ログ・オプト List of Products and Services

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, the modeling often leads to significant differences in the scale of problems that can be solved and the speed of solutions. Our company not only sells the solver but also excels in the technical aspects of modeling, providing excellent modeling support (for a fee) to user companies, as well as various consulting services (for a fee) to solve real-world problems using mathematical optimization techniques. Types of problems that can be solved: - 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 - Rapidly finds feasible solutions through advanced MIP heuristic algorithms - Barrier algorithms that fully leverage the latest computer architectures - Support for an intuitive, easy-to-use, and lightweight range of APIs

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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 has world-class search capabilities. It can efficiently solve large-scale problems within a limited computation time. • Data input is possible using a simple modeling language.

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OptSeq is a general-purpose scheduling optimization solver specialized in scheduling optimization. It allows for the description of various practical constraints and employs algorithms specifically designed for scheduling optimization problems, enabling it to produce good solutions in a short time for large-scale scheduling optimization problems that cannot be solved by mathematical optimization solvers. In OptSeq, while considering 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, it is a solver capable of addressing due date minimization scheduling and mixed scheduling of forward and backward scheduling. Features: It allows for modeling 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 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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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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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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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 few parameter settings, and does not need corrections due to unsatisfactory results. By 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.

• Scientific Calculation and Simulation Software