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.

Manufacturer encroachment with production cost reduction under asymmetric information

Abstract: This paper considers manufacturer encroachment with the cost reduction decision under either asymmetric or symmetric demand information. By solving a signaling game, we find that encroachment motivates the manufacturer to invest more in cost reduction if and only if the direct selling channel is relatively efficient. Furthermore, both members benefit from the cost reduction action without encroachment, while encroachment allows the manufacturer to monopolize all of the benefit. In addition, encroachment benefits the manufacturer when the direct selling cost is sufficiently low, while it benefits the retailer when this cost is sufficiently high. Finally, we obtain some insights into information management.

Embodied carbon emissions in the supply chains of multinational enterprises

Abstract: Enterprises are at the forefront of climate actions and multinational enterprises (MNEs) engage in foreign direct investment, allowing them substantial influence over the entire supply chain. Yet emissions embodied in the international supply chains of MNEs are poorly known. Here we trace the carbon footprints of foreign affiliates of MNEs and show that the gross volume of global carbon transfer through investment peaked in 2011, mainly driven by the decline in carbon intensity. Despite declining carbon footprints of developed country-based MNEs, there has been a notable increase in carbon transfer sourced from the Chinese mainland. We propose an investment-based accounting framework to allocate carbon footprints of MNEs to the investing country. Investment-based accounting of emissions could inform targeted and effective climate policies and actions. For instance, some large MNEs play a crucial role in carbon transfer, therefore their originating country should bear more responsibilities of carbon emission reduction as an investor. Multinational enterprises and their international supply chains can have large carbon footprints, but there is mitigation potential. Global carbon transfer through investment has declined in recent years, and this framework, assigning emissions to the investing country, would inform further action.

Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing tabu search algorithm to solve the symmetrical traveling salesman problem

Abstract: AHSATS-d-CM: Adaptive Hybrid Simulated Annealing Tabu Search Algorithm with Dynamic Neighborhood Based on Circle-directed Mutation.AHSATS-2-opt: Adaptive Hybrid Simulated Annealing Tabu Search Algorithm with 2-opt.Conclusions: This paper develops an adaptive hybrid meta-heuristic algorithm that combines simulated annealing and tabu search algorithms with a dynamic neighborhood structure to solve the Traveling Salesman Problem (TSP). The experimental results demonstrated that the proposed algorithm can obtain satisfactory solutions within a reasonable time. Moreover, the proposed algorithm can overcome the individual disadvantages of simulated annealing and tabu search. The new hybrid algorithm provides a fast decrease rate and a clear convergence process, and is not dependent on initial solutions. Furthermore, compared with the classical 2-opt neighborhood, the dynamic neighborhood can significantly reduce computational time by decreasing the number of calculations and simultaneously improve solution quality. Finally, the adaptive parameters are appropriate for solving almost all benchmark test examples.Display Omitted A hybrid simulated annealingtabusearch algorithm is applied to solve the traveling salesman problem.A dynamic neighborhood based on circle-directed mutation is developed for the hybrid algorithm.Adaptive parameters are proposed for the hybrid algorithm.Experiments are conducted to verify the accuracy and efficiency of the hybrid algorithm proposed.Experimental results are compared with other algorithms derived from literature. This paper applies a hybrid simulated annealing tabu search algorithm to solve the Traveling Salesman Problem (TSP). Fully considering the characteristics of the hybrid algorithm, we develop a dynamic neighborhood structure for the hybrid algorithm to improve search efficiency by reducing the randomness of the conventional 2-opt neighborhood. A circle-directed mutation is developed to achieve this dynamic neighborhood structure. Furthermore, we propose adaptive parameters that can be automatically adjusted by the algorithm based on context specific examples. This negates the need to frequently readjust algorithm parameters. We employ benchmarks obtained from TSPLIB (a library of sample instances for the TSP) to test our algorithm, and find that the proposed algorithm can obtain satisfactory solutions within a reasonable amount of time. The experimental results demonstrate that the proposed hybrid algorithm can overcome the disadvantages of traditional simulated annealing and tabu search methods. The results also show that the dynamic neighborhood structure is more efficient and accurate than the classical 2-opt. Also, adaptive parameters are appropriate for almost all of the numerical examples tested in this paper. Finally, the experimental results are compared with those of other algorithms, to demonstrate the improved accuracy and efficiency of the proposed algorithm.