Fast solving of large-scale set cover optimization problems.
OptCover is a solver designed to quickly solve large-scale set cover optimization problems. The set cover optimization problem (not a strict definition) involves enumerating feasible solutions and finding the best combination among them. It can solve any problem where feasible solutions can be enumerated, including delivery optimization problems, scheduling problems, and other combinatorial optimization problems. Features: • Based on metaheuristics, it possesses world-class search capabilities. Even for large-scale problems, it can efficiently find solutions within a limited computation time. • Data input is possible using a simple modeling language.
Inquire About This Product
basic information
Supported OS environments: Mac OS 64-bit Linux (Ubuntu) 64-bit
Price range
Delivery Time
Applications/Examples of results
Benchmark results: https://www.logopt.com/download/OptCover_benchmark.pdf The comparison results with commercial solvers such as CPLEX, Gurobi, and LocalSolver are also included. Within the same computation time, better solutions are obtained in most problem instances compared to these commercial solvers. Practical examples: It is possible to efficiently solve problems such as the proper allocation of rare resources like aircraft, trains, and buses in the aviation, railway, and bus industries. Due to the structure of these problems, general algorithms for personnel allocation or assignment problems cannot solve them efficiently. For example, in the aviation industry, based on customer demand forecasts, it is possible to efficiently create flight schedules between airports while considering various constraints, as well as allocate appropriate aircraft and crew. In addition, other combinatorial optimization problems such as delivery optimization and scheduling optimization can also be solved more quickly and effectively with OptCover when their problem structure (which varies depending on the actual problem data) is better suited to be treated as a set cover problem.
Line up（5）
| Model number | overview |
|---|---|
| Scheduling Optimization Solver OptSeq | Solver for quickly solving scheduling optimization problems https://www.logopt.com/optseq/ |
| Delivery Optimization Solver METRO | Solver for quickly solving delivery optimization problems https://www.logopt.com/metrosolver/ |
| 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/ |
| Constraint Optimization Solver SCOP | Solver for quickly solving large-scale combinatorial optimization problems https://www.logopt.com/scop2/ |
catalog（1）
Download All Catalogs
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.
![Quantum Annealing Mathematical Optimization Solver [For Education, Research, and PoC]](https://image.mono.ipros.com/public/product/image/648/2001503305/IPROS98878039991510392350.jpeg?w=280&h=280)
