ログ・オプト Product Lineup

It is one of the most widely used and high-performance mathematical optimization solvers in the world, with a broad range of applicable problem types. While the performance of the solver itself is important, significant differences often arise in the scale of problems that can be solved and the speed of solutions due to modeling. Our company not only sells the solver but also excels in the technical aspects of modeling, providing excellent modeling support (for a fee) and various consulting services (for a fee) to solve real-world problems using mathematical optimization techniques. Types of solvable problems: - Linear Programming (LP) - Mixed Integer Linear Programming (MILP) - Quadratic Programming (QP) - Quadratic Constraints (QCP) - Mixed Integer Quadratic Programming (MIQP) - Mixed Integer Quadratic Constraints (MIQCP) - Mixed Integer Non-convex Quadratic Constraints (Non-convex MIQCP) Features: - Code designed to maximize parallel processing - Unmatched performance in cutting plane processing - Advanced MIP heuristic algorithms for quickly finding feasible solutions - Barrier algorithms that fully utilize the latest computer architectures - Support for an intuitive, easy-to-use, and lightweight API across a wide range of applications

• Other embedded systems (software and hardware)

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.

• Other embedded systems (software and hardware)

OptSeq is a general-purpose scheduling optimization solver specialized in scheduling optimization. It can describe various practical constraints and uses algorithms tailored for scheduling optimization problems, enabling it to provide good solutions in a short time for large-scale scheduling optimization problems that cannot be solved by mathematical optimization solvers. OptSeq considers renewable resources such as machines and people, non-renewable resources that are consumed such as money and materials, setup times, interruptions during work, resource occupancy and non-occupancy during interruptions, parallel tasks, selection of work modes, and arbitrary time constraints between tasks, allowing it to solve scheduling problems that minimize delays and mixed scheduling of forward and backward scheduling. Features: It allows modeling of scheduling optimization problems in a more natural expression (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 be solved extremely efficiently within limited computation time. It provides data input through a simple modeling language and a Python language interface.

• Production Scheduler

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.

• Other embedded systems (software and hardware)

SCOP (Solver for Constraint Programming) is a solver designed to quickly solve large-scale combinatorial optimization problems. By utilizing solution principles specialized for combinatorial optimization problems, SCOP can efficiently explore good solutions even for large 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 that is 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.

• Other analyses

Demand forecasting, production optimization, inventory optimization, delivery optimization, logistics network optimization, personnel allocation optimization, revenue optimization, and more are possible. 'SCMOPT ZERO' is an integrated optimization system that can provide fast and good answers even for large-scale problems by designing models and selecting algorithms (solvers) based on the correct methodology of supply chain optimization. It can consider various constraints of real-world problems, requires minimal parameter settings, and does not need corrections due to unsatisfactory results. Taking into account the overall objectives and constraints, it automatically calculates balanced and appropriate answers from a vast number of combinations. Additionally, by adopting carefully selected solvers and models, it is capable of solving problems of the largest scale that can be addressed with existing technologies. [Benefits] ■ Cost reduction ■ Supply chain resilient to environmental changes ■ Improved customer satisfaction and sales *For more details, please download the PDF or feel free to contact us.

• Production Scheduler