Papers Job Shop Scheduling
“Job Shop Scheduling” 태그가 달린 논문 92편 · 필터 해제
A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints
The classical Job Shop Scheduling Problem (JSSP) focuses on optimizing makespan under deterministic constraints. Real-world production environments introduce additional complexities that cause traditional scheduling appr…
Job Shop SchedulingReinforcement Learning (RL)SchedulingSolving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment
The job shop scheduling problem is an NP-hard combinatorial optimization problem relevant to manufacturing and timetabling. Traditional approaches use priority dispatching rules based on simple heuristics. Recent work ha…
Combinatorial OptimizationImitation LearningJob Shop SchedulingSchedulingREMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models
Multi-objective optimization is fundamental in complex decision-making tasks. Traditional algorithms, while effective, often demand extensive problem-specific modeling and struggle to adapt to nonlinear structures. Recen…
BenchmarkingDecision MakingDiversityJob Shop Scheduling+1ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning
Large language models (LLMs) excel at rapid generation of text and multimodal content, yet they falter on transaction-style planning that demands ACID-like guarantees and real-time disruption recovery. We present Adaptiv…
Job Shop SchedulingSchedulingAdaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem
The Flexible Job-Shop Scheduling Problem (FJSSP) is an NP-hard combinatorial optimization problem, with several application domains, especially for manufacturing purposes. The objective is to efficiently schedule multipl…
Combinatorial OptimizationJob Shop SchedulingSchedulingUnraveling the Rainbow: can value-based methods schedule?
Recently, deep reinforcement learning has emerged as a promising approach for solving complex combinatorial optimization problems. Broadly, deep reinforcement learning methods fall into two categories: policy-based and v…
Atari GamesCombinatorial OptimizationDeep Reinforcement LearningJob Shop Scheduling+3Neural Combinatorial Optimization via Preference Optimization
Neural Combinatorial Optimization (NCO) has emerged as a promising approach for NP-hard problems. However, prevailing RL-based methods suffer from low sample efficiency due to sparse rewards and underused solutions. We p…
Combinatorial OptimizationJob Shop SchedulingSchedulingDiscrete Differential Evolution Particle Swarm Optimization Algorithm for Energy Saving Flexible Job Shop Scheduling Problem Considering Machine Multi States
As the continuous deepening of low-carbon emission reduction policies, the manufacturing industries urgently need sensible energy-saving scheduling schemes to achieve the balance between improving production efficiency a…
Job Shop SchedulingSchedulingStarjob: Dataset for LLM-Driven Job Shop Scheduling
Large Language Models (LLMs) have shown remarkable capabilities across various domains, but their potential for solving combinatorial optimization problems remains largely unexplored. In this paper, we investigate the ap…
Combinatorial OptimizationJob Shop SchedulingSchedulingLearning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling
Long-horizon combinatorial optimization problems (COPs), such as the Flexible Job-Shop Scheduling Problem (FJSP), often involve complex, interdependent decisions over extended time frames, posing significant challenges f…
Combinatorial OptimizationJob Shop SchedulingSchedulingSelf-Evaluation for Job-Shop Scheduling
Combinatorial optimization problems, such as scheduling and route planning, are crucial in various industries but are computationally intractable due to their NP-hard nature. Neural Combinatorial Optimization methods lev…
Combinatorial OptimizationDecision MakingGraph Neural NetworkJob Shop Scheduling+2Investigating the Monte-Carlo Tree Search Approach for the Job Shop Scheduling Problem
The Job Shop Scheduling Problem (JSSP) is a well-known optimization problem in manufacturing, where the goal is to determine the optimal sequence of jobs across different machines to minimize a given objective. In this w…
Job Shop SchedulingSchedulingA Benchmarking Environment for Worker Flexibility in Flexible Job Shop Scheduling Problems
In Production Scheduling, the Flexible Job Shop Scheduling Problem (FJSSP) aims to optimize a sequence of operations and assign each to an eligible machine with varying processing times. For integration of the workforce,…
BenchmarkingEvolutionary AlgorithmsJob Shop SchedulingSchedulingNeural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has been widely applied to job shop scheduling …
Combinatorial OptimizationJob Shop SchedulingSchedulingAutomatic programming via large language models with population self-evolution for dynamic job shop scheduling problem
Heuristic dispatching rules (HDRs) are widely regarded as effective methods for solving dynamic job shop scheduling problems (DJSSP) in real-world production environments. However, their performance is highly scenario-de…
Deep Reinforcement LearningEvolutionary AlgorithmsJob Shop SchedulingPrompt Engineering+1Offline reinforcement learning for job-shop scheduling problems
Recent advances in deep learning have shown significant potential for solving combinatorial optimization problems in real-time. Unlike traditional methods, deep learning can generate high-quality solutions efficiently, w…
Combinatorial OptimizationDeep LearningDeep Reinforcement LearningJob Shop Scheduling+5A Flexible Job Shop Scheduling Problem Involving Reconfigurable Machine Tools Under Industry 5.0
The rise of Industry 5.0 has introduced new demands for manufacturing companies, requiring a shift in how production schedules are managed to address human centered, environmental, and economic goals comprehensively. The…
Job Shop SchedulingSchedulingA History-Guided Regional Partitioning Evolutionary Optimization for Solving the Flexible Job Shop Problem with Limited Multi-load Automated Guided Vehicles
In a flexible job shop environment, using Automated Guided Vehicles (AGVs) to transport jobs and process materials is an important way to promote the intelligence of the workshop. Compared with single-load AGVs, multi-lo…
Job Shop SchedulingSchedulingOptimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics
This paper explores the potential application of Deep Reinforcement Learning in the furniture industry. To offer a broad product portfolio, most furniture manufacturers are organized as a job shop, which ultimately resul…
Deep Reinforcement LearningERPJob Shop SchedulingSchedulingOffline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization problem. While online Reinforcement Learning (RL) has shown promise by quickly finding acceptable solutions for JSSP, it faces key limitation…
Combinatorial OptimizationcounterfactualJob Shop SchedulingQ-Learning+4