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This paper aims to study the impact of existence and lack of discount on the relationships between one manufacturer and one retailer under the cooperative and the non-cooperative games and the members’ profits are compared.

In the first approach, the manufacturer’s price function is constant, and in the second approach, this price function is a decreasing function with respect to lot size. These approaches are modeled through three games structure, including two Stackelberg games and one cooperative game.

Some numerical instances comprising sensitivity analysis are provided, and then the members’ profits in different scenarios are compared. This paper reveals that in the presented models, whether the members are inclined to change their profits.

This paper presents a tool of decision-making for the type of relationships of members in two different circumstances, and an approach is also presented to maximize the members’ profit.

In this paper, the relationships between one manufacturer and one retailer are studied under six different circumstances, where pricing, cooperative advertising and inventory cost are considered simultaneously. Also, a different model is presented to make a balance in individual profits and gain more profit for each member compared to the cooperative and non-cooperative game.

The purpose of this paper is to discuss how wind farms attract wind turbine manufacturers to get involved in wind turbines’ maintenance service with revenue sharing contract of bundled service under which the background of operation and maintenance (O&M) aftermarket of wind turbine exists. The authors also try to extend the results to the application of product plus service business mode on large-scale equipment O&M service. At present, Chinese wind power industry is suffering from production capacity redundancy. The profit levels for both wind farm and wind turbine manufacturers are relatively low. It is significant for Chinese wind power industry development to coordinate the supply chain of wind power in order to reduce O&M costs and increase revenues.

The present paper discusses product plus aftermarket service contract design on the background of closed-loop product service chain and uncertain equipment demand using revenue sharing contract model.

If centralized decision making is assumed, the authors find that the wind turbine order increases as the aftermarket service effort level and aftermarket service profit increase; aftermarket service effort level is positively correlative to the service efficiency. On the other hand, if decentralized decision making is assumed, the wind turbine order increases as share of the aftermarket service chain by manufacturer to wind farm increases and share of product supply chain by wind farm to manufacturer decreases. The optimal effort level of wind farm increases as the share of aftermarket service chain increases while the optimal effort level of the manufacturer is a concave function of share of aftermarket service chain if service quality linear correlates with effort level. Meanwhile, the authors find that the revenues of the product supply chain and aftermarket service chain have a concave relationship. This relationship is not affected by the format of relationship between service quality and effort level (linear or exponential).

The results could potentially be used to provide the wind turbine manufacturer with a greater profit space and satisfy wind farm’s equipment maintenance demand at the same time. It can also guide the practice of revenue sharing in the aftermarket service and manufacturing servitization.

In this model, the authors assumed that both the forward revenue sharing of power generation by wind farm to manufacturer and the backward revenue sharing of maintenance service by the manufacturer to wind farm exist in closed-loop product service chain. Then the authors discussed channel coordination of such cross-revenue sharing contract.

To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.

The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.

The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.

(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

The aim of this study is to investigate profit efficiency in the banking industries of 11 Central and Eastern European (CEE) countries for the period 1998‐2005.

The authors employ a directional technology distance function approach to measure profit efficiency and decompose it into its technical and allocative components. They use these efficiency measures to investigate potential differences in banking performance across countries and across banks of different size and with different ownership status.

The results indicate that the highest proportion of profit inefficiency in the CEE region is attributed to allocative inefficiency, recognizing that considerable variation and different patterns in inefficiency levels across banking systems can be observed. Small and domestic private banks appear to be the most efficient. A negative relationship between efficiency and bank size, the capitalization ratio and market concentration, and a positive relationship with the European Bank for Reconstruction and Development index of banking reform are also found.

Bank performance relative to best practice is measured across the CEE region. While it is found that on average technical inefficiency is relatively small and about one quarter of the banks lie on the technological frontier, the size of technical inefficiencies is likely to be exacerbated if the sample were to include Western European banks.

The effects of banking reforms are evident by recent positive trends in profit and allocative efficiencies estimated for CEE banking sectors. These trends suggest that policy makers should intensify efforts to further improve the financial services regulatory and supervisory framework while freeing any remaining explicit or implicit barriers to bank competition.

The study departs from the traditional literature of efficiency. It uses a directional distance function approach to model multi input – multi output banking technology and to investigate profit efficiency in CEE countries.

By studying the competition between a B2C platform and a third-party seller, the purpose of this paper is to analyze and compare their optimal decisions and profits between cases with and without sales effort of the platform or third-party seller.

This paper studies the competition between a B2C platform and a third-party seller. The platform sells a product directly, and allows the third-party seller to sell a competing product on the platform. Based on whether the platform or the third-party seller makes sales effort, there are four scenarios. The paper analyzes the optimal decisions and profits of platform and third-party seller under each scenario, respectively.

The transaction fee has a negative effect on third-party seller’s sales effort level. What is more, the platform can take a free riding from the third-party seller’s sales effort, but the platform’s sales effort has a negative effect on the profit of third-party seller.

These results provide managerial insights for the platform and the third-party seller to make decisions.

This paper is among the first papers to study the competition between B2C platform and third-party seller.

This study examines the potential of contracts as one of the supply chain coordination mechanisms under competitive conditions. It also investigates a two-echelon supply chain model with two manufacturers and two retailers to develop a competitive structure in grey stochastic demand.

Supply chain demand is considered as a stochastic phenomenon depending on the selling price of the product. Also, products can be replaced by market manufacturers. Each retailer faces the pricing of products from two manufacturers, leading to competition between downstream retailers. In the present study, the duopoly supply chain model was presented based on the wholesale price contract, revenue-sharing contract and quantity discount contract separately.

Grey optimization and analysis of their coordination were presented. The results showed the high performance of revenue-sharing contracts in the supply chain. Thus, manufacturers will give the next priority to quantity discount contracts.

Ordering is the main factor contributing to competitive decision-making. Meanwhile, decision-making along with ordering and pricing will be required due to the nature of the demand.

The purpose of this study is to investigate the impacts of surface ozone pollution on rice profit, output and variable inputs in China.

This study estimates the rice profit function using county-level rice production data and ozone monitoring data in 2014 and 2015 to capture the impact of ozone pollution on rice profit. Then, it uses dual approach to identify the impacts of ozone on the supply of rice and the demand for variable inputs. The ozone concentration data are obtained from 1,412 monitoring stations established by the National Environmental Monitoring Centre of China.

The results show that surface ozone would significantly reduce rice profits; a 1% increase in (the daily average ozone concentration from 9 am to 4 pm) leads to a 0.1% decrease in profits. In addition, ozone has a negative impact on the levels of inputs and the supply of rice, and the elasticities of rice output, fertilizer input and labour input with respect to are −0.87, −0.86 and −0.78%, respectively. These results suggest that ozone pollution affects rice production via two channels: the direct damage on rice growth and the indirect negative impact of reducing variable inputs.

This study estimates the impacts of surface ozone pollution on rice profit and output, and quantifies its influence on variable inputs in China, which provides a better understanding of farmers' adaptation behaviour.

Providers of cloud computing storage services (CCSS) charge offers in several unit bundles for a lump sum per bundle. This non-linear pricing approach is known as a bucket-pricing plan (BPP). If a customer exploits the purchased bucket, he/she can opt for the next higher bucket or refrain from further CCSS use. CCSS suppliers are faced with an optimization problem concerning the number of buckets as well as their lower and upper storage volume boundaries. The purpose of this paper is to develop a model, which supports CCSS suppliers in deriving a BPP-structure and which maximizes their profit in varying market constellations.

The authors develop a multi-period model of tariff choice decisions of private customers of CCSS. The model is applied in Monte Carlo simulations to determine profit-maximal tariff structures as a function of different market characteristics such as median demand saturation, demand heterogeneity, average price per storage unit and bucket ceiling allocation (identical size of each bucket within the frame set by the lower and upper overall boundary, varying sizes of the buckets offered, so that the interval between two ceilings consecutively increases for subsequent buckets) and type of a customer’s utility function.

The simulation analysis suggests that demand heterogeneity and average price per unit are the most influential factors for CCSS tariff structure optimization. Price plans with more than two buckets tend to generate higher profits than simple schemes with two buckets only if demand heterogeneity is low and the average price per storage unit is high and/or median saturation level of customers is low.

Despite the popularity of BPP among providers of CCSS for consumers, there is a lack of scholarly modeling work on the profit implications of the number of buckets entailed in a scheme and the size/ceilings of the various buckets on offer. The model suggested in this paper is a first step toward narrowing this research gap.

Proposes a possibilistic group support system (PGSS) for the retailer pricing and inventory problem when possibilistic fluctuations of product parameters are controlled by a set of possibilistic optimality conditions. Experts in various functional areas convey their subjective judgement to the PGSS in the form of analytical models (for product parameters estimation), fuzzy concepts (facts), and possibilistic propositions (for validation and choice procedures). Basic probability assignments are used to elicit experts’ opinions. They are then transformed into compatibility functions for fuzzy concepts using the falling shadow technique. Evidence is processed in the form of fuzzy concepts, then is rewritten back to basic probability assignments using the principle of least ignorance on randomness. The PGSS allows the user (inventory control) to examine a trade‐off between the belief value of a greater profit and a lower amount of randomness associated with it.

The 21st century is a new century of environmental protection. Environmental protection is one of the most important subject matters yet to come. Moreover, as the public pays more attention to environmental problems, enterprises should increase their investment in environmental management. Therefore, determining the investment level for environmental management and allocating the investment to associated environmental management activities has become a major task. The principal and agent theory and sales response functions are used for analysis in this research. The allocation of capital investment in environmental management is found to have significant impact on the aggregate sales response, aggregate profit and investment level. Therefore, in preparing the budget for environmental management, enterprises should focus on investment allocation decisions, determine the investment level and allocation method using integrated means, and apply submarket data in the allocation decision‐making process. In other words, in setting the investment level, executive management should take managers’ willingness into consideration. In allocating capital investment, managers should identify the optimal allocation method based on submarket characteristics.