Rapidly solving large-scale scheduling optimization.
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.
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Supported OS environments: Windows 64-bit Mac OS 64-bit Linux (Ubuntu) 64-bit
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Benchmark result link: http://www.logopt.com/download/optseq_benchmark_result.pdf Benchmark for JSP (Job Shop Scheduling (Minimizing Makespan)) Benchmark for RCPSP/max (Resource-Constrained Project Scheduling Problem with Time Constraints between Activities) The above two types of benchmarks only compete on solution accuracy, with no time limit for solving, and it is acceptable to create or tune algorithms specialized for the problem. However, OptSeq was tested with default parameters and without any algorithm customization. A comparison was also made with Google's OR-tools, confirming that as the problem size increases, OptSeq produces better solutions (in the same amount of time). Applications: Production scheduling with a mix of manual and automated tasks Production scheduling in chemical plants Casting schedule planning Shipbuilding process planning Pipeline repair scheduling Scheduling of inspection tasks Production scheduling in semiconductor factories Shipping scheduling (such as resource imports) Various types of production scheduling, etc.
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| Model number | overview |
|---|---|
| Delivery Optimization Solver METRO | A solver for quickly solving large-scale delivery optimization problems https://www.logopt.com/metrosolver/ |
| Set Cover Optimization Solver OptCover | A solver for quickly solving large-scale set cover problems https://www.logopt.com/optcover/ |
| Mathematical Optimization Solver Gurobi Optimizer | A fast mathematical optimization solver https://www.logopt.com/gurobi/ |
| Supply Chain Integrated Optimization System SCMOPT | A system for supply chain optimization https://www.logopt.com/demo/ |
| Constraint Optimization Solver SCOP | A solver for quickly solving large-scale combinatorial optimization problems https://www.logopt.com/scop2/ |
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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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