ESG (Environmental, social, and governance) has become increasingly crucial in the investment of pension funds. This paper develops a multi-objectivedynamic model to analyze the ESG-integration investment strategy of...
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ESG (Environmental, social, and governance) has become increasingly crucial in the investment of pension funds. This paper develops a multi-objectivedynamic model to analyze the ESG-integration investment strategy of Target Date Funds (TDFs). The model optimizes three objectives: expected return, variance, and ESG score, incorporating human capital in the budget constraint and considering inflation-linked bonds as one of the candidate assets. Our model facilitates ESG into the long-term investment of pension funds and extends the multi-objective framework for ESG investment into a dynamic context. To solve the model, we propose a bipolar genetic algorithm that addresses challenges arising from the varying magnitudes of objectives and time inconsistency. In the numerical experiments, we compare our model to the non-ESG model, demonstrating the benefits of ESG in the long term. Our findings show that, unlike the traditional glide path of TDFs, the ESG-integration glide path prefers equity assets and achieves superior performance. Additionally, the glide paths and TDFs’ performance are associated with human capital and preferences among the three objectives, highlighting the need for customized glide paths of different investors. This study provides insights for TDFs fund managers to incorporate ESG into investment decisions and develop strategies accommodating investor heterogeneity.
There are many problems related to the choice of the best route in real life. For example, the transportation of urban food or urban tourism problems, as well as common express delivery problems. These are all related...
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ISBN:
(纸本)9781728176871
There are many problems related to the choice of the best route in real life. For example, the transportation of urban food or urban tourism problems, as well as common express delivery problems. These are all related to the selection of the best route, the maximization, the most effective use of resources. In order to solve the problem of cargo transportation route selection, this paper sets up two scenarios in real life through field investigation. And then the model and manual adjustment are given, finally determines the optimal path.
This dissertation develops algorithms and frameworks to obtain the optimal design and control solutions for a non-linear dynamic system in a computationally efficient manner. These methods and their advantages are dem...
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This dissertation develops algorithms and frameworks to obtain the optimal design and control solutions for a non-linear dynamic system in a computationally efficient manner. These methods and their advantages are demonstrated by applying them to a Plug-in Hybrid Electric Vehicle (PHEV) powertrain's optimal design and supervisory control. Since a PHEV draws energy from the electric grid it is important to consider these interactions in optimal design and control decisions of the PHEV. At the same time the battery size significantly affects the amount of grid energy transferred to propulsion and consequently the on-road power management decisions. Thus, we develop and apply algorithms capable of highlighting the optimal PHEV battery size and control decisions that result in a synergistic interaction with the electric grid. First, we develop a dynamicprogramming (DP) based optimal control algorithm capable of evaluating optimal on-road power management for a series PHEV. This algorithm was based on a backward looking implementation of the PHEV powertrain's dynamic model. Such an implementation of the DP algorithm avoided the need to interpolate the value function or enforce constraints through penalty functions, thereby alleviating crucial computational concerns. The performance of two supervisory control strategies for series PHEVs was compared using this algorithm. For a series PHEV, the results show that a charge deplete and sustain approach is comparable in performance (in $ costs) to the optimal strategy in most cases (esp. when gasoline is more expensive per mile than electricity). Then, we extend this algorithm to consider optimal charging on the electric grid. This extension was made possible by understanding and utilizing the conditions at the boundaries of the optimal charging and driving problems, and the computational attractiveness offered by the above DP algorithm. The results showed the tradeoffs between optimal charging and power management decisions
This paper analyzes a multi-objective variant of the well-known Traveling Salesman Problem (TSP) and the Traveling Repairman Problem (TRP) in order to address the classical conflict between cost minimization (represen...
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This paper analyzes a multi-objective variant of the well-known Traveling Salesman Problem (TSP) and the Traveling Repairman Problem (TRP) in order to address the classical conflict between cost minimization (represented by the TSP) and customer waiting time minimization (represented by the TRP). By simultaneously considering different scenarios with individual travel times, uncertainty in travel data is handled. We interpret each travel time scenario as an individual objective function and introduce deterministic multi-objective counterpart models denoted as the multi-objective TSP (MOTSP), multi-objective TRP (MOTRP), and the combined multi-objective TSP and TRP models (MOTSRP), respectively. Problems with and without additional deadline restrictions are considered, and the complexity status of computing the Pareto fronts of various problem variants for different underlying networks is resolved. As a particularly interesting case, we consider the MOTSRP with deadlines on a line and show that the problem is intractable even in this simple setting. Nevertheless, we propose a dynamicprogramming approach that solves random instances to optimality in reasonable time. Moreover, the computational study additionally evaluates the average complexity of the Line-MOTSRP with deadlines for different numbers of scenarios. The computational study also analyzes the Pareto fronts that are generated for specifically designed extremal instances. (C) 2019 Elsevier Ltd. All rights reserved.
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