p–ISSN: 2723 - 660 e-ISSN: 2745-5254
Vol. 5, No. 10 October 2024 http://jist.publikasiindonesia.id/
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 10, October 2024 4472
Dynamic Programming Implementation for Delivery Route
Optimization in E-Commerce Logistics
Selfi Audy Priscilia
1*
, Zulfahmi Indra
2
, Fahra Pebiana Putri
3
Universitas Negeri Medan, Indonesia
Email: [email protected]
1*
, [email protected]
2
,
[email protected]
3
*Correspondence
ABSTRACT
Keywords: Dynamic
Programming; Route
Optimization; E-commerce
Logistics
The rapid growth of e-commerce has created new challenges in
logistics optimization, particularly in terms of delivery route
efficiency. This research develops a dynamic programming model
to optimize delivery routes in the context of e-commerce in
Indonesia. Using a modified Vehicle Routing Problem with Time
Windows (VRPTW) approach, we implemented an algorithm that
considers various factors such as distance, time, and cost.
Simulations using synthetic datasets showed efficiency
improvements of 18.7% in travel distance and 22.3% in delivery
time compared to conventional methods. Field trials with an e-
commerce partner resulted in a 21.5% reduction in travel distance
and an increase in on-time delivery rate from 87% to 94%.
Sensitivity analysis revealed that the algorithm's performance is
most affected by demand fluctuations and changes in traffic
conditions. Implementation challenges include integration with
existing systems and consideration of workforce impact. This
research opens avenues for further development in algorithm
scalability, integration of sustainability factors, and adaptation to
various geographical contexts, demonstrating significant potential
for improving e-commerce logistics efficiency in the future.
Introduction
The rapid development of e-commerce in recent years has significantly changed
the global trade landscape. According to a report from eMarketer (2023), global e-
commerce sales growth is expected to reach 20.8% by 2023, with a total transaction value
reaching $6.3 trillion. This growth has created new challenges in logistics and delivery,
especially in terms of efficiency and optimization of delivery routes. Efficient e-
commerce logistics are crucial to maintaining customer satisfaction and competitive
advantage. A study conducted by Saputra et al. (2024) shows that 43% of e-commerce
consumers consider delivery speed as a major factor in their purchasing decisions. This
emphasizes the importance of optimizing delivery routes to meet increasingly high
customer expectations.
Dynamic Programming Implementation for Delivery Route Optimization in E-
Commerce Logistics
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 10, October 2024 4473
One promising approach to address these challenges is the use of dynamic
programming in delivery route optimization. Dynamic programming, as a mathematical
optimization technique, has proven to be effective in solving complex optimization
problems in various fields (Rangkuti, 2014). In the context of e-commerce logistics,
dynamic programming can be used to find optimal delivery routes that minimize delivery
time and cost. Recent research by Wijayanti (2022) demonstrated that the implementation
of dynamic programming in delivery route optimization can result in a reduction in
operational costs by up to 15% and an increase in delivery time efficiency by 20%.
However, the application of dynamic programming in the context of e-commerce logistics
still requires further exploration, especially given the complexity and unique dynamics of
this sector.
Although several studies have examined the use of dynamic programming in route
optimization, such as those conducted by Garside et al. (2024), there is still a gap in the
literature regarding the specific implementation for e-commerce logistics. The unique
characteristics of e-commerce, such as high demand fluctuations, geographical variation
of customers, and tight delivery time constraints, require a more specialized approach. In
addition, the integration of dynamic programming with current technologies such as the
Internet of Things (IoT) and artificial intelligence (AI) opens up new opportunities to
improve the accuracy and effectiveness of route optimization. Research by Guan et al.
(2023) showed that the combination of dynamic programming with real-time data
analysis can improve the precision of delivery time prediction by up to 30%.
In the context of Indonesia, the growth of e-commerce reaching 31% by 2022
(Bank Indonesia, 2023) reinforces the urgency of this research. Indonesia's geographical
challenges as an archipelago with diverse infrastructure add to the complexity of
optimizing delivery routes, making implementing dynamic programming even more
relevant and important. Given the significant potential of dynamic programming in
improving e-commerce logistics efficiency, as well as the gaps in existing research, this
study aims to explore and implement dynamic programming for delivery route
optimization in the context of e-commerce logistics in Indonesia. This research is
expected to contribute both theoretically and practically to improving the efficiency and
effectiveness of e-commerce logistics operations.
Based on the background that has been described, this research focuses on three
main problem formulations. First, how the implementation of dynamic programming can
optimize delivery routes in e-commerce logistics. Second, what is the effectiveness of
dynamic programming in improving the efficiency of delivery time and cost compared to
conventional methods. Third, what are the challenges and constraints in the
implementation of dynamic programming for delivery route optimization in the context
of e-commerce in Indonesia. To answer these questions, this research has three main
objectives. The first objective is to develop and implement a dynamic programming
model for delivery route optimization in e-commerce logistics. The second objective is to
analyze the effectiveness of dynamic programming in improving the efficiency of
delivery time and cost compared to conventional methods. The third objective is to
Selfi Audy Priscilia, Zulfahmi Indra, Fahra Pebiana Putri
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 10, October 2024 4474
identify challenges and constraints in the application of dynamic programming for
delivery route optimization in the context of e-commerce in Indonesia and formulate
solutions to overcome them.
This research is expected to provide significant benefits both theoretically and
practically. Theoretically, this research will enrich the literature on dynamic
programming applications in e-commerce logistics, especially in the context of
developing countries such as Indonesia. Practically, the results of this research can be a
reference for e-commerce and logistics companies in implementing dynamic
programming to improve their operational efficiency. In addition, for policymakers, this
research can provide insights into developing regulations and infrastructure that support
the optimization of e-commerce logistics in Indonesia. Thus, this research not only
contributes to the development of science but also has practical implications that can
encourage the growth and efficiency of the e-commerce sector in Indonesia.
Methods
This research adopts a quantitative approach with a focus on developing and
implementing dynamic programming algorithms for delivery route optimization in the
context of e-commerce. The research method used combines mathematical modeling
techniques, computer simulation, and empirical data analysis to produce a comprehensive
and applicable solution. The first stage of the research involves developing a
mathematical model that represents the delivery route optimization problem. The model
will consider various key variables such as distance between delivery points, travel time,
operational cost, vehicle capacity, and delivery time constraints. The problem formulation
will utilize a modified Vehicle Routing Problem with Time Windows (VRPTW)
approach to suit the unique characteristics of e-commerce logistics.
Furthermore, a dynamic programming algorithm will be developed based on the
mathematical model. The algorithm will be designed to break down complex route
optimization problems into smaller sub-problems that can be solved recursively. This
approach allows the algorithm to find the global optimal solution by considering all
possible route combinations. The implementation of the algorithm will use the Python
programming language, which was chosen due to its flexibility and the availability of
relevant libraries such as NumPy and SciPy for numerical computing. To test the
effectiveness of the algorithm, simulations will be conducted using a synthetic dataset
that reflects the characteristics of e-commerce delivery in Indonesia. This dataset will
cover various scenarios, including variations in the number of delivery points,
geographical distribution, and demand fluctuations. Simulations will be conducted using
AnyLogic software, which enables complex system modeling and "what-if" scenario
analysis.
Validation of the models and algorithms will be done by comparing historical data
from e-commerce and logistics companies in Indonesia. For this, partnerships will be
established with several leading e-commerce companies to gain access to their operational
data. The comparative analysis will involve key performance metrics such as total
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Commerce Logistics
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mileage, delivery time, and operational cost. To assess the superiority of the proposed
method, a performance comparison with conventional route optimization methods such
as genetic algorithm and tabu search will be conducted. The evaluation will include
aspects of solution quality (i.e., how optimal the generated route is) and computational
efficiency (i.e., the time taken to create the solution).
Sensitivity analysis will also be conducted to test the robustness of the algorithm
to changes in input parameters such as demand fluctuations, changes in traffic conditions,
and variations in operational costs. This is important to ensure that the algorithm can
adapt to the fast-changing dynamics of e-commerce logistics. Finally, the algorithm's
implementation in a real operational environment will be tested through a pilot project
with one of the e-commerce partners. This will enable the evaluation of the algorithm's
performance under real conditions and the identification of potential implementation
challenges. Feedback from end users, including logistics managers and couriers, will be
collected for further refinement.
Throughout the research process, ethical considerations will be observed,
especially when it comes to the use and protection of customer data. All data used will go
through an anonymization process to protect individual privacy and business
confidentiality. In addition, the potential impact of route optimization on courier working
conditions will also be evaluated to ensure that increased efficiency does not come at the
expense of worker welfare. With this comprehensive methodology, the research aims to
produce a route optimization solution that is not only theoretically effective but also
applicable and useful in the context of e-commerce operations in Indonesia. The results
of the research are expected to contribute significantly to the improvement of e-commerce
logistics efficiency and, ultimately, support the growth of the digital economy in the
country.
Results and Discussion
Dynamic Programming Model Development
This research successfully developed an effective dynamic programming model
for delivery route optimization in the context of e-commerce. The model is based on a
modified Vehicle Routing Problem with Time Windows (VRPTW) formulation to
accommodate the unique characteristics of e-commerce logistics in Indonesia. The
developed algorithm uses a bottom-up approach, where the optimal solution is built
incrementally from smaller sub-problems. Bellman's optimality principle is used as the
basis to ensure that every decision taken at each stage is optimal (Cox, 2021).
Table 1. Main Components of Dynamic Programming Model
Component
Description
Function
Objective Function
Total cost minimization
f(i,S) = min{cij + f(j,S-{j}) : j ∈ S}
Decision Variable
Route selection
xij = 1 if route i to j is chosen, 0
otherwise
Limitations
Vehicle capacity, time
windows
∑q_i <= Q, a_i <= t_i <= b_i
Selfi Audy Priscilia, Zulfahmi Indra, Fahra Pebiana Putri
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 10, October 2024 4476
Optimization
Technique
Memorization
Avoid repetitive calculations
The implementation of this algorithm uses memoization techniques to avoid
repeated calculations of the same sub-problem, thus improving computational efficiency.
Our research shows that this approach can reduce the time complexity from O(n!) to
O(n^2*2^n), where n is the number of delivery points.
Algorithm Performance Analysis
To evaluate the performance of the developed algorithm, we conducted a series
of simulations using a synthetic dataset that reflects the characteristics of e-commerce
delivery in Indonesia. This dataset includes variations in the number of delivery points
(50, 100, 200, 500), geographical distribution (urban, suburban, rural), and demand
fluctuations (low, medium, high).
Table 2. Comparison of Algorithm Performance
Methods
Distance (km)
Cost (Rp)
Efficiency (%)
Dynamic Programming
387.5
2,325,000
100 (Reference)
Genetic Algorithm
456.2
2,737,200
84.9
Tabu Search
442.8
2,656,800
87.5
Simulation results show that the developed dynamic programming algorithm is
able to generate optimal routes in terms of total travel distance and delivery time.
Compared to heuristic methods such as genetic algorithm and tabu search, our algorithm
shows an average efficiency improvement of 18.7% in terms of total mileage and 22.3%
in terms of delivery time. This result is in line with the findings of (Zenezini et al., 2022),
who reported an efficiency improvement of 15-20% using dynamic programming in the
context of urban logistics. However, our study shows a more significant improvement,
which may be due to the additional optimizations we applied to the specific characteristics
of e-commerce in Indonesia.
Sensitivity Analysis
To test the robustness of the algorithm, we conducted sensitivity analysis on
various input parameters. The results show that our algorithm remains effective under
various scenarios, but its performance is most sensitive to changes in demand patterns
and traffic conditions.
Table 3. Algorithm Sensitivity Analysis
Parameters
Change (%)
Effect on Efficiency (%)
Demand Fluctuations
+50
-7 to -12
Traffic Conditions
+100
-15
Total of points
+100
-5
Time Windows
-50
-8
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High demand fluctuations (coefficient of variation > 0.5) lead to a 7-12% decrease
in efficiency, while extreme changes in traffic conditions can reduce efficiency by up to
15%. These findings emphasize the importance of integrating real-time data and
predictive techniques in the practical implementation of algorithms, in line with the
recommendations of (Herdiana, 2023).
Implementation in a Real Operational Environment
A pilot project conducted with one of Indonesia's leading e-commerce partners
provided valuable insights into the practical implementation of the algorithm. During the
3-month trial period, the company reported a 21.5% reduction in total mileage and an
increase in on-time delivery rate from 87% to 94%.
Table 4. Results of Pilot Project Implementation
Performance Metrics
Before Change
After Change
Change (%)
Total Distance Traveled (km)
15.000
11.775
-21.5
On-Time Delivery Rate (%)
87
94
+7
Operating Costs (Million IDR)
90
73.5
-18.3
Customer Satisfaction (Scale 5)
3.8
4.2
+10.5
However, the implementation also revealed some operational challenges. One of
them is the need for seamless integration with existing inventory management and
shipment tracking systems. (Pambudi et al., 2024) Also identified similar challenges in
their study on e-commerce logistics optimization in China. Feedback from end users,
including logistics managers and couriers, was generally positive. 85% of respondents
reported that the algorithm helped them make better routing decisions. However, 23% of
couriers expressed concerns about increased workload due to more efficient routes. This
highlights the importance of considering human factors in the implementation of
optimization technologies, a point also emphasized by (Siti Masrichah, 2023) in their
study on the impact of AI on the logistics workforce.
Theoretical and Practical Implications
Theoretically, this research extends our understanding of the application of
dynamic programming in the context of e-commerce logistics. We demonstrate that with
appropriate modifications, these classical techniques can be highly effective in addressing
the unique complexities and dynamics of e-commerce delivery.
Table 5. Theoretical and Practical Implications
Research
Aspects
Theoretical Implications
Practical Implications
VRPTW
Model
Develop a more comprehensive VRPTW model,
including various constraints (e.g., waiting time,
time window, vehicle capacity) and objectives
(e.g., minimization of distance, time, and cost).
Improved accuracy in route
planning, thereby optimizing
resource usage and reducing
operational costs.
Selfi Audy Priscilia, Zulfahmi Indra, Fahra Pebiana Putri
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 10, October 2024 4478
Algorithm
Development of more efficient algorithms (e.g.,
heuristics, metaheuristics) for solving large-scale
VRPTW problems
Faster decision-making, so as to
respond to real-time changes in
demand and increase operating
flexibility.
Data
Integration
Development of a framework for the integration
of real-time data (e.g., traffic data, demand data)
into the route planning system
Better adaptation to changing
dynamic conditions, thereby
improving reliability and
operating efficiency.
Social
Impact
Understanding of the impact of automation on the
workforce (e.g., role changes, training needs)
The need for effective change
management, including training
programs and organizational
restructuring.
The mathematical model we developed, which incorporates factors such as time
windows, variable vehicle capacity, and customer preferences, provides a more
comprehensive framework compared to the standard VRPTW model. This is in line with
the trend identified by (Adirinekso et al., 2024) on the need for more nuanced
optimization models in e-commerce. From a practical perspective, this research offers
solutions that can be applied directly by e-commerce and logistics companies to improve
their operational efficiency. The significant reduction in mileage and delivery time
observed in our pilot project shows great potential for cost savings and improved
customer satisfaction.
Challenges and Opportunities for Future Research
While this study's results are promising, several challenges remain and open up
opportunities for further research. One key area is the integration of real-time data into
the optimization model. While our algorithm is able to handle demand fluctuations and
changing traffic conditions in simulation, the practical implementation of a system that is
fully responsive to real-time changes is still a significant technical challenge(Harahap, L.
M. et al., 2024).
(Cahyaningati & Vikaliana, 2021) Has started to address this issue by proposing
a framework for IoT data integration in dynamic route optimization. Future research can
build on this work to develop more adaptive and responsive systems. Another challenge
identified is the scalability of the algorithm for very large-scale e-commerce operations.
While the performance of our algorithm remains robust for up to 500 delivery points, its
computational efficiency decreases significantly for larger numbers. The use of parallel
and distributed computing techniques may be necessary to address this issue, as suggested
by (Bello et al., 2020) in their study on large-scale logistics optimization.
Another aspect that requires further research is the integration of sustainability
factors into the optimization model (Alam & Mustafa, 2024). With the increasing
awareness of the environmental impact of logistics operations, there is a need to develop
algorithms that not only optimize operational efficiency but also minimize carbon
emissions. A recent study by (Purbasari et al., 2020) has begun to explore the trade-off
between economic efficiency and sustainability in e-commerce logistics, providing a
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Commerce Logistics
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 10, October 2024 4479
good basis for further research in this area. Finally, while our research focuses on the
Indonesian context, cross-country comparisons can provide valuable insights into how
factors such as infrastructure, regulation, and consumer behavior affect the effectiveness
of route optimization algorithms. This can help in the development of more customizable
and effective solutions for various geographical and economic contexts.
Conclusion
This research has successfully developed and implemented a dynamic
programming model for delivery route optimization in the context of e-commerce
logistics in Indonesia. Through a series of simulations and field trials, we demonstrate
that the developed algorithm is able to significantly improve delivery efficiency, with a
reduction in distance traveled of up to 21.5% and an increase in on-time delivery rate of
7%. The model shows consistent advantages over conventional methods such as genetic
algorithm and tabu search. Nonetheless, the research also revealed some challenges in
practical implementation, including the need for seamless integration with existing
systems and consideration of the impact on the workforce. Sensitivity analysis shows that
the performance of the algorithm is most affected by demand fluctuations and changes in
traffic conditions, emphasizing the importance of real-time data integration in future
developments. This research paves the way for further exploration in several key areas,
including improved scalability of the algorithm, integration of sustainability factors, and
adaptation of the model to various geographical contexts. By continuing to develop and
refine this approach, we can expect significant improvements in the efficiency and
sustainability of e-commerce logistics in the future.
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