Optimizing Logistics and Transport Planning with Smart Mapping Solutions

Since 2019, j‑labs has been a Technology Partner for a global logistics provider, delivering highly specialized engineering teams. The core focus is backend development (Java/Kotlin) and frontend development (Angular).

The projects described above were executed by a cross-functional team of:

• 1x Team Leader
• 4x Backend Developers
• 1x Frontend Developer

The engineering team, in collaboration with the client, has delivered innovative solutions. High-quality project execution and effective cooperation have led to positive feedback and an ongoing partnership.

GeoData Solutions

Project

The primary goal of this project is to provide accessible, precise, and up-to-date geographical data alongside modern tools supporting logistics. We support all business units and regions within the company by delivering solutions tailored to their individual needs. We leverage advanced digital mapping technologies and diverse data sources—both internal and external—to create and manage dedicated geographic solutions that address the most demanding business requirements.

Tech stack

Kotlin, Spring Boot, Hibernate, PostgreSQL, Docker, Kubernetes, Kafka, AWS, Angular, Angular Material, Karma, RxJS, ELF (an alternative to NgRx)

Task

• Develop a user interface and API for visualizing geographical data on maps.
• Support the presentation of postal codes for selected regions and offer advanced visualization features.
• The application had to include:
  • Highlighting regions based on their assignment to logistics terminals.
  • Visualize parcel transport routes, including intermediary terminals (e.g. routes from a terminal in city a to postal codes in different country).
  • Mark regions based on predicted delivery times (e.g. 1, 2, 3 days).

Implementation

• Designed an intuitive user interface for dynamic map visualizations tailored to business needs.
• Implemented an API supporting queries for routes, estimated delivery times, and logistics terminal assignments.
• Developed visualizations of transport routes as lines and points on maps and color-coded regions based on delivery time predictions.
• Integrated advanced digital mapping technologies with internal and external geographical data sources.
• Optimized application performance for seamless operation with large datasets and complex queries.

Route Optimalization

Project

The project aimed to integrate with a cloud-based transport planning system to simplify transport planning and carrier route management. This integration automated several processes related to transport and geographical data analysis, such as optimizing routes considering road constraints and estimating toll charges based on vehicle specifications like the number of axles and weight.

Tech stack

Kotlin, AWS, Spring Boot, Kubernetes, Hibernate, Docker

Task

• Enable the use of a third-party transport optimization system.
• Build a proxy that allows users to interact with the external system.
• Develop and deploy the system.

Implementation

Integrated with the transport optimization system and utilized its APIs:

• Routing: Planning routes considering time, distance, and road restrictions.
• Geocoding and Reverse Geocoding: Converting addresses to coordinates and vice versa.
• Traffic Information: Providing real-time traffic data.
• Toll Calculation: Estimating toll charges.

Self-hosted Mapping Solution

Project

This project involved creating and setting up a locally hosted map server to eliminate the need for external service subscriptions. The solution supports user interface (UI) customization, adding new map layers, and modification of the existing structure.

Tech stack

JavaScript, CSS, mapping library, Docker

Task

• Create a locally hosted map server.
• Customize map appearance and functionality to remove reliance on external providers.

Implementation

• Established an environment for a self-hosted map server.
• Deployed map instances using an open-source mapping library.
• Supplied the server with map layers in .mbtiles format.
• Tailored map appearance and functionality to application-specific needs.
• Resolved migration-related challenges.

Optimized Ferry Operations

Project

This project focused on developing a system to assist dispatchers. When transporting goods requires ferry services, dispatchers need to:

• Obtain ferry schedules from operators.
• Check the availability of space on ferries (height, width, length).
• Place bookings if enough space is available for shipments.
• Track transport by monitoring when the cargo:
  • Arrives at the port.
  • Is loaded onto the ferry.
  • Is unloaded at the destination port.
  • Leaves the port.

Previously, these tasks were done manually through phone calls or individual operators’ websites.

Tech stack

Kotlin, Spring Boot, JUnit, Kotest, Wiremock, Testcontainers, PSQL, Flyway, MuleSof

Task

• Develop a system to simplify the ferry transport management process.
• Create an easily integrable system with ferry operators.
• Design user-friendly APIs for dispatchers.
• Integrate with internal logistics systems for centralized process management.

Implementation

• We collected information from each carrier about ferry schedules and the cargo capacity available for each voyage, which allowed us to place bookings and track transport.
• The system was developed to integrate a unified API with ferry operators’ APIs.
• System was integrated with multiple ferry operators.
• The database, WireMock, and Spring Context were initialized only once to optimize performance.
• WireMock was used to mock the operators’ APIs.