Dynamic Job Shop Scheduling Research Articles

Multi-objective scheduling of dynamic job shop using variable neighborhood search

Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as objective function that consists of makespan and tardiness. The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem. Results illustrate the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions.

Dynamic job shop scheduling using variable neighbourhood search

In this paper a scheduling method based on variable neighbourhood search (VNS) is introduced to address a dynamic job shop scheduling problem that considers random job arrivals and machine breakdowns. To deal with the dynamic nature of the problem, an event-driven policy is selected. To enhance the efficiency and effectiveness of the scheduling method, an artificial neural network with a back propagation error learning algorithm is used to update parameters of the VNS at any rescheduling point according to the problem condition. The proposed method is compared with some common dispatching rules that have been widely used in the literature for the dynamic job shop scheduling problem. Results illustrate the high efficiency and effectiveness of the proposed method in a variety of shop floor conditions.

Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.

Intelligent Job Shop Scheduling Based on MAS and Integrated Routing Wasp Algorithm and Scheduling Wasp Algorithm

Dynamic scheduling algorithms are gaining more and more special attention for their satisfying robustness when confronted with unexpected events as well as their considerably high performance in scheduling. An organization structure for intelligent job shop scheduling based on MAS (Multiple Agents System) is put forward to achieve effective and efficient production. A hybrid multilayer agent structure is put forward to facilitate constructing various agents. Consequently, all resources in a manufacturing system are reorganized into an agile manufacturing network of agents of autonomous and cooperative characteristics. Regarding wasps as a specific kind of agents, wasp colony algorithms are used to solve job shop dynamic scheduling problem. Based on the principle of the wasp colony algorithm, two different algorithms, namely the routing wasp algorithm and the scheduling wasp algorithm, are combined to solve the job shop dynamic scheduling problem. The algorithms are modified to better adapt to job shop dynamic scheduling environment. The algorithms are developed based on Eclipse 3.2 and J2SE 6.0. Simulation experiments are accomplished and experimental data are analyzed. The results show that the principle of the algorithms is simple, their computational quantity is small, and they can be applied to multi-batch dynamic scheduling with unpredictable entry time due to their favorable potential.

Simulating the generic job shop as a multi-agent system

A model combining Discrete Event System (DES) and Multi-Agent System (MAS) is proposed to simulate a real-time job shop so that it can work as a test bed to systematically study the performance of control rules and algorithms in dynamic job shop scheduling. A definition of a generic job shop is given considering dynamic events, shop floor layout and the Material Handling System (MHS). It is first simulated as a DES and then implemented as an MAS so that data recording and analysis can be naturally distributed to the most related entities and events can be executed simultaneously at different locations. The state changes of agents and the communication among them are illustrated with state charts and sequential diagrams in Unified Modelling Language (UML). The proposed model is validated with a case study through statistical analysis and comparison with reported works.

Applying Ant Colony Optimisation (ACO) algorithm to dynamic job shop scheduling problems

Ant Colony Optimization (ACO) is applied to two dynamic job scheduling problems, which have the same mean total workload but different dynamic levels and disturbing severity. Its performances are statistically analysed and the effects of its adaptation mechanism and parameters such as the minimal number of iterations and the size of searching ants are studied. The results show that ACO can perform effectively in both cases; the adaptation mechanism can significantly improve the performance of ACO when disturbances are not severe; increasing the size of iterations and ants per iteration does not necessarily improve the overall performance of ACO.

Dynamic job-shop scheduling algorithm of the non-close-joining operations

Dynamic job-shop scheduling algorithm of the non-close-joining operations

Research on Dynamic Scheduling of Job-Shop Production with the Ant Colony Optimal Algorithm

Job-shop dynamic scheduling is an important subject in the fields of production management and combinatorial optimization. It is usually hard to achieve the optimal solution with classical methods due to the high computational complexity of the problem. A solution of job-shop scheduling problem based on multi-agent is presented for the comparability between the dynamic scheduling problem of job-shop production and the TSP problem. The dynamic scheduling of job-shop production is designed according to the pattern of TSP problem which can be applied with ACO. By the application case, the ACO is the new method to solve the dynamic scheduling of job-shop production.

A Complete Multiagent Framework for Robust and Adaptable Dynamic Job Shop Scheduling

This paper presents a complete multiagent framework for dynamic job shop scheduling, with an emphasis on robustness and adaptability. It provides both a theoretical basis and some experimental justifications for such a framework: a job dispatching procedure for a completely reactive scheduling approach, combining real-time and predictive decision making. It resolves various disruptions as flexibly as dispatching rules while providing more stability. It is ready to be implemented in a distributed environment where agents have minimum global information thereby improving system fault tolerance. Computational experiments on dynamic job arrivals provide the experimental justification of the framework. First, a comparison of computational results on unpredictable job arrivals among the presented framework and commonly used dispatching rules is presented to show the effectiveness and robustness of the developed framework. Then, a comparison of the computational results among four cases of dynamic job arrivals is presented to demonstrate the effects of making full use of available uncertain information about disruptions using this framework for the enhancement of scheduling robustness.

Workflow-based dynamic scheduling of job shop operations

In a job shop such as a mould-making shop, dispatching decisions as to which job should be loaded on a machine when it becomes free are termed dynamic scheduling. A practical approach to dynamic job-shop scheduling is the use of discrete event simulation, and their dedicated simulation-based dynamic scheduling systems, often known as manufacturing execution systems or advanced planning systems, are available. In the present paper, we propose a workflow-based dynamic scheduling framework, in which a workflow management system (WfMS) serves as a dynamic job-shop scheduler. For this purpose, we have developed an algorithm for embedding a discrete-event simulation mechanism into a WfMS, and have implemented a prototype job-shop scheduler. Illustrative performance evaluation shows that the proposed framework is logically valid and computationally efficient.

Dynamic job-shop scheduling with sequence-dependent setup times: simulation modeling and analysis

This paper presents the salient aspects of a simulation-based experimental study of scheduling rules for scheduling a dynamic job shop in which the setup times are sequence-dependent. A discrete event simulation model of the job shop system is developed for the purpose of experimentation. Seven scheduling rules from the literature are incorporated in the simulation model. Five new setup-oriented scheduling rules are proposed and implemented. Simulation experiments were conducted under various experimental conditions characterized by factors such as shop load, setup time ratios, and due date tightness. The results indicate that setup-oriented rules provide better performance than ordinary rules. The difference in performance between these two groups of rules increases with the increase in shop load and setup time ratio. One of the proposed rules performs better for mean flow time and mean tardiness measures.

Parameter setting in a bio-inspired model for dynamic flexible job shop scheduling with sequence-dependent setups

This paper addresses the parameter modelling and optimisation issues in the application of a bio-inspired model for the scheduling of dynamic flexible job shop with sequence-dependent setups. The study sets the model parameters using two steps, termed mapping and tuning. Mapping establishes a set of coefficients to link the model parameters with the scheduling problem characteristics. Tuning determines good values of these coefficients and is then used to compute the model parameters. Such a tuning procedure is accomplished by extensive computational experiments and statistical analyses. A data set from semiconductor manufacturing was used to show the effectiveness of the parameter setting approach. The performance of the proposed multiagent model was compared with that of another scheduling method which is based on dispatching rules. It is concluded that the proposed parameter setting method is effective and worth considering when applying bio-inspired division of labour to dynamic manufacturing scheduling. [Received 5 October 2006; Revised 2 January 2007; Accepted 1 March 2007]

An efficient priority rule for scheduling job shops to minimize mean tardiness

The ability to respond quickly to customer demands is a key factor in successfully competing in today's globally competitive markets. Thus, meeting due dates could be the most important goal of scheduling in many manufacturing and service industries. Meeting due dates can naturally be translated into minimizing job tardiness. In this paper we present a priority rule for dynamic job shop scheduling that minimizes mean job tardiness. The rule is developed based on the characteristics of existing dispatching rules. With job due dates set by the generalized total work content rule, the computational results of simulation experiments show that the proposed dispatching rule consistently, and often, significantly outperforms a number of well-known priority rules in the literature. The proposed rule is also robust being the best or near-best rule for reducing the mean flowtime, for all the shop load levels and due date tightness factors tested.

HEURISTIC METHOD FOR DYNAMIC JOB SHOP SCHEDULING PROBLEM WITH OPERATION RELATIVITY

A dynamic job shop scheduling problem with operation relativity and job releasing in batch are proposed. Based on defining operation relativity and giving its mathematical description systematically, the optimization model of the dynamic job shop scheduling is put forward. A new combinatorial dispatching rule, RAN(FCFS,ODD), and a heuristic method are designed. In order to validate the method and compare and evaluate the new combinatorial dispatching rule, a simulation method is presented, then seven dispatching rules proposed in literatures and RAN(FCFS,ODD) are adopted in scheduling simulation. Simulation results indicate that the heuristic method is effective and RAN(FCFS,ODD) outperforms the other seven dispatching rules with respect to the dynamic job shop scheduling problem proposed.

Dynamic Scheduling Using the Mixture of a Genetic Algorithm and a Priority Rule (The Effect of Priority Rule with Consideration to the Due-Date Allowance)

We have proposed a dynamic scheduling method using the mixture of a genetic algorithm and a priority rule. In the method, a dynamic scheduling problem is assumed to be a series of static problems and scheduled on a rolling basis. In this paper, we examine the effect of priority rule used in the method. For this purpose, two types of the ATC rules having two parameters are used. The first type is the rule whose parameters are tuned for the dynamic scheduling environment. The second type is the rule whose parameters are tuned for the imminent static scheduling problem. Numerical experiments show the followings : The proposed scheduling method searches effective schedules guided by the priority rule and the rule has important effect to the scheduling performance. For the dynamic job shop scheduling, the scheduling method using the first type of ATC rule greatly outperforms the one using the second type of ATC rule. The reason can be explained by considering the due-date allowance contained in the optimized schedules.

Scheduling in dynamic assembly job-shops with jobs having different holding and tardiness costs

Most studies on scheduling in dynamic job-shops assume that the holding cost of a job is given by the flowtime of the job and that the tardiness cost of a job is given by the tardiness of the job. In other words, unit holding and unit tardiness costs of a job are assumed. However, in reality, such an assumption need not hold, and it is quite possible that there are different costs for holding and tardiness for different jobs. In addition, most studies on job-shop scheduling assume that jobs are independent and that no assembly operations exist. The current study addresses the problem of scheduling in dynamic assembly job-shops (manufacturing multilevel jobs) with the consideration of different holding and tardiness costs for different jobs. An attempt is made to develop efficient dispatching rules by incorporating the relative costs of holding and tardiness of jobs in the form of scalar weights. The primary objective of scheduling considered here is the minimization of the total scheduling cost consisting of the sum of holding and tardiness costs. The performance of the scheduling rules in minimizing the individual components of total scheduling cost is also observed. The results of an extensive simulation study on the performance of different dispatching rules show that the proposed rules are effective in minimizing the means and maximums of the primary measure, and are quite robust with respect to different job structures and experimental settings.

Dynamic rescheduling that simultaneously considers efficiency and stability

Dynamic job shop scheduling is a frequently occurring and highly relevant problem in practice. Previous research suggests that periodic rescheduling improves classical measures of efficiency; however, this strategy has the undesirable effect of compromising stability and this lack of stability can render even the most efficient rescheduling strategy useless on the shop floor. In this research, a rescheduling methodology is proposed that uses a multiobjective performance measures that contain both efficiency and stability measures. Schedules are generated at each rescheduling point using a genetic local search algorithm that allows efficiency and stability to be balanced in a way that is appropriate for each situation. The methodology is tested on a simulated job shop to determine the impact of the key parameters on the performance measures.

Efficient dispatching rules for dynamic job shop scheduling

This study attempts to provide efficient dispatching rules for dynamic job shop scheduling by combining different dispatching rules. A dispatching rule is used to select the next job to be processed from a set of jobs awaiting service. A job shop will be treated as dynamic, when conditions such as continuously arriving new jobs and deviations from current schedule need to be accommodated, and a job shop should be treated as an integrated part of a manufacturing system. The discussion includes a simulation technique which uses ARENA 4.0. software to simulate the dynamic model of a job shop under various rules and performance measures . Results of the simulation show that, for most of the performance measures, combined rules perform well. In this study, the combined rules MWKR_FIFO and TWKR_SPT do well under most conditions.

Dynamic Job Shop Scheduling Using a Neural Network. 1st Report. A Two-Stage Training Method to Minimize Tardiness Cost.

We consider dynamic job shop scheduling problems with a tardiness cost criterion. The most practical scheduling method in dynamic scheduling environments is using dispatching rules. However, there is no dispatching rule that performs best under all scheduling conditions. In this paper, we apply a multi-layer feed-forward neural network to the scheduling, and propose a two-stage training method to minimize the average tardiness cost of jobs under various scheduling conditions. In the first stage, each neural network for its specified scheduling condition is trained separately by a simulated annealing method. In the second stage, a final neural network for all scheduling conditions is trained by a back-propagation algorithm. Simulation results show that the trained neural network outperforms the best dispatching rules in any levels of due-date tightness and job arrival rate.

An Inter-Machine Activity Coordination based Approach for Dynamic Job Shop Scheduling

Article An Inter-Machine Activity Coordination based Approach for Dynamic Job Shop Scheduling was published on December 1, 2001 in the journal Journal for Manufacturing Science and Production (volume 4, issue 2).