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To help investors find an investment policy with strong competitiveness, the purpose of this paper is to construct a multi-period investment decision model with practicality and superior performance.

The paper uses a suitable multi-period risk measure to construct a multi-period portfolio selection model, where target returns at intermediate periods and market frictions are taken into account simultaneously. An efficient scenario tree generation approach is proposed in order to transform the complex multi-period portfolio selection problem into a tractable one.

Numerical results show the new scenario tree generation algorithms are stable and can further reduce the tree size. With the scenario tree generated by the new scenario tree generation approach, the optimal investment strategy obtained under the multi-period investment decision model has more superior performance and robustness than the corresponding optimal investment strategy obtained under the single period investment model or the multi-period investment model only paying attention to the terminal cash flow.

The new risk measure and multi-period investment decision models can stimulate readers to find even better models and to efficiently solve realistic multi-period portfolio selection problems.

The empirical results show the superior performance and robustness of optimal investment strategy obtained with the new models. What's more important, the empirical analyses tell readers how different market frictions affect the performance of optimal portfolios, which can guide them to efficiently solve real multi-period investment decision problems in practice.

The paper first derives the concrete structure of the time consistent generalized convex multi-period risk measure, then constructs a multi-period portfolio selection model based on the new multi-period risk measure, and proposes a new extremum scenario tree generation algorithm. The authors construct a realistic multi-period investment decision model. Furthermore, using the proposed scenario tree generation algorithm, the authors transform the established stochastic investment decision model into a deterministic optimization problem, which can provide optimal investment decisions with robustness and superior performance.

The purpose of this paper is to present some dynamic hesitant fuzzy aggregation operators to tackle with the multi-period decision-making problems where all decision information is provided by decision makers in hesitant fuzzy information from different periods.

First, the notions and operational laws of the hesitant fuzzy variable are defined. Then, some dynamic hesitant fuzzy aggregation operators involve the dynamic hesitant fuzzy weighted averaging (DHFWA) operator, the dynamic hesitant fuzzy weighted geometric (DHFWG) operator, and their generalized versions are presented. Some desirable properties of these proposed operators are established. Furthermore, two linguistic quantifier-based methods are introduced to determine the weights of periods. Next, the paper extends the results to the interval-valued hesitant fuzzy situation. Furthermore, the authors develop an approach to solve the multi-period multiple criteria decision making (MPMCDM) problems. Finally, an illustrative example is given.

The presented hesitant fuzzy aggregation operators are very suitable for aggregating the hesitant fuzzy information collected at different periods. The developed approach can solve the MPMCDM problems where all decision information takes the form of hesitant fuzzy information collected at different periods.

The presented hesitant fuzzy aggregation operators and decision-making approach can widely apply to dynamic decision analysis, multi-stage decision analysis in real life.

The paper presents the useful way to aggregate the hesitant fuzzy information collected at different periods in MPMCDM situations.

Many investors have been disappointed with the practical results of portfolio insurance programs, which attempt to achieve option‐like results through dynamic hedging. This article takes the simplest form of dynamic hedging, constant proportion portfolio insurance (CPPI), as a model for developing a more optimal approach. The author uses Monte Carlo simulation to model the multi‐period median growth in discretionary wealth. In addition, he constructs leverage policies that mitigate the practical drawbacks to dynamic hedging. The article also shows that self‐imposed ex ante borrowing constraints (not the ex post constraint imposed by a margin call) can, under certain conditions, improve the performance of dynamic hedging with respect to median terminal wealth.

Entrepreneurship as a development engine has a distinct character in the economic growth of countries. Therefore, governments must support entrepreneurship in order to succeed in the future. The best way to improve the performance of this entrepreneurial advocacy is through efficient measurement methods. For this reason, the purpose of this paper is to propose a new integrated dynamic multi-attribute decision-making (MADM) model based on neutrosophic set (NS) for assessment of the government entrepreneurship support.

Due to the nature of entrepreneurship issues, which are multifaceted and full of uncertain, indeterminate and ambiguous dimensions, this measurement requires multi-criteria decision-making methods in spaces of uncertainty and indeterminacy. Also, due to the change in the size of indicators in different periods, researchers need a special type of decision model that can handle the dynamics of indicators. So, in this paper, the authors proposed a dynamic neutrosophic weighted geometric operator to aggregate dynamic neutrosophic information. Furthermore, in view of the deficiencies of current dynamic neutrosophic MADM methods a compromised model based on time degrees was proposed. The principle of time degrees was introduced, and the subjective and objective weighting methods were synthesized based on the proposed aggregated operator and a nonlinear programming problem based on the entropy concept was applied to determine the attribute weights under different time sequence.

The information of ten countries with the indicators such as connections (C), the country's level of education and experience (EE), cultural aspects (CA), government policies (GP) and funding (F) over four years was gathered and the proposed dynamic MADM model to assess the level of entrepreneurial support for these countries. The findings show that the flexibility of the model based on decision-making thought and we can see that the weights of the criteria have a considerable impact on the final evaluations.

In many decision areas the original decision information is usually collected at different periods. Thus, it is necessary to develop some approaches to deal with these issues. In the government entrepreneurship support problem, the researchers need tools to handle the dynamics of indicators in neutrosophic environments. Given that this issue is very important, nonetheless as far as is known, few studies have been done in this area. Furthermore, in view of the deficiencies of current dynamic neutrosophic MADM making methods a compromised model based on time degrees was proposed. Moreover, the presented neutrosophic aggregation operator is very suitable for aggregating the neutrosophic information collected at different periods. The developed approach can solve the several problems where all pieces of decision information take the form of neutrosophic information collected at different periods.

This article aims to apply a multi‐period model of insurance market equilibrium to solve for the insureds' optimal demand for insurance, as well as insurers' optimal supply.

Most approaches to competitive equilibrium in the insurance market involve the construction of demand and supply curves based on maximizing the insureds' and insurers' expected utility for a single time period. However, it is important to recongnize that, for a given utility function, the demand (supply) decisions of insureds (insurers) in a single‐period model may differ substantially from those under a multi‐period formulation. In this article, first, separate multi‐period models of demand and supply are constructed, and then a dynamic solution for equilibrium price and quantity is provided.

Although a single‐period model generally requires the assumption of an exact loss distribution to compute expected utilities, the multi‐period model requires only the expected loss and its associated stochastic process (in this case, a Brownian motion). One implication of this approach is that it may explain phenomena of market prices failing to achieve Pareto optimality for a single period.

This approach may be used to generate new hypotheses related to the underwriting cycle. Specifically, the insureds' demand and insurers' supply decisions may both be based on expected discounted future cash flows. The non‐trivial multi‐period equilibrium insurance price may provide additional insights into the volatility of insurance market prices.

The purpose of this paper is to study the effects of hedging with options and cardinality constraints in multi‐period portfolio management systems.

The paper focuses on a recursive multi‐period portfolio management formulation (SHAREX) subject to hedging with cardinality constraints and options. The problem formulation is tested with observed and simulated data.

The yield of the multi‐period cardinality constrained option hedging framework under integer‐valued transactions and fixed and variable transactions costs exceeds the riskless return predicted by the Black‐Scholes model in equilibrium.

The paper demonstrates that the multiple representations framework constructed to generate optimal predictions provides accurate forecasts with obvious value for portfolio management.

The literature on the facilities location problem is quite extensive with a wide variety of solution methods for addressing these problems where the objective is cost minimization. Develops a branch and bound algorithm for solving the uncapacitated, multi‐period facility location problem where the objective is to maximize profits. The solution method uses a number of simplification and branching decision rules to solve the problem efficiently. Extensive computational results on the algorithm’s performance are provided. The results indicate that the algorithm provides optimal solutions in substantially less time than LINDO.

This paper aims to propose an integrated supplier selection, order allocation, transportation planning model, along with investment planning for corporate social responsibility (CSR), over a given multi-period horizon under uncertainty. Furthermore, a customer’s behavior to pay more money for items with CSR attributes is considered in the total market demand.

The objective functions, i.e. social value of purchasing, total profit (TP), total delivery lead-time, total air pollution, total water pollution and total energy consumption with regard to a number of constraints are jointly considered in a multi-product system. It is worth noting that operational- and sustainable-related parameters are usually vague and imprecise in this area. Therefore, this paper develops a new fuzzy multi-objective optimization model to capture this inherent fuzziness in critical data.

Through the numerical examples in the textile industry, the application of the model and usefulness of solution procedures are carried out. The numerical results obtained from the proposed approach indicate the efficiency of the solution algorithm in different instances. Moreover, the authors observe that social investment of the buyer, to stimulate market demand, can affect the TP and also involve the total contribution of suppliers in social responsibility.

This research work concentrates on providing a procurement and inventory model through the lens of sustainability to enable textile supply chain managers and related industries to apply the approach to their inventory control and supply management. Totally, the proposed methodology could be applied by many fabric buyers of textile industry tackling purchasing issues and attempting to perfect understanding of social supply chains.

This article studies formal optimal decision approaches for a multi‐period asset/liability management model for a pension fund. The authors use Conditional Value‐at‐Risk (CVaR) as a risk measure, the weighted average of the Value‐at‐Risk (VaR) and those losses exceeding VaR. The model is based on sample‐path simulation of the liabilities and returns of financial instruments in the portfolio. The same optimal decisions are made for groups of sample‐paths, which exhibit similar performance characteristics. Since allocation proportions are time‐dependent, these techniques are more flexible than more standard allocation procedures, e.g. “constant proportions.” Optimization is conducted using linear programming. Compared with traditional stochastic programming algorithms (for which the problem dimension increases exponentially in the number of time stages), this approach exhibits a linear growth of the dimension. Therefore, this approach allows the solution of problems with very large numbers of instruments and scenarios.