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About This Product
■Summary
・Solver specialized for delivery optimization problems
- Possible to optimize delivery from multiple customer points to customer points (delivery similar to a sharing service)
・It is possible to optimize delivery based on warehouses and distribution centers.
・Most practical constraints can be described, such as time constraints, vehicle and location-related constraints, driver breaks, truck multidimensional capacity (combination of refrigerated and frozen cargo can be considered), simultaneous collection and delivery, etc.
■Features
- Allows for more natural logical constraint descriptions (easier for humans to understand) than mathematical optimization solvers.
・Since it is based on metaheuristics, it has one of the world's fastest search capabilities.
・Even large-scale problems can be solved extremely efficiently within a limited calculation time.
・Data can be exchanged using Excel, JSON, and Python.
Applicable problems (It is possible to solve various delivery optimization problems)
■Depot to customer type (general delivery issues)
This is a type of delivery problem in which packages are loaded at a depot (warehouse or distribution center), delivered and collected while visiting each customer, returned to the depot, and unloaded the collected packages. For example, a delivery such as a courier that departs from a center and returns to the center is an example of this type of delivery problem.
At METRO, if the truck only performs collection (there is no cargo to be loaded at the center), the truck can depart from a point other than the center and return to the center, and if the truck only performs delivery (there is no cargo to be unloaded at the center), It is also possible to solve cases where the truck does not need to return to the center. Simultaneous optimization of multiple depots is also possible.
■Customer-to-customer type (sharing service delivery problem)
This is a delivery problem that determines which truck should move cargo from multiple customer points to customer points. For example, this type of delivery problem involves deciding which vehicle to assign to which customer point-to-point journey in a shared taxi or shared truck service.
In an example of application to a real problem with various constraints (time zone specification, taking breaks into account, taking into account weight and capacity restrictions, taking into account restrictions such as car class and car type), even a problem with 800 locations can be improved by 10% or more from the current situation in a few tens of seconds. We are seeing good results. Improvements from the current situation also depend on how efficient the current situation was, the constraints to be considered, and the scale of the problem. In some cases, the improvement is over 30%.
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Product
Delivery optimization solver METRO
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