1. Introduction
The demand for medical products such as dietary supplements has globally increased because many people have become interested in health care and improving their immunity against infectious diseases due to the advent of the COVID-19 pandemic [
1]. In addition, good health has generally been considered an essential basis of life. As a result, the medical product industries such as those for vitamins, minerals, herbs or other botanical products, and health examinations have also grown [
2]. According to a survey conducted by the Korea Health Functional Food Association, it is expected that the annual growth rate of the health functional food market would be 10.8% on average [
3]. Moreover, with the growth of the medical product industry, the logistics industry has grown because of an increase in demand for online shopping due to COVID-19 as well. Various national policies such as social distancing were implemented during the period of the COVID-19 pandemic, and it caused consumers to depend on online shopping, particularly for food [
4].
The trend in logistics networks is changing along with the growth of the logistics industry. One of the prominent changes is the expansion of the Micro-fulfillment Center (MFC). MFCs are small-scale logistics centers located in urban areas, unlike large logistics centers located in suburban areas [
5], and enable same-day delivery and efficient logistics management [
6]. As a result, customer satisfaction and repurchase rates can increase because of fast delivery, and delivery costs can be reduced by shorter distances between consumers to urban logistics centers. Moreover, a reduction in delivery distance leads to a decrease in CO
2 emissions from delivery vehicles, which has a positive environmental impact. Several countries operate MFCs in urban areas. For example, the food/logistics company ‘Coupang’ in South Korea offers the ‘Rocket Delivery’ service, which uses an urban logistics center to deliver products within 24 h. As shown in
Figure 1, the health/beauty company ‘CJ Olive Young’ in South Korea also offers the ‘O Neul Dream’ service, which means delivery tonight, and uses an urban logistics center to deliver products within 3 h. In other countries such as the United States, Whole Foods Market uses urban logistics centers to enhance logistics efficiency and provide fast delivery services within two hours for Amazon Prime customers [
7]. Approximately 7% of global carbon emissions originate from the logistics industry. In countries like the United States, where long-distance delivery is common due to the vastness of the territory, 20–30% of CO
2 emissions are attributed to transportation. Consequently, environmental issues related to the logistics industry are recognized as significant concerns [
8]. In summary, urban logistics centers are gaining attention as alternatives to traditional large logistics centers due to the great advantage of fast and sustainable delivery. In this situation, the purpose of this study is to reduce the CO
2 emissions of the ‘L’ medical company by constructing a new logistics center in an urban area. A new logistics center is to be constructed in one of the 25 candidate sites in Seoul, and the candidate site with the lowest total CO
2 emissions from vehicles will be determined. The company sells powdered medical products that are effective for hydration in South Korea. The ‘L’ medical company has four large logistics centers located in the suburban areas of Seoul, and each logistics center only processes orders received from pharmacies, home shopping, online platforms, and military marts. According to the data analysis in
Section 5, the proportion of pharmacy orders is 44.65% around Seoul including suburban cities. Therefore, it seems that the logistics network can be improved by reducing delivery distance when an urban logistics center is built in Seoul, in addition to the existing logistics center in Gunpo. Furthermore, the company will surely be able to increase its economic benefits and improve customer satisfaction by reducing delivery costs and time by using an urban logistics center from a long-term perspective [
9]. Thus, a strategy for determining the optimal location of an urban logistics center in Seoul is proposed to reduce CO
2 emissions by minimizing the delivery distances of the ‘L’ medical company’s logistics network.
The logistics network of the ‘L’ medical company can be improved by determining the optimal location for the urban logistics center through a developed mathematical model-based optimization technique. The optimal solution solver and genetic algorithm are adopted to solve the proposed mathematical model by deriving an optimal or near-optimal solution. The scientific gap of this study is that the input parameters such as demands and capacities are derived from the ‘L’ medical company’s actual data for the practical and rational aspects. The delivery distance and time differ depending on the location of the urban logistics center constructed within Seoul. At this point, the candidate locations for constructing a logistics center with higher demands have a higher probability of being determined as the optimal location. It means that the accessibility of the candidate locations to the pharmacies is crucial. In summary, this study aims to find the optimal location in Seoul for the urban logistics center by using actual data such as the demand and capacity of medical companies and the actual distance between candidate locations and pharmacies and examines how many orders of the existing logistics center in Gunpo are assigned to the new urban logistics center in Seoul through the numerical experiment.
This paper is organized as follows.
Section 2 presents a literature review related to the environmental impacts of logistics centers and vehicle routing problems. In
Section 3, the problem description of this research is explained including the actual logistics networks of the ‘L’ medical company and the development of a mathematical model that can minimize the total CO
2 emissions of all vehicles. In
Section 4, various methodologies for solving the mathematical model are introduced and how to apply them in this study is explained. In
Section 5, the procedure of processing raw data is described and the numerical experiments for reasonable-size problems are conducted. Finally, the findings and insights from this study are presented as concluding remarks in
Section 6.
2. Literature Review
The delivery and logistics industries have been striving to enhance customer satisfaction and reduce CO
2 emissions over the past few years. Generally, studies related to logistics centers have been conducted on topics such as location decision making for logistics centers, storage and inventory management, delivery staff scheduling, and transportation route optimization [
10]. In this situation, many companies tried to locate logistics centers in urban areas close to customers to reduce CO
2 emissions by reducing delivery distance and time. Furthermore, many studies have been conducted to rationally determine the location of logistics centers. Kazançoğlu et al. [
11] sought to determine the location of logistics centers in emerging countries using a hybrid multi-criteria decision-making method, which combines the fuzzy Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). As a result, 12 criteria were derived to select the location of the logistics centers in the Sivas region. Wang et al. [
12] used a heuristic genetic algorithm to schedule the process of logistics centers and aimed to achieve improving the circulation efficiency of logistics networks and reducing the cost by recommending the number of vehicles and working hours. As a result, the goal of improving the circulation efficiency of the logistics center and reducing circulation fees was achieved by deriving the optimal number and path of picking carts. Cattaruzza et al. [
13] performed a study that contributed to reducing traffic congestion and enhancing the mobility of freight transportation services in urban areas at a minimum cost by optimizing the vehicle routing of urban logistics centers. Consequently, the study found that vehicle routing optimization can indeed play a key role in the urban goods movement. Thus, the logistics network can be operated more efficiently by optimizing the routes and the number of vehicles that are used within the logistics center like picking carts and delivery vehicles for external customers. Based on this literature review, this study emphasized environmental and economic factors in the selection of logistics center locations. Furthermore, aiming to optimize the number of vehicles and delivery routes, a genetic algorithm was developed based on mathematical models.
The development of land transportation infrastructure and the increasing demands for delivery services have caused the adoption of eco-friendly management in the logistics industry [
14]. Liu et al. [
15] present a sustainable management aimed at reducing the carbon emissions of cold chain logistics companies. In this study, based on actual business data from 4 cold chain companies and 28 client firms, a Joint Distribution-Green Vehicle Routing Problem (JD-GVRP) model was constructed. Subsequently, the simulated annealing (SA) algorithm was applied to optimize the routing of cold chain vehicles. As a result, a joint logistics network effectively reduced the total costs and carbon emissions. Scaburi et al. [
16] aimed to optimize the newspaper delivery process of a Brazilian printing company by applying a mathematical model-based VRP (Vehicle Routing Problem). Based on the results, the reduction in greenhouse gas emissions was assessed using the Greenhouse Gas Protocol calculation tool. Gupta et al. [
17] implemented a vehicle routing problem in urban areas based on actual company data from Singapore. This study considered time windows at customer locations, simultaneous pickup and delivery demands, a heterogeneous fleet of vehicles, and the heterogeneity of traffic congestion levels in urban transportation networks and compared the results of various cases to derive the optimal route that minimizes CO
2 emissions. Numerous studies exist that effectively reduce CO
2 emissions through optimizing logistics networks. However, this study stands out as research focused on determining the location of logistics centers, considering both CO
2 emission reduction and economic factors such as real estate prices.
Various industries use the strategy for finding an optimal location that is used in this study. Ahmad et al. [
18] conducted a study about the optimal location of electric vehicle charging facilities using mathematical models and genetic algorithms. This research set the cost, net benefit, power loss, distance, covered trip, and power supply moment balance index as objective functions and found the optimization strategies with the handling of uncertainty and integration of renewable energy sources. Subsequently, discussions on the optimal location of electric vehicle charging facilities were conducted through an evaluation of the distribution network and the environmental and economic impact. Ko et al. [
19] used mathematical models and genetic algorithms to find the optimal location, capacity, and capability of emergency medical centers. The objective is to minimize costs while satisfying the functions and capacities of emergency medical centers considering the types of transportation that patients primarily use when visiting emergency medical centers and the types of diseases. The optimal solutions were derived from the proposed strategy in this research and were examined by the simulation studies at the stochastic scheme. Gavina et al. [
20] proposed that strategically arranging the beehives in the situation of the limited availability of nectar and pollen near the beekeeping site leads to competition among bees and a scarcity of pollinators relative to the abundance of flowers, which could result in less-than-optimal crop pollination. In this situation, the research established a mathematical model aimed at maximizing objective functions such as the beekeeper’s variable preferences, the count of bee colonies that can be utilized, and the expected productivity of the flora surrounding them and utilized linear programming to derive conclusions. Similarly, in various industries such as charging facilities, emergency medical centers, and beekeeping, while the objectives may differ from those in the logistics industry, optimization techniques based on mathematical models are employed to find optimal locations.
The difference between the optimal location selection in the logistics industry and others is that the Vehicle Routing Problem is usually applied. The Vehicle Routing Problem (VRP) was first proposed by Dantzig and Ramser based on the issues of the transportation/logistics field [
21]. The VRP plays a significant role in reducing costs in the transportation/logistics sector by 5% to 20%. Traditionally, the VRP in the logistics industry encompasses the process of planning the optimal routes for vehicles loading and displacing products within the logistics center, as well as for vehicles delivering products externally. The general objective of the traditional VRP is to search for the optimal route to satisfy various customer demands under given circumstances [
22]. Ray et al. [
23] proposed an ILP model to find the depot locations and vehicle routes for commodity delivery. They also suggested an efficient heuristic-based mechanism that can solve the model optimally. Moreover, vehicle routes were segmented by multiple depots. However, this strategy had a limitation in that it only assumed a situation where the total demand was less than the vehicle’s load, and it did not account for additional work after the vehicle returned to the logistics center. Wang et al. [
24] proposed a low-carbon cold chain logistics distribution route optimization model that minimizes various costs such as transportation, cooling, and carbon emissions of cold chain logistics centers in China and derived the optimal solution by cycle evolutionary genetic algorithm (CEGA). However, the working time of vehicles considered only the delivery time, excluding the loading/unloading time, and the vehicles did not operate additional delivery tasks after returning to the cold chain logistics center.
Moreover, the study applying the VRP has been conducted beyond the logistics industry. Markov et al. [
25] developed a mathematical model with the goal of minimizing waste transportation costs in the process of waste collection and disposal through vehicle operation. However, this study appeared to present an inefficient routing plan in that it assumes all available waste-collecting vehicles are operating simultaneously, and this previous study differed from the present study in that the departure and destination of the vehicles can be different. Zhou & Wu [
26] introduced a Green Vehicle Routing Problems with Pick-up and Delivery (GVRPPD) model to establish an eco-friendly route that minimizes fuel emissions from vehicles. The results of the design experiment revealed that the GVRPPD model significantly reduced carbon emissions without reducing customer satisfaction. However, they had the limitations of not reflecting reasonable situations due to the problem design being based on only one depot for the delivery vehicles.
Studies related to the location selection of logistics centers have been prolongedly conducted. It is easy to find research that applies the VRP to select the location of logistics centers. This paper also uses the VRP considering demands and vehicle capacity and derives solutions by a mathematical model and a meta-heuristic algorithm. The development of the Internet has led to a vast amount of information about users, making it crucial to derive the direction of user preferences [
27]. In this regard, this study can be seen as reflecting customer preference clearly and more empirical research because it sets the problems using several months of actual data from the ‘L’ medical company in South Korea. Furthermore, the health care provider currently operates multifaceted supply chains to reduce logistics costs and improve the quality and efficiency of medical products transport [
28]. The way to consider features in real life to construct practical models is a challenge that has always received attention in VRP problem research [
29]. Therefore, it is expected that this study would be evaluated as one of the practical strategies in a company’s decision-making process in that it treats the problem based on the actual data of the company.
5. Numerical Experiment
In the existing logistics network, a logistics center located in Gunpo, which is a suburban area of Seoul, handled all of the demand in Seoul. However, there is a plan to construct a new logistics center within the Seoul urban area to reduce CO2 emissions and establish an eco-friendly logistics network. Therefore, in the numerical experiments, the carbon emissions of the existing logistics network are compared with those of the new logistics network, and the location within Seoul that results in the lowest carbon emissions is selected for the logistics center. A total of six delivery vehicles are operated, and they are distributed to the existing and new logistics centers according to the optimal delivery routes. Additionally, since the company has a fixed budget for logistics center construction, the size of the new logistics center that can be built varies by local governments, based on the 2023 real estate prices in Seoul. This study only considers the routes between local governments and does not plan routes between each pharmacy within local governments. Therefore, delivery delay times are assigned based on the number of pharmacies in each local government. Furthermore, considering the circumstances of the ‘L’ medical company, 1-ton trucks are used, and the maximum operation time is based on the standard working hours per day in South Korea. Lastly, the operating time for each vehicle is calculated as the sum of the round-trip travel time from the logistics center to the demand area, the travel time between demand areas, the delay time at the demand area, and the unloading time for the number of demands. CO2 emissions per kilometer are derived by considering the type of vehicle.
5.1. Data Analysis
The mathematical model in this study uses input parameters such as demands, locations of pharmacies in Seoul, capacity of vehicles, and volume of packaged products. The parameters above are derived from the actual data from the ‘L’ medical company in South Korea. Therefore, data analysis is conducted to derive insights and the input parameters for the developed mathematical model. The raw data are shown in
Figure 7 to understand the demand of the pharmacy among various data. Seoul has a total of 25 local governments, and there are 958 pharmacies that receive products from the ‘L’ medical company. Additionally, the demand for products by the district is set based on the average value of the ‘L’ medical company’s actual data over one month.
The ‘L’ medical company uses 1-ton trucks due to the narrow roads when delivering. The maximum loading amount per vehicle is set according to the volume of the 1-ton truck and the size of the ‘L’ medical company’s product. As shown in
Figure 8, each carton contains 40 boxes of products, and a vehicle can accommodate 40 cartons, which means a vehicle can carry a total of 1600 boxes. In addition, each vehicle cannot work on delivery tasks for more than 480 min a day, and the working time of the vehicle consists of the sum of delivery time and loading/unloading time.
5.2. Parameters Setting
The specific system parameter values are shown in
Table 1. Seoul has 25 local governments, and the demands of each local government’s pharmacies are given in the value of
. Although the delivery routes between the logistics center and the pharmacy demand areas are considered, the routes between each pharmacy are not. Therefore, vehicles will stay in the demand areas according to the number of pharmacies in local governments (
). Due to the real estate prices in Seoul, there are limitations on the size of logistics centers that can be constructed in each local government. Therefore, the maximum number of products that can be delivered is assigned through
. According to Jabali [
33], the CO
2 emission rate per kilometer for a 1-ton truck is 1200 g. As described in
Section 5.1, the capacity of the vehicle allows for 1600 products. Additionally, according to the Seoul Research Data Service, the average speed of vehicles in Seoul is about 20 km/h. Based on the standard working hours per day in South Korea, each vehicle cannot work for more than 480 min.
The genetic algorithm is evaluated by comparing the optimal solution derived by CPLEX. After this process, GA is applied to the actual situation, and parameters for the GA are set as shown in
Table 2.
5.3. Results
Initially, this study compared the vehicle routes and CO
2 emissions for cases where logistics centers were constructed in the ‘Gangnam’, ‘Gwangjin’, and ‘Guro’ areas to verify the performance of the GA. CPLEX took over 48 h to find the optimal solution, which was considered an inefficient process. Subsequently, comparing the optimal solution from CPLEX with the results from the GA revealed a difference of less than 5%. The specific compared results of CPLEX and GA are shown in
Table 3. Therefore, by utilizing the GA, this study aimed to find the most optimal location among the 25 local governments of Seoul, and the results can be found in
Table 4.
Before the examination of cases, the existing logistics network of the ‘L’ medical company is examined. The company operates a logistics center that is only located in Gunpo, a suburb of Seoul, for deliveries to pharmacies. The six vehicles move until they meet the demand of pharmacies located in the 25 local governments within Seoul. Under the current logistics network environment, using only the logistics center located in Gunpo, the total CO
2 emissions amount to 577,360 g/km. Next, when comparing the CO
2 emissions resulting from the construction of logistics centers by local governments, constructing in Dongdaemun resulted in the lowest CO
2 emissions, at 190,800 g/km. Following Dongdaemun, the area with the next lowest CO
2 emissions is Jung, emitting 206,400 g/km. This means that compared to the existing logistics network, there is a 66.74% reduction in CO
2 emissions with the new logistics network. Dongdaemun and Jung are located in the heart of Seoul, offering excellent accessibility. Additionally, while Yongsan and Seocho are also located in the center of Seoul, the high real estate prices in these areas impose limitations on the size of logistics centers, leading to a larger allocation of vehicles to Gunpo and an increase in CO
2 emissions due to the increased travel distance. Therefore, the comparison is made between the existing logistics network, the optimal solution, and areas located in the center of Seoul, which have shown high carbon emissions, as in
Table 5.
Table 5 shows the comparison of 3 cases. In the first case, the existing logistics networks of the ‘L’ medical company are described, where all vehicles start from Gunpo, visit all demand points, and then return to Gunpo. In the second case, it represents the delivery routes based on the optimal results, where all vehicles start from the logistics center in Dongdaemun, to reduce CO
2 emissions according to delivery distance. In the case of Dongdaemun, the relatively low real estate prices allow for the construction of a logistics center large enough to accommodate the demands of all pharmacies in Seoul. This is why all vehicles were able to start from the Dongdaemun logistics center. The final case focuses on the results for Yongsan, an area located in the center of Seoul that has shown high CO
2 emissions despite its central location. Yongsan, being an area with high real estate prices, makes it challenging to build a logistics center large enough to accommodate the demand from all pharmacies in Seoul. As a result, while 4 vehicles start from the logistics center in Yongsan, 2 vehicles should start from Gunpo, leading to an increase in delivery distances and, consequently, higher CO
2 emissions.
6. Conclusions
This study proposed the optimal location for an urban logistics center to minimize the total CO2 emissions to make the logistics network of the ‘L’ medical company sustainable. According to the numerical experiment results, the location with high accessibility and capacities was identified as the location that could minimize the total CO2 emissions. Additionally, in areas adjacent to the optimal location, which have excellent accessibility but insufficient logistics center capacity due to high real estate prices, the increased delivery distance resulted in higher CO2 emissions. These findings demonstrate that the CO2 emissions were reduced by 66.74% compared to the existing logistics network of the ‘L’ medical company.
The contributions of this study can be divided into practical and theoretical implications. Firstly, this study proposes a mathematical model that can derive the optimal location of logistics centers to minimize the total CO
2 emissions from all vehicles. Selecting the location of a logistics center requires consideration of various factors, including economic, environmental, social, and political aspects. In this situation, the theoretical implication of this study is that it establishes a model for selecting the location of logistics centers based on the environmental aspects to construct a sustainable logistics network. The practical implications of this study are as follows. This study reflects the actual trends in the logistics industry and provides important insights for company decision making. The logistics industry is highly interested in constructing smart and green logistics infrastructure and the construction of MFC in urban areas as national projects [
34]. In this situation, this study is meaningful in that it proposed the optimal location for a new logistics center using actual data from the ‘L’ medical company. It can be considered a highly realistic study as it incorporates actual data and reflects the logistics network currently in operation from the ‘L’ company. Recently, industries that provide delivery services by using land vehicles such as buses, taxis, and trucks are seeking to reduce environmental burdens by utilizing electric vehicles [
35]. In this situation, the result in minimizing CO
2 emissions in the study is evaluated as one of the suitable methods to deal with environmental issues and enable a company to practice sustainable management.
This study only considered routing between logistics centers and demand areas and did not account for routing between pharmacies within demand areas. CO
2 emissions vary depending on the vehicle’s speed, the length of congested areas, and the road’s gradient. Therefore, future research plans to consider additional factors that can influence vehicle CO
2 emissions, as mentioned previously. Moreover, as these considerations increase, there is a possibility that the optimal location may shift to another location. Additionally, constructing a smaller-scale logistics center implies the need for multiple logistics centers in Seoul. Thus, research can be conducted to determine the optimal number of logistics centers and their optimal locations. Moreover, factors for selecting the location of urban logistics centers may include not only environmental factors but also various economic, social, and political factors. Therefore, future studies will address practical considerations for logistics center location selection by integrating factors such as infrastructure, geographical connectivity, transportation costs, and market demand [
36]. Also, this study has set a single objective of minimizing CO
2 emissions and utilized heuristic algorithms to derive insights. However, future studies will design reward functions that assign weights to multiple optimization objectives such as equilibrium in delivery volume per vehicle and suitability for government regulations [
37]. These multi-objective functions will be used in reinforcement learning to address the problem [
38]. Furthermore, the study aiming to ensure sustainable management of resources and contribute to environmental protection and public health through the implementation of green logistics systems is actively underway [
39]. Therefore, future studies also aim to explore optimizing the network of waste collection vehicles in urban areas or optimizing systems that bundle delivery tasks to adjacent destinations for execution.