Conflicting objectives becomes a common issue in many supply chain network optimization problems. In this paper, a new model is formulated to design a green supply chain network through a new mixed integer linear programming problem. Uncertain demand and stochastic environmental respect levels...

Natural disasters, such as earthquakes, affect thousands of people and can cause enormous financial loss. Therefore, an efficient response immediately following a natural disaster is vital to minimize the aforementioned negative effects. This research paper presents a network design model for...

Natural disasters, such as earthquakes, affect thousands of people and can cause enormous financial loss. Therefore, an efficient response immediately following a natural disaster is vital to minimize the aforementioned negative effects. This research paper presents a network design model for...

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics...

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the real-world problems often deal with imprecise or ambiguous data. In this paper, we propose a novel robust data envelopment model (RDEA) to investigate the efficiencies of...

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics...

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the real-world problems often deal with imprecise or ambiguous data. In this paper, we propose a novel robust data envelopment model (RDEA) to investigate the efficiencies of...

In this paper we study distributionally robust constraints on risk measures (such<br/>as standard deviation less the mean, Conditional Value-at-Risk, Entropic Value-at-Risk) of decision-dependent random variables. The uncertainty sets for the discrete probability distributions are defined using...

In this paper we propose a methodology for constructing decision rules for in-<br/>teger and continuous decision variables in multiperiod robust linear optimization<br/>problems. This type of problems finds application in, for example, inventory management, lot sizing, and manpower management. We show...

Portfolio optimization results are strongly dependent on the model parameters. To circumvent this shortcoming, this paper proposes a new modeling approach to address data uncertainty. The model offers full control over the degree of conservatism and underlines its interaction with robustness for...

This paper analyses the problem of finding a robust spanning tree. The problem consists of determining a minimum spanning tree of a graph with uncertain edge costs. We should determine a spanning tree that minimizes the difference in costs between the tree selected and the optimal tree. While...

This paper constructs a robust optimization framework of the uncertain worst-case return. The model defines an adjustable discrete uncertainty set which controls the conservatism of the optimal asset allocation. Without prior assumptions on the data generating process, the model also develops an...

This paper constructs a robust optimization framework of the uncertain worst-case return. The model defines an adjustable discrete uncertainty set which controls the conservatism of the optimal asset allocation. Without prior assumptions on the data generating process, the model also develops an...

Conditional value at risk (CVaR) has been widely used as a risk measure in finance. When the confidence level of CVaR is set close to 1, the CVaR risk measure approximates the extreme (worst scenario) risk measure. In this paper, we present a quantitative analysis of the relationship between the...

We examine the problem of setting optimal incentives for a portfolio manager hired by an investor who wants to induce ambiguity-robust portfolio choices with respect to estimation errors in expected returns. We consider a one-period model with a set of risky assets (with multivariate normal...

In the portfolio optimization, the goal is to distribute the fixed capital on a set of investment opportunities to maximize return while managing risk. Risk and return are quantities that are used as input parameters for the optimal allocation of the capital in the suggested models. But these...

Research from the United States shows that gaps in early cognitive and non-cognitive ability appear early in the life cycle. Little is known about this important question for developing countries. This paper provides new evidence of sharp differences in cognitive development by socioeconomic...