本文研究了将作业车间调度问题分解为时间窗，通过多轮 ASP 求解逐步调度优化，结合重叠与压缩技术，在严格时间限制内比单轮优化获得显著更好的调度质量。
摘要：
作业车间调度问题（JSP）是一个著名且具有挑战性的组合优化问题，其中共享一台机器的任务需要被排列成一个序列，使得包含这些任务的作业能够尽早完成。在本文中，我们研究了将问题分解为时间窗，其操作可以通过多轮 Answer Set Programming（ASP，答案集编程）求解来逐步调度和优化。从计算的角度来看，分解旨在将高度复杂的调度任务拆分为更易于管理的子问题，这些子问题具有平衡的操作数量，从而能够在极少的运行时间内可靠地找到高质量甚至最优的局部解。我们设计并研究了多种分解策略，涉及时间窗的数量和大小以及选择其操作的启发式方法。此外，我们将时间窗重叠和压缩技术纳入迭代调度过程，以抵消由于限制于逐窗局部调度而导致的优化局限性。我们在不同的 JSP 基准集上的实验表明，在严格的运行时间限制内，通过多轮 ASP 求解进行的逐步优化比对整个问题进行单轮优化能产生显著更好的调度。特别是，我们发现将由熟练的启发式方法获得的初始解分解为时间窗，能够提升解的质量。
英文摘要：
The Job-shop Scheduling Problem (JSP) is a well-known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. In this paper, we investigate problem decomposition into time windows whose operations can be successively scheduled and optimized by means of multi-shot Answer Set Programming (ASP) solving. From a computational perspective, decomposition aims to split highly complex scheduling tasks into better manageable subproblems with a balanced number of operations such that good-quality or even optimal partial solutions can be reliably found in a small fraction of runtime. We devise and investigate a variety of decomposition strategies in terms of the number and size of time windows as well as heuristics for choosing their operations. Moreover, we incorporate time window overlapping and compression techniques into the iterative scheduling process to counteract optimization limitations due to the restriction to window-wise partial schedules. Our experiments on different JSP benchmark sets show that successive optimization by multi-shot ASP solving leads to substantially better schedules within tight runtime limits than single-shot optimization on the full problem. In particular, we find that decomposing initial solutions obtained with proficient heuristic methods into time windows leads to improved solution quality.
Investigation of the Generalisation Ability of Genetic Programming-evolved Scheduling Rules in Dynamic Flexible Job Shop Scheduling
Centre for Data Science；Artificial Intelligence \& School of Engineering；Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
DyRo-MCTS: A Robust Monte Carlo Tree Search Approach to Dynamic Job Shop Scheduling
Centre for Data Science & Artificial Intelligence（数据科学与人工智能中心）；School of Engineering & Computer Science（工程与计算机科学学院）；Victoria University of Wellington（惠灵顿维多利亚大学）
RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
School of Computing and Information Systems, Singapore Management University, Singapore（新加坡管理大学计算机与信息系）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）；Institute of Marine Science and Technology, Shandong University, China（山东大学海洋科学与技术研究院）
Policy-Based Reinforcement Learning with Action Masking for Dynamic Job Shop Scheduling under Uncertainty: Handling Random Arrivals and Machine Failures
South Westphalia University of Applied Sciences（西南弗劳恩霍夫应用科学大学）
Models and Strategies for Variants of the Job Shop Scheduling Problem
Cork Constraint Computation Centre（科克约束计算中心）；University College Cork（科克大学学院）；CNRS（法国国家科学研究中心）；LAAS（法国航空航天实验室）；Université de Toulouse（图卢兹大学）；UPS（图卢兹第三大学）；INSA（法国国立应用科学学院）；INP（法国国立理工学院）；ISAE（法国高等航空航天学院）；UT1（图卢兹第一大学）；UTM（图卢兹第二大学）
Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Scheduling
Faculty of Applied Sciences, Macao Polytechnic University（澳门理工学院应用科学学院）；Pazhou Lab (Huangpu), Guangzhou（广州 Pazhou 实验室（黄埔））；College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering（仲恺农业工程学院动物科学与技术学院）；Macao Polytechnic University（澳门理工学院）
Instance Configuration for Sustainable Job Shop Scheduling
Universitat Politècnica de València（巴塞罗那理工大学）；valgrAI - Valencian Graduate School and Research Network of Artificial Intelligence（瓦伦西亚人工智能研究生学院和研究网络）；Research Network of Artificial Intelligence（人工智能研究网络）
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
Eindhoven University of Technology（埃因霍温理工大学）；Eindhoven Artificial Intelligence Systems Institute（埃因霍温人工智能系统研究所）；Delmia R&D, Dassault Systèmes（达索系统Delmia研发部）
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；Eindhoven University of Technology（埃因霍温理工大学）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术研究院）
Online Operator Design in Evolutionary Optimization for Flexible Job Shop Scheduling via Large Language Models
School of Computer Science, Guangdong University of Technology（广东工业大学计算机科学学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics
Faculty of Wood Technology and Construction（木材技术与建筑学院）；Rosenheim Technical University of Applied Sciences（罗森heim应用技术大学）；Center for Research, Development and Transfer（研究、开发与转让中心）；Faculty of Management and Engineering（管理与工程学院）
Discrete Differential Evolution Particle Swarm Optimization Algorithm for Energy Saving Flexible Job Shop Scheduling Problem Considering Machine Multi States
Business School, Shandong Normal University（山东师范大学商学院）；School of Mathematics, Yunnan Normal University（云南师范大学数学学院）；School of Physics and Electronics, Shandong Normal University（山东师范大学物理与电子科学学院）
ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection
School of Computer Science and Engineering（计算机科学与工程学院）；Beihang University（北京航空航天大学）；Qingdao Research Institute（青岛研究院）；Hangzhou Innovation Institute（杭州创新研究院）；Zhongguancun Laboratory（中关村实验室）
Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）；College of Software（软件学院）；College of Computer Science and Technology（计算机科学与技术学院）；Singapore Management University（新加坡管理大学）；Nanyang Technological University（南洋理工大学）
