Research on Crude Oil Trade Procurement Model Based on DEA-Malmquist Algorithm

To retain valuable information to the maximum extent and enhance the ability to mine the crude oil trade purchase price demand, this paper proposes a crude oil trade purchase model based on the DEA-Malmquist algorithm. The intranet of the management and control platform shall share the same database, and the intranet shall only allow managers to access and manage the system and only allow all registered users to access and realize data exchange between the intranet and the intranet through two-dimensional code scanning; moreover, due to the resource sharing between the intranet and the intranet for crude oil trade procurement, suppliers and other registered users can immediately grasp the procurement trends of enterprises. Under the DEA-Malmquist algorithm, the uncertainty of procurement management is analyzed by fuzzy theory, and the refined procurement decision model with fuzzy parameters is established. The optimal order time and purchase quantity are determined through the symbol distance and the method of the center of gravity. Experimental results show that the method can effectively retain valuable information in the initial sequence and has better practical application value of material procurement demand intelligent mining. The proposed model obtained the highest accuracy of 98.62%.

Artificial intelligence in agricultural value chain: review and future directions

PurposeThis paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.Design/methodology/approachThe authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.FindingsFifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.Research limitations/implicationsThe authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.Originality/valueEarlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

Research on Multichannel Supply Chain Pricing Under Different Dominance

In a multichannel supply chain comprising of dual-channel retailers with both physical and online channels as well as single-channel e-tailers with online channels, a multichannel demand model for e-commerce is constructed based on customer channel preferences, and a Stackerberg game model with price competition dominated by dual-channel retailers and single-channel e-tailers as well as a Bertrand game model with equal rights are established to analyze the impact of different channel rights structures on the price, demand, and profit of the two retailers. The results show that the single-channel e-tailer under the dual-channel retailer-dominated game has the highest profit, and the dual-channel retailer under the single-channel e-tailer-dominated game has the highest profit; thus, both retailers should accept the other’s dominant channel rights for profit maximization.

A study of boundedly rational behaviour in housing choice: evidence from Malaysia

Purpose Housing choice is always a complicated decision with its dual functions as a roof over the head and as an investment good. This paper aims to investigate the boundedly rational behaviours that affect the housing choice three bounded behaviours play roles in explaining the decision-making behaviour of homebuyers when they acquire/sell a property. These behaviours are endowment effect, loss aversion and herding, which have implications on the decision-making process. Design/methodology/approach The research is based on cross-sectional questionnaires and collected from 587 respondents. Factor analysis and reliability tests were used to identify the latent construct of bounded rational housing choice behaviour. In the meantime, the study used one-way analysis of variance (ANOVA) to examine whether there are any differences in the housing choice based on the respondents’ demographic backgrounds. Findings The findings indicated that a total of 11 items were reduced to three factors that accounted for the decision-making in housing choice. There are significant differences in herding behaviour amongst respondents with different level of education and their purpose of looking for a house. Research limitations/implications This paper helps to identify latent constructs that shed light on the housing choice, especially on the bounded rational behaviour. Originality/value This is one of the few studies to explore boundedly rational behaviours in housing choice from the angle of homebuyers. Previous studies addressed housing choice in terms of price, demand and supply in general but not on individual homebuyers. The results will be useful to developers, policymakers, homebuyers as well as scholars in understanding the decision-making process in housing choice.

Impact of Wind and Solar Generation on the Italian Zonal Electricity Price

This paper assesses the impact of increasing wind and solar power generation on zonal market prices in the Italian electricity market from 2015 to 2019, employing a multivariate regression model. A significant aspect to be considered is how the additional wind and solar generation brings changes in the inter-zonal export and import flows. We constructed a zonal dataset consisting of electricity price, demand, wind and solar generation, net input flow, and gas price. In the first and second steps of this study, the impact of additional wind and solar generation that is distributed across zonal borders is calculated separately based on an empirical approach. Then, the Merit Order Effect of the intermittent renewable energy sources is quantified in every six geographical zones of the Italian day-ahead market. The results generated by the multivariate regression model reveal that increasing wind and solar generation decreases the daily zonal electricity price. Therefore, the Merit Order Effect in each zonal market is confirmed. These findings also suggest that the Italian electricity market operator can reduce the National Single Price by accelerating wind and solar generation development. Moreover, these results allow to generate knowledge advantageous for decision-makers and market planners to predict the future market structure.

Determinants of Wheat Consumption, Irrigated Agriculture, and Food Security Challenges in Egypt

Wheat is one of the most widely cultivated cereal crops and consumable staple food globally. Internal production efforts are essential to reduce the ever increasing gap between production and consumption of wheat in Egypt. The production-consumption gap can be reduced through advanced agriculture, innovative wheat varieties, land expansion, bio-saline agriculture, and other water management practices. This research study aims to investigate the determinants of wheat consumption in Egypt, find the price, income, and cross price demand elasticities of wheat. For empirical analysis, the annual time series data from 1961 through 2020 is collected from different sources. The data is analyzed through the Autoregressive Distributive Lag (ARDL) model to investigate the long-run demand determinants of wheat in Egypt. The estimated results indicate the presence of a long-term relationship among determinants of wheat consumption. The results of own price, GDP per capita, and population reveal that wheat is a necessity food. Similarly, the estimates of rice price, corn consumption, and barley consumption indicate that such commodities are substitutes for wheat in Egypt. Based on these estimates, it is suggested that the policy makers in the Egyptian government and all other stakeholders need to concentrate on a comprehensive policy for parallel consumption of wheat, rice, corn, and barley. It is also recommended that the Egyptian government must focus on exploring ways including bio-saline agriculture to increase domestic wheat production to reduce wheat imports, save valuable foreign exchange, and overcome some of the food security challenges in Egypt

An Inventory Model for Perishable Items with Price-, Stock-, and Time-Dependent Demand Rate considering Shelf-Life and Nonlinear Holding Costs

Nowadays, consumers are more health conscious than before, and their demand of fresh items has intensely increased. In this context, an effective and efficient inventory management of the perishable items is needed in order to avoid the relevant losses due to their deterioration. Furthermore, the demand of products is influenced by several factors such as price, stock, and freshness state, among others. Hence, this research work develops an inventory model for perishable items, constrained by both physical and freshness condition degradations. The demand for perishable items is a multivariate function of price, current stock quantity, and freshness condition. Specific to price, six different price-dependent demand functions are used: linear, isoelastic, exponential, logit, logarithmic, and polynomial. By working with perishable items that eventually deteriorate, this inventory model also takes into consideration the expiration date, a salvage value, and the cost of deterioration. In addition, the holding cost is modelled as a quadratic function of time. The proposed inventory model jointly determines the optimal price, the replenishment cycle time, and the order quantity, which together result in maximum total profit per unit of time. The inventory model has a wide application since it can be implemented in several fields such as food goods (milk, vegetables, and meat), organisms, and ornamental flowers, among others. Some numerical examples are presented to illustrate the use of the inventory model. The results show that increasing the value of the shelf-life results in an increment in price, inventory cycle time, quantity ordered, and profits that are generated for all price demand functions. Finally, a sensitivity analysis is performed, and several managerial insights are provided.

Optimal Emission Reduction and Pricing in the Tourism Supply Chain Considering Different Market Structures and Word-of-Mouth Effect

In the context of carbon tax policy and word-of-mouth, local operators and tour operators in the tourism supply chain need to determine optimal wholesale price, carbon reduction level, and retail price of tour packages strategies. To address these decision-making issues, while considering the word-of-mouth effect, our paper considers a local operator determining wholesale price and carbon reduction level of the tour package and a tour operator determining retail price of the tour package. According to different bargaining powers, we study three scenarios: the local operator leading Stackelberg (LL), the tour operator leading Stackelberg (TL), and the static Nash game (NG). We develop three theoretical models and present some insights. We find that tourist’s sensitivity to word-of-mouth has positive (negative) impacts on optimal wholesale price, carbon reduction level, retail price, demand, and profits if the impact of word-of-mouth is positive (negative), while the impact of word-of-mouth is always having positive impacts on optimal decisions, demand, and profits. Interestingly, the NG market structure contributes the most environmentally-friendly products but mostly hurts the environment. The local operator under LL can obtain the largest profit, which is even larger than the profit of the tour operator, while the tour operator under NG and TL can obtain more profit than the local operator.

Design of Distributed Electricity Trading Mechanism Based on Contract for Difference

A contract for difference is a medium and long-term financial contract, which can be used in the electricity market to lock the electricity price in advance and avoid the risk of electricity price fluctuations in the spot market. The construction of the domestic power spot market has just started. With the release of relevant policies and the gradual improvement of the market structure, it is urgent to design a corresponding trading mechanism to ensure the smooth transition of the market. The current day-ahead transactions, real-time transactions and other short-term transactions for distributed power generation, on the one hand power load forecasting, electricity price demand response and other related technologies need to be further improved, on the other hand due to the randomness and uncertainty of distributed energy, participating in the short-term spot market has large price fluctuations, which is not conducive to the stability of the electricity market, and it is also not conducive to the consumption of distributed energy. Aiming at the above problems, this paper uses the characteristics of CFDs to restrain market power to design a distributed energy trading mechanism to achieve the purpose of energy saving and emission reduction.