Growing concerns about the climate change have forced governments to initiate tighter environmental regulations and tougher emission reduction targets, increasing the interest on electromobility. Logistics operators started employing electric vehicles (EVs) and must face new operational planning challenges. Moreover, with an ever-growing interest in e-commerce, parcel delivery is taking new shapes by offering flexible delivery options to the customers. To mitigate these issues, we introduce the Electric Vehicle Routing Problem with Flexible Deliveries (EVRP-FD), where the customers are served using a fleet of EVs that can recharge their batteries along their routes. In this problem, a customer may specify different delivery locations for different time windows. Our objective is to serve the customers while minimising the total travelled distance using minimum number of vehicles. We first give the mathematical model and then develop a hybrid Variable Neighbourhood Search coupled with Tabu Search by proposing new mechanisms to solve the problem effectively. Then, we verify the performance of our algorithm on instances from the literature. We also introduce new instances for the EVRP-FD and perform an extensive computational study to investigate the trade-offs associated with different operational factors. Finally, we present a case study in Nottingham, UK to provide further insights.

In this paper, we deal with a portfolio optimization model involving fuzzy random variabels. Portfolio optimization is an important research field in modern finance. We consider the problem to maximize the the degree of both possibility and necessity that the objective function values satisfy the fuzzy goals. Using the possibility and necessity-based model, we reformulate the problem as a linear programming problem. In order to find the optimum solution, we propose two-level linear programming model to calculate the upper bound and lower bound of the objective function value separately. The lower bound calculates by historical data and the upper bound calculates by new information of stock market which is received during the constant time. Finally, we provide a numerical example to illustrate the proposed model