Showing papers on "Job shop published in 2019"

Flexible Job-Shop Rescheduling for New Job Insertion by Using Discrete Jaya Algorithm

Abstract: Rescheduling is a necessary procedure for a flexible job shop when newly arrived priority jobs must be inserted into an existing schedule. Instability measures the amount of change made to the existing schedule and is an important metrics to evaluate the quality of rescheduling solutions. This paper focuses on a flexible job-shop rescheduling problem (FJRP) for new job insertion. First, it formulates FJRP for new job insertion arising from pump remanufacturing. This paper deals with bi-objective FJRPs to minimize: 1) instability and 2) one of the following indices: a) makespan; b) total flow time; c) machine workload; and d) total machine workload. Next, it discretizes a novel and simple metaheuristic, named Jaya, resulting in DJaya and improves it to solve FJRP. Two simple heuristics are employed to initialize high-quality solutions. Finally, it proposes five objective-oriented local search operators and four ensembles of them to improve the performance of DJaya. Finally, it performs experiments on seven real-life cases with different scales from pump remanufacturing and compares DJaya with some state-of-the-art algorithms. The results show that DJaya is effective and efficient for solving the concerned FJRPs.

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

Abstract: With the rapid advancement and widespread applications of information technology in the manufacturing shop floor, a huge amount of real-time data is generated, providing a good opportunity to effectively respond to unpredictable exceptions so that the productivity can be improved. Thus, how to schedule the manufacturing shop floor for achieving such a goal is very challenging. This paper addresses this issue and a new multiagent-based real-time scheduling architecture is proposed for an Internet of Things-enabled flexible job shop. Differing from traditional dynamic scheduling strategies, the proposed strategy optimally assigns tasks to machines according to their real-time status. A bargaining-game-based negotiation mechanism is developed to coordinate the agents so that the problem can be efficiently solved. To demonstrate the feasibility and effectiveness of the proposed architecture and scheduling method, a proof-of-concept prototype system is implemented with Java agent development framework platform. A case study is used to test the performance and effectiveness of the proposed method. Through simulation and comparison, it is shown that the proposed method outperforms the traditional dynamic scheduling strategies in terms of makespan, critical machine workload, and total energy consumption.

An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals

Abstract: Random job arrivals that happen frequently in manufacturing practice may create a need for dynamic scheduling. This paper considers an issue of how to reschedule the randomly arrived new jobs to pursue both performance and stability of a schedule in a job shop. Firstly, a mixed integer programming model is established to minimize three objectives, including the discontinuity rate of new jobs during the processing, the makespan deviation of initial schedule, and the sequence deviation on machines. Secondly, four match-up strategies from references are modified to determine the rescheduling horizon. Once new jobs arrive, the rescheduling process is immediately triggered with ongoing operations remain. The ongoing operations are treated as machine unavailable constraints (MUC) in the rescheduling horizon. Then, a particle swarm optimization (PSO) algorithm with improvements is proposed to solve the dynamic job shop scheduling problem. Improvement strategies consist of a modified decoding scheme considering MUC, a population initialization approach by designing a new transformation mechanism, and a novel particle movement method by introducing position changes and a random inertia weight. Lastly, extensive experiments are conducted on several instances. The experiments results show that the modified rescheduling strategies are statistically and significantly better than the compared strategies. Moreover, comparative studies with five variants of PSO algorithm and three state-of-the-art meta-heuristics demonstrate the high performance of the improved PSO algorithm.

Deep neural networks based order completion time prediction by using real-time job shop RFID data

Abstract: In the traditional order completion time (OCT) prediction methods, some mutable and ideal production data (e.g., the arrival time of work in process (WIP), the planned processing time of all operations, and the expected waiting time per operation) are often used. Thus, the prediction time always deviates from the actual completion time dramatically even though the dynamicity of the production capacity and the real-time load conditions of job shop are considered in the OCT prediction method. On account of this, a new prediction method of OCT using the composition of order and real-time job shop RFID data is proposed in this article. It applies accurate RFID data to depict the real-time load conditions of job shop, and attempts to mine the mapping relationship between RFID data and OCT from historical data. Firstly, RFID devices capture the types and waiting list information of all WIPs which are in the in-stocks and out-stocks of machining workstations, and the real-time processing progress of all WIPs which are under machining at machining workstations. Secondly, a description model of real-time job shop load conditions is put forward by using the RFID data. Next, the mapping model based on the composition of order and real-time RFID data is established. Finally, deep belief network, which is one of the major technologies of deep neural networks, is applied to mine the mapping relationship. To illustrate the advantages of the proposed method, a numerical experiment compared with back-propagation (BP) network based prediction method, multi-hidden-layers BP network based prediction method and the principal components analysis and BP network based prediction method is conducted at last.

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

Abstract: Designing effective dispatching rules for production systems is a difficult and time-consuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learn...

A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling

Abstract: Dynamic flexible job shop scheduling (DFJSS) is an important and a challenging combinatorial optimisation problem. Genetic programming hyper-heuristic (GPHH) has been widely used for automatically evolving the routing and sequencing rules for DFJSS. The terminal set is the key to the success of GPHH. There are a wide range of features in DFJSS that reflect different characteristics of the job shop state. However, the importance of a feature can vary from one scenario to another, and some features may be redundant or irrelevant under the considered scenario. Feature selection is a promising strategy to remove the unimportant features and reduce the search space of GPHH. However, no work has considered feature selection in GPHH for DFJSS so far. In addition, it is necessary to do feature selection for the two terminal sets simultaneously. In this paper, we propose a new two-stage GPHH approach with feature selection for evolving routing and sequencing rules for DFJSS. The experimental studies show that the best solutions achieved by the proposed approach are better than that of the baseline method in most scenarios. Furthermore, the rules evolved by the proposed approach involve a smaller number of unique features, which are easier to interpret.

Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation.

Abstract: In this paper, a hybrid deep neural network scheduler (HDNNS) is proposed to solve job-shop scheduling problems (JSSPs) In order to mine the state information of schedule processing, a job-shop scheduling problem is divided into several classification-based subproblems And a deep learning framework is used for solving these subproblems HDNNS applies the convolution two-dimensional transformation method (CTDT) to transform irregular scheduling information into regular features so that the convolution operation of deep learning can be introduced into dealing with JSSP The simulation experiments designed for testing HDNNS are in the context of JSSPs with different scales of machines and jobs as well as different time distributions for processing procedures The results show that the MAKESPAN index of HDNNS is 9% better than that of HNN and the index is also 4% better than that of ANN in ZLP dataset With the same neural network structure, the training time of the HDNNS method is obviously shorter than that of the DEEPRM method In addition, the scheduler has an excellent generalization performance, which can address large-scale scheduling problems with only small-scale training data

A novel dynamic assignment rule for the distributed job shop scheduling problem using a hybrid ant-based algorithm

Abstract: Distributed scheduling problems are among the most investigated research topics in the fields of Operational Research, and represents one of the greatest challenges faced by industrialists and researchers today. The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories and with determining the sequence of operations on each machine in distributed manufacturing environments. The objective is to minimize the global makespan over all the factories. Since the problem is NP-hard to solve, one option to cope with this intractability is to use an approximation algorithm that guarantees near-optimal solutions quickly. Ant based algorithm has proved to be very effective and efficient in numerous scheduling problems, such as permutation flow shop scheduling, flexible job shop scheduling problems and network scheduling, etc. This paper proposes a hybrid ant colony algorithm combined with local search to solve the Distributed Job shop Scheduling Problem. A novel dynamic assignment rule of jobs to factories is also proposed. Furthermore, the Taguchi method for robust design is adopted for finding the optimum combination of parameters of the ant-based algorithm. To validate the performance of the proposed algorithm, intensive experiments are carried out on 480 large instances derived from well-known classical job-shop scheduling benchmarks. Also, we show that our algorithm can process up to 10 factories. The results prove the efficiency of the proposed algorithm in comparison with others.

A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines

Abstract: In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem.

Large-scale medical examination scheduling technology based on intelligent optimization

Abstract: In recent years, the scale of the health examination business has increased rapidly, and research on the combinatorial optimization of medical examinations has become more important In this context, a special large-scale flexible open shop scheduling problem (FOSP) is introduced based on the idea of the multi-processor open shop scheduling problem A mixed integer programming model is developed for the FOSP, which regards client satisfaction as the most important objective As the FOSP is particularly complex, three different intelligent optimization algorithms are examined, namely a genetic algorithm, hybrid particle swarm optimization, and simulated annealing According to the medical examination preferences of the clients, a group of large-scale test problems are created on the basis of benchmark instances of the flexible job shop problem, and these are used to evaluate the performance of each algorithm The experimental results show that the genetic algorithm outperforms both simulated annealing and hybrid particle swarm optimization, especially in large-scale problems

Improving Energy Efficiency in Discrete Parts Manufacturing System Using an Ultra-Flexible Job Shop Scheduling Algorithm

Abstract: Improving energy efficiency has been one of main objectives in modern manufacturing enterprises. Various approaches aiming at efficient energy management have been proposed/developed, among which minimizing energy consumption by energy-sensible production scheduling techniques has emerged as a promising one. However, reported workshop models are quite simple and unrealistic. This paper studies a more realistic workshop model called ultra-flexible job shop (uFJS). In an uFJS, the sequence among operations for a job can be changed within certain constraints. To formulate this energy-efficient scheduling problem, a mixed-integer linear programming model was developed. To deal with large-sized problems, a specially designed genetic algorithm (GA) was subsequently proposed and implemented. Numerical results showed the proposed GA worked with decent effectiveness and efficiency. Finally, several comparative studies are carried out to further demonstrate its efficacy in terms of energy efficiency improvement. The advantage of the uFJS as compared to other relative simple workshop models is also shown. By considering the flexibility in operation sequencing in each job, the uFJS effectively integrates process planning and scheduling in discrete parts manufacturing system, thus providing a much larger solution space for more energy-efficient solutions. It therefore provides an excellent platform for decision-makers when developing energy-efficient techniques and strategies

A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission

Abstract: The manufacturing industry is undergoing transformation and upgrading from traditional manufacturing to intelligent manufacturing, in which Internet of Things (IoT) technology plays a central role in promoting the development of intelligent manufacturing. In order to solve the problem that low production efficiency and machine utilization lead to serious pollution emissions in the workshop caused by untimely transmission of information in all links of the production and manufacturing process to whole supply chains, this study establishes an intelligent production scheduling and logistics delivery model with IoT technology to promote green and sustainable development of intelligent manufacturing. Firstly, an application framework of IoT technology in production–delivery supply chain systems was established to improve efficiency and achieve the integration of production and delivery. Secondly, an integrated production–delivery model was constructed, which takes into account time and low carbon constraints. Finally, a two-layer optimization algorithm was proposed to solve this integration problem. Through a case study, the results show this integration production–delivery model can reduce the cost of supply chains and improve customer satisfaction. Moreover, it proves that carbon emission cost is a major factor affecting total cost, and it could help enterprises to realize the profit and sustainable development of the environment. The production–delivery model could also support the last kilometer distribution problem and extension under E-commerce applications.

An integrated approach for scheduling flexible job-shop using teaching–learning-based optimization method

Abstract: In this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, then the FJSP comes under integrated approach. Otherwise, it becomes a hierarchical approach. Very less research has been done in the past on FJSP problem as it is an NP-hard (non-deterministic polynomial time hard) problem and very difficult to solve till date. Further, very less focus has been given to solve the FJSP using an integrated approach. So an attempt has been made to solve FJSP based on integrated approach using TLBO. Teaching–learning-based optimization is a meta-heuristic algorithm which does not have any algorithm-specific parameters that are to be tuned in comparison to other meta-heuristics. Therefore, it can be considered as an efficient algorithm. As best student of the class is considered as teacher, after few iterations all the students learn and reach the same knowledge level, due to which there is a loss in diversity in the population. So, like many meta-heuristics, TLBO also has a tendency to get trapped at the local optimum. To avoid this limitation, a new local search technique followed by a mutation strategy (from genetic algorithm) is incorporated to TLBO to improve the quality of the solution and to maintain diversity, respectively, in the population. Tests have been carried out on all Kacem’s instances and Brandimarte's data instances to calculate makespan. Results show that TLBO outperformed many other algorithms and can be a competitive method for solving the FJSP.

An Energy-Efficient Scheduling Approach for Flexible Job Shop Problem in an Internet of Manufacturing Things Environment

Abstract: This paper addresses the energy-efficient scheduling and real-time control of flexible job shop that requires rescheduling affected operations and updating the scheduling. For energy-efficient scheduling shop floor efficiently, we propose a metaheuristic algorithm called PN-ACO algorithm, which combines a timed transition Petri nets (TTPN) based representation tool and an ant colony optimization (ACO) heuristic search method. To address the real-time control problem in energy-efficient scheduling of the shop floor, we apply the Internet of Things (IoT) technology to product production to form an Internet of Manufacturing Things environment (IoMT). In the IoMT environment, there are usually many abnormal event disturbances, including machine breakdown and urgent order arrival. To quickly handle the disturbance problem of abnormal events, the distributed control system architecture is proposed, the core of which is the negotiation and cooperation between manufacturing resources based on the wireless communication network. The proposed approach is further illustrated by a case energy-efficient of scheduling for a flexible job shop through which the optimal scheduling and correct supervisory control instructions can be obtained easily and quickly. In sum, the proposed optimization algorithm obtains a good effect in engineering applications while the validity of optimization is proved.

Bad-scenario-set robust scheduling for a job shop to hedge against processing time uncertainty

Abstract: This paper proposed two robust scheduling formulations in real manufacturing systems based on the concept of bad scenario set to hedge against processing time uncertainty, which is described by dis...

Surgical case scheduling problem with fuzzy surgery time: An advanced bi-objective ant system approach

Abstract: We address the bi-objective surgical case scheduling problem under uncertain service times. The goal is to simultaneously minimize (i) makespan and (ii) number of unscheduled surgical cases. We optimize two decisions in our surgical case scheduling problem: the allocation of the resources to the surgical cases and their starting times. We formulate our problem as a novel bi-objective no-wait multi-resource flexible job shop problem. We use fuzzy numbers to represent the inherent stochasticity in the length-of-stays of patients in different stages of an operating theater. Due to the intractability of the problem even for small instances, we develop a novel bi-objective ant system: Fuzzy Pareto Envelope-based Selection Ant System. The performance of the new algorithm on all test instances is compared to a basic bi-objective ant system under the fuzzy condition: Pareto strength ant colony optimization. Finally, we demonstrate computationally that our approach outperforms the state-of-the-art algorithm in literature in terms of both efficiency and effectiveness.

An Imperialist Competitive Algorithm With the Diversified Operators for Many-Objective Scheduling in Flexible Job Shop

Abstract: A number of related works have been done on multi-objective flexible job shop scheduling problem, however, many-objective flexible job shop scheduling problems (MaOFJSP) with at least four objectives are seldom considered. In this paper, an imperialist competitive algorithm with the diversified operators (DOICA) is proposed for MaOFJSP with the minimization of makespan, total tardiness, total workload, and total energy consumption. In DOICA, the diversified assimilation and revolution are used according to the features of empires, a novel imperialist competition is implemented by excluding the strongest empire and doing multiple neighborhood search of a solution in the strongest empire. The extensive experiments are conducted by using a number of benchmark instances to test the impact of strategies of DOICA on its performance and compare DOICA with other algorithms from literature finally. The computational results validate that the new strategies of DOICA are effective and DOICA can provide promising results for the considered MaOFJSP.

A New Representation in Genetic Programming for Evolving Dispatching Rules for Dynamic Flexible Job Shop Scheduling.

Abstract: Dynamic flexible job shop scheduling (DFJSS) is a very important problem with a wide range of real-world applications such as cloud computing and manufacturing. In DFJSS, it is critical to make two kinds of real-time decisions (i.e. the routing decision that assigns machine to each job and the sequencing decision that prioritises the jobs in a machine’s queue) effectively in the dynamic environment with unpredicted events such as new job arrivals and machine breakdowns. Dispatching rule is an ideal technique for this purpose. In DFJSS, one has to design a routing rule and a sequencing rule for making the two kinds of decisions. Manually designing these rules is time consuming and requires human expertise which is not always available. Genetic programming (GP) has been applied to automatically evolve more effective rules than the manually designed ones. In GP for DFJSS, different features in the terminal set have different contributions to the decision making. However, the current GP approaches cannot perfectly find proper combinations between the features in accordance with their contributions. In this paper, we propose a new representation for GP that better considers the different contributions of different features and combines them in a sophisticated way, thus to evolve more effective rules. The results show that the proposed GP approach can achieve significantly better performance than the baseline GP in a range of job shop scenarios.

Energy-Conscious Scheduling Problem in a Flexible Job Shop Using a Discrete Water Wave Optimization Algorithm

Abstract: As more and more attention is paid to green manufacturing, production scheduling has been proved to be an efficient method for the reduction of environmental pollution. It is well-known that the flexible job shop scheduling problem (FJSP) is a very complex combinatorial optimization problem with strong theoretical and background for application. However, the problem has been extensively investigated and historically concerned with some indicators related to time, e.g., flow time, makespan, and workload. In this study, an energy-conscious FJSP is investigated with the consideration of the energy consumption. First, a mathematical model of the energy-conscious FJSP is built with the objective of optimizing the sum of the energy consumption cost and the completion-time cost. Due to the fact that the basic water wave optimization (WWO) was developed for various continuous problems, a discrete water wave optimization (DWWO) algorithm is proposed to solve the model. In our DWWO algorithm, a three-string encoding approach is first adopted to represent each individual wave. To make the algorithm adapt for the considered scheduling problem, three discrete evolutionary operations are redesigned according to the characteristics of the problem, i.e., propagation, refraction, and breaking. Finally, extensive experimental simulations are conducted to test the proposed DWWO algorithm. The comparison results demonstrate that the proposed DWWO algorithm is efficient for the energy-conscious FJSP.