In recent years, eco-conscious and eco-friendly logistics have grown exponentially as a competitive element of logistics (Lee et al. 2008). Considering the environmental bur- dens of manufacturing, the elements of modern day SC networks tend to operate on low carbon emissions. This becomes possible when the usage of energy-efficient vehicles, waste reduction, recycling, along with the deployment of optimisation techniques is considered. Greening a logistics system may occur in four phases: (i) green inbound logistics, (ii) operations and manufacturing, (iii) marketing/outbound logistics, and (iv) reverse logistics (Sarkis 2003).
Significant control on the emissions from the distribution of products through transpor- tation is a pre-requisite as it is one of the major sources for environmental concern in transportation. There is an increasing concern regarding the growth of GNP in the in- dustrialised world (Aronsson and Brodin, 2006). Therefore, substantial efforts are being made by the European Union (EU) to decrease the total emissions from the transporta- tion sector (European Commission 2001). However, plenty of scope is still available to optimise the carbon emissions from the SC logistics.
Chapter Two
22
One of the benefits of the Kyoto protocol carbon trading mechanism is that it encour- ages firms to minimise carbon emissions throughout their operations (Diabat and Sim- chi-Levi 2009). The Kyoto protocol identifies six greenhouse gases, viz., Carbon diox- ide (CO2), Methane (CH4), Nitrous oxide (N2O), Hydrofluorocarbons (HFC), Perfluoro- carbons (PFC), Sulphur hexafluoride (SF6). According to the United Nations Frame- work Convention on Climate Change (UNFCCC 2012), CO2 is considered as the prin- cipal greenhouse gas in the ‘carbon market’. Often the quantity of emitted greenhouse gases is expressed as CO2 equivalent (CO2e) in carbon footprints. The ‘total amount of CO2e emissions that is directly and indirectly caused by an activity or is accumulated over the life stages of a product’ is considered as the carbon footprint (Wiedmann and Minx 2008). More precisely, CO2e gases emitted across a SC for a single unit of a product is referred to as the carbon footprint (Reclay Holding GmbH 2012). Therefore, it is recommended to measure the total amount of CO2e and propose possible ways to minimise the carbon footprint in a SC in order to enhance the efficiency of today’s green-SC network. In this research the term ‘low-carbon’ is referred to as an alternative of ‘carbon footprint’.
There are plenty of recommended low-carbon SC principles in businesses. Recom- mended principles are: the in-depth discernment of the impact of the carbon footprint in manufacturing locations and raw material sources, alternative sourcing options, operat- ing speed of SCs, reduction of the use of packaging, proportionate increase in reverse logistics and re-design of distribution channels etc. In a SC network, logistics service providers are required to contribute by increasing SC efficiency and simultaneously reducing associated costs and carbon emissions. Transportation activities are one of the significant sources of air pollution and greenhouse gas emissions within a SC (Wang et al. 2011). These activities leave harmful effects on human health and the environment. Therefore, transportation activities of products from plants to customers via roadways are required to be investigated thoroughly. Minimisation of the traversed distance and maximum utilisation of the vehicles during transportation are possible solutions to min- imise gas emissions. Considering the principles of a low-carbon SC and the effect of transportation activities on society and the environment, this research is focused on op- timising the efficiency of SC carbon management through location-routing models, which are a variant of facility location problems.
Chapter Two
23 2.3. Facility Location Problems
Facility location is a well-established area of research and within the operations research (OR) domain (Melo et al. 2009). Facility location models and techniques have begun to appear gradually in a SC context (Chopra and Meindl 2007) and have become one of its most important applications. In the literature, facility location is also much discussed and studied in the context of logistic decisions (e.g. Lanagevin and Riopel 2010). Location theory formulation started in 1909 when Alfred Weber tried to position a sin- gle warehouse to minimise the total distance between the warehouse and several cus- tomers. Following this in 1964 Hakimi tried to locate switching centres in a communi- cation network and police stations in a highway system (Owen and Daskin, 1998). Since the mid-1960s the study of location theory, specifically the mathematical science of facility location (ReVelle and Eiselt, 2005), has attracted much research attention and as a result many models have been developed.
Location analysis has been defined by ReVelle and Eiselt (2005) as: ‘modelling, formu- lation, and solution of a class of problems that can be best described as sitting facilities in some given space’. ReVelle et al. (2008) suggest that ‘even though the context in which Facility Location models are situated may differ, their main features are always the same: a space including a metric, customers whose locations in the given space are known, and facilities whose locations have to be determined according to some objec- tive function’. These four components characterise the location problems (ReVelle and Eiselt 2005):
‘(1) Customers: who are presumed to be already located at points or on routes, (2) Fa-
cilities that will be located, (3) A Space in which customers and facilities are located, and (4) A Metric that indicates distances or times between customers and facilities’.