Global sourcing & distribution practices are creating large and complex logistics networks. Consequently, long distance product movements and inter-related shipment flows are resulting in high energy costs and increased CO2 emissions. On the other hand in the name of expediting orders or just-in-time (JIT) practices, multiple direct and repeated small shipments are adding to traffic congestion, further aggravating fuel costs and CHG emissions. Besides, emissions per mile also increase with the level of congestion as vehicles move at unproductive speeds. In this way, most of the companies have become liable for increased carbon footprints in the environment.
Studies
have shown that, supply chain network optimization can cut cost by eleven
percent and reduce CO2 emissions by ten percent. A well designed supply chain network with an
aim to reduce length of product movements can minimize both costs and
environmental impacts. As a result, shipping
distances shrink lowering fuel consumption, distribution costs and carbon emissions.
Further, supply chain risks such as
volatile fuel prices and long lead times will lessen. Therefore, network
optimization should be the key strategic objective to realize additional cost and
green benefits.
Best
network-related decisions regarding facility locations and flows should be made
using scientific and advanced network design methods. While network optimization
is one way to protect environment, companies also can rethink their supply or
sourcing strategies. Shifting to local sourcing
though increases material cost, product movement can be curtailed considerably.
Additional costs due to local sourcing can be easily offset through savings in shipping
and distribution.
Operations Research (OR) tools and methods have an important role to play in Green Logistics. This includes Supply Chain Reengineering, Vehicle Routing Problem, Location-Routing problem and the development of software component of Freight Intelligent transportation Systems (ITSs).
ReplyDeleteAs far as transportation activities by logistics networks are concerned, reducing environmental externalities requires a simultaneous effort toward both cutting down on fuel consumption, and exploiting cleaner alternative fuels, such as natural gas (liquid-LNG- or compressed-CNG), biodiesel, electricity, ethanol, hydrogen, methanol, and propane.
Fleets owned by businesses and government agencies, that have fixed daily routes, are, on average, driven twice as far as household vehicles on an annual basis (Nesbitt & Sperling, 1998) and road freight alone is responsible for 30% to 40% of total CO2 emissions from the transport sector (ITF, 2010). These observations combined with new environmental regulations and tax incentives, and the prevalent lower price of most alternative fuels has convinced government agencies and private companies to convert their fleets to include Alternative Fuel Vehicles (AFVs). This new opportunity to more reduce costs and emissions from logistics activities of fleets, besides providing the potential for extra capacity, creates a new intricate decision making situation of course, and re-highlights the role of the Vehicle Routing Problem (VRP), which is a conventional OR problem and central to transportation planning for logistics.
In one of my studies I introduced Alternative Fuel Vehicle Routing Problem (AFVRP) as an extension to VRP which aids Logistics networks with a bi-fuel vehicle fleet. The Implementation of the approach in an Iranian case study which has a fleet running on both Gasoline and CNG, proved a possible reduction in CO2 emissions by up to 29.40% and in costs by up to 13.15% compared to conventional VRP.