Article
Engineering, Industrial
Kaiping Luo, Jianfei Sun, Liuwei Guo
Summary: This article focuses on the problem of flexible process planning and presents linear integer programming models to solve it. The proposed models have lower complexity and better performance compared to the latest mathematical programming models for process planning.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Chemical
Florian Joseph Baader, Andre Bardow, Manuel Dahmen
Summary: The increasing volatility of electricity prices highlights the importance of simultaneous scheduling optimization for production processes and their energy systems. In this study, we propose an efficient scheduling formulation that takes into account both process dynamics and binary on/off-decisions in the energy system. By considering three different aspects, we demonstrate the feasibility of achieving fast optimization for real-time scheduling.
Article
Mathematics
Teeradech Laisupannawong, Boonyarit Intiyot, Chawalit Jeenanunta
Summary: This paper presents the application of a new mixed-integer linear programming model to the short-term scheduling of the pressing process in the fabrication process of multi-layer printed circuit board (PCB) manufacturing. The proposed model shows better size complexity compared to the previous model and outperforms it in terms of computational complexity.
Article
Mathematics
Teeradech Laisupannawong, Boonyarit Intiyot, Chawalit Jeenanunta
Summary: This paper discusses the scheduling of the pressing process in PCB manufacturing, presenting a novel MILP optimization model and a heuristic algorithm. Experimental results show that the MILP model can find optimal schedules for small- to medium-sized problems within 2 hours, while the 3P-PCB-PH can find optimal schedules in a shorter computational time.
Article
Management
Qihao Liu, Xinyu Li, Liang Gao, Jiaxin Fan
Summary: Process planning and shop scheduling are two independent subsystems in traditional flexible manufacturing systems. Integrated process planning and scheduling (IPPS) is the main focus of production research. This study proposes two MILP models to solve IPPS problems, with full-flexibility and semi-flexibility for small-scale and larger-scale problems respectively. Experimental results demonstrate the superiority of the two models in solving IPPS problems.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Mathematics
Fatima Pilar, Eliana Costa e Silva, Ana Borges
Summary: This study focuses on scheduling mechanical repairs at a Portuguese firm in the automotive sector. By developing a mathematical model that considers available resources, interventions, and repair time, the aim is to reduce vehicle downtime. The model, based on mixed-integer linear programming, effectively schedules interventions, allocates resources, and determines start times for each vehicle. Real-world instances provided by the company were successfully solved using the AMPL modeling language and Gurobi solver. The results demonstrate significant improvements, with an average 67% reduction in vehicle downtime and the ability to automatically generate accurate repair schedules, enabling faster delivery to customers.
Article
Engineering, Multidisciplinary
Qihao Liu, Xinyu Li, Liang Gao
Summary: The intelligent process planning (PP) is an important component in intelligent manufacturing systems, serving as a bridge between product design and actual manufacturing processes. The proposed mixed-integer linear programming (MILP) mathematical model considers network topology structure and OR nodes, effectively solving PP problems and outperforming state-of-the-art algorithms in obtaining optimal solutions.
Article
Engineering, Industrial
Xavier Delorme, Gerard Fleury, Philippe Lacomme, Damien Lamy
Summary: Reconfigurable manufacturing systems (RMS) aim to bridge the gap between dedicated and flexible manufacturing systems. While most literature focuses on the design and tactical planning of RMS, there is limited research on operational-level scheduling. This paper formalizes the problem through integer linear programming and proposes an iterative search method for larger-scale instances. Results show that managing even a few configurations can significantly improve solution quality, but the extended search space poses challenges for finding good solutions within a reasonable computation time.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Robotics
Anbang Liu, Peter B. Luh, Bing Yan, Mikhail A. Bragin
Summary: This letter presents a novel ILP formulation for job-shop scheduling problems, which significantly reduces computational requirements while ensuring quality by improving existing ILP formulations. By enhancing the SAVLR method under the new formulation, near-optimal solutions are efficiently obtained for large problems where traditional methods have difficulties.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Bing Yan, Mikhail A. Bragin, Peter B. Luh
Summary: This article introduces an innovative and systematic approach to tighten the formulations of individual parts in the data preprocessing stage, linking integer variables to binary variables and obtaining vertices of the convex hull based on LP problem vertices. This significantly improves solution quality and computational efficiency, and can be applied to other complex ILP and MILP problems with similar characteristics.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Janis Brammer, Bernhard Lutz, Dirk Neumann
Summary: This study introduces a reinforcement learning approach to minimize work overload situations in the mixed model sequencing problem. By generating sequences in a constructive way and using metaheuristics, the trained policy can quickly create an initial sequence to improve solution quality. Numerical evaluation on benchmark datasets shows superior performance to established methods when demand plan distribution aligns with learning process expectations.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Management
Kaiping Luo, Guangya Shen, Liheng Li, Jianfei Sun
Summary: Flexible Process Planning (FPP) is a key intelligent manufacturing technique that is formulated using 0-1 mathematical programming. The new formulation simultaneously considers alternative operation selection and sequencing and operational method assignment under two optimization criteria. The proposed linear models have lower complexity and better performance in solving benchmark instances compared to existing mathematical programming models.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Chemistry, Multidisciplinary
Soukaina Oujana, Lionel Amodeo, Farouk Yalaoui, David Brodart
Summary: This paper discusses a research project that aims to optimize the scheduling of production orders in the packaging field. The problem is modeled as an extended version of the hybrid and flexible flowshop scheduling problem with precedence constraints, parallel machines, and sequence-dependent setups. Two methodologies, mixed-integer linear programming (MILP) and constraint programming (CP), are used to tackle the problem. Resource calendar constraints are added to the models, and a novel heuristic is designed for quick solutions. The proposed problem can be easily modified to suit real-world situations involving similar scheduling characteristics.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Jiahui Qian, Zhijing Zhang, Lingling Shi, Dan Song
Summary: This paper proposes a novel assembly timing planning method based on knowledge and mixed integer linear programming. By constructing a knowledge base and adopting a group planning strategy, the assembly timing planning for automatic assembly system is achieved. The proposed method significantly reduces assembly time, improves assembly efficiency, and provides guidance for assembly process design through the developed software for timing planning visualization.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Management
Quang-Vinh Dang, Thijs van Diessen, Tugce Martagan, Ivo Adan
Summary: This paper addresses the scheduling problem of a set of tasks on identical parallel machines in a work center, considering the complex characteristics, objectives, and decision-making process, and proposes a mathematical model and a new matheuristic to solve the problem, demonstrating its superiority through empirical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)