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This chapter presents the differential approach to applied demand analysis. The demand systems of this approach are general, having coefficients that are not necessarily constant. We consider the Rotterdam parameterization of differential demand systems and derive the absolute and relative price versions of the Rotterdam model, due to Theil (1965) and Barten (1966). We address estimation issues and point out that, unlike most parametric and semi-nonparametric demand systems, the Rotterdam model is econometrically regular.

Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.

Based on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.

(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.

This paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.

1. Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

2. Investment strategy of energy storage power stations on the supply side of wind power generators.

3. Impact of pricing method on the investment decisions of energy storage power stations.

4. Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

5. A two-stage wind power supply chain including energy storage power stations.

Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

Investment strategy of energy storage power stations on the supply side of wind power generators.

Impact of pricing method on the investment decisions of energy storage power stations.

Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

A two-stage wind power supply chain including energy storage power stations.

Estimates of the UK demand for money function are obtained utilising the Box‐Cox family of power transformations based on a Bank of England adjusted data set for the period 1963I — 1979IV. The functions are subjected to functional and structural stability testing with careful consideration of the resulting error structure. First‐order autocorrelation problems are encountered in the narrow money series Ml and attempts to consider a more flexible dynamic structure are investigated.

The purpose of this paper is to study joint inventory and pricing strategy for a continuous inventory review system. While dynamic pricing decisions are often studied in the literature along with inventory management, the authors' aim in this study is to obtain a single long‐run optimal price; also to gain insight about how to obtain the optimal price and inventory control variables simultaneously and then the benefits of joint optimization of the inventory and pricing decisions over the sequential optimization policy often followed in practice.

A general (R;Q) policy system with fixed cost of ordering is modelled and then the case where unsatisfied demand is lost is studied. General forms of both the additive and multiplicative demand models are used to obtain structural results.

By showing optimality conditions on the price and inventory decision variables, two algorithms on how to obtain optimal decision variables, one for additive and another for multiplicative demand‐price model are provided. Through extensive numerical analyses, the potential profit increases are reported if the price and inventory problem are solved simultaneously instead of sequentially. In addition, the sensitivities of optimal decision variables to system parameters are revealed.

Although there are several studies in the literature investigating emergency price change models, they use arbitrary exogenous prices menus. However, the value of a price change can be better appreciated if the long‐run price is optimal for the system.

Very few researchers have investigated constant price and inventory optimization, and while there are several past studies demonstrating the benefits of dynamic pricing over a static one, there still are not many findings on the benefit of joint price and inventory optimization.

This paper analyses the general equilibrium and disequilibrium effects of fiscal policy when fiscal instruments have direct impacts on both aggregate supply and demand. A model is specified which incorporates the direct impacts of expenditure and tax instruments on the behavioural function for individuals and firms and which explicitly recognises the role of public production and supply. In contrast to simple Keynesian and neoclassical models, this model involves direct supply‐side crowding out and budget composition effects that operate on both aggregate demand and supply. It also reveals the relative efficiency of various “balanced instruments” under Keynesian and neoclassical conditions.

This note shows that much conventional macro‐economic literature uses two inconsistent definitions of equilibrium in the commodity market. Equilibrium is defined as income equalling expenditure when deriving the IS curve; but when overall equilibrium is treated the requirement for equilibrium is that planned supply equals planned demand. The note shows that these inconsistent definitions lead to a confusing and often erroneous exposition of disequilibrium behaviour.

Information sharing is one of essential collaboration methods for building effective system-level disruption responses and communication for supply chain resilience. However, supply chain members are often reluctant to share the members' business information for fear of losing competitiveness. To facilitate the cooperation among these members, the supply chain members' should be made aware of the value of information. As a result, the purpose of this paper is to quantify the benefit of information sharing and evaluate its magnitude under various factors.

In this paper, information sharing is measured in a two-stage supply chain containing a manufacturer and a retailer. A demand function is constructed as a linear combination of a first-order autoregressive [AR(1)] process, the retail and reference prices. The values of information sharing are quantified for four scenarios: (1) no information sharing, (2) full information sharing, (3) limited information sharing and (4) partial information sharing. Based on the four scenarios, the conditions for valuable information sharing are determined. In addition, the impact of several demand parameters on the usefulness of information sharing is analyzed.

When the demand function is a pure AR(1) process (i.e. there is no impact from the retail and reference prices), information sharing is always valuable regardless of the autoregressive coefficient. Under the influence of the retail price and consumer behavior via the reference price, information sharing is not always beneficial. The boundaries for useful information sharing are analytically constructed. In addition to full information sharing, this study also quantifies the value of information under a partial sharing scheme. The results indicate that the information is more valuable as long as the information is inducible.

This study highlights several specific conditions for a beneficial information sharing agreement in consideration of consumer behaviors. These conditions enable supply chain members to design a sustainable partnership.

Group buying (GB) is a shopping strategy through which customers obtain volume discounts on the products they purchase, whereas retailers obtain quick turnover. In the scenario of GB, the optimal discount strategy is a key issue because it affects the profit of sellers. Previous research has focused on exploring the price discount and order quantity with a fixed selling price of the product assuming that customer demand is uncertain (but follows a known distribution). This study aims to look at the same problem but goes further to examine the case where not only customer demand is certain but also the demand distribution is unknown.

In this study, optimal price discount and order quantity of a GB problem cast as a price-setting newsvendor problem were obtained assuming that the distribution of customer demand is unknown. The price–demand relationship is considered in addition form and product form, respectively. The bootstrap sampling technique is used to develop a solution procedure for the problem. To validate the usefulness of the proposed method, a simulated comparison of the proposed model and the existing one was conducted. The effects of sample size, demand form and parameters of the demand form on the performance of the proposed model are presented and discussed.

It is revealed from the numerical results that the proposed model is appropriate to the problem at hand, and it becomes more effective as sample size increases. Because the two forms of demand indicate restrictive assumptions about the effect of price on the variance of demand, it is found that the proposed model seems to be more suitable for addition form of demand.

This study contributes to the growing literature on GB models by developing a bootstrap-based newsvendor model to determine an optimal discount price and order quantity for a fixed-price GB website. This model can assist the sellers in making decisions on optimal discount price and order quantity without knowing the form of customer demand distribution.

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.