Enterprise AI Analysis
Applying Decision Transformers to Enhance Neural Local Search on the Job Shop Scheduling Problem
Background: The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades. In recent years, machine learning (ML) has been playing an increasingly important role in advancing existing solutions and building new heuristic solutions for the JSSP, aiming to find better solutions in shorter computation times. Methods: In this study, we built on top of a state-of-the-art deep reinforcement learning (DRL) agent, called Neural Local Search (NLS), which can efficiently and effectively control a large local neighborhood search on the JSSP. In particular, we developed a method for training the decision transformer (DT) algorithm on search trajectories taken by a trained NLS agent to further improve upon the learned decision-making sequences. Results: Our experiments showed that the DT successfully learns local search strategies that are different and, in many cases, more effective than those of the NLS agent itself. In terms of the tradeoff between solution quality and acceptable computational time needed for the search, the DT is particularly superior in application scenarios where longer computational times are acceptable. In this case, it makes up for the longer inference times required per search step, which are caused by the larger neural network architecture, through better quality decisions per step. Conclusions: Therefore, the DT achieves state-of-the-art results for solving the JSSP with ML-enhanced local search.
Executive Summary: AI-Driven Job Shop Optimization
This paper introduces a significant advancement in solving the Job Shop Scheduling Problem (JSSP) by integrating Decision Transformers (DTs) with state-of-the-art Neural Local Search (NLS) agents. The DTs are trained on NLS search trajectories, learning to make more effective local search decisions by considering a history of past actions. This innovative approach has led to an average improvement in optimality gaps by 1.1-1.2% over various NLS models, setting a new benchmark for ML-enhanced local search. While DTs require slightly longer inference times per step due to their larger neural network architecture, they achieve superior makespans when longer computational times (beyond 7 seconds) are acceptable. This makes DTs particularly valuable for complex scheduling scenarios where solution quality is paramount. The study also reveals that DTs' effectiveness primarily stems from their ability to leverage contextual information from past search steps, rather than being solely driven by explicit return-to-go priors, highlighting the power of sequential decision-making models in combinatorial optimization.
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Avg. Optimality Gap Improvement
Contextual
Enhanced Search Strategy
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Time for DT Performance Advantage
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Understanding Job Shop Scheduling with AI
Effective and efficient scheduling presents an ongoing challenge that is critical for success in many sectors, from manufacturing to cloud computation. Scheduling, broadly, deals with the allocation of resources to tasks over time with the goal of optimizing a given objective. The job shop scheduling problem (JSSP) is an abstracted combinatorial scheduling problem that underlies many real-world problems and has been extensively studied in the literature. To this day, new solution methods for scheduling problems are developed and tested on the JSSP due to its interesting properties and the availability of popular public datasets that allow for rigorous benchmarking. Much of the research aims at finding solutions with smaller makespans with less computational effort.
In recent years, machine learning has emerged as a promising technique for new solution methods for scheduling problems by enhancing or replacing heuristic decisions in existing dispatching rules, metaheuristics and search heuristics, and optimal solvers to varying degrees. A notable advancement in this area is the integration of deep reinforcement learning (DRL) with local search algorithms.
Neural Local Search Decision Process
Acceptance Decision
→
Neighborhood Operation Selection
→
Perturbation Decision
The Neural Local Search (NLS) approach for JSSP involves a DRL agent making iterative decisions within a local search heuristic. These decisions include accepting or rejecting the last iteration's solution, choosing a new neighborhood operation to explore the search space, or applying a perturbation operator to jump to a different area of the search space.
Decision Transformers: A New Paradigm for Control
Decision Transformers (DTs) offer a novel approach by abstracting Markov Decision Processes as sequence modeling problems. Unlike traditional RL that predicts actions from current states, DTs consider a history of states, actions, and target return-to-go values to generate action sequences. This contextual understanding allows for more nuanced decision-making in complex environments like JSSP.
Decision Transformer Training & Evaluation Flow
Dataset Generation
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DT-Training
→
DT-Testing
The Decision Transformer development process involves three main stages. First, a dataset is generated by recording state-action-reward tuples from NLS model interactions. Second, the DT is trained on this dataset to predict actions based on past sequences and a target return-to-go. Finally, the trained DT is tested on new instances, aiming to achieve improved solutions.
No Significant
Impact of Return-to-Go Prior on Mean Makespan
Surprisingly, experiments reveal that the return-to-go prior, a core concept in Decision Transformers for guiding behavior towards desired outcomes, had no statistically significant influence on the mean makespans achieved. This suggests the DT learned superior strategies primarily through leveraging the broader context of past search steps via its transformer architecture, rather than being strongly driven by the explicit return-to-go target in this specific JSSP application.
Benchmarking DT Performance in JSSP
The integration of Decision Transformers into Neural Local Search shows a promising advancement in solving the Job Shop Scheduling Problem. While DTs require a more complex neural network architecture and consequently longer inference times per step, their ability to leverage a broader context of past search steps enables them to make more effective decisions, leading to superior overall solution quality, especially over longer search durations.
1.1-1.2%
Average Optimality Gap Improvement over NLS Teachers
Decision Transformers (DTs), particularly DT100, demonstrate a consistent average improvement in optimality gaps over various Neural Local Search (NLS) teacher models (NLSA, NLSAN, NLSANP) across multiple problem sizes. This indicates the DT's capability to learn more effective local search strategies, even surpassing the performance of its training source.
| Performance Metric | Computational Time (<7s) | Computational Time (>7s) |
|---|---|---|
| Solution Quality (Makespan) | Similar/NLS Faster | DT Better |
| Search Speed | NLS Faster | DT More Efficient |
| Neural Network Complexity | NLS (Smaller) | DT (Larger) |
The Decision Transformer (DT) demonstrates a trade-off between computational time and solution quality compared to the Neural Local Search (NLS) teacher models. For short search durations (under 7 seconds), NLS is faster and achieves comparable results. However, for search times exceeding 7 seconds, the DT consistently achieves better makespans, leveraging its more complex architecture and contextual decision-making over longer search trajectories.
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Phase 1: Discovery & Strategy
Assessment of current scheduling processes, identification of key pain points, and definition of AI integration objectives and success metrics. Exploration of data readiness and infrastructure requirements.
Phase 2: Data Engineering & Model Training
Collecting and cleaning operational data, constructing relevant features, and training Decision Transformer models on historical and simulated scheduling trajectories. Customizing models for specific JSSP complexities.
Phase 3: Pilot Deployment & Validation
Initial deployment of the AI scheduling agent in a controlled environment. Rigorous testing against baseline methods and NLS teachers, validating performance, makespan improvements, and computational efficiency gains.
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