Hybrid ASP-based multi-objective scheduling of semiconductor manufacturing processes (Extended version)
University of Klagenfurt（克拉根福大学）；Cairo University（开罗大学）；Graz University of Technology（格拉茨技术大学）
Mohammed M. S. El-Kholany, Ramsha Ali, Martin Gebser
备注：
17 pages, 1 figure, 4 listings, 1 table; a short version of this paper is presented at the 18th European Conference on Logics in Artificial Intelligence (JELIA 2023)
本文针对现实半导体制造过程的调度问题，采用带差异逻辑的混合 Answer Set Programming 进行建模，纳入灵活的机器处理、设置、批量和维护操作，研究多优化目标下的大规模调度潜力。
摘要：
现代半导体制造包含数百道操作的复杂生产过程，从批次发放到完成可能需要数月。这些高科技机器种类繁多，在多个阶段对单个晶圆、批次或大批次进行操作，并需要针对特定产品的设置和专门的维护程序。这种情况不同于传统作业车间调度场景，后者的生产过程和机器较不复杂，主要关注解决高度组合但抽象的调度问题。在本工作中，我们通过使用带差异逻辑的混合 Answer Set Programming 对特定需求进行建模，来解决现实半导体制造过程的调度问题，纳入了灵活的机器处理、设置、批量和维护操作。与现有方法使用贪婪启发式局部调度或独立优化特定机器组分配不同，我们研究了受多个优化目标约束的大规模调度潜力。
英文摘要：
Modern semiconductor manufacturing involves intricate production processes consisting of hundreds of operations, which can take several months from lot release to completion. The high-tech machines used in these processes are diverse, operate on individual wafers, lots, or batches in multiple stages, and necessitate product-specific setups and specialized maintenance procedures. This situation is different from traditional job-shop scheduling scenarios, which have less complex production processes and machines, and mainly focus on solving highly combinatorial but abstract scheduling problems. In this work, we address the scheduling of realistic semiconductor manufacturing processes by modeling their specific requirements using hybrid Answer Set Programming with difference logic, incorporating flexible machine processing, setup, batching and maintenance operations. Unlike existing methods that schedule semiconductor manufacturing processes locally with greedy heuristics or by independently optimizing specific machine group allocations, we examine the potentials of large-scale scheduling subject to multiple optimization objectives.
An ASP-based Solution to the Medical Appointment Scheduling Problem
University of Pisa, Italy（意大利比萨大学）；Department of Information Engineering, Computer Science（信息工程系）；Mathematics, University of L’Aquila,Italy（数学，意大利拉奎拉大学）
Train Scheduling with Hybrid Answer Set Programming
SBB Switzerland（瑞士联邦铁路）；Potassco Solutions Germany（德国Potassco解决方案公司）；Simon Fraser University（西蒙弗雷泽大学）；Griffith University（格里菲斯大学）；University of Potsdam（波茨坦大学）
提出了一种基于回答集编程（ASP）的训练调度问题的解决方案。为此，我们追求一种混合的方法，扩展ASP与不同的约束，以考虑细粒度的时间。更确切地说，我们举例说明如何混合ASP系统clingo[DL]可以用来解决苛刻的计划和调度问题。特别是，我们研究了如何提高性能相结合的不同ASP解决技术，如近似和算法，预处理和编码技术，以解决大规模的，现实世界的训练调度实例。逻辑程序设计理论与实践（Theory and Practice of Logic Programming，TPLP）
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School of Advanced Manufacturing and Robotics, Peking University（先进制造与机器人学院，北京大学）；Department of Computer Science and Engineering, School of Engineering, The Hong Kong University of Science and Technology（计算机科学与工程系，香港科技大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（计算机科学与技术学院，华中科技大学）
Refined Iterated Pareto Greedy for Energy-aware Hybrid Flowshop Scheduling with Blocking Constraints
Department of Computer Science（计算机科学系）；University College Cork（科尔克大学）；Process, Energy and Transport Engineering（过程、能源与运输工程）；Munster Technological University（穆斯林技术大学）
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）
Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks
CNRS（法国国家科学研究中心）；LAAS（系统分析与架构实验室）；Université de Toulouse（图卢兹大学）；UPS（图卢兹第三大学（保罗·萨巴蒂埃大学））；INSA（法国国立应用科学学院）；INP（图卢兹国立理工学院）；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）；INSAT（突尼斯国立应用科学与技术学院）
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）
A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery
School of Electromechanical Engineering, Guangdong University of Technology（广东工业大学机电工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Coordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing
School of Computer Science, University of Sydney（悉尼大学计算机科学学院）；Khoury College of Computer Sciences, Northeastern University（美国东北大学库里计算机科学学院）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Life and Environmental Sciences, University of Sydney（悉尼大学生命与环境科学学院）；College of Business and Economics, Australian National University（澳大利亚国立大学商业与经济学院）
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Intelligent Systems Science and Engineering, Jinan University（暨南大学智能系统科学与工程学院）
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）
