production-scheduling

Program for managing orders, planning and scheduling in job shop production system using popular heuristics alghorithms.

Frepple Production Scheduling Tool integration to Frappe web Framework

Flow Shop Scheduling example using the Quantum Hybrid NL Solver.

⚙️ Effortless and efficient task scheduling tailored for production, built with numpy.

Metaheuristic- and RL-powered production scheduling & optimization toolkit in Python

AI-powered production scheduling with constraint satisfaction — optimizes across machines, materials, labor, and orders to maximize throughput and minimize tardiness

Mixed Integer Linear Programming (MILP) model to optimize SmartFab's production scheduling for an IoT module assembly line, minimizing production, inventory, and setup costs using lp_solve.

3DSTU FarmFlow — 3D printing farm production OS for orders, printers, scheduling, files, todos, maintenance, alerts, and operations.

Parallel simulated annealing for setup- and deadline-aware machine scheduling in off-site construction, with modular neighbourhoods and calendar-aware evaluation

GX 工厂多产线两阶段（成型+贴标）智能排产系统：FastAPI 排产算法后端 + Next.js 甘特图前端 + LLM 自然语言交互

Enterprise-grade finite-capacity Production Scheduling & Optimization (PSO) system using Python, Pyomo, and MILP (HiGHS/CBC). Optimizes machine scheduling, operator assignment, batch planning, inventory, and production costs under real-world manufacturing constraints.

Genetic Algorithm for minimizing total tardiness in identical parallel machine scheduling problems

A browser-based production scheduling tool with Johnson and Petrov–Sokolitsyn methods, Gantt charts, and BI analytics.

町工場の工程盤。納期に間に合わない案件を、遅れる前に出す。

Multi-objective workforce allocation for off-site construction assembly using parallel Pareto Simulated Annealing and context-aware neighbourhood moves to minimise labour cost, total tardiness, and delay severity.

Estudo que utiliza Reinforment Learning no processo de Production Scheduling.

Deep Reinforcement Learning (DRL) framework for dynamic chemical production scheduling under uncertainty.

End-to-end Material Requirements Planning (MRP) system built from scratch with Claude Code — demand forecasting, BOM netting, capacity scheduling, and an AI advisory layer. Validated against real manufacturing data. 205 tests. A supervised AI-assisted engineering case study.