The empirical study on the optimal distribution route of minimum carbon footprint of the retail industry

Journal of Cleaner Production xxx (2015) 1e10

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The empirical study on the optimal distribution route of minimum carbon footprint of the retail industry Yan Li a, *, Wenru Tan b, Ruili Sha a a b

School of Environment and Nature Resources, Renmin University of China, China School of Business, Renmin University of China, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 July 2014 Received in revised form 28 April 2015 Accepted 26 May 2015 Available online xxx

In recent years, with the enlarging scale of the Chinese retail industry and the rapid growth of retail turnover, the energy consumption and relevant carbon emissions of the Chinese retail industry have seen a gradual upward tendency. Therefore, how to effectively reduce its carbon emissions has become increasingly important. Carbon emissions in the process of logistics distribution account for nearly 60% of the total carbon emissions in the Chinese retail industry, so the improved efficiency in logistics transportation can not only save operational costs but also significantly reduce carbon emissions of the Chinese retail industry. This paper aims to research on the optimization of vehicle routes of logistics distribution with their distribution centers and distribution vehicles. First, Model Z1 is built to describe logistics carbon emissions relative to the shortest optimal route, which is used to simulate the distribution distances of different routes. Compared with Model Z1, Model Z2 which describes the optimal vehicle routes relative to carbon emission reduction is established with the objective constraint function of carbon emission included. It is used to simulate carbon emissions of different routes. The two models are then compared and their differences in route and distribution order are obtained. Based on the actual data of the distribution center and the chain stores responsible for distribution of a chain retail company in south China, the annealing simulation method is employed in the computations of Models Z1 and Z2 and results of the optimal routes are obtained with the software Matlab. The results show that the distribution distance is a critical factor in influencing carbon emissions. The shortest vehicle route significantly affects the volume of carbon emission. Optimizing the distribution order further reduces carbon emissions after a constraint function of carbon emission is presented. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Retail industry Logistics efficiency Optimal vehicle routing Carbon emission reduction

1. Introduction With the continual development and expansion of the Chinese economy, the scale of the Chinese retail industry maintains its rapid growth. During 2007e2011, the sales of the retail industry increased 27.9% (CSB, 2008, 2009, 2010, 2012) on average, and the industry's energy consumption accounted for about 8% of the national data. Researches on low carbon supply chain revealed (Accenture, 2009) that as an indispensible part of human activities: logistics transportation emitted 2800 trillion tons of carbon dioxide, approximately 5.5% of the total emissions. The study also showed that the carbon emissions during logistics transportation accounted for 5%e15% of the emissions in the life cycle of a product.

* Corresponding author. E-mail address: [email protected] (Y. Li).

Therefore, there exists a huge potential for low carbon distribution and significant business opportunities. The operation condition and efficiency of logistics transportation influence not only the carbon emissions of companies, but also the logistics cost and financial returns of retail companies. At present, the average logistics cost of the Chinese retail companies is about 10% of their sales volume. It even reaches 20e30% for Fast Moving Consumer Goods (FMCG) while the figure is only about 4e6% in developed European and American countries. For example, Wal-Mart's logistics cost is only about 2% of its total sales volume (Chen, 2011). Logistics distribution cost plays one of the major roles in retail costs and has direct impact on the existence and development of retail companies. Its proportion in the total cost is only second to leasing cost and labor cost. Therefore, enhancing logistics efficiency and reducing logistics costs are significant measures to increase retail profits. American Commercial Committee (ACC) pointed out (CCFA, 2012) in its research report

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that the ratio of transportation and distribution costs of the Chinese supermarkets to its total cost is 4 times that of the U.S. Among the factors causing the increase in energy consumption and a high cost level, the key elements are the energy consumption of the logistics centers themselves and the methods of logistics distribution (Kohn and Bodin, 2008; McKinnon, 2003). The retail industry has started to study the environmental influence of logistics distribution from the perspective of self strategic development. (Rodrigue et al., 2001; McKinnon, 2003). The operational characteristics and the widely located stores of the retail industry have decided the diversified and small-scale logistics distribution as well as a larger proportion of energy consumption and carbon emissions than other sectors. Carbon emissions in the process of logistics distribution accounts for nearly 80% of the total carbon emissions in the retail industry (de Brito and van der Laan, 2010), to which transportation contributes most of carbon emissions in logistics distribution amounting to about 60% (Ahlborn, 2011). This figure is far bigger than the 5e15% average logistics transportation carbon emissions of all industries. Therefore, it is one of the critical factors in reducing carbon emissions of the retail industry to improve the efficiency of logistics distribution. Priorities given to develop multimodal transportation and lesspolluted vehicles can be an effective approach to realize low carbon logistics (McKinnon, 2007; Zachariadis, 2005; Schade and Schade, 2005). The modes of multimodal transportation and route selection are restrained by methods of transaction, and the demand and capacity of the transport system largely influence carbon dioxide emissions (Zhu et al., 2013); so the optimal combination of modes and routes of multimodal transportation can reduce carbon emissions (Zhu and Sarkis, 2004; Ubeda et al., 2011; Kam et al., 2006). The multimodal transportation is especially suitable for the Chinese small and medium sized companies (Zschinsky and Char, 2010). It can enhance the carrying capacity by 30%, significantly improving the distribution efficiency, and in the meantime reduce nearly half of the carbon emissions. Sbihi and Eglese (2007) thought that low carbon distribution can be realized in the following two ways: one is to incorporate carbon emission into the distribution optimization

model and directly reduce carbon emissions in physical distribution; the other is to refer to the optimal distribution time model to help reduce carbon emissions because the shorter the distribution time, the less traffic congestion. Logistics transportation cost is related to its fuel efficiency, which involves vehicle spices, carrying capacity and logistics distance etc (Kam et al., 2006; Christie and Satir, 2006). So, improving fuel efficiency will both save cost and reduce carbon emission. Researches on logistics distribution routes of China mainly cover the optimization of transportation cost and transportation time: goods consolidation of vehicles, the process of cargo loading and delivery of goods. Moreover, the optimization of distribution routes of vehicles has great impact on the transportation speed, cost and profit of physical distribution (Chen, 2008; Li and Li, 2008).Some scholars constructed the optimal vehicle dispatch model with time windows based on the shortest optimal distribution route to optimize the existing distribution sites (Li, 2007). Other scholars included the objective of “minimum carbon emission” in the multiobjective programming model of transportation method selection (Christofer and Brodin, 2008; Luo, 2011). It has offered companies a feasible idea of choosing transportation methods and making decisions to reduce carbon emissions. Optimization and reconstruction of various algorithms to optimize distribution routes have enhanced computation efficiency and accuracy of the simulated results (Liu and Xuan, 2005; Jia et al., 2007; Wang et al., 2009).

2. Analysis on the status quo of logistics distribution and the approaches to improve logistics distribution efficiency of the Chinese retail industry Logistics distribution has very important functions in the operation process of the retail industry. Its methods and routes will greatly affect the operating costs of companies. Its characteristics are feeder branch road transportation, small quantity, and high frequency which bring about more complex distribution process, as shown in Fig. 1 below.

Determining basic distribution

Stores Location

District Vehicle’s Capacity

Merchandise Trait

Distribution Order (preliminary)

Delivery Time

Goods

Traffic Condition

Vehicle’s Arrangement

Specific Location

Bulk, Weight, Quantity etc

Vehicle

Optimizing vehicle routing

Volume and Load

Time Restrict Determining distribution Order

Cost

Distribution Fig. 1. Retail's distribution process.

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Table 1 A comparative analysis of each distribution method. Methods of distribution

Advantages

Disadvantages

Scope of application

Supplier distribution (distributed by suppliers)

Low cost; transportation risk to suppler transferred to suppler

Suitable for big independent marts and warehouse comprehensive supermarkets

Self-distribution (distributed by retail company itself) The third party distribution (distributed by professional distribution companies)

Beneficial to companies' integration and highly systematic Helpful for companies to concentrate on its core business activities; Relatively low cost The adversity of retail companies such as lacking resources and functions of distribution

Bargaining power of retail companies reduced; Unfavorable for the integration of the retail supply chain; One-time bulk investment

Suitable for large chain operation companies

Hard for a company to control logistics

Suitable for retail companies with weak distribution ability

Strong organizational and coordinating skills required

Suitable for small retail companies with weak distribution capacity

Joint distribution (distributed by several joint retail companies)

The current transportation cost accounts for about 52% in the total logistics costs in China (China Chain Store, 2010). It is also a hot issue in the international theory and practice of realizing carbon emission reduction via optimizing the methods and routes on the basis of the normal running of logistics distribution of the retail industry (Metro Group, 2010; RILA, 2013). For instance, data of 28 best green retail company practices collected by the Greening Retail program in Canada revealed that carbon emission reduction was mainly achieved through cargo capacity management, vehicle return trip management, and optimization of distribution routes; and 32.1% of these companies have realized carbon emission reduction by means of route optimization.1 At present, the main distribution modes of the Chinese retail industry include supplier distribution, self-distribution, the third party distribution, and joint distribution (China Chain Store, 2012; China Chain Store, 2013). Supplier distribution covers products of high value, small quantity or short shelf life; while the other three distributions apply the method of having logistics distribution centers as the core to radiate out to each store, as shown in Table 1. Due to their different operation patterns and scales, retail companies will choose different distribution methods. Although the logistics models of supplier distribution and the third party distribution can transfer part of the logistics cost and risk, it is hard for the outsourced distribution to always offer service in time due to a wide range of products, small demand of each transaction, and high frequency of logistics distribution (China Chain Store, 2012). When the scale of a company is not large enough, due to its hefty upfront investments, the long investment cycle, and the large amount of fund needed to run the logistics center, self-distribution cannot realize scale economy and reduce logistics cost (Ramanathan et al., 2014); on the contrary, it will increase the distribution cost. The study (CCFA, 2013) shows that 60% of chain operation companies employ the self-distribution model, and only 5% uses the third party distribution. Joint distribution means that many retail companies distribute products jointly through alliance, agreement, or joint contribution to develop a scale advantage and reduce distribution cost. The level of uniform distribution in selfdistribution and joint distribution represents the distribution efficiency level of the whole retail industry. The average uniform distribution only takes 30e60% of the total distribution of the Chinese retail companies. According to a survey, one Chinese distribution center bears the distribution work of 20 stores and each delivery vehicle responsible for 2e3 stores on average. In contrast, each foreign distribution center is responsible for the distribution of 70 stores but needs only 4e5 vehicles to do the work. The number of stores for which each

1

http://www.greeningretail.ca/best/best transportation.dot.

vehicle is responsible is 5e8 times of that of the Chinese ones. The efficiency gap is apparent. The reasons for the wide gap are the problem of optimizing logistics distribution transportation and the Vehicle Routing Problem (VPR) (Mittenmahal and Noon, 1992) besides the problem of site selection and scale of the distribution centers. Given the locations of the distribution centers and stores as well as the demand of each store, companies can effectively control their logistics costs and drastically reduce carbon emission in logistics distribution with a highly efficient and rational low cost allocation and delivery. There are definitely methods to optimize of different distribution modes, and this study focuses on the optimization of uniform distribution modes under the conditions that the distribution network structure and the sites of the distribution centers are known. The supplier delivers the products to the distribution centers from where articles of different stores are sorted out and loaded on a delivery truck according to the capacity of the vehicle and the need for enhancing transportation efficiency, and the distribution route is selected as needed, in the dotted frame in Fig. 1. 3. Models and estimation Since the problem of optimizing vehicle scheduling was first presented by Dantzig and Ramser in 1959 (Danzig and Ramser, 1959), Vehicle Routing Problems(VRPs) have been among the most important Combinatorial Optimization problems, because of their difficulties and their practical relevance. When the goods distribution problems are proposed, the solution requires determining a set of routes, one for each vehicle starting and finishing at the tie depots, so that all customer requirements are fulfilled, all operational constraints are satisfied, and the transportation cost is minimized. 3.1. The optimal vehicle routing model Z1 based on the shortest route No matter which distribution method of the retail industry in Table 1 is adopted, the logistics distribution cost links directly to the distribution company, such as the supplier, the retailer, or the professional independent third party distribution company. Due to the great difference between the logistics deployment models of different distribution methods, the quantitative approaches for logistics cost control vary. This research aims at the route optimization of unified distribution vehicles of each logistics center. Therefore, all the methods of self distribution, supplier distribution with their own distribution centers, the professional third part distribution, and joint distribution are included in this research. On the premise that the distance between each store and each distribution center and the distance between each demand point

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and each store is known, and it is assumed the demand for goods of per demand point is no more than the carrying capacity of each vehicle and the distribution center can meet the quantity demands of each store and has enough transport capacity available including by renting or cooperating with transportation companies. Furthermore, it is required that the one-time load capacity of each delivery truck does not exceed the rated carrying capacity and the total traveling distance of each vehicle has an upper limit. Thus shortening the total traveling distance of each vehicle through vehicle dispatch and logical routing can effectively improve the vehicle utilization efficiency. To accomplish a transportation task, a distribution center must send several delivery vehicles. All the routes are main routes (loop). After leaving the distribution center, each vehicle delivering goods along a main route (loop) with several consumers are serviced and returns to the distribution center. A distribution center also needs to make sure that consumers are assigned to the loop, which consumers' goods are arranged to be loaded on the vehicle on the loop, and set priorities for the consumers around each route, etc. In additional, in order to deliver goods on time and avoid traffic congestion causing extra transportation cost, retail uniform distribution in China is usually arranged at night in order to avoid traffic jam. Thus in the study, time window is not considered. Based on the distribution characteristics of the retail industry and the actual need, this research constructs the following hypothesis for the transportation dispatch model under the distribution modes:

4 Each demand point can get the distribution service; 5 Each demand point is serviced with only one vehicle. Based on the above constraint conditions, the constraint functions (2)e(8) are established:

X

ai yik  bk ; k ¼ 1; //; k

(2)

i

X



k; i ¼ 0 1; i ¼ 1; //; n

(3)

yik ¼ 0 or 1; i ¼ 0; //n; k ¼ 1; //; k

(4)

yik ¼

k

X

xijk ¼ yjk ; j ¼ 0; //; n

(5)

xijk ¼ yik ; i ¼ 0; //; n

(6)

i

X j

X

xijk  jsj  1; s  f1; //; ng; 2  jsj  n  1

(7)

ies;jes

xijk ¼ 0 or 1; i ¼ 0; //; n; j ¼ 0; //; n

(8)

Among them, (a) The types and quantity of the distributed goods are sufficient; (b) The location and demand of each client are known; (c) The transportation distances between each logistics center and each client are known; (d) The logistics centers have abundant resources and transport capacity. The objective function is established based on the shortest route:

Z1 ¼ min

X

Cij Xijk

(1)

ijk

Among them, C i j is the transportation cost from store i to store j; K is the number of distribution vehicles;

8 < 1; when vehicle k completes delivery at i; Xijk ðisjÞ ¼ it will drive to j : 0; others The construction of the shortest route model is based on the condition that the shortest route can minimize the cost of logistics distribution. In the distribution process, distribution is value added only when vehicle k sets out to deliver goods from the distribution center to store i, or from store i to store j while the other routes are regarded void. At the same time, the optimal distribution route must meet the following basic constraint requirements: 1 It must meet all the consumers' requirements for type, quantity and demand; 2 It must be ensured that the total demand of all demand points on every distribution route must not exceed the carrying capacity of the vehicle; 3 The number of demand points on each route must not exceed that of all demand points;

N is the distribution depot, with 0 representing the distribution center, and 1 to n standing for the depots of each store; bk is the carrying capacity of vehicle k; ai is the demand for goods at depot i; Constraint functions (4)e(7) include two optimization combinations, in which constraint functions (2)e(4) are the constraint conditions for the generalized assignment problem. Each distribution vehicle is assigned according to the demand of consumers and the locations of the store depots, which is to guarantee that the central depot 0 (distribution center) is the starting and ending point of each route. Meanwhile, it must be ensured that a specific vehicle is assigned to each depot and the task assigned to each vehicle is no more than the rated carrying capacity of the vehicle. If yjk can meet the above constraint conditions, the Traveling Salesman Problem is described based on the constraint functions (5)e(8) of each assigned vehicle k. The purpose is to calculate the route of each vehicle in order to minimize the cost of the trip. The traveling cost in the model refers to the logistics distribution cost. In order to minimize logistics cost, retailers would like to optimize the distribution process. The vehicle optimization model Fisher and Jaikumar established in 1981 is also applicable to the distribution of large retail chain companies. The transportation cost Cij correlates closely to not only the type of delivery vehicle, the transportation route, and the carrying capacity of the vehicles, but also the traffic conditions. To optimize the model, it is necessary to simplify the transportation cost at C ij from store i to store j. First, it is assumed that all vehicles are of the same type. Second, in view of the logistics distribution situation of the Chinese retail industry and to avoid traffic congestion and waste of time and money, distribution work is done at night when road traffic is better, so its impact on the cost can be ignored. Third, on condition that the traffic is smooth, it is assumed that all vehicles move at a constant speed except for unloading at specific stores, and the cost of per unit distance is

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fixed if the carrying capacity of the vehicles is not considered. Therefore, Cij can be regarded as the traveling distance between stores i and j. 3.2. The construction of the optimal vehicle routing model Z2 based on carbon emission reduction Model Z1 assumes that the cost for the same traveling distance is fixed, that is to say, fuel consumption or carbon emission per kilometer is identical. But in the actual distribution process when the load of the vehicle changes with unloading at each store, the fuel consumption and carbon emission closely relate to the cargo capacity. The cargo capacity does not correlate with the traveling distance, but with the fuel consumption and the volume of carbon emissions (Lu et al., 2013). The construction of the route optimization Model Z1 of the logistics distribution vehicle based on the shortest route can somewhat reduce carbon emission due to the shortened route through decreasing the cost of logistics transportation; but because emissions of carbon dioxide are not only related to the transportation distance but also to the carrying capacity of the vehicle, the optimal route model based on the shortest route Z1 can not accurately describe the optimal route scenario of minimum carbon emission. To more accurately describe the carbon emission, this research includes the objective functiond carbon emissiondin the shortest route model. In view of the characteristics of CVRP(Carbon Vehicle Routing Problem)of the distribution modes of the retail industry and the research purpose, this research establishes the following hypothesis of the transport dispatch model under the distribution mode: i there is only one starting point of distribution in the modelddall the vehicles set out from the starting point and return to it; ii to simplify the model, it is assumed that all vehicles of the fleet are trucks of the same model with the same rated carrying capacity and speed, etc.; iii the scale of the distribution fleet is known and all vehicles will take part in the distribution task; iv each store has a truck to offer distribution service and the total demand of each depot on each route does not exceed the rated carrying capacity of the vehicle; v the location and demand of each store are known; vi all the routes between the demand depots are the same, and regardless of traffic congestion; vii there are no time window restraints, and every depot is covered by distribution; viii the goods to be distributed are supposed to be the same type and convenient for loading; ix the distribution cost and distance are proportional, and when the distances are the same, the carbon emission and the carrying capacity are proportional.

5

Carbon emission during transportation is influenced by many factors, such as modes of transportation, weather conditions, and traffic congestion (Van Woensel et al., 2001). At present there are two prevailing international quantitative research approaches: on the basis of fuel types and transportation distance (Lu et al., 2013). Which approach is to be adopted depends on the availability of data. For the reason that the fuel of almost all of the transportation vehicles of China is diesel, the accounting of this article is based on transportation distance. To simplify the research, vehicle uniformity is also assumed. In the meantime, it is assumed that the carbon in the diesel during transportation is completely burnt and emitted in the form of carbon dioxide, so the carbon emissions are influenced by the two factors of cargo capacity and traveling distance. Existing research results have been consulted for the fuel consumptions per hundred kilometer for different loads(Ubeda et al., 2011). The conversion coefficient of carbon dioxide(e) is set as 2.61 kg/l. Diesel exhaust C13H28 þ 20O2/13CO2 þ 14H2O, according to mass conservation, diesel exhaust per kilogram can produce 13  44/184 ¼ 3.11 kg carbon dioxide, and the known density of diesel is 0.84 kg/l, it can be calculated that diesel exhaust per liter can produce 3.11  0.84 ¼ 2.61 kg carbon dioxide, so the diesel carbon conversion coefficient is considered as 2.61 kg/l. Under the condition that the fuel emission coefficient is known, the carrying capacity and the emission coefficient of carbon dioxide ε(kg CO2/km) are shown in Table 2. Based on the coefficient between the carbon emission coefficient and the carrying capacity in Table 2, a function relationship between the carbon emission coefficient and the carrying capacity is obtained. There is a linear relation between the carbon emission and the carrying capacity, as shown in Fig. 2. To minimize the carbon dioxide emission during transportation, set the following objective function:

Z2 ¼ min

n X n X m X

ðisjÞ

(9)

i; jef1; 2; /; ng

(10)

eij xijk

i¼0 j¼1 k¼1

Among them,

  eij ¼ dij  ε qVij

There is a relationship between carbon dioxide emissions from depot i to j and the driving distance dij, as well as the carrying capacity ε(qVij). However, before a route is planned, the carrying capacity between every two depots is not known. If the demands for goods at every depot are known, assume that the carrying capacity of each vehicle arriving at every depot is just the demand of that depot. As shown in the following formulas,

eij ¼ dij  εðqi Þ

i; jef0; 1; 2; /; ng

(11)

The constraint function of the objective function of minimum carbon emission is:

Table 2 Carbon emission coefficient of vehicles. The status of vehicle carrying capacity

Percentage in the rated carrying capacity (%)

Fuel consumption (1/100 km)

Conversion coefficient (kg CO2/l)

The emission coefficient of carbon dioxide (kg CO2/km)

No-load Under-load Half-load High-load Full-load

0 25 50 75 100

29.6 32.0 34.4 36.7 39.0

2.61 2.61 2.61 2.61 2.61

0.773 0.831 0.900 0.958 1.018

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Fig. 2. Correlation between the coefficients of carbon emission and the carrying capacity.

m X

y1k ¼ m

(12)

k¼1 m X

yik ¼ 1

ði; 2; 3; //; nÞ

(13)

k¼1 n X

xijk ¼ yjk

ðj ¼ 1; //; n; isj; k ¼ 1; //; mÞ

(14)

xijk ¼ yik

ði ¼ 1; //; n; isj; k ¼ 1; //; mÞ

(15)

i¼1 n X j¼1 n X

qi yik  Qk

ðk ¼ 1; //; mÞ

(16)

i¼1

XX ieS

xijk  jSj  1

ðS3V; jSj  2; k ¼ 1; //; m; isjÞ

(17)

jeS

Constraint function (12) ensures that all vehicles set out from depot 0 of the distribution center. In this model, the depot of the distribution center is set at 0, while the depots of other stores are respectively 2, 3 … n; The constraint function (13) ensures that there is only one vehicle that passes all depots except for the distribution center. The constraint functions (14) and (15) ensure that when the vehicle passes all depots except for the distribution center, each depot it passes is different from the others. Function (14) constrains the previous depot while the function (15) constrains the next depot. Constraint function (16) ensures that the carrying capacity of every vehicle does not exceed its maximum carrying capacity. The constraint function (17) excludes the circumstance that there is a return trip between every two depots. 4. The solution to the problem in the vehicle routing models Z1 and Z2 The vehicle stowage problem of logistics distribution is an entirely NP (Nondeterministic Polynomial) problem, a difficult issue in Nondetrministic Polynomial algorithm. There is less likely a highly efficient accurate algorithm but it is necessary and feasible to find heuristics for VPRs. This field of research is very active around the world. Branch-and-Bound (B&B) algorithms

(Land and Doig, 1960) for capacitated VPR is still the most successful approach to solve VPRs. However, the genetic algorithm and simulated annealing algorithm are used more frequently in solving the problem of combination optimization. Simulated annealing algorithm is used in this research because of its powerful local search capability, short running time (Jia, 2007), quick optimal solution, simple description, flexible usage, and high operating efficiency. In the above optimal routing model based on carbon emission reduction, it is obviously not the exact route of minimum carbon emission to calculate objective function (9) and constraint functions (12)e(17) with the matrix (eij)expressed with constraint function (11). The simulated annealing algorithm can omit constraint condition (11) and directly find solutions to the objective function and constraint functions. The results are more truthful and accurate. First of all, the objective function in the two optimal distribution vehicle routing models based on the shortest route and carbon emission reduction is defined as the general energy function E(x). Then the initial temperature T is introduced into the model, which may not have physical significance. The general way is to produce a group of conditions randomly and determine the difference in the maximum target values between every two conditions. Based on the difference, the initial temperature is obtained by certain functions such as T0 ¼ △max/pr, where pr is the initial probability of acceptance. Under the condition that the initial temperature T0 is included in the model, generally the metropolis approach is applied to simulate the heat balance process, that is energy E is first computed under the present system configuration, then the system configuration is changed randomly and its changed energy E0 is computed. Afterwards, the probability of the successful alteration

   0 E E P ¼ min 1; exp  KT is computed. If P is greater than any random number in [0,1], then the alteration is successful. Otherwise, it will be the basis of deciding whether the system configuration has changed. Finally, an annealing strategy, which is the temperature-fall period is made. It is the basis of getting the retention time and cooling proportion of each temperature. This research uses the Matlab programming software to find a quick solution to the model

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with the help of the application software to simulate the annealing method. The optimal vehicle dispatching Model Z1 can improve the vehicle utilization efficiency (Butcher and Coronado-Mondragon, 2007). It means that how to arrange the routes and dispatch the vehicles to meet the distribution needs and shorten the traveling route. In other words, to accomplish the transportation task, a distribution center must send out several vehicles. All the routes are main routes (loop). After leaving the distribution center, each vehicle delivers goods along a main route (loop) with several stores are covered and returns to the distribution center. Solutions to Model Z1 can show which stores should be arranged on the same loop (that is which stores' goods are to be delivered by the same vehicle) and what priorities of the vehicles should be given on each route. The optimal carbon emission Model Z2 is based on Model Z1 with the constraints relevant to cargo capacity. This model can solve the problem of vehicle arrangement of the stores and the order of vehicles on the loop with minimum carbon emission.

5. The empirical case study on the optimal vehicle routing of the Chinese retail industry This paper constructs the optimal distribution routing model based on the actual data of a large Chinese retail chain company. The company has business all over the country and has its own logistics distribution centers. One logistics distribution center in the southeast region is responsible for distributing goods to its 13 chain stores. The distribution center has 4 trucks with the rated carrying capacity of 200 pieces each. The trucks set out from its the distribution center. After delivering the goods, the trucks will return to the distribution center. The distribution center is chosen as the origin of the coordinates, as (0, 0). The location of each chain store and its distance of it from the distribution center are on the X-axis and Y-axis respectively, which east to distribution center in X-axis represents positive and north to distribution center in Y-axis is positive. The scale and performance of each chain store are different and the quantity of goods needed by each store is also varied. The average quantity of goods needed for each delivery of each store is known by the actual survey data. Please see Table 3. The simulated annealing method is used to compute the objective function of the optimal vehicle route based on the shortest route and carbon emission reduction respectively. Different results of the low cost operation and low carbon emissions are compared.

Table 3 Location and demand of each chain store. Point

X-axis

Y-axis

Demand for goods (piece)

Distribution center Chain store 1 Chain store 2 Chain store 3 Chain store 4 Chain store 5 Chain store 6 Chain store 7 Chain store 8 Chain store 9 Chain store 10 Chain store 11 Chain store 12 Chain store 13

0 0 6 7 9 15 20 17 7 1 15 20 7 2

0 12 5 15 12 3 0 2 4 6 6 7 9 15

48 36 43 92 57 16 56 30 57 47 91 55 38

7

5.1. The solution to the shortest optimal vehicle routing model Z1 First, the objective function based on the shortest route (1) is determined.

Z1 ¼ min

n X n X m X

dij xijk

ðisjÞ

i¼1 j¼1 k¼1

With the Matlab software, and the programming language, the objective function and the constraint functions of the model are obtained. Firstly, the initial temperature is set at 10  C. The criterion of the inside and outside circulation suspension is that the variance of cyclical function value is less than 0.0001. After that, the heat balance process of the inside circulation and the annealing process of the outside circulation; and set the criterion of the inside and outside circulation suspension are simulated, that is the best value of several consecutive steps the algorithm has found changed from little to stable. After all the factors have been set, the polar coordinates of each demand point and the quantity demand for goods are included into the algorithmic routine. Initial simulation is done in the circumstance of 283 K initial temperature. Run the program “mySAA2” in the “workspace” interface (the evaluation function of the shortest route), and the following quasi-optimal route and corresponding carbon emissions are obtained. The time for this result is about 30 s. The detailed route map is shown in Fig. 3. The order for the above route map of the vehicles is, as each route corresponding to a vehicle: Route Route Route Route

1: 2: 3: 4:

0 0 0 0

/5/7/0 / 9 / 13 / 12 / 8 / 0 / 10 / 11 / 6 / 2 / 0 / 4 / 3 / 1/ 0

Quasi-optimal route path fare ¼ 162.96 km. Quasi-optimal carbon emission CO2 fare ¼ 126.07 kg. On the condition that the initial temperature is 10  C, the iterations were about 8500 and the system quickly attained a stable state; but the simulated distribution routes overlapped which is not in conformity with the optimal principle, so the solution is not the global optimal one. When the initial temperature was raised to 100  C, there existed the global optimal solution, but it needed more running times and was slow in positioning the optimal state. When the initial temperature continues to be optimized to 300  C, which was included in the store data in Table 3, the stable optimal state is achieved after running the program for 2 min in the workspace interface, as shown in follow Fig. 4. However, there is little difference in the optimal route and the order of vehicles for the optimal route when the initial temperature is raised up to 300  C. See Fig. 5. The order for the vehicle route map is as follows: Route Route Route Route

1: 2: 3: 4:

0 0 0 0

/ 4 /3 /1 /0 / 12 / 13 / 9 / 0 /7/6/5/2/0 / 11 / 10 / 8 / 0

Quasi-optimal route path length ¼ 158.75 km. Quasi-optimal carbon emission CO2 fare ¼ 122.84 kg. The simulated annealing method is applied to compute the stores each vehicle delivers goods to and the delivery order of each vehicle. The results show when the initial temperature T0 is gradually raised, the distribution route of the logistics vehicles can be

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Fig. 3. Route map of the computation results at 10  C initial temperature (T0 ¼ 283  C).

optimized. The simulated route distance is 162.96 km under the condition of T0 ¼ 10  C, while the simulated route distance is 158.75 km under the condition of T0 ¼ 300  C. Therefore, the total route distance is shortened maximally and the distribution efficiency of the vehicles is increased. 5.2. The solution to the vehicle route optimization model of minimum carbon emission Z2 The objective function of minimum carbon emissions (9) is:

Z2 ¼ min

n X n X m X

eij xijk

ðisjÞ

i¼0 j¼1 k¼1

Fig. 4. Computation iteration fluctuation graph of the optimization model based on the shortest route (T0 ¼ 300  C).

Temperature T0 was set at 300  C. The optimal state solution was obtained after the empirical data was introduced into the program of the model, as shown in Fig. 6. On the condition of the same running time and the initial temperature T0, the simulated annealing method is used to find solutions to models Z1 and Z2. The optimal routes of the two models are the same, the stores the four trucks responsible for are also the same, and the optimal distribution distance is 158.75 km (Table 4).

Fig. 5. The vehicle route optimization result based on the shortest routing Model Z1 at the initial temperature 300  C.

Please cite this article in press as: Li, Y., et al., The empirical study on the optimal distribution route of minimum carbon footprint of the retail industry, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.05.104

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Fig. 6. Optimal distribution route based on the carbon emission reduction Model Z2 (T0 ¼ 300  C).

Table 4 Simulated optimization results of the two models.

Distribution distance (kilometer) Carbon emission (Kilogram) Distribution routes and order

The optimal routing model of the shortest route Z1

The optimal routing model of carbon emission reduction Z2

158.75

158.75

122.84

122.79

Route 1: 0 / 4 / 3 / 1 / 0 Route 2: 0 / 12 / 13 / 9 / 0 Route 3: 0 / 7 / 6 / 5/2 / 0 Route 4: 0 / 11 / 10 / 8 / 0

Route 1: 0 / 4/3 / 1 / 0 Route 2: 0 / 9/13 / 12 / 0 Route3: 0 / 2/5 / 6 / 7 / 0 Route 4: 0 / 8 / 10 / 11 / 0

This result shows that the shortest distribution distance can maximize logistics efficiency and reduce carbon emissions. However, due to the differences in the distribution order, there is a carbon emission gap between the two models. The carbon emission amount in the shortest route model is 122.84 kg while it is 122.79 kg in the carbon emission reduction model. It means that even though the distribution distance is the same, carbon emissions can be different on account of distribution routes and distribution order. Although the difference in carbon emissions of the two models is slightdwith only 0.04% less in the carbon emission reduction modeldit can absolutely achieve considerable carbon emission reduction per year through a change in the distribution order without changing the shortest distribution route. The reasons are the huge number of stores, the long logistics distribution distance, the high energy consumption, the short delivery cycle and the high delivery frequency of the retail industry. 6. Conclusions This paper employs two optimal vehicle routing models of logistics distribution: optimal Model Z1 based on the shortest route and optimal Model Z2 based on carbon emission reduction by adding the constraint factor of carbon emissions. The paper proposes optimal models to better understand how various distribution routes and orders influence carbon emission in the course of retail logistics.

The results show that the distribution route distances correlate directly to carbon emissions: the shorter the route, the lower the carbon emission. Previous researches in distribution process pay very much attention to distribution route distance and time, route distance plays an important role in carbon emission without considering distribution time (McKinnon, 2007; Mingzhou et al., 2014). Based on that foundation, a constraint condition of vehicle carbon emission has been taken into account in this research: The carbon emissions are closely related to not only the transportation distance but also the cargo capacity of vehicles. There is a linear relationship between the carbon emissions and cargo capacity of vehicles. The cargo capacity of full-load trucks setting out from the distribution center is different at every specific store, and the same route distance varies with the distribution order. Comparison Model Z1 and Z2 with the carbon emission constraint shows that carbon emission can also be affected by distribution order. Whether in the optimal Model Z1 based on the shortest route or in the optimal Model Z2 based on carbon emission reduction, the optimal distribution distance is identical, which means that the shortest route can reduce both the cost of retailers and carbon emissions. However, the presence of carbon emission as a new variant in the optimal distribution Model Z2 makes up for Model Z1 which neglects the impact of cargo capacity on carbon emissions. Based on the actual data of the logistics distribution of Chinese retail industry, this paper uses simulated annealing method to compare the optimal results of the two models Z1 and Z2. The results show that the optimal distribution order in the carbon emission reduction model can further reduce carbon emissions in the logistics process. The lower carbon emission Model Z2 can better reduce carbon emission than the shortest route Model Z1. In the future practice of retailing logistics, the lowest carbon emission can be realized by the lower emission Model Z2 fitting the shortest distribution route to each store and the distribution order to maximize the lowest carbon emission and reduce the influence of logistics on environment and climate change. Neither Model Z1 nor Model Z2 has the time windows limit, i.e. the distribution time to each store is not fixed. However, in reality the stores have requirements for distribution time. This is the limitation of this research. It will accord more with the actual logistics process if time windows are included as constraint condition in the future studies.

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Please cite this article in press as: Li, Y., et al., The empirical study on the optimal distribution route of minimum carbon footprint of the retail industry, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.05.104