Dynamic Job Research Articles

Responsiveness to sudden disturbances in manufacturing through dynamic job shop scheduling using Quantum Annealing

An efficient production planning and control is inevitable for the economic operation of a manufacturing system. As an essential part of production planning and control, process scheduling aims to assign processes to the available resources of a manufacturing system under consideration of its objectives. This forms an optimization problem also known as job shop scheduling which can be solved with computer aided techniques. Exact solution methods are only practical up to a certain number of functional units and processes, therefore, approximation methods are used in industry. However, as the problem size increases, the computational solving time increases significantly and the solution quality decreases in equal measure. In order to react to the current effects of global crises, such as disruptions in supply chains, approaches for fast and efficient rescheduling are needed. A dynamic shop job scheduling approach using Quantum Annealing bears the potential to close this research gap. Previous work has shown that Quantum Annealing is able to solve static job shop scheduling problems within seconds while finding good solutions. However, in a flexible environment such as a manufacturing system, the static approach is not suitable for process scheduling. Therefore, a dynamic Quantum Annealing based approach for job shop scheduling with consideration of machine breakdowns and new job arrivals is proposed. The approach monitors a manufacturing system and reacts to changes in the job pool or availabilities in functional units with rescheduling. The method is tested with several use cases involving small and large-scale problems and is compared with a simulated annealing approach. Thereby, the Quantum Annealing based computations show better results regarding solution quality and computing time. Moreover, the dynamic approach bears the potential for industrial application, especially as a supplement to a conventional advanced planning system.

EXAMIMIG CAREER READINESS AMONG UNIVERSITY SENIOR STUDENTS AND ALUMNI: DEVELOPMENT OF DIAGNOSTIC TOOLS

This article investigates the importance of career readiness among university students and alumni in today's dynamic job market. It emphasizes the significance of professionalism, advanced qualifications, and adaptability for a successful professional career. The research aims to develop a diagnostic toolkit to assess and measure career readiness, exploring the various components that contribute to students' preparedness. The study involves a comprehensive literature review and employs surveys and interviews to gather data from stakeholders involved in career guidance. The toolkit evaluates values orientation, career motivations, self-efficacy, decision-making readiness, emotional intelligence, and organizational skills. The article provides valuable insights for career counseling and educational programs to enhance students' preparedness for their future careers. By empowering educators and institutions with this comprehensive approach, it facilitates successful career development and helps graduates achieve their professional aspirations in today's competitive job market. The study is presented as a practical integrated career counseling support for university students and graduates to assess how much students' future career readiness is improved.

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Genetic Programming (GP) has been widely employed to create dispatching rules intelligently for production scheduling. The success of GP depends on a suitable terminal set of selected features. Specifically, techniques that consider feature selection in GP to enhance rule understandability for dynamic job shop scheduling (DJSS) have been successful. However, existing feature selection algorithms in GP focus more emphasis on obtaining more compact rules with fewer features than on improving effectiveness. This paper is an attempt at combining a novel GP method, GP via dynamic diversity management, with feature selection to design effective and interpretable dispatching rules for DJSS. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules for the three objective functions. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 13.28%, 12.57%, and 15.62% in the mean tardiness (MT), mean flow time (MFT), and mean weighted tardiness (MWT) objective, respectively.

Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems

• The manufacturing equipment in the workshop is constructed as an equipment agent with the support of edge computing node, and a multi-agent manufacturing system with the ability of online scheduling and scheduling strategy optimization is presented. • A self-organizing negotiation mechanism based on contract network protocol (CNP) is proposed to guide the cooperation and competition among multiple agents , and to provide support for intelligent decision-making. • The decision-making module of equipment agent called artificial intelligence (AI) scheduler is established based on the multi-layer perceptron, and enables the manufacturing equipment to intelligently generate an optimal production strategy according to the perceived workshop state. • An adaptive learning strategy of AI scheduler based on proximal policy optimization (PPO) algorithm is proposed to improve its decision-making performance under the disturbance of orders and resources. • Experimental results show that the proposed methodology achieves better performance in terms of production strategy learning and disturbance handling compared with other optimization-based approaches and dispatching rules . Personalized orders bring challenges to the production paradigm, and there is an urgent need for the dynamic responsiveness and self-adjustment ability of the workshop. Traditional dispatching rules and heuristic algorithms solve the production planning and control problems by making schedules. However, the previous methods cannot work well in a changeable workshop environment when encountering a large number of stochastic disturbances of orders and resources. Recently, the potential of artificial intelligence (AI) algorithms in solving the dynamic scheduling problem has attracted researchers' attention. Therefore, this paper presents a multi-agent manufacturing system based on deep reinforcement learning (DRL), which integrates the self-organization mechanism and self-learning strategy. Firstly, the manufacturing equipment in the workshop is constructed as an equipment agent with the support of edge computing node, and an improved contract network protocol (CNP) is applied to guide the cooperation and competition among multiple agents, so as to complete personalized orders efficiently. Secondly, a multi-layer perceptron is employed to establish the decision-making module called AI scheduler inside the equipment agent. According to the perceived workshop state information, AI scheduler intelligently generates an optimal production strategy to perform task allocation. Then, based on the collected sample trajectories of scheduling process, AI scheduler is periodically trained and updated through the proximal policy optimization (PPO) algorithm to improve its decision-making performance. Finally, in the multi-agent manufacturing system testbed, dynamic events such as stochastic job insertions and unpredictable machine failures are considered in the verification experiments. The experimental results show that the proposed method is capable of obtaining the scheduling solutions that meet various performance metrics, as well as dealing with resource or task disturbances efficiently and autonomously.

Job Shop 제조라인의 설비 가공지연 최소화를 위한 시계열 예측 기반의 자율주행 로봇 할당 제어

제조 운영 환경의 빠른 변화에 유연하게 대응하기 위해 기존의 rail 기반의 AGV, OHT 제조물류에서 벗어나 rail이 필요 없는 AMR 기반의 물류로 전환하는 새로운 시도들이 나타나고 있다. AMR은 주행용 rail을 설치할 필요가 없으므로 설비 증설이나 이동과 같은 레이아웃 변화에 즉시 대응 가능한 장점이 있다. 그러나 AMR은 이동 경로가 다양하며 상대적으로 속도가 느려서 기존 rail 기반 물류 시스템 대비 운반 요청 대응력이 떨어진다는 단점이 있다. 따라서 발생하는 작업요청을 적절한 AMR에 배정하는 작업 할당이 전체 제조물류 운반 성능에 미치는 영향도가 커졌다. 본 연구에서는 동적인 job shop 제조 환경에서 예측기반의 AMR 작업할당 방법을 제안하고 성능을 평가한다. 구체적으로, 운반을 요청할 설비를 예측하고 해당 설비까지 이동거리를 최소화하는 위치로 AMR을 미리 이동시켜서 실제 운반 요청 발생 시 재빠르게 대응하려는 것이다. 실험 결과 기존의 방법에 비해 AMR 대기시간이 감소하는 성능을 보였다.

Real-time scheduling for distributed permutation flowshops with dynamic job arrivals using deep reinforcement learning

Distributed manufacturing plays an important role for large-scale companies to reduce production and transportation costs for globalized orders. However, how to real-timely and properly assign dynamic orders to distributed workshops is a challenging problem. To provide real-time and intelligent decision-making of scheduling for distributed flowshops, we studied the distributed permutation flowshop scheduling problem (DPFSP) with dynamic job arrivals using deep reinforcement learning (DRL). The objective is to minimize the total tardiness cost of all jobs. We provided the training and execution procedures of intelligent scheduling based on DRL for the dynamic DPFSP. In addition, we established a DRL-based scheduling model for distributed flowshops by designing suitable reward function, scheduling actions, and state features. A novel reward function is designed to directly relate to the objective. Various problem-specific dispatching rules are introduced to provide efficient actions for different production states. Furthermore, four efficient DRL algorithms, including deep Q-network (DQN), double DQN (DbDQN), dueling DQN (DlDQN), and advantage actor-critic (A2C), are adapted to train the scheduling agent. The training curves show that the agent learned to generate better solutions effectively and validate that the system design is reasonable. After training, all DRL algorithms outperform traditional meta-heuristics and well-known priority dispatching rules (PDRs) by a large margin in terms of solution quality and computation efficiency. This work shows the effectiveness of DRL for the real-time scheduling of dynamic DPFSP.

Numerical Simulation and Assessment of Meta Heuristic Optimization Based Multi Objective Dynamic Job Shop Scheduling System

In today's world of manufacturing, cost reduction becomes one of the most important issues. A successful business needs to reduce its cost to be competitive. The programming of the machine is playing an important role in production planning and control as a tool to help manufacturers reduce their costs maximizing the use of their resources. The programming problem is not only limited to the programming of the machine, but also covers many other areas such such as computer and information technology and communication. From the definition, programming is an art that involves allocating, planning the allocation and utilization of resources to achieve a goal. The aim of the program is complete tasks in a reasonable amount of time. This reasonableness is a performance measure of how well the resources are allocated to tasks. Time or time-dependent functions are always it used as performance measures. The objectives of this research are to develop Intelligent Search Heuristic algorithms (ISHA) for equal and variable size sub lot for m machine flow shop problems, to Implement Particle Swarm Optimization algorithm (PSO) in matlab, to develop PSO based Optimization program for efficient job shop scheduling problem. The work also address solution to observe and verify results of PSO based Job Shop Scheduling with help of graft chart.

Assessing the Effect of Setup Times and Shop Utilization Levels on Performance of ORR Policies in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Times: A Simulation Approach

Assessing the Effect of Setup Times and Shop Utilization Levels on Performance of ORR Policies in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Times: A Simulation Approach

Surrogate-assisted automatic evolving of dispatching rules for multi-objective dynamic job shop scheduling using genetic programming

Dispatching rules are simple but efficient heuristics to solve multi-objective job shop scheduling problems, particularly useful to face the challenges of dynamic shop environments. A promising method to automatically evolve non-dominated rules represents multi-objective genetic programming based hyper-heuristic (MO-GP-HH). The aim of such methods is to approximate the Pareto front of non-dominated dispatching rules as good as possible in order to provide a sufficient set of efficient solutions from which the decision maker can select the most preferred one. However, one of the main drawbacks of existing approaches is the computational demanding simulation-based fitness evaluation of the evolving rules. To efficiently allocate the computational budget, surrogate models can be employed to approximate the fitness. Two possible ways, that estimate the fitness either based on a simplified problem or based on samples of fully evaluated individuals making use of machine learning techniques are investigated in this paper. Several representatives of both categories are first examined with regard to their selection accuracy and execution time. Furthermore, we developed a surrogate-assisted MO-GP-HH framework, incorporating a pre-selection task in the NSGA-II algorithm. The most promising candidates are consequently implemented in the framework. Using a dynamic job shop scenario, the two proposed algorithms are compared to the original one without using surrogates. With the aim to minimize the mean flowtime and maximum tardiness, experimental results demonstrate that the proposed algorithms outperform the former. Making use of surrogates leads to a reduction in computational costs of up to 70%. Another interesting finding shows that the enhanced ability to identify duplicates based on the phenotypic characterization of individuals is particularly helpful in increasing diversity within a population. This study illustrates the positive effect of this mechanism on the exploration of the entire Pareto front.

An End-to-End Deep Learning Method for Dynamic Job Shop Scheduling Problem

Job shop scheduling problem (JSSP) is essential in the production, which can significantly improve production efficiency. Dynamic events such as machine breakdown and job rework frequently occur in smart manufacturing, making the dynamic job shop scheduling problem (DJSSP) methods urgently needed. Existing rule-based and meta-heuristic methods cannot cope with dynamic events in DJSSPs of different sizes in real time. This paper proposes an end-to-end transformer-based deep learning method named spatial pyramid pooling-based transformer (SPP-Transformer), which shows strong generalizability and can be applied to different-sized DJSSPs. The feature extraction module extracts the production environment features that are further compressed into fixed-length vectors by the feature compression module. Then, the action selection module selects the simple priority rule in real time. The experimental results show that the makespan of SPP-Transformer is 11.67% smaller than the average makespan of dispatching rules, meta-heuristic methods, and RL methods, proving that SPP-Transformer realizes effective dynamic scheduling without training different models for different DJSSPs. To the best of our knowledge, SPP-Transformer is the first application of an end-to-end transformer in DJSSP, which not only improves the productivity of industrial scheduling but also provides a paradigm for future research on deep learning in DJSSP.

Feature selection approach for evolving reactive scheduling policies for dynamic job shop scheduling problem using gene expression programming

Dispatching rules are one of the most widely applied methods for solving Dynamic Job Shop Scheduling problems (DJSSP) in real-world manufacturing systems. Hence, the automated design of effective rules has been an important subject in the scheduling literature for the past several years. High computational requirements and difficulty in interpreting generated rules are limitations of literature methods. Also, feature selection approaches in the field of automated design of scheduling policies have been developed for the tree-based GP approach only. Therefore, the aim of this study is to propose a feature selection approach for the Gene Expression Programming (GEP) algorithm to evolve high-quality rules in simple structures with an affordable computational budget. This integration speeds up the search process by restricting the GP search space using the linear representation of the GEP algorithm and creates concise rules with only meaningful features using the feature selection approach. The proposed algorithm is compared with five algorithms and 30 rules from the literature under different processing conditions. Three performance measures are considered including total weighted tardiness, mean tardiness, and mean flow time. The results show that the proposed algorithm can generate smaller rules with high interpretability in a much shorter training time.

Dynamic Scheduling of Crane by Embedding Deep Reinforcement Learning into a Digital Twin Framework

This study proposes a digital twin (DT) application framework that integrates deep reinforcement learning (DRL) algorithms for the dynamic scheduling of crane transportation in workshops. DT is used to construct the connection between the workshop service system, logical simulation environment, 3D visualization model and physical workshop, and DRL is used to support the core decision in scheduling. First, the dynamic scheduling problem of crane transportation is constructed as a Markov decision process (MDP), and the corresponding double deep Q-network (DDQN) is designed to interact with the logic simulation environment to complete the offline training of the algorithm. Second, the trained DDQN is embedded into the DT framework, and then connected with the physical workshop and the workshop service system to realize online dynamic crane scheduling based on the real-time states of the workshop. Finally, case studies of crane scheduling under dynamic job arrival and equipment failure scenarios are presented to demonstrate the effectiveness of the proposed framework. The numerical analysis shows that the proposed method is superior to the traditional dynamic scheduling method, and it is also suitable for large-scale problems.

Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning

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. However, 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. The idea consists of using the insights obtained with the latter to guide the empirical search of the former. We hypothesise that this guided empirical learning process should result in effective and interpretable dispatching rules that generalise well to different scenarios. We test our approach in the classical dynamic job shop scheduling problem minimising tardiness, one of the most well-studied scheduling problems. The simulation experiments include a wide spectrum of scenarios for the first time, from highly loose to tight due dates and from low utilisation conditions to severely congested shops. Nonetheless, results show that our approach can find new state-of-the-art rules, which significantly outperform the existing literature in the vast majority of settings. Overall, the average improvement over the best combination of benchmark rules is 19%. Moreover, the rules are compact, interpretable, and generalise well to extreme, unseen scenarios. Therefore, we believe that this methodology could be a new paradigm for applying machine learning to dynamic optimisation problems.

A novel feature selection for evolving compact dispatching rules using genetic programming for dynamic job shop scheduling

Because of advances in computational power and machine learning algorithms, the automated design of scheduling rules using Genetic Programming (GP) is successfully applied to solve dynamic job shop scheduling problems. Although GP-evolved rules usually outperform dispatching rules reported in the literature, intensive computational costs and rule interpretability persist as important limitations. Furthermore, the importance of features in the terminal set varies greatly among scenarios. The inclusion of irrelevant features broadens the search space. Therefore, proper selection of features is necessary to increase the convergence speed and to improve rule understandability using fewer features. In this paper, we propose a new representation of the GP rules that abstracts the importance of each terminal. Moreover, an adaptive feature selection mechanism is developed to estimate terminals’ weights from earlier generations in restricting the search space of the current generation. The proposed approach is compared with three GP algorithms from the literature and 30 human-made rules from the literature under different job shop configurations and scheduling objectives, including total weighted tardiness, mean tardiness, and mean flow time. Experimentally obtained results demonstrate that the proposed approach outperforms methods from the literature in generating more interpretable rules in a shorter computational time without sacrificing solution quality.

Scheduling with Maintenance Technique in Dynamic Job Shop System including Machine Breakdowns

Scheduling with Maintenance Technique in Dynamic Job Shop System including Machine Breakdowns

A c/μ‐Rule for Job Assignment in Heterogeneous Group‐Server Queues

We study a dynamic job assignment problem in queueing systems with one class of Poisson arrivals and K groups of heterogeneous servers. A scheduling policy prescribes the job assignment among servers in each group at every state n (number of jobs in the system). Our goal is to obtain the optimal policy to minimize the long‐run average cost, which involves the increasingly convex holding cost for jobs and the operating cost for working servers. This problem has wide application scenarios in operations management, such as job scheduling in manufacturing systems, packet routing in communication systems, and staffing in service systems. We prove that the optimal policy has monotone structures and quasi bang–bang control forms. Specifically, we discover that the optimal policy is governed by the marginal cost rate c − μG( n), where c is the operating cost rate, μ is the service rate, and G( n) is called the perturbation realization factor at state n. Under the condition of scale economies which can be guaranteed by any increasingly concave operating cost in μ, we prove that the optimal policy obeys a so‐called c/ μ‐ rule: Servers with a smaller c/ μ should be occupied by jobs with higher priority. Optimality of multi‐threshold type policies is further proved when the c/ μ‐rule is applied. Our c/ μ‐rule in group‐server queues can be viewed as a counterpart of the famous cμ‐rule in polling queues, which both significantly reduce the complexity of optimization problems. By utilizing these optimality structures, we also develop computational‐efficient algorithms to determine the optimal policy numerically. Simulation experiments demonstrate the good scalability and robustness of the c/ μ‐rule, which are important for managerial practice.

A novel independent job rescheduling strategy for cloud resilience in the cloud environment

Purpose Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance. Design/methodology/approach A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed. Findings The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches. Originality/value This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

Network-based dynamic dispatching rule generation mechanism for real-time production scheduling problems with dynamic job arrivals

Although the concept of Industrial 4.0 has been well accepted, only few studies have dealt with real-time production scheduling of smart factories. Due to the advantages of simplicity, efficiency and quick response, heuristic rules have become the most promising technology to solve such problems. However, they suffer some drawbacks, such as high development and maintenance costs, low solution quality, and excessive emphasis on local information. To design heuristics from the perspective of system optimization and ensure the performance of heuristics in real-time production scheduling environments, this study develops a network-based dynamic dispatching rule generation mechanism. The complex network theory is introduced to extract a series of low-level heuristics from the perspective of system optimization, while the automatic heuristic generation problem is formulated as a multiple attribute decision making problem. Given that the dispersity of local features indicates their value for decision-making, the entropy weighting method is employed to automatically produce an adequate combination of the provided easy-to-implement low-level heuristics. Finally, the open shop scheduling problem with dynamic job arrivals is taken as an example to evaluate the effectiveness of the proposed algorithm. Numerical results demonstrate the excellent performance of the proposed algorithm in terms of algorithm effectiveness and computational time.

Real-time scheduling simulation optimisation of job shop in a production-logistics collaborative environment

ABSTRACT In a complex and dynamic job shop containing logistics factor, schedule needs to be generated rapidly, so the real-time scheduling method is more suitable for such scenario. Such method takes advantage of local information within a short time due to the rapid changes of information under uncertain environment. Therefore, how to make use of the future information by prediction while ensuring the robustness of schedule is a valuable problem. To solve it, firstly, a new real-time scheduling model and algorithm is proposed. There is a new kind of release moment of task information which can give AGVs the longest time to prepare for the task than existing research. Secondly, a real-time information update mechanism is designed to increase schedule’s robustness. Finally, a large-scale and dynamic job shop simulation experimental platform is developed. Dynamic factors include the random insertion of orders and failures of equipment. Results show that the method proposed outperforms existing research in terms of customer satisfaction, equipment utilisation and energy consumption. The robustness of schedule can also be acceptable. This paper also finds a rule that in job shop with the large proportion of logistics transportation time, the above method can achieve more competitive results.

Simulation Study of Scheduling Heuristics for Parallel Machines with Sequence-Independent Setups

In this paper we examine a dynamic job scheduling problem on identical, parallel machines. Jobs that belong to different families arrive to the system at random time intervals. Stochastic processing times of jobs are considered and each family has different due date allowances. The manufacturing system consists of a single input queue and several machines. When switching from one job type to another on some machine, a sequence-independent setup is incurred. The scheduling of jobs is performed by some priority rule and nine alternative rules are considered in this study. The discrete-event simulation model of the system is developed and the performance of the scheduling heuristics is assessed in terms of the metrics of mean cycle time, mean earliness and mean tardiness. A total of thirteen simulation cases is considered, where the differentiating factors of the cases are the average job interarrival times, the due date and the setup parameters of jobs.