🗃️ Models
3 items
📄️ Finalization Type
The Finalization Type is a post-processing instruction that fine-tunes the exact scheduled time for a stop after the optimal route has already been determined.
📄️ Booking Penalty
A Booking Penalty (also known as an "order penalty") is an artificial cost, set as an integer, that you assign to a specific order. This penalty is applied only if the optimization engine chooses not to fulfill that order, leading to a "no offer" status.
📄️ Vehicle Capacity and Node Demand
In Vehicle Routing Problems (VRP), vehicle capacity and node demand are fundamental constraints that define the problem's boundaries. The optimizer's goal is to create the most efficient routes while ensuring that the vehicle's capacity is not exceeded at any point along its route.
📄️ Route compactness
In the Vehicle Routing Problem (VRP), optimizing routes involves more than just vehicle capacity and customer demand. Route compactness and clusterization are two key concepts used to manage and optimize routes based on spatial location.
📄️ Compound Zones: Setting Entry and Exit Time Penalties
Certain delivery or pickup areas, like large industrial parks, gated communities, or restricted city centers, require additional time simply to enter or exit the area, regardless of the specific stop location within it. These "compound zones" might involve navigating security gates, internal road networks, or specific traffic patterns. This model is supported by SWAT APIs using a compound_zones parameter to add these time penalties.
📄️ Limiting capacity of the warehouse
Warehouses often have limited capacity, requiring restrictions on the number of trucks serviced concurrently. This model is supported by SWAT APIs and a cumulative limitation parameter can be used to enforce this constraint.
📄️ Vehicle depot
In the context of the Vehicle Routing Problem (VRP), a depot is a central location where vehicles start and end their routes. It serves as the origin and destination for all vehicle trips. The depot is a critical component of the VRP, as it represents the base of operations for the fleet.
📄️ Driver breaks
Both the SWAT Optimization API and Integration API support driver breaks, allowing dynamic allocation to align with operational needs or legal requirements. For example, Route Optimization can incorporate the length of required breaks after specific driving durations into its execution plan while respecting other set constraints. Two main use cases apply, which can be used in a mutually exclusive manner when simulating the addition of driver breaks to optimization:
📄️ Dynamic Service Time
In logistics, the time it takes to service an order at a drop-off location (like a warehouse or distribution center) is often not a fixed number. It can vary based on the size, weight, or type of goods being delivered. Dynamic Service Time is a feature that allows the optimization engine to calculate the service time for an order based on these variable factors, rather than using a single static value.
📄️ Just-In-Time (JIT) Employee Transport: Optimizing Workforce Mobility
Just-in-time (JIT) employee transport focuses on precisely coordinating the movement of employees to align with their work schedules and real-time needs, eliminating unnecessary wait times and maximizing operational efficiency. It's about ensuring the right people are in the right place at the right time, every time.
📄️ Maximum trip duration
In the Vehicle Routing Problem (VRP), a trip refers to the sequence of nodes visited by a single vehicle, starting from the depot and ending back at the depot. A route can consist of one or more trips, especially when dealing with constraints like maximum trip duration or vehicle capacity. The maximum trip duration constraint limits the total time a vehicle can spend on a single trip.
📄️ Mutually Exclusive Groups
The Vehicle Routing Problem (VRP) often involves complex constraints beyond simply matching vehicle characteristics with order requirements.
📄️ Nodes and bookings (orders)
The Vehicle Routing Problem (VRP) revolves around efficiently delivering goods or services to a set of customers. Two fundamental concepts underpin the VRP: nodes and orders.
📄️ On demand routing and operations
On-demand operations in the Vehicle Routing Problem (VRP) refer to scenarios where customer requests (orders) are not known in advance but rather arise dynamically in real-time. This contrasts with traditional VRP models where all orders are typically assumed to be known beforehand.
📄️ Path Equalizer
In complex vehicle routing scenarios, it's common for optimization algorithms, focused solely on minimizing total cost, to produce unbalanced routes. For example, one vehicle might be assigned a long, 8-hour route while another gets a short, 3-hour route. This can lead to issues with driver fairness, overtime costs, and inconsistent vehicle utilization.
📄️ CVRPTW vs. PDP Scheduling Modes
The Stateless Optimization API is designed to solve two primary classes of Vehicle Routing Problems (VRP) by offering two distinct scheduling modes: prebook_cvrptw and prebook (PDP). Each mode is tailored for different operational workflows, and choosing the correct one is crucial for achieving an efficient and accurate optimization result.
📄️ Vehicle slack
In the context of a Vehicle Routing Problem (VRP), slack (also commonly called waiting time) is the unproductive idle time that a vehicle and driver must spend at a stop.
📄️ Time windows in CPVRPTW problems
The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) extends the classic VRP by incorporating two key real-world constraints: vehicle capacity and time windows. Time windows, which restrict when a customer can be serviced, are crucial for modeling realistic scenarios and significantly impact solution feasibility and cost.
📄️ Trip cost
In the Vehicle Routing Problem (VRP), the concept of "trip cost" is used to model the cost or penalty associated with a vehicle making a trip to serve a specific order. This cost can represent various factors, such as the distance traveled, the time spent, or other operational expenses. By assigning a trip cost to each order, the optimization algorithm can prioritize routes that minimize these costs, leading to more efficient and cost-effective solutions.
📄️ Maximum Trips Per Vehicle
In some logistics scenarios, it's necessary to limit the number of trips a vehicle can make from a depot. For example, a driver might have a maximum number of routes they can perform in a shift, or a vehicle might need to return to the depot for reloading or maintenance after a certain number of tours.
📄️ Vehicle Characteristics
Vehicle characteristics encompass the various attributes and capabilities of vehicles that influence their suitability for specific tasks or routes. These characteristics can include factors like capacity, vehicle type, specialized equipment, driver skills, and availability.
📄️ Vehicle cost
In the Vehicle Routing Problem (VRP), Vehicle Cost is a fixed, one-time cost that is added to the total solution cost for each vehicle that is used to serve one or more orders. Think of it as an "activation fee" for deploying a vehicle.
📄️ Vehicle Labels and Order Labels
One way to manage these relationships between vehicles and orders is through the use of vehicle labels and order labels. These labels act as tags or identifiers that provide additional information about the vehicles and orders, enabling more sophisticated and nuanced route optimization.
📄️ Distance Ratio Constraint
Optimizing route directness is crucial for balancing operational costs and carrier agreements. The distance ratio constraint allows planners to control how much a route can deviate from the most direct path, ensuring efficiency and cost predictability.