Whenever referring to VRSP, words like “time”, “distance”, “capacity” and “cost”will jump into mind immediately. VRSP realizes the models where the objective to be optimized is minimized cost. However, how many people have ever thought about that VRSP is also beneficial to the environment not just about cutting the cost.
According to our previous study in class, transportation has an environmental significance to entail the responsibility to the environment which means to balance between green logistics and efficient transportation system. The assumption of VRSP to reduce total distance is equal to say reduce the fuel emission and consequent pollutant. For a simple instance, many companies like UPS use “no left turns” routing method can shave off great amount of fuel bill which also implies less CO2 emissions. Based on the research we got, two subcategories of VRSP play a critical role in the green logistics which are time-dependent VRSP and hazardous materials transportation.
Time-dependent VRSP refers to optimally route a fleet of vehicles of fixed capacity when the traveling times between nodes depend on the time of the day that the trip on that arc was initiated which stands for reducing number of total vehicles used and total travelling time. The travelling time is measured by knowing the departing time and an accurate estimate of the average speed of the vehicle while traveling on the arc. In this system, vehicles are required to drive at a faster speed without being caught in congestion. Despite a longer distance may be applied, there is likely to be environmental benefit because less pollution is created when vehicles are traveling at the best speeds for the environment and for shorter times. Meanwhile, this system can satisfy delivery time windows more reliably. The next one is hazardous materials transportation. This concept is finding a route to reduce the accidents and hazardous impacts on human and environment as low as possible, especially in large urban areas. However, this risk-analysis-based routing methodology has a high implementation degree to minimize the risk, alongside cost, it needs justify different objective quantities and trade-off the objectives. In others words, a great deal of data base and accurate information are required.