College of Management and Economics

About: College of Management and Economics is a based out in . It is known for research contribution in the topics: Supply chain & Stock market. The organization has 2184 authors who have published 2193 publications receiving 28830 citations.

The impact of subsidies in a transboundary pollution game with myopic players

Abstract: In this paper, we study the impact of green subsidies in an international transboundary pollution game. In the game, the developed country only suffers from paying the subsidy but does not reduce emissions, while developing countries reduce their emissions after receive the subsidy. Furthermore, we assume that developing countries can be myopic, which means that they choose their reduction policy without taking into account the future levels of pollution. The results show that when some of the developing countries are myopic, the optimal reduction policy is much lower than that in the case where all of the developing countries are farsighted. The level of pollution is much higher for the former case than the latter one. However, for developed countries, the presence of a certain number of myopic developing countries can maximize their payoffs. Finally, we find the critical condition for a farsighted developing country to adopt a myopic framework. The findings of this paper could offer valuable guidances for climate policy makers.

Capacity Sharing and Capacity Investment of Environment-Friendly Manufacturing: Strategy Selection and Performance Analysis.

Abstract: Many small manufacturing factories suffer insufficient environment-friendly capacity after eliminating the outdated and environmental-harmful production capacity according to stringent environmental rules and regulations. This paper analyzes two strategies that the manufacturer with limited environment-friendly capacity may take to tackle this problem, i.e., investing in building environment-friendly capacities and collaborating with the manufacturer with sufficient environment-friendly capacity in capacity sharing. In a supply chain with two competing manufacturers, this paper builds game-theoretical models and investigates equilibrium solutions under three scenarios (no capacity investment or sharing, capacity investment, and capacity sharing). Then this research investigates the feasible regions of these two strategies and compares the performance of each manufacturer under each scenario. The findings show that both capacity investment and capacity sharing can effectively reduce the profit loss of the manufacturer with limited capacity, while only capacity sharing benefits both manufacturers. The feasibility of these two strategies depends on the initial capacity volume and the capacity investment cost coefficient of the manufacturer with limited capacity. Moreover, the preference of the manufacturer with limited capacity for each strategy depends on the capacity investment cost coefficient. When the capacity investment cost coefficient is relatively high, the win-win situation exists for supply chain members. Furthermore, with the use of chaos theory, the paper shows how to adjust the capacity investment in each period to keep the system stable.

Optimizing Joint Production Planning, Pricing and Retailer Selection with Emission Control based on Stackelberg Game and Nested Genetic Algorithm

Abstract: We address the joint decision making of production planning, pricing, and retailer selection while considering emission control. In view of the interactive decision-making processes, we adopt the Stackelberg game theory and develop a 0–1 mixed nonlinear bi-level programming model to maximize the profits of a manufacturer and his retailers. Involving two submodels representing the decision-making processes of the manufacturer and retailers, respectively, the Stackelberg game model is very complex and impossible to be solved analytically. Therefore, we further develop a nested genetic algorithm (NGA) to solve the game model. Numerical examples demonstrate (i) the applicability of the Stackelberg game model in making joint decisions and (ii) the robustness of the NGA. Sensitivity analysis sheds light on several important managerial implications.

Learning the Compositional Visual Coherence for Complementary Recommendations.

Abstract: Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. %However, it is challenging due to its complexity and subjectivity. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a \textit{Global Coherence Learning} (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a \textit{Focal Coherence Learning} (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods.