal purchase price combination.

The purpose of this paper is to study how supermarkets can maximize profits of selling perishable food through price adjustment based on real‐time product quality and values.

The value of the perishable food can be traced based on an automatic product identification technology radio frequency identification (RFID). With the support of the RFID, an optimization model can be developed to enable product tracking.

The analysis of the model shows promising benefits of applying a dynamic pricing policy and obtains the optimal ordering decision in respects of deterministic and stochastic demand function with RFID.

Although technological approaches for tracking products have attracted increasing attentions in both research and practice, little research have proved the profit using RFID by mathematics, the result of this paper can prove the benefit by using RFID.

The result of this paper can tell the supermarket how to make the price and the ordering decision by using the RFID.

This study proves the benefit of using the RFID by mathematical model based on the conceptual model before, and tell the method how to use RFID for pricing and making ordering decision.

The purpose of this paper is to determine the inventory replenishment policies when demand rate is a function of the inventory space allocated to the products on retail shelves. Existing results on inventory policies with inventory-level-dependent demand (ILDD) assume deterministic functional forms of the demand rate. In this paper, the authors model the inventory decisions when demand is a function of shelf-space allocation and random uncertainty. The authors provide managerial insights of this paper's results.

The demand rate is assumed to be a function of shelf-space allocation based on two settings in the literature. First, the authors model the demand rate as a function of initial shelf-space allocation. In the next setting, the authors assume that the demand rate is a function of instantaneous inventory level on shelves. In both the settings, the authors also model random demand uncertainty in addition to the shelf-space dependency of demand rate. The objective is to maximize the expected profit and determine the inventory parameters.

In addition to the demand uncertainty, the authors consider linear, power and exponential functional forms of demand rate. Inventory policy that maximizes expected profit is determined when demand rate is a function of initial allocation and displayed inventory level. The results are implementable for practitioners for optimizing the shelf-space allocation and related inventory policy.

Most of the extant results on inventory policy with shelf-space-dependent demand do not model the demand uncertainty. The authors model a variety of functional forms of demand rate with ILDD in addition to the demand uncertainty. The results are a building block for more applications in inventory management for real-life applications.