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）
Yunhui Zeng, Zijun Liao, Yuanzhi Dai, Rong Wang, Xiu Li, Bo Yuan
The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasks that specifically consider the inherent uncertainties such as changing order requirements and possible machine breakdown in realistic smart manufacturing settings. Since traditional methods cannot dynamically generate effective scheduling strategies in face of the disturbance of environments, we formulate the DJSP as a Markov decision process (MDP) to be tackled by reinforcement learning (RL). For this purpose, we propose a flexible hybrid framework that takes disjunctive graphs as states and a set of general dispatching rules as the action space with minimum prior domain knowledge. The attention mechanism is used as the graph representation learning (GRL) module for the feature extraction of states, and the double dueling deep Q-network with prioritized replay and noisy networks (D3QPN) is employed to map each state to the most appropriate dispatching rule. Furthermore, we present Gymjsp, a public benchmark based on the well-known OR-Library, to provide a standardized off-the-shelf facility for RL and DJSP research communities. Comprehensive experiments on various DJSP instances confirm that our proposed framework is superior to baseline algorithms with smaller makespan across all instances and provide empirical justification for the validity of the various components in the hybrid framework.
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）
University of Klagenfurt（克拉根福大学）；Graz University of Technology（格拉茨技术大学）
Scheduling is a fundamental task occurring in various automated systems applications, e.g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste. Nevertheless, finding such schedules is often intractable and cannot be achieved by Combinatorial Optimization Problem (COP) methods within a given time limit. Recent advances of Deep Reinforcement Learning (DRL) in learning complex behavior enable new COP application possibilities. This paper presents an efficient DRL environment for Job-Shop Scheduling -- an important problem in the field. Furthermore, we…
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem. A well-known technique to improve generalization is to learn on increasingly complex instances using Curriculum Learning (CL). However, as many works in the literature indicate, this technique might suffer from catastrophic forgetting when transferring the learned skills between different problem size…
The Flexible Job-shop Scheduling Problem (FJSP) is an important combinatorial optimization problem that arises in manufacturing and service settings. FJSP is composed of two subproblems, an assignment problem that assigns tasks to machines, and a scheduling problem that determines the starting times of tasks on their chosen machines. Solving FJSP instances of realistic size and composition is an ongoing challenge even under simplified, deterministic assumptions. Motivated by the inevitable randomness and uncertainties in supply chains, manufacturing, and service operations, this paper investig…
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术研究院）；Eindhoven University of Technology（埃因霍温理工大学）
Nanyang Technological University（南洋理工大学）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术研究院）；National University of Singapore（新加坡国立大学）；Singapore Institute of Manufacturing Technology, A*STAR（新加坡科技研究局制造技术研究院）
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large…
Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
Centre for Artificial Intelligence, Zurich University of Applied Sciences（人工智能中心，苏黎世应用科学大学）；Inspire AG（Inspire公司）；Automatic Control Laboratory, ETH Zurich（自动控制实验室，苏黎世联邦理工学院）
Baidu Inc.（百度公司）；Soochow University（苏州大学）；Auburn University（奥本大学）；North Carolina State University（北卡罗来纳州立大学）
Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine learning jobs because of regulations and laws. As a practical approach to handling decentralized data, Federated Learning (FL) enables collaborative global machine learning model training without sharing sensitive raw data. The servers schedule devices to jobs within the training process of FL. In contrast, device scheduling with multiple jobs in FL remains a critical and open problem. In this paper, we propose a novel…
University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）
In recent years, the power demonstrated by Machine Learning (ML) has increasingly attracted the interest of the optimization community that is starting to leverage ML for enhancing and automating the design of algorithms. One combinatorial optimization problem recently tackled with ML is the Job Shop scheduling Problem (JSP). Most of the works on the JSP using ML focus on Deep Reinforcement Learning (DRL), and only a few of them leverage supervised learning techniques. The recurrent reasons for avoiding supervised learning seem to be the difficulty in casting the right learning task, i.e., wha…
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）
We are interested in the optimal scheduling of a collection of multi-component application jobs in an edge computing system that consists of geo-distributed edge computing nodes connected through a wide area network. The scheduling and placement of application jobs in an edge system is challenging due to the interdependence of multiple components of each job, and the communication delays between the geographically distributed data sources and edge nodes and their dynamic availability. In this paper we explore the feasibility of applying Deep Reinforcement Learning (DRL) based design to address…
One of the main challenges in Grid systems is designing an adaptive, scalable, and model-independent method for job scheduling to achieve a desirable degree of load balancing and system efficiency. Centralized job scheduling methods have some drawbacks, such as single point of failure and lack of scalability. Moreover, decentralized methods require a coordination mechanism with limited communications. In this paper, we propose a multi-agent approach to job scheduling in Grid, named Centralized Learning Distributed Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS i…
Research Center of Smart Networks and Systems, Fudan University（复旦大学智慧网络与系统研究中心）；School of Operations Research and Information Engineering, Cornell University（康奈尔大学运筹学与信息工程学院）
Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years. The incoming jobs require different CPU and memory units, and span different number of time slots. The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions. In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters. Our study reveals that deep reinforcement learning method has the potential to outperform traditional…
Department of Computer Science Texas Tech University（得克萨斯理工大学计算机科学系）
Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy g…
Novosibirsk State Technical University（新西伯利亚国立技术大学）
In the paper, the new approach to the scheduling problem are described. The approach deals with the problem of planning the cyclic production and proposes to consider such scheduling problem as the cyclic job-shop problem of the order k, where k is the number of reiterations. It was found out that planning of only one iteration of the loop is less effective than planning of the entire cycle. To the experimental research, a number of test instances of the job-shop scheduling problem by Operation Research Library were used. The Simulated Annealing was applied to solve the instances. The experime…
Syracuse University（雪城大学）；Georgia Institute of Technology（佐治亚理工学院）
The Jobs shop Scheduling Problem (JSP) is a canonical combinatorial optimization problem that is routinely solved for a variety of industrial purposes. It models the optimal scheduling of multiple sequences of tasks, each under a fixed order of operations, in which individual tasks require exclusive access to a predetermined resource for a specified processing time. The problem is NP-hard and computationally challenging even for medium-sized instances. Motivated by the increased stochasticity in production chains, this paper explores a deep learning approach to deliver efficient and accurate a…
We characterize the search landscape of random instances of the job shop scheduling problem (JSP). Specifically, we investigate how the expected values of (1) backbone size, (2) distance between near-optimal schedules, and (3) makespan of random schedules vary as a function of the job to machine ratio (N/M). For the limiting cases N/M approaches 0 and N/M approaches infinity we provide analytical results, while for intermediate values of N/M we perform experiments. We prove that as N/M approaches 0, backbone size approaches 100%, while as N/M approaches infinity the backbone vanishes. In the p…
INESC TEC（INESC TEC（葡萄牙系统与计算机工程研究所））；Faculdade de Engenharia, Universidade do Porto（波尔图大学工程学院）
The emergence of Industry 4.0 is making production systems more flexible and also more dynamic. In these settings, schedules often need to be adapted in real-time by dispatching rules. Although substantial progress was made until the '90s, the performance of these rules is still rather limited. The machine learning literature is developing a variety of methods to improve them, but the resulting rules are difficult to interpret and do not generalise well for a wide range of settings. This paper is the first major attempt at combining machine learning with domain problem reasoning for scheduling…
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
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）；Nanyang Technological University（南洋理工大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；College of Computing and Data Science（计算与数据科学学院）；College of Economics and Management（经济与管理学院）；Department of Industrial Engineering & Innovation Sciences（工业工程与创新科学系）
The subject of Job Scheduling Optimisation (JSO) deals with the scheduling of jobs in an organization, so that the single working steps are optimally organized regarding the postulated targets. In this paper a use case is provided which deals with a sub-aspect of JSO, the Job Shop Scheduling Problem (JSSP or JSP). As many optimization problems JSSP is NP-complete, which means the complexity increases with every node in the system exponentially. The goal of the use case is to show how to create an optimized duty rooster for certain workpieces in a flexible organized machinery, combined with an …
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）
In manufacturing, the production is often done on out-of-the-shelf manufacturing lines, whose underlying scheduling heuristics are not known due to the intellectual property. We consider such a setting with a black-box job-shop system and an unknown scheduling heuristic that, for a given permutation of jobs, schedules the jobs for the black-box job-shop with the goal of minimizing the makespan. Here, the jobs need to enter the job-shop in the given order of the permutation, but may take different paths within the job shop, which depends on the black-box heuristic. The performance of the black-…
School of Computer Science and Informatics, University College Dublin（都柏林大学学院计算机科学与信息学院）；University of Artois（阿图瓦大学）
While cyclic scheduling is involved in numerous real-world applications, solving the derived problem is still of exponential complexity. This paper focuses specifically on modelling the manufacturing application as a cyclic job shop problem and we have developed an efficient neural network approach to minimise the cycle time of a schedule. Our approach introduces an interesting model for a manufacturing production, and it is also very efficient, adaptive and flexible enough to work with other techniques. Experimental results validated the approach and confirmed our hypotheses about the system …
This report contains the description of two novel job shop scheduling benchmarks that resemble instances of real scheduling problem as they appear in industry. In particular, the aim was to provide large-scale benchmarks (up to 1 million operations) to test the state-of-the-art scheduling solutions on problems that are closer to what occurs in a real industrial context. The first benchmark is an extension of the well known Taillard benchmark (1992), while the second is a collection of scheduling instances with a known-optimum solution.
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）
The job-shop scheduling is one of the most studied optimization problems from the dawn of computer era to the present day. Its combinatorial nature makes it easily expressible as a constraint satisfaction problem. In this paper, we compare the performance of two constraint solvers on the job-shop scheduling problem. The solvers in question are: OR-Tools, an open-source solver developed by Google and winner of the last MiniZinc Challenge, and CP Optimizer, a proprietary IBM constraint solver targeted at industrial scheduling problems. The comparison is based on the goodness of the solutions fou…
University of Luxembourg（卢森堡大学）；University of São Paulo（圣保罗大学）
In this work, the online printing shop scheduling problem introduced in (Lunardi et al., Mixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem, Computers & Operations Research, to appear) is considered. This challenging real scheduling problem, that emerged in the nowadays printing industry, corresponds to a flexible job shop scheduling problem with sequencing flexibility; and it presents several complicating specificities such as resumable operations, periods of unavailability of the machines, sequence-dependent setup times, partial…
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
A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP). It uses smart initialization approaches to enrich the first generated population, and proposes various crossover operators to create a better diversity of offspring. Especially, the MIP-EGO configurator, which can tune algorithm parameters, is adopted to automatically tune operator probabilities. Furthermore, different local search strategies are employed to explore the neighborhood for better solutions. In general, the algorithm enhancement strategy can b…
Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel constructive search technique that performs a series of resource-limited tree searches where each search begins either from an empty solution (as in randomized restart) or from a solution that has been encountered during the search. A small number of these "elite solutions is maintained during the search. We introduce the technique and perform three sets of experiments on the job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS is carried out to evaluate the performance impact of various parameter se…
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）
Many optimization techniques evaluate solutions consecutively, where the next candidate for evaluation is determined by the results of previous evaluations. For example, these include iterative methods, "black box" optimization algorithms, simulated annealing, evolutionary algorithms and tabu search, to name a few. When solving an optimization problem, these algorithms evaluate a large number of solutions, which raises the following question: Is it possible to learn something about the optimum using these solutions? In this paper, we define this "learning" question in terms of a logistic regre…
Flow Shop Scheduling (FSS) has been widely researched due to its application in many types of fields, while the human participant brings great challenges to this problem. Manpower scheduling captures attention for assigning workers with diverse proficiency to the appropriate stages, which is of great significance to production efficiency. In this paper, we present a novel algorithm called Self-encoding Barnacle Mating Optimizer (SBMO), which solves the FSS problem considering worker proficiency, defined as a new problem, Flow Shop Manpower Scheduling Problem (FSMSP). The highlight of the SBMO …
