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 in 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, multi-dimensional capacity of trucks (considering mixed refrigerated and frozen loads, 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. There is no need for complex mathematical formulations or programming; 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 issues (one example): - VRPTW (Solomon benchmark) benchmark: 100 job instances, median computation time of 484 ms, median solution accuracy of +1.2% - PDPTW (Li&Lim benchmark) benchmark: 100 job instances, median computation time of 130 ms, median solution accuracy of +0.1% - In applications to various constrained practical problems (considering time slots, breaks, weight and capacity restrictions, vehicle class and type restrictions, etc.), even for problems with 800 locations, results better than the current situation by over 10% have been achieved in just a few tens of seconds. (Improvements from the current situation vary depending on how efficient the current situation was, the constraints considered, and the problem size. In some cases, improvements of over 30% have been 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 Cover Optimization Solver OptCover | Solver for quickly solving large-scale set cover 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/ |
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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 since 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 address, thus offering world-class optimization solutions.
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