Solving large-scale combinatorial optimization problems quickly.
SCOP (Solver for Constraint Programming) is a solver designed to quickly solve large-scale combinatorial optimization problems. By using solution principles specialized for combinatorial optimization problems, SCOP can efficiently explore good solutions even for large-scale problems that traditional mathematical optimization solvers cannot handle. Features: By defining variables in a different way than mathematical optimization solvers, it can significantly reduce the number of variables, enabling fast solutions. It allows for a more natural logical constraint description (easier for humans to understand) compared to mathematical optimization solvers. Based on metaheuristics, it possesses world-class search capabilities. Even for large-scale problems, it can solve them extremely efficiently within limited computation time. It provides data input through a simple modeling language and a Python language interface.
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Supported OS environments: Windows 64-bit Mac OS 64-bit Linux (Ubuntu) 64-bit
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Applications/Examples of results
Benchmark results: Creation of Timetable - 2nd International Competition ITC2007: Track 1 (Exam Timetable): 3rd place, Track 2 (University Timetable): 2nd place, Track 3 (High School Timetable): 3rd place (The only algorithm that placed in the top three in all three tracks is the one that underlies SCOP) Nurse Scheduling Shift Creation - 1st International Competition INRC 2010: Track 1 (10 seconds): 2nd place, Track 2 (10 minutes): 3rd place, Track 3 (10 hours): 4th place Applications: Capable of solving a wide range of combinatorial optimization problems, it allows for very natural modeling that is easy for humans to understand for assignment problems, and can quickly produce good solutions. - Personnel allocation optimization - Meeting scheduling - Job assignment - Room allocation - Production optimization - Determining input order - Advertising allocation, etc. In personnel allocation optimization, even when considering shifts and tasks simultaneously (a problem that determines which tasks to perform during assigned shifts, which is significantly more difficult than creating shifts on a daily basis or in a three-shift system), good solutions can be obtained in just a few minutes for a one-month schedule for 80 people in 30-minute increments. (SCOP is designed to provide the best solution within the computation time set by the user.)
Line up（5）
| Model number | overview |
|---|---|
| Scheduling Optimization Solver OptSeq | Solver for quickly solving large-scale scheduling optimization problems https://www.logopt.com/optseq/ |
| Delivery Optimization Solver METRO | Solver for quickly solving large-scale delivery optimization problems https://www.logopt.com/metrosolver/ |
| 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/ |
| Supply Chain Integrated Optimization System SCMOPT | 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.
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