This paper addresses the multi-period facility location problem during the market expansion stage, where the decision maker plans to add a certain number of new facilities in each period to gradually increase the accessibility of the infrastructural collection network. In order to consider the trade-off between cost efficiency and service coverage, we propose a multi-period bi-objective 0–1 integer programming formulation for the problem. We develop three metaheuristics to solve the problem. The first metaheuristic is based on the NSGA II framework with the traditional operators for the single-period location problem. The second metaheuristic is based on the local search strategy, where five new neighbourhood structures are designed. The last metaheuristic integrates the former two algorithms. The proposed modelling framework is justified by a case study of the system infrastructure design in Vancouver for E-waste collection. To demonstrate the computational performance of the proposed modelling framework, sixty random instances of different sizes (200 or 500 demand points), with different demand distributions (Uniform, Normal and Gamma distributions) are generated. We compare the proposed modelling framework with two popular multi-objective metaheuristics, MOEA/D and NNIA. Computational results show that the proposed metaheuristic based on local search is the most efficient for searching Pareto solutions of the problem.

This paper investigates a Stackelberg stochastic differential reinsurance–investment game problem, in which the reinsurer is the leader and the insurer is the follower. The unequal status of the reinsurer and the insurer in the financial market is characterized by the asymmetric information model. We consider their wealth processes with delay to characterize bounded memory. The objective of the reinsurer is to find the optimal premium pricing strategy and investment strategy such that its constant absolute risk aversion (CARA) utility of the combination of terminal wealth and average performance wealth is maximized. The objective of the insurer is to find the optimal reinsurance strategy and investment strategy such that its CARA utility of the relative performance is maximized. We derive the equilibrium strategy explicitly for the game by solving corresponding Hamilton–Jacobi–Bellman equations sequentially. Then, we establish the verification theorem. The equilibrium investment strategy indicates that the insurer with less information completely imitates the investment strategy of the reinsurer who has more information on the financial market. Further, we find that the effect of the delay weight on the equilibrium strategy is related to the length of delay time. Finally, we present some numerical examples to demonstrate the findings.

The traditional portfolio theory has relied heavily on historical asset returns while ignoring future information. Based on ensemble learning and maximum Sharpe ratio portfolio theory, this paper proposes a two-stage portfolio optimization method by considering asset forecast information, aming to improve the performance and robustness of a portfolio. In the first stage, concerning the underlying asset selection, we integrate six individual prediction models using the ensemble learning method to forecast the future return of assets where the assets with higher potential returns are selected for portfolio optimization. In the second stage, we propose a novel investment strategy by combining the forecasted returns of selected assets with the maximum Sharpe ratio portfolio model. In the empirical analysis, we employ the constituent stocks of China Securities Index 300 (CSI 300) to test the out-of-sample performance of the proposed strategy with several traditional portfolio strategies, including minimum variance portfolio strategy, traditional maximum Sharpe ratio portfolio strategy, 1/N portfolio strategy, and CSI 300 index. Our analytical results show that (1) compared to individual forecasting models, the ensemble learning method is more accurate in forecasting stock returns, and (2) the proposed portfolio strategy largely outperforms most of its competitors in terms of the Sharpe ratio, Sortino ratio, Omega ratio, and Calmar ratio. This indicates that the proposed two-stage portfolio optimization method is of potential to construct a promising investment strategy due to its trade-off between historical and future information of assets.