Fast solving of large-scale distribution optimization problems.
It is a solver specialized in delivery optimization problems, capable of solving large-scale delivery optimization issues quickly. It can express almost all practical constraints of delivery optimization problems, making it a highly practical delivery optimization solver. • It is possible to optimize delivery from multiple sets of customer locations to customer locations (such as delivery for sharing services). • It can optimize delivery based on warehouses or distribution centers. • Practical constraints such as time constraints, vehicle and location-related constraints, driver breaks, multidimensional capacity of trucks (considering mixed loads of refrigerated and frozen items, etc.), and simultaneous pickups and deliveries can be described.
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basic information
It is provided as an API for the delivery optimization system. No complicated formulas or programming are required; simply input the data in the specified format, and it will return the results in the specified format.
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Applications/Examples of results
Benchmark problems and performance in practical applications (one example): - VRPTW (Solomon benchmark) benchmark: 100 job instances, median computation time of 484ms, median solution accuracy of +1.2% - PDPTW (Li&Lim benchmark) benchmark: 100 job instances, median computation time of 130ms, median solution accuracy of +0.1% - In applications with various constraints (designated time slots, consideration of breaks, weight and capacity restrictions, vehicle class and type restrictions, etc.), even for problems with 800 locations, results over 10% better than the current state can be achieved in just a few dozen seconds. (Improvements from the current state depend on how efficient the current state is, as well as the constraints considered and the scale of the problem. In some cases, improvements of over 30% are observed.)
Line up（5）
| Model number | overview |
|---|---|
| Scheduling Optimization Solver OptSeq | Solver for quickly solving large-scale scheduling optimization problems https://www.logopt.com/optseq/ |
| Set Covering Optimization Solver OptCover | Solver for quickly solving large-scale set covering problems https://www.logopt.com/optcover/ |
| Mathematical Optimization Solver Gurobi Optimizer | Fast mathematical optimization solver https://www.logopt.com/gurobi/ |
| Constraint Optimization Solver | Solver for quickly solving large-scale combinatorial optimization problems https://www.logopt.com/scop2/ |
| Supply Chain Integrated Optimization System | System for supply chain optimization https://www.logopt.com/demo/ |
Company information
Our company was established in 1991 to provide the highest level of technology for optimization in logistics (supply chain). Since then, we have expanded the scope of our optimization solutions beyond the supply chain and, starting around 2016, we have also been providing data analysis solutions using AI. We operate in Japan, China, and South Korea. With technical support from university professors who have extensive practical experience in the field of mathematical optimization both domestically and internationally, we develop products and provide services that enable us to solve difficult optimization problems that other companies cannot, thus offering world-class optimization solutions.