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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.

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.

Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected.

Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention.

Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention.

Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.

We propose a framework for the measurement of income mobility over several time periods, based on the notion that multi-period mobility amounts to measuring the degree of association between the individuals and the time periods. More precisely we compare the actual income share of individuals at a given time in the total income of all individuals over the whole period analyzed, with their “expected” share, assumed to be equal to the hypothetical income share in the total income of society over the whole accounting period that an individual would have had at a given time, had there been complete independence between the individuals and the time periods. We then show that an appropriate way of consistently measuring multi-period mobility should focus on the absolute rather than the traditional (relative) Lorenz curve and that the relevant variable to be accumulated should be the difference between the “a priori” and “a posteriori” shares previously defined. Moving from an ordinal to a cardinal approach to measuring multi-period mobility, we then propose classes of mobility indices based on absolute inequality indices. We illustrate our approach with an empirical application using the EU-SILC rotating panel dataset. Our empirical analysis seems to vindicate our approach because it clearly shows that income mobility was higher in the new EU countries (those that joined the EU in 2004 and later). We also observe that income mobility after 2008 was higher in three countries that were particularly affected by the financial crisis: Greece, Portugal, and Spain.

This study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one (inbound) model considers the bill of materials (BOM), supply failure risks (SFR) and customer demand uncertainty. The secondary objective is to study the influence of potential time-dependent model variables on the overall supply network costs based on a full factorial design of experiments (DOE).

A five-step solution approach is proposed to derive the optimal inventory levels, best sourcing strategy and vehicle route plans for a multi-period discrete manufacturing product assembly IRP. The proposed approach considers an optimal risk mitigation strategy by considering less risk-prone suppliers to deliver the required components in a specific period. A mixed-integer linear programming formulation was solved to derive the optimal supply network costs.

The simulation results indicate that lower demand variation, lower component price and higher supply capacity can provide superior cost performance for an inbound supply network. The results also demonstrate that increasing supply capacity does not necessarily decrease product shortages. However, when demand variation is high, product shortages are reduced at the expense of the supply network cost.

A two-echelon supply network for a single assembled discrete product with homogeneous vehicle fleet availability was considered in this study.

The proposed multi-period inbound IRP model considers realistic SFR, customer demand uncertainties and product assembly requirements based on a specific BOM. The mathematical model includes various practical aspects, such as supply capacity constraints, supplier management costs and target service-level requirements. A sensitivity analysis based on a full factorial DOE provides new insights that can aid practitioners in real-life decision-making.

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.

This paper aims to address the problem of optimal allocation of demand of items among candidate suppliers to maximize the purchase value of items. The purchase value of the items directly relates to cost and quality of raw materials purchased from the supplier. In an increasingly competitive environment, firms are paying more attention to selecting the right suppliers for procurement of raw materials and component parts for their products. The present research work focuses on this issue of supply chain management.

This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

The supply chain network is witnessing a changing business environment due to government policies aimed at promoting new small manufacturing enterprises (small and medium-sized enterprises) for intermediate parts and components. Hence, the managers have an option to select a new group of suppliers and allocate the optimal multi-period demand among the new group of suppliers to maximize their purchase value. In this context, the proposed hybrid model would be beneficial for the managers to operate their supply chain effectively and efficiently. The present research work will be helpful for the managers who are interested to reconfigure their supply chain under the failure of any supply chain partner or in a changing business environment. The model provides flexibility to the managers for evaluation of the different available alternatives to take a decision of optimal demand allocation among the suppliers. The proposed hybrid (fuzzy, TOPSIS and MILP) model provides more objective information for supplier evaluation and demand allocation among suppliers in a supply chain. The managers can use the proposed model to the analysis of other management decision-making problems.

This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. This hybrid algorithm prioritizes the suppliers and then allocates the multi-period demand among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

The purpose of this study is to compare the impact of multi‐period pricing, as an example of more dynamic pricing and discounting strategy with that of a present less dynamic alternative on customer satisfaction and consumers' willingness to make trade‐offs between price and remaining shelf‐life.

The authors conducted interviews with three food retail managers in South Korea to gather practical information about the management of perishable foods, which informed the design of a survey in which consumers in South Korea were questioned about their perceptions of the two strategies, with respect to nine perishable food products in three categories. The data collected were analysed by one‐way ANOVA and the t‐test.

The findings of this research present an improved understanding of the impact of a multi‐period pricing strategy on consumer satisfaction and customer behaviour for perishable foods. The conclusions have the potential to significantly assist food retailers to understand the consumers' perspective on the benefits of a more dynamic pricing strategy.

The findings suggest that food retailers can enhance customer satisfaction by offering an earlier but lower discount, and increasing it as perishable food items approach their expiry date, rather than a higher discount when the expiry date is imminent.

The findings in this study are significant since they serve as the first step in measuring the value of dynamic pricing approaches that provide better trade‐off options between price and remaining shelf‐life from consumers' perspectives.

The purpose of this paper is to put forward a multi-period dynamic pricing strategy for perishable food considering consumers’ price fairness perception. The impacts of the multi-period retail price, food freshness and inventory shortage risk on consumers’ heterogeneous willingness to pay (WTP) and their strategic purchasing behaviours are studied.

The authors present a price optimization model for perishable food, and conduct a laboratory experiment to justify the theoretical model. The data collected are analysed by correlation analysis and nonparametric test.

The results obtained reveal, first, food freshness and inventory shortage risk have effect on consumers’ heterogeneous WTP. Second, different retail prices lead to consumers’ strategically purchasing behaviours. Finally, consumers’ intertemporal price fairness perception and the food retailer’s long-term utility maximization can be achieved by developing multi-period dynamic pricing strategy.

This study suggests the perishable food retailer to apply a step-by-step price markdown strategy. It aims at eliminating price unfairness perceptions caused by loss of freshness and high shortage risk of the perishable food in the subsequent selling periods within the shelf life. Some valuable managerial insights towards perishable pricing for food retailers are discussed.

This study serves as the first step to utilize a laboratory experiment to dig out consumers’ intertemporal WTP towards perishable food. It also presents a novel way for describing consumers’ intertemporal price fairness perception by equalizing consumers’ average utilities considering consumer surplus, food freshness and shortage risk at different selling periods. The line of research on dynamic pricing concerning consumers’ price fairness perception is quite new in academic research, and has arisen due to its importance for food retailers of maximizing their long-term revenues and also of constructing mutual benefit and lasting connections with the consumers.