Optimizing Routes for Sustainability: A Comparative Analysis of Parcel Distribution Methods in South Jakarta

This study focuses on improving the efficiency of PT. XYZ’s logistics services by examining the optimization of parcel distribution routes in South Jakarta. It compares the Nearest Neighbor Method and the Clarke and Wright Savings Method to determine the optimal strategy for delivering 53 packages to 8 locations. The Nearest Neighbor Method generates two routes, one encompassing 32 packages across four locations and the other encompassing 21 packages across four locations. This method significantly reduces travel distance, saving 27,3 kilometers in comparison to actual courier routes. The Clarke and Wright Savings Method generates two routes, one covering 35 packages across six locations and the other covering 18 packages across two locations. Compared to the actual van courier route, this method reduces the distance by 24,8 kilometers. The Nearest Neighbor Method reduces travel distances by 27.3 kilometers compared to actual routes, surpassing the Clarke and Wright Savings Method by 2.5 kilometers. Reduced travel distances reduce fuel consumption and greenhouse gas emissions, making logistics operations more sustainable on an environmental and economic level. This study concludes that the Nearest Neighbor Method is the optimal strategy for improving parcel distribution efficiency in South Jakarta, thereby substantially reducing the travel distances of PT. XYZ’s logistics operations.


Introduction
In the ever-changing world of logistics and transportation of goods, businesses face a landscape with shifting challenges and opportunities.This environment is influenced by globalization, the rapid growth of e-commerce, and shifting consumer preferences.As globalization creates new international markets, it also poses difficult supply chain management challenges.The expansion of e-commerce has transformed shipping and delivery expectations, necessitating more rapid and adaptable services.Constant innovation is driven by evolving consumer demands, such as the need for faster deliveries and improved tracking.In light of these profound changes, logistics companies must maintain a leading position in terms of innovation and operational efficiency to thrive.
PT. XYZ is a company that operates within the logistics and goods delivery industry, with a focus on providing services to its customers.The company, which was founded in 1984, has accumulated more than three decades of expertise in the realm of logistics.PT.XYZ is distinguished by its comprehensive logistics approach, known as the One Stop Logistics concept, which encompasses the delivery requirements of both the general populace and clients, encompassing a wide range of activities from upstream to downstream.The company has implemented a range of innovative services that include International Express, Logistics Warehouse, Distribution Trucking, Domestic Express, International Freight, Custom Clearance, Enabler, and Transit Warehouse.PT.XYZ has established a widespread presence throughout Indonesia, operating through a network of 22 stations and branches.This extensive infrastructure enables the company to effectively conduct its operations across the country.In order to support its various activities, PT.XYZ has employed a workforce consisting of 1,000 personnel.
PT XYZ has established partnerships with numerous companies as a vendor, primarily operating at the HLP station situated in Pondok Pinang.The South Jakarta area exhibits the highest level of dominance in the utilization of shipping services provided by PT XYZ.This region is home to numerous companies that have established collaborative partnerships with PT XYZ Group, resulting in the seamless and customary exchange of documents and other related materials among these entities.Enhancing service quality poses a significant challenge for the organization.One potential strategy for optimization involves identifying the most efficient route within the designated destination area.The determination of the route traversed by the transportation equipment employed by the company can be achieved through the process of calculation.The methods used as the basis of this optimization program are Nearest Neighbor and Clarke and Wright Savings to find the most optimal route.
The objective of the current study is to evaluate the results of route optimization calculations in the South Jakarta area, with a specific focus on the implementation of the Nearest Neighbor Method and the Clarke and Wright Savings method.Additionally, a comparative analysis will be conducted to assess the effectiveness of these two techniques.
It is imperative to underscore the congruence between this research and the United Nations Sustainable Development Goals (SDGs), a worldwide endeavor crafted to tackle urgent global predicaments and foster sustainable development across various facets.The optimization of transportation routes and logistics operations plays a significant role in advancing SDG 9: Industry, Innovation, and Infrastructure.This optimization process aims to improve efficiency and sustainability within the logistics industry.Furthermore, this concept aligns with Sustainable Development Goal 11, which focuses on creating sustainable cities and communities.The implementation of efficient logistics and transportation systems is crucial in mitigating issues such as congestion, emissions, and resource depletion within urban areas.By delving into sophisticated methodologies for enhancing transportation routes, this study showcases a pragmatic strategy for enhancing operational efficiency while concurrently aligning with worldwide sustainability goals.

Route Optimization
Transportation, logistics, and network routing are just a few of the domains where route optimization is a crucial operation.It involves determining the most effective and efficient routes for vehicles, buses, ships, and aircraft, as well as data transmission in wireless sensor networks.The concept of route optimization has evolved over time, expanding beyond the traditional shortest-path assumption to incorporate variables such as traffic conditions, historical data, and specific constraints.
Lima et al. [1] emphasize the importance of understanding individual routing behavior in the context of transportation.They define "optimal routes" as those suggested by a well-known online routing service that considers typical traffic conditions based on historical data.This suggests that historical information plays an important role in determining optimal routes.
Similarly, Li & Boley [2] discuss various routing strategies in the field of network routing, including shortest-path, multi-path, and potential-based ("all-path") routing.They develop a unifying theoretical framework for flow optimization with mixed 1/2-norms, highlighting the relationship between routing and flow optimization.
Companies in the transportation, logistics, and other related industries can reap numerous benefits from route optimization.These advantages include cost savings, enhanced efficiency, decreased fuel consumption, increased customer satisfaction, and improved resource allocation.Sridhar et al. [3] examine the advantages of wind-optimal operations for transatlantic flights.Consideration of wind conditions during the optimization of a flight's trajectory can reduce travel time and fuel consumption, saving airlines money.Merchán et al. [4] describe the Amazon Last Mile Routing Research Challenge, which sought innovative and improved solutions to real-world routing issues.Participants could identify opportunities for cost reduction, improved delivery performance, and increased customer satisfaction by utilizing actual operational data.In the context of capital structure decisions, Binsbergen et al. [5] emphasize the significance of companies' optimal debt decisions.They contend that the optimal capital structure exists when the marginal benefit of debt equals its marginal cost, resulting in enhanced financial performance and the creation of value.Overall, route optimization plays a crucial role in a variety of fields, providing companies with significant cost savings, efficiency improvements, and improved decision-making.

Nearest Neighbour
The Nearest Neighbour algorithm is a heuristic method commonly used for the optimization of routes.This approach entails determining an alternative pathway by ascertaining the closest distance from the initial location to the subsequent point of visitation [6].
The application of the Nearest Neighbor method in computational analysis can facilitate the resolution of problems by identifying the most optimal solutions.This methodology is suitable for generating a route within the context of goods distribution, as it aims to ascertain the most efficient travel route, thereby optimizing the overall distance of delivery [7].

Clarke and Wright Savings
The savings matrix technique known as the "Clarke and Wright Savings" method was initially devised by Clarke and Wright in 1964 [8].This methodology functions as a valuable instrument for addressing issues pertaining to the computation of optimal pathways.The algorithm computes the nodes by considering the maximum savings distance, thereby enabling the identification of the optimal route.The Clarke and Wright Savings method incorporates the consideration of the vehicle's load weight capacity into its computational procedures [9].The calculation for determining the savings distance can be derived using the following formula:

Sik= Total distance Cio= Distance between point 1 to depot Coj= Distance between point 2 to depot Cij= distance between point 1 and to 2
The savings formula is utilized to determine the total distance between the depot node (i) and the starting point (o), taking into account the distance between the depot node (i) and the first delivery point (j), and subsequently subtracting the distance between the starting point (o) and the second delivery point (j).The algorithm employed in the Clarke and Wright Savings Method calculates the aggregate savings in distance in order to ascertain the necessary delivery distance.This approach takes into account both the distance and the capacity of the vehicle in order to optimize the overall distance and time effectively [10].

Methodology
This study utilizes quantitative methods by combining two specific optimization methods in logistics and routing: Nearest Neighbor and Clarke and Wright Saving.The data was collected through direct engagement in the package distribution activities within the South Jakarta region, involving active participation and careful observation.In addition, the author collected essential data pertaining to the frequented destinations of couriers, the distances separating delivery locations, and the volume of dispatched items.
PT. XYZ is a corporate entity that specializes in the provision of parcel delivery services.This organization has established collaborative alliances with clients in multiple urban areas.South Jakarta is renowned for its significant customer base and frequent delivery demands.The provided dataset presents a compilation of buildings that engage in collaborative efforts with PT.XYZ in the South Jakarta region.This dataset encompasses a total of eight locations, namely Building A, Building B, Building C, Building D, Talavera Building E, Avoskin Warehouse G, Building H, and Building I.
Building F, also known as Warehouse H LP, has been designated as the parcel delivery depot.The following data presents the distances, measured in kilometers, from Building F to various delivery locations.This information is displayed in Table 1.The data collection process was carried out through active participation in field observations during check rides with van couriers operating in the South Jakarta region.The transportation of packages to 8 designated locations within South Jakarta was carried out systematically using vans.Each van was capable of accommodating a maximum of 35 packages.Table 2 provides the relevant data pertaining to the delivery routes and corresponding distances in the specified region.
According to the data presented in Table 2, the transportation of goods to the eight designated locations within the specified area is accomplished through the utilization of two distinct routes.The reason for this limitation is due to the van's maximum capacity, which can accommodate up to 35 packages.The delivery itinerary for route 1 commences at point F and proceeds in the following order: F -> G -> B -> D -> E -> H -> F. This route entails the transportation of 35 packages and spans a total distance of 53.8 kilometers.Subsequently, the courier retraces their path back to the initial point of departure, denoted as point F, and proceeds to distribute packages along the route F -> A -> I -> C -> F. This particular delivery entails the transportation of a total of 18 packages, spanning a distance of approximately 16.6 kilometers.The courier successfully completes the delivery of 53 packages within a single day, encompassing a cumulative distance of 70.4 kilometers.The authors analyze and discuss the results obtained from the calculations based on the processed data in order to identify issues and propose solutions.

Optimization of routes using the Nearest Neighbor Method
The Nearest Neighbor Method is employed to ascertain the most efficient route for distributing packages to eight locations within the South Jakarta region.The present methodology computes the closest distance from each point under consideration.The computation of the Nearest Neighbor Method for allocating 53 packages among 8 points is presented in Table 3.The first calculation is done by determining the nearest route and calculating the total demand on route 1.Each time a delivery is made, the route calculation starts from the depot and returns to the depot.

Route 1
= F -> E -> B -> A -> H -> F Distance of Route 1 = 4.3 km + 3.2 km + 0.6 km + 0.6 km + 5.9 km = 14.6 km Demand on Route 1 = A + B + E + H = 8 packages + 9 packages + 9 packages + 4 packages = 32 packages Upon the conclusion of route 1, which commenced at the depot point F and encompassed visits to points E, B, A, H, and a subsequent return to F, a total of 32 packages were transported over a distance of 14.6 km.This route entailed the delivery of packages to 4 distinct points.The remaining points yet to be delivered will be managed from the initial departure point at F. In the context of employing the Nearest Neighbor Method for route 2, the algorithm will identify the closest unvisited destination from point F for the courier to traverse.Upon employing the Nearest Neighbor Method to compute the courier's route, the resulting routes will be amalgamated to ascertain the overall distance covered by the courier while delivering packages to the eight designated points within the specified region.

Total Route = Route 1 + Route 2 Total Route = (F E B A H F) + (F C I D G F)
= (4.3+ 3.2 + 0.6 + 0.6 + 5.9) + (7.1 + 0.8 + 4.4 + 3.7 + 12.5) = 14.6 km + 28.5 km = 43.1 km The application of the Nearest Neighbor Method to optimize the route calculation for the eight points located in the South Jakarta area resulted in a total distance covered by the courier of 43.1 km, while successfully delivering a total of 53 packages.

Optimization route calculation using the Clarke and Wright Savings Method
The process of determining the most efficient route for delivering goods to multiple destinations simultaneously is achieved through the application of the Clarke and Wright Savings Method.This method aims to optimize the distance traveled by maximizing savings in delivery distance.The calculation in this method is performed by determining the distances from each point through the utilization of the savings formula.The distance matrix data pertaining to each point can be located in Table 4.The aforementioned equation computes the amount of savings obtained by consolidating the routes from points i and j to the depot, in comparison to delivering them individually.The objective is to ascertain pairs of points (i, j) that yield substantial cost reductions when amalgamated into a singular route.The calculations of distances at each point can be seen on Table 5.
Once the distances for each point have been calculated, the subsequent step involves determining the optimal routes for the eight points based on the obtained values.The sequence of the route will be determined by calculating the distances in descending order, starting with the longest distance and ending with the shortest.The initial delivery point will be determined based on the destination with the greatest distance.Subsequent delivery routes will be established until the total demand of 35 units of goods is met.= 35 packages Upon the conclusion of Route 1, which commenced at the depot location denoted as F and encompassed a cumulative distance of 31.3 kilometers, encompassing visits to 5 delivery points and the transportation of a total of 35 packages, the outstanding delivery points shall be attended to, with the initial departure point at F being utilized.The delivery itinerary for Route 2 can be located in Table 7.The table is expected to present a comprehensive analysis of the optimized route, utilizing the Clarke and Wright Savings calculations for the remaining points.The outcome of the computation for Route 2, employing the Clarke and Wright Savings Method, indicates that the courier will transport goods along the path F -> B -> E -> F. The courier is expected to distribute a cumulative quantity of 18 packages, encompassing a combined distance of 14.3 kilometers.

Table 7. Route 2 Clarke and Wright Savings Method
Upon employing the Clarke and Wright Savings Method, the computed routes of the courier will be amalgamated to ascertain the aggregate distance covered in the process of delivering packages to the eight designated locations within the specified vicinity.The application of the Clarke and Wright Savings Method to optimize the route calculation for the eight points located in the South Jakarta area yields a total distance of 45.6 km covered by the courier, who successfully delivers a total of 53 packages.
PT. XYZ is a logistics enterprise that specializes in the provision of parcel delivery services, guided by its overarching vision of "One Stop Logistics."In light of the dynamic nature of the business environment, PT.XYZ is confronted with a growing level of competition, necessitating the organization to prioritize the maintenance of service quality.The duration of distribution is a critical factor in the context of parcel delivery.PT.XYZ engages in extensive collaboration with its customers to provide tailored package delivery solutions that align with their specific business requirements.This collaborative effort is particularly prominent in the South Jakarta delivery area, which serves as a bustling hub due to the multitude of companies that have established partnerships with PT.XYZ for their delivery needs within the region.
The author conducted an observational assessment with a van courier tasked with the distribution of goods in the South Jakarta region.The courier consistently transports packages to eight specific destinations within the South Jakarta area.During the assessment, it was noted that PT.XYZ's delivery vans operating in the Jakarta region possess a maximum load capacity of 35 packages per trip.This limitation primarily arises from the comparatively larger dimensions of the packages being distributed.
The distance from the initial departure point, Warehouse HLP (F), to its return to F was calculated by the author using the route F -> G -> B -> D -> E -> H -> F -> A -> I -> C -> F. This particular route amalgamates two distinct routes, encompassing a cumulative delivery distance of 70.4 kilometers and accommodating a total of 53 packages.Utilizing the aforementioned data, the author endeavored to devise a novel and improved route with the objective of augmenting the efficiency of parcel distribution.
The computations utilizing the Nearest Neighbor Method entail traversing the closest route starting from the depot and subsequently choosing the closest route from the delivery point.To effectively distribute a quantity of 53 items across 8 distinct locations, it is necessary to devise a logistical plan consisting of 2 delivery routes.
In the initial route, the courier will distribute a total of 32 packages and traverse a distance of 14.6 km, following the sequence of locations F, E, B, A, H, and F. Subsequently, the courier is required to transport a total of 21 packages to the remaining route that has not yet been visited.In the second route, the courier will distribute goods by following the sequence of locations F, C, I, D, G, and F, resulting in a total distance of 28.5 kilometers.This route encompasses a visit to four distinct points.The application of the Nearest Neighbor Method in the optimization calculation yields a courier's route that encompasses the delivery of 53 packages to a total of 8 distribution points.These distribution points, in sequential order, are denoted as F, E, B, A, H, F (again), C, I, D, and G, with the final destination being F. The route in question spans a cumulative distance of 43.1 kilometers.
The subsequent optimization calculation employs the Clarke and Wright Savings Method.The present methodology computes the most efficient path by evaluating the reduction in distance achieved when the courier visits multiple distribution points concurrently.The Clarke and Wright Savings Method employs a savings formula to determine the aggregate distance reduction achievable at each juncture.The distances obtained through the utilization of the savings formula are arranged in descending order to ascertain the most efficient route.The initial leg of the journey is determined by selecting the route with the greatest distance, followed by the subsequent routes in a similar manner.
By employing this approach, a comprehensive analysis reveals the identification of two optimal routes for the purpose of distributing a total of 53 packages to eight designated locations within the South Jakarta area.The initial path follows the sequence F -> D -> G -> C -> I -> A -> H -> F, encompassing a total distance of 31.3 kilometers and accommodating the transportation of 35 packages.The subsequent computation ascertains the alternative route for distributing the remaining packages to the three unvisited points.Upon completion of the second round of goods distribution, it has been determined that the optimal route to be followed is F -> B -> E -> F, resulting in a total travel distance of 14.3 kilometers.
The calculations conducted to ascertain the optimal routes for distribution within the South Jakarta region yield the subsequent outcomes: By employing the Nearest Neighbor Method, it is determined that the distribution of 53 packages to 8 designated points will necessitate the courier to undertake 2 separate trips.The initial route allocates a total of 32 packages to four designated locations, each separated by a distance of 14.6 kilometers.In contrast, the subsequent route involves the distribution of 21 packages to the same four locations but requires a longer travel distance of 28.5 kilometers.The cumulative distance covered utilizing this approach amounts to 43.1 kilometers.Through the implementation of this approach, PT.XYZ is able to achieve a reduction of 27.3 kilometers in comparison to the distance covered by the courier in reality.
In contrast, the Clarke and Wright Savings Method also generates two routes for distributing 53 items to eight points.The first route covers 35 items across six locations, spanning a total distance of 31.3 kilometers.The second route, covering 18 items between two points, is 14.3 kilometers long.Utilizing the Clarke and Wright Savings Method results in a total distribution route length of 45.6 kilometers, which is 24.8 kilometers shorter than the van courier's actual route.
Reducing travel distances has a positive impact on various aspects, including decreased fuel consumption, lower greenhouse gas emissions, and enhanced sustainability in both environmental and economic terms.These reductions are associated with lower fuel consumption, waiting times, energy usage, vehicle utilization, alternative energy sources, and reduced CO2 emissions [11,12].When comparing the Nearest Neighbor Method to the actual distribution distance, it demonstrates higher efficiency by saving 27.3 kilometers.Furthermore, it surpasses the Clarke and Wright Savings Method by an additional 2.5 kilometers in terms of efficiency.
The importance of these reductions in travel distance cannot be underestimated.They not only result in cost savings and operational improvements, but also have a significant impact in reducing the carbon emissions associated with transportation operations.This approach makes a direct contribution to sustainable logistics practices by reducing fuel consumption and emissions.It is in line with global efforts to address climate change and promote environmental responsibility.

Conclusion
The conclusions drawn from the data analysis conducted by the author provide insights into addressing the research questions at hand.
1.When utilizing the Nearest Neighbor Method for route optimization in the context of distributing 53 items among 8 points, the analysis reveals the existence of two distinct routes.
The first route encompasses a distance of 14.6 kilometers, whereas the second route spans 28.5 kilometers.Consequently, the cumulative travel distance amounts to 43.1 kilometers.2. In contrast, the utilization of the Clarke and Wright Savings Method yields two distinct routes for the aforementioned task.The first route spans a travel distance of 31.3 km, while the second route covers a distance of 14.3 km.The cumulative distance traveled for both routes totals 45.6 kilometers.. 3. The findings from both approaches demonstrate that the Nearest Neighbor Method exhibits superior effectiveness, exhibiting a discrepancy of 27.3 km in relation to the actual route and 2.5 km in relation to the Clarke and Wright Savings Method.
In conclusion, the Nearest Neighbor Method emerges as the most efficacious strategy for optimizing delivery routes, yielding considerably reduced travel distances in comparison to both the actual routes and the Clarke and Wright Savings Method.
Route 2 = F -> C -> I -> D -> G -> F Distance of Route 2 = 7.1 km + 0.8 km + 4.4 km + 3.7 km + 12.5 km = 28.5 km Demand for Route 2 = C + D + G + I = 5 packages + 7 packages + 4 packages + 5 packages 1324 (The outcome of the computation for route 2 utilizing the Nearest Neighbor Method indicates that the courier will transport goods along the path F -> C -> I -> D -> G -> F. The courier is expected to transport a cumulative quantity of 21 packages, encompassing a combined travel distance of 28.5 kilometers.

Table 1 .
Package Distribution Place Data

Table 2 .
Matrix of Routes and Actual Distances (in km) for Package Distribution in the South Jakarta Area

Table 3 .
Nearest Neighbor Method Calculation

Table 4 .
Clarke and Wright Savings Method Delivery Distance Matrix

Table 5 .
Calculations of distances using Clarke and Wright Savings

Table 6
displays the table utilized for the computation of Route 1 using the Clarke and Wright Savings Method.The following table presents a comprehensive analysis of the optimized route, taking into account the calculations of potential savings.

Table 6 .
Route 1 Clarke and Wright Savings Method