Quality Improvement of Agricultural Products Supply Chain under Social Preference and CSR by Big Data Analysis

Purpose- The aim of this study is to analyze the effect of corporate social responsibility (CSR) and social preference on quality improvement of the agricultural products supply chain composed of agricultural products producer and processor (A3P) and supermarket by theoretical analysis and empirical evidence. Methodology- This paper sets Stackelberg game model under A3P’s CSR by considering supermarket’s altruistic reciprocity and A3P’s fairness conern, respectively. By comparative analysis, we study the effect of CSR, altruistic reciprocity and fairness conern on the quality improvement of the agricultural products supply chain. Then, we adopt the empirical evidence to analyze the correlation between CSR, altruistic reciprocity (fairness concern) and quality improvement and the mediating effect of altruistic reciprocity (fairness concern) by investigating the agricultural enterprises.

Incentive mechanism design for citizen reporting application using Stackelberg game

<p>The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crowdsensing under the limited budget then allocates the budget to several regional working units that are responsible for the specific city problems. We propose an incentive mechanism for mobile crowdsensing based on several identified incentive parameters using the Stackelberg game model and applied the MOOP (multi-objective optimization problem) to the incentive model in which the participant reputation is taken into account. The evaluation of the proposed incentive model is performed through simulations. The simulation indicated that the result appropriately corresponds to the theoretical properties of the model.</p>

Bilevel Optimization of Taxing Strategies for Carbon Emissions Using Fuzzy Random Matrix Generators

Bilevel (BL) optimization of taxing strategies in consideration of carbon emissions was carried out in this work. The BL optimization problem was considered with two primary targets: (1) designing an optimal taxing strategy (imposed on power generation companies) and (2) developing optimal economic dispatch (ED) schema (by power generation companies) in response to tax rates. The resulting interaction was represented using Stackelberg game theory – where the novel fuzzy random matrix generators were used in tandem with the cuckoo search (CS) technique. Fuzzy random matrices were developed by modifying certain aspects of the original random matrix theory. The novel methodology was tailored for tackling complex optimization systems with intermediate complexity such as the application problem tackled in this work. Detailed performance and comparative analysis are also presented in this chapter.