CHAPTER 1
INTRODUCTION
1.1 Background
Supply chain is an integrated manufacturing process where the raw materials are converted into final products, and then delivered to customers. At its highest level, a supply chain is comprised of two basic, integrated processes: (1) the Production Planning and Inventory Control Process, and (2) the Distribution and Logistics Process. These processes are illustrated in Figure. 1.1. It provides the basic framework for the conversion and movement of raw materials into final products. (Beamon, 1998).
Figure 1.1 The supply chain process.
Transportation is an essential component to any supply chain. As product moves through the supply chain, from raw material to the finished good, transportation costs contribute to the overhead value being added to the final cost of the finished product. Each of the following five basic modes of transportation are used in modern day supply chains: (1) rail carrier, (2) motor carrier, (3) water carrier, (4) air carrier, and (5) pipeline. Intermodal transport which involves the transportation of freight by multiple modes of transportation such as truck and rail, or truck and water is also a commonly accepted practice in most industries. (Reed et al., 2010)
Transportation cost is one of the main components in the structure of the total logistics cost. Stock and Lambert (2001) reports that 60% of the total logistics cost is transportation costs. Bowersox et al. ( 2010) note that the largest element of logistical cost incurred in a supply chain is due to the mode of transportation. Hence, unless the transportation component of the supply chain is managed effectively, the overall performance of the supply chain will be affected.
On the other side, the transportation sector plays a significant role in emissions production, particularly in GHG production. In 2009, the transport sector contribute by approximately 24% of CO2 emissions from fossil fuel combustion and the split between transport modes, the road transport approximately contribute 16,7% of overall GHG emissions as depicted in figure 1.2. (Forum, 2010).
Figure 1.2 World CO2 emissions from fossil fuel combustion
In the United States (U.S.), the transportation sector contributes 28% (U.S. EPA, 2009) of national greenhouses gas (GHG) emissions. This is in large part because 97% of U.S. transportation energy comes from petroleum-based fuels (U.S. DOT, 2010a). Facing this problem, US government has energy policies in place with the aim of reducing fossil-fuel usage so as to reduce GHG emissions, break dependency on foreign oil, increase homeland security and support renewable energy use (e.g. the Energy Policy Act (EPAct), 1992, 2005; Executive Order (EO) 13423 and the Energy Independence and Security Act (EISA), 2007). These policies have led to the creation of regulations, mandates, tax incentives, etc. that motivate or require companies and agencies to use alternative fuel vehicle (AFVs). In fact, federal agencies with a fleet of 20 motor vehicles or more are required to reduce petroleum consumption by a minimum of 2% per year through the end of fiscal year 2015 from the 2005 baseline usage. (Erdogan & Miller-Hooks, 2012)
Today, many companies adopt environmental programs and invests in new and environmental friendly technology on the basis that it is a possibility to gain or maintain competitive advantage in the future (Sarkis, 2003). Bacallan (2000) describes three reasons behind companies’ effort to enhance their competitiveness by improving their environmental performance. Those are to comply with existing or emerging environmental regulations, to address the environmental concerns of their customers, or to mitigate the environmental impact from their products or services. One example of a company that supports the U.S. government program in terms of reducing GHG emissions is Raley’s Supermarkets (Raley’s). Raley’s, a large retail Grocery Company based in Northern California, decided to take participation on utilizing heavy-duty trucks powered by liquefied natural gas (LNG) in 1997. It was found that the LNG truck emits lower levels of oxides of nitrogen and particulate matter than the diesel trucks. (Chandler et al., 2000)
The researches that attempt to solve transportation problems are grouped in Vehicle Routing Problem (VRP). VRP started and introduced by Dantzig and Ramser (1959) that was concerned with the optimum routing of fleet of gasoline delivery trucks between a bulk terminal and the large number of service station supplied by terminal. The objective is to find minimal distance using linier programming formulation. Since introduced, VRP has been extensively studied. By considering additional requirements and various constraints, different VRP have been formulated. Lin et al. (2014) conduct survey in the VRP as depicted in figure 1.3.
Figure 1.3 Researches on VRP
Researches on VRP are divided in two groups, namely traditional VRP and green VRP (GVRP). Traditional VRP focuses on the economic impact of vehicle routes on the organization that carries out the distribution service. In addition, GVRP are characterized by the objective of harmonizing the environmental and economic costs by implementing effective routes to meet the environmental concerns and financial indexes.
1.2 Problem Statement
The problem that faced in this research is concerned with those companies or agencies that employ an alternative fleet of vehicles to serve customers or other entities located over a wide geographical region. Such companies rely on tools to aid in forming low cost tours, so as to save money and time resulting from traveling to customer locations. These routes typically begin at a depot, visit customers and then return to the depot. In order to visit customer, the tour some time need to visit alternative fuel station (AFS). The example of a company that employs AFV is FedEx. The AFV, in the FedEx, run on biodiesel, liquid natural gas (LNG) or compressed natural gas (CNG) (Erdogan & Miller-Hooks, 2012). In this problem, It is generally known as the vehicle or vehicle routing problem (VRP). Due to using AFV and the vehicle has limited in capacity, so the problem called the Capacitated Green Vehicle Routing Problem (CG-VRP) as new a variant of the VRP.
1.3 Objective
Vehicle routing problem has been extensively studied by worldwide researcher. In addition, it needs improving and developing of the existing problem. The objectives of this research are to:
a. Develop new variant of GVRP on the capacitated problem so called Capacitated Green Vehicle Routing Problem (CG-VRP).
b. Implement meta-heuristic method, Simulated Annealing, to solve CG-VRP.
1.4 Research Contribution
This research is expected to contribute in:
a. Developing research that related to the use of alternative fuels on the VRP
b. Developing GVRP model that appropriate to practical application in the companies that called CG-VRP.
c. Evaluating the performance of meta-heuristic approach in solving CG-VRP
1.5 State of the Art
Researches on GVRP divide into three topic. There are GVRP, Pollution routing problem and VRP in reverse logistic. Research on GVRP concerns in the optimization of energy (fuel consumption) of transportation and use an alternative fuel. Research in the energy (fuel consumption) minimization start by Kara et al. (2007). He conduct research on the transportation cost is affected by load and distance. The objective is energy minimizing and solving using CPLEX 8.0. Kuo (2010) conduct research to calculating total fuel consumption for the time-dependent vehicle routing problem (TDVRP). In the model, he considers the transportation speed to calculate the fuel consumption in time-dependent VRPs. The other research, Xiao et al. (2012) is focusing on the fuel consumption. The objective is minimizing fuel consumption and shown a formulation of fuel consumption rate (FCR). FCR is taken as a load dependent function, where FCR is linearly associated with the vehicle’s load. They use Simulated Annealing to solve the problem. Another research focusing on the use of an alternative fuel shown by Erdogan and Miller-Hooks (2012). They conduct research deals with the recharging or refueling of the vehicles, particularly, the alternative-fuel powered vehicle (AFV) by using the Modified Clarke and Wright Savings (MCWS) heuristic and the Density-Based Clustering Algorithm (DBCA). Yasin and Yu (2013) conduct research on G-VRP and solve using Simulated Annealing.
Comparative research that related with GVRP from Kara et al. (2007), Kuo (2010), Xiao et al. (2012), Erdogan and Miller-Hooks (2012), Yasin and Yu (2013) are shown in Table 1.1.
Table 1.1 State of the art
No Aspect of review Kara et al. (2007)
Kuo (2010)
Xiao et al. (2012)
Erdogan and Miller-Hooks (2012)
Yasin and Yu (2013)
This research
(2014)
1 Topic Energy minimization Fuel consumption Fuel consumption Using AFV & AFS Using AFV & AFS Using AFV & AFS
2 Objective Minimize energy and distance Minimization fuel consumption Minimization fuel cost Minimize distance Minimize distance Minimize distance
3 Decision variable Vehicle travel from vertex i to j or not and the weight of vehicle if goes from i to j Vehicle travel from vertex i to j or not Vehicle travel from vertex i to j or not and the weight of the (cargo) vehicle if goes from i to j Vehicle travel from vertex i to j or not, fuel level, time Vehicle travel from vertex i to j or not, fuel level, time Vehicle travel from vertex i to j or not, fuel level, time
4 Method/ Solver CPLEX 8.0 Simulated Annealing Simulated Annealing MCWS & DBCA Simulated Annealing Simulated Annealing
5 Number of depot Single Single Single Single Single Single
6 Capacity of vehicle Available Available Available Not available Not available Available
7 Demand Available Available Available Not available Not available Available
According to the literature review, the research related to GVRP is still limited, especially for the usage of AFS. Therefore this research focus on G-VRP related to the usage of AFS which considers the capacity of vehicle, called CG-VRP. For the solution purpose, this research develops Simulated Annealing (SA) approach. SA has some attractive advantages such as its ability to deal with highly nonlinear models chaotic and noisy data and many constraints. In addition, SA is also empowered by the flexibility and ability to approach global optimality. SA does not rely on any restrictive properties of the model and this makes this method very versatile. Parameter settings significantly impact the computational results of SA. The coefficient used to control speed of the cooling schedule, Boltzmann constant used in the probability function to determine the acceptance of worse solution and the number of iterations the search proceeds at a particular temperatures are some of those. These parameters need to be adjusted and numerous trials are required to make sure that SA can provide good results (Lin et al., 2011).
1.6 Research scope
This research focuses on the G-VRP with the following characteristics:
1. Using AFV and AFS
2. Considering the capacity of the vehicle
1.7 Assumption
This research has some assumption, namely:
1. The vehicle that employed satisfied for the alternative fuel
2. Tour duration is limited
3. Travel speed is constant
4. Demand is deterministic and known
5. Each customer can be visited only once
6. Each vehicle start and end at the depot.
7. Capacitated in each vehicle is same
1.8 Organization of thesis
This thesis is organized as follows: Chapter 1 discusses background, problem statement, objective, research contribution, state of the art, limitation and organizations. Chapter 2 elaborate essential literatures related to this study which consists of green supply chain management, vehicle routing problem, capacitated VRP, G-VRP, AFV and AFS, and simulated annealing. Chapter 3 consists of problem statement, assumptions and mathematical formulation. Chapter 4 discusses the proposed algorithm including solution representation and simulated annealing algorithm. Chapter 5 explains test instance and numerical experiments. Chapter 6 provides conclusion, research contribution and recommendation for future research.
Reference
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