Stackbox Advanced Vehicle Route Optimization Engine

Venktesh Kumar

MD, Co-Founder | Stackbox

‍

‍

‍Introduction

In today's fast-paced customer service  landscape, businesses face mounting challenges, including rising fuel costs,delivery delays, and inefficient fleet utilization. Vehicle route optimization is no longer optional—it’s essential for companies looking to maintain a competitive edge. As a core challenge in logistics and transportation, optimizing vehicle routes impacts businesses that depend on seamless   deliveries, timely pickups, and effective fleet management. The field of Operations Research (OR) provides the mathematical foundation for solving these complex routing problems, which are often described using acronyms such as:

1. TSP (Traveling Salesman Problem)
2. VRP (Vehicle Routing Problem)
3. CVRP (Capacitated VRP)
4. HFVRP (Heterogeneous fleet VRP)
5. PDVRP (Pickup and Delivery VRP)
6. CVRPTW (Capacitated Time Window VRP)

These models define how routes should be planned based on business constraints such as distance, capacity, pickup/delivery sequencing, and time windows.

Key Routing Models and Constraints

A.  Traveling Salesman Problem (TSP)

The TSP is one of the simplest routing problems. It involves finding the shortest  possible route for a single vehicle to visit a set of locations before returning to its starting point. In modern optimization platforms like StackBox,the TSP is referred to as a "Vehicle model," where:

• The vehicle has a start and end location.
• The sequence of stops is optimized to minimize the total travel distance.
• Distance and Travel time calculations are based on geographical coordinates using the actual  road distance and traffic speed.

B.   Vehicle Routing Problem (VRP)

‍

The VRP extends the TSP by considering multiple vehicles and the need to distribute deliveries efficiently among them. VRP models help businesses optimize fleet operations by:

• Assigning delivery requests to the most suitable vehicle.
• Minimizing overall travel distance and fuel consumption.
• Ensuring balanced workloads across vehicles.

C.   Capacitated Vehicle Routing Problem (CVRP)

This  Model involves a fleet of identical vehicles with a fixed capacity. Capacity constraints ensure that a vehicle does not exceed its carrying limit while minimizing total cost. In a delivery network:

• Each vehicle has a  maximum number of deliveries P)/maximum weight (W)/maximum volume (V) it  can carry.
• Overloading a vehicle is prevented by optimizing assignments accordingly.

D.   Heterogeneous Fleet VRP (HFVRP):

This  Model is similar to CVRP except this model considers fleets composed of  different types of vehicles, each with varying capacities and costs. Utilizing A heterogeneous fleet can lead to significant cost savings in many practical  applications.

E:  Pickup and Delivery Sequencing (PDVRP):

This model focuses onscenarios where items must be picked up from specific locations and  delivered to others. Each pickup and delivery pair is linked, and routes must  be planned to respect these dependencies.

 Pickup and deliveryconstraints enforce logical order in routes. For example:

• A courier cannot  deliver a package before picking it up.
• Restaurant food deliveries must ensure that pickup occurs before customer delivery.

F. CVRPTW (CapacitatedTime Window VRP)

This  variant adds time constraints to the CVRP. Each customer must be servicedwithin a specific time window, and vehicles may wait if they arrive early. The goal remains to minimize total cost while adhering to both capacity and timeconstraints.

Time windows define the allowable time range for arrivals at each location.

• Reducing failed delivery attempts.
• Ensuring  Deliveries occur when the recipient is available.
• Synchronizing fleet schedules with customer expectations.

G.  Other Variants:

• Multi Compartment  Routing: A multi-compartment vehicle routing problem (MCVRP) that aims to plan the delivery of different products to a set of geographically dispatched  customers. It is applicable in many industries e.g. QSR industry where truck has dry, frozen and/or chilled compartments.
• Electric Vehicle  Routing:Addresses the challenges associated with electric vehicles, such as limite range and the need for recharging at specific locations.
• Dynamic ReRouting: Considers real-time changes like traffic conditions or new customer requests, allowing for route adjustments on-the-fly.
• AMR Routing : Intralogistics use  case like Autonomous vehicle routing within a facility.  

VRP Optimization in Practice: Stackbox Approach

Stackbox advanced VRP optimizer allowusers to configure a large number of real world constraints like capacity, cost,availability, exclusive zone, pickup/delivery rules, and time windows etc. toensure that Plan output is the most practical and feasible plan output fromexecution perspective.

a.   Objective Function:

The primary objective of the Stackbox VRP optimizer is to minimize the total cost to serve while ensuring adherence to service level commitments. This is  achieved through:

• Reducing total travel time  by optimizing routes dynamically based on real-time traffic conditions.
• Minimizing the number  of vehicles required  while ensuring timely delivery windows are met.
• Optimizing fleet utilization  by balancing loads across vehicles and reducing empty return trips.
• Enhancing customer  satisfaction  by ensuring on-time deliveries within the required constraints.

‍b.    Key Constraints Considered in Optimization

    1.  Time Constraints

• Maximum work duration per driver
• Travel time between  stops.
• Service time per  delivery (based on number of lines, volume, cash on delivery, etc.)
• Distance from the parking area to the delivery location
• Customer-specific  waiting time requirements

    2.  Vehicle Constraints

• Vehicle type and  capacity (weight, volume, # of handling units limitation
• Multi-trip  constraints for return and additional deliveries.
• Compatibility between  specific customer requirement
• Compatibility between specific customerrequirements and vehicle types.
• Multi Compartment  constraints e.g. Dry and Froze.
• Fixed and Variable   costs for vehicles
• Max distance covered by a vehicle e.g. Electric Vehicles
• Vehicle service zone  constraints due to Permit requirement etc.

    3.  Drop Point Constraints

• Defined time windows   for delivery visit
• Maximum number of  drop points per route to maintain efficiency

    4. Traffic Constraints

• Real-time and historical traffic speed data for routeplanning.
• No-entry time restrictions for certain vehicle types inurban areas.
• Vehicle-type-specific lane access constraints.

     5.   Order Servicing  Constraints

• Delivery window commitments for different customers &orders
• Planning at Customer vs Order vs Line vs Line Splitlevel  
• Flexibility in order batching to optimize multi-shipmentfulfillment.

     6.  Product Constraints

• Transportation compatibility based on product sensitivity(e.g., fragile, perishable, high-density, low-density items).
• Loading constraints for balancing vehicle weightdistribution.

     7.  Warehouse Constraints

• Facility working hours and cut-off times for orderprocessing.
• Throughput capacity of distribution centers toprevent bottlenecks

            Stackbox  Vehicle  Route Optimization deployed in Real-World Scenarios:

      1. Direct to Store Deliveries

• Optimized delivery routes for retailers receiving shipments directly from distribution center

     2. Direct to Home  Deliveries

• Efficient home delivery planning for E-commerce shipmentswith real-time tracking and customer notifications.
• Supports cash on delivery, contactless delivery,and multi-attempt strategies for failed deliveries

     3. First Mile Pickup  Optimization

• Streamlined pickup operations from suppliers, warehouses,or collection hubs.
• Reduces empty miles and ensures smooth inbound logisticsfor further distribution

     4. Drop-shipping to End  Customers (Multiple Dependent Picks & Drops)

• Enables direct order fulfillment from suppliers to endcustomers without intermediate storage.
• Optimizes multi-stop routes with dependency handling for interlinked deliveries.

     5. Field Service Dispatch  Optimization

• Ensures efficient routing of field service personnel formaintenance, repairs, or installations.
• Reduces service response time and optimizes technicianworkload allocation

     6. Backhaul Operations

• Utilizes return trips effectively by assigning pickuploads on the way back.
• Reduces transportation costs and optimizes vehicleutilization.

     7. Interleaving (Multiple Independent Picks and Drops)

• Intelligent sequencing of multiple independent pickupsand drop-offs.
• Optimizes fleet efficiency by reducing empty trips andbalancing route loads.

‍

‍

Stackbox's Vehicle Routing Optimization is built to address the growing complexities of last-mile logistics. With cutting-edge technology, real-time optimization, and data-driven decision-making, businesses can enhance delivery efficiency, reduce costs, and improve customer satisfaction. Our commitment to innovation ensures that logistics operations remain agile and competitive in a rapidly evolving market.

"Businesses that adopt advancedroute optimization can achieve up to 20% cost savings and 30% service levelimprovements. Contact Stackbox today for a demo and see how our technology cantransform your logistics operations."

‍

For more details, visit Stackbox.xyz.

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍‍

‍

‍

‍

‍

‍

‍

‍

‍

‍