With the increasing availability of real-time information and communication systems in logistics, the need for appropriate planning algorithms arises. Customers in transport markets increasingly expect quicker and more flexible fulfillment of their orders, especially in the electronic marketplace. Fleischmann et al. [19] considered a dynamic routing system that dispatches a fleet of vehicles according to customer orders arriving at random during the planning period by using dynamic travel time information. Routing in a stochastic and dynamic (time-dependent) network is a crucial transportation problem. With the utilization of perfect online information of continuous real-time link travel time, Ardakani and Sun [20] proposed a new variant of adaptive routing problem, and developed an adaptive approach to tackle the continuous dynamic shortest path problem. Boriboonsomsin et al. [21] presented an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination by using advanced traveler information systems. Mendoza et al. [22] developed a DSS that integrates commercial systems with a distance-constrained routing module. Suzuki [23] developed a DSS that helps motor carriers route vehicles. These vehicles visit all customers in time (without violating time-windows), and utilize the cheapest gas stations (cheapest truck stops in a region) as refueling points during a tour. Pillac et al. [24] designed an event-driven framework that anticipates unknown changes in a dynamic VRP.