Dynamic Job Shop Scheduling Problem Research Articles

Multi Objective Dynamic Job Shop Scheduling using Composite Dispatching Rule and Reinforcement Learning

The applications of composite dispatching rules for multi objective dynamic scheduling have been widely studied in literature. In general, a composite dispatching rule is a combination of several elementary dispatching rules, which is designed to optimize multiple objectives of interest under a certain scheduling environment. The relative importance of elementary dispatching rules is modeled by weight factors. A critical issue for implementation of composite dispatching rule is that the inappropriate weight values may result in poor performance. This paper presents an offline scheduling knowledge acquisition method based on reinforcement learning using simulation technique. The scheduling knowledge is applied to adjust the appropriate weight values of elementary dispatching rules in composite manner with respect to work in process fluctuation of machines during online scheduling. Implementation of the proposed method in a two objectives dynamic job shop scheduling problem is demonstrated and the results are satisfactory.

An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem

An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job-shop scheduling problem (JSP) is presented. Though the traditional genetic algorithm (GA) exhibits implicit parallelism and can retain useful redundant information about what is learned from previous searches by its representation in individuals in the population, yet GA may lose solutions and substructures due to the disruptive effects of genetic operators and is not easy to regulate GA’s convergence. The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed. The proposed IAGA is characterized by simplifying operations, high search precision, overcoming premature phenomenon and slow evolution. The proposed method by employing operation-based encoding is effectively applied to solve a dynamic job-shop scheduling problem (DJSP) and a complicated contrastive experiment of JSP in manufacturing system. Meanwhile, in order to ensure to create a feasible solution, a new method for crossover operation is adopted, named, partheno-genetic operation (PGO). The computational results validate the effectiveness of the proposed IAGA, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing genetic algorithms reported recently in the literature. By employing IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.

A Three-Fold Approach to Solve Dynamic Job Shop Scheduling Problems by Artificial Immune Algorithm

This paper aims to propose a novel three-fold approach to solve dynamic job-shop scheduling problems by artificial immune algorithm. The proposed approach works in three phases. Firstly, priority rules are deployed to decrease problem scale instead of using scheduling algorithms directly. Secondly, immune algorithm is applied to optimize the individual scheduling modules. Finally, integration schema is employed to reschedule operations and minimize makespan of gross schedule. The integration schema is carried out in a dynamic manner that the previous modules’ machine idle time is searched continuously. In this way, the machine utilization is increased while the objective of makespan minimization is maintained. Efficacy of the proposed approach has been tested with test instances of job-shop scheduling problems. The experimentation results clearly show effectiveness of the proposed approach.

Earliness-tardiness-lost sales dynamic job-shop scheduling

In the real world production environment, the decision about the acceptance or rejection of new orders is made by both the sale department and the production department, cooperatively. However, as far as we searched, no published paper has considered this fact so far. Job-shop scheduling is one of the most complex problems in scheduling. In job-shop environment, there are some production stations, and every job (order; In this paper job and order are used in the same meaning) has a specific production sequence which is not necessarily the same as the other jobs’ sequences. This paper studies the earliness-tardiness-lost sales dynamic job-shop scheduling problem. In this problem, whenever some new orders arrive, a decision is made about the acceptance or the rejection of each of these orders. In this way, all of the alternatives, including the acceptance or rejection of each new order, should be compared. If at least one new order is accepted, the new schedule which includes the accepted order(s) will be generated. By defining some variables, this comparison is done by the developed models. Because of NP-hardness of this problem, exact methods can not be used to solve it in large or medium scales. So, in this paper a hybrid metaheuristic algorithm is developed, which is composed of a genetic algorithm to determine the sequence of the operations and a simulated annealing algorithm to achieve a near-optimal schedule based on this sequence. Finally, the efficiency and effectiveness of the algorithm is evaluated using some numerical results.

A New Adaptive Genetic Algorithm for Job-Shop Scheduling

In order to minimize makespan for job-shop scheduling problem (JSP), an improved adaptive genetic algorithm (IAGA) based on hormone modulation mechanism is proposed. This algorithm has characteristics with avoiding overcoming premature phenomenon and slow evolution. The proposed IAGA algorithm is applied to dynamic job-shop scheduling problem (DJSP) and the satisfied result is obtained. By employing the proposed IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently. Therefore it embodies good adaptation to the DJSP (rush order, machine malfunction, and so on).

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.

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.

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 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 analysis of heuristics in a dynamic job shop with weighted tardiness objectives

Meeting due dates as a reflection of customer satisfaction is one of the scheduling criteria that is frequently encountered in today's manufacturing environments. The natural quantification of this qualitative goal involves tardiness related measures. In this study, we consider the dynamic job shop scheduling problem with the weighted tardiness criterion. After we present a comprehensive literature survey on the topic, we measure the long-run performances of more than 20 single-pass dispatching rules under various experimental conditions. In this study, we pay special attention to recently proposed dispatching heuristics such as CEXSPT, CR+ SPT, S/RPT+ SPT, and Bottleneck Dynamics (BD). We also investigate the effects of six resource pricing schemes proposed recently for BD. Moreover, we extend the earlier versions of inserted idleness and identify the conditions in which these techniques can be applied without incurring too much computational cost. Future research directions are also outlined in light of the computational results.

A batch splitting heuristic for dynamic job shop scheduling problem

The job shop scheduling problem has been a major target for many researchers. Unfortunately, though, most of the past studies assumed that a job consists of only a single part. If we assume that a job consists of a batch as in many real manufacturing environment, then we can obtain an improved schedule. However, then, the size of the scheduling problem would become too large to be solved in practical time limit. So, we proposed an algorithm to get an improved schedule by splitting the original batch into smaller batches, and thereby can meet the due date requirement, and adapt to unexpected dynamic events such as machine failure, rush order and expediting.

An expert neural network system for dynamic job shop scheduling

The objective of this research is to apply the neural network approach to the dynamic job shop scheduling problem. A feed-forward back propagation neural network is designed and trained to recognize the individual contributions of traditional dispatch rules. The network is incorporated into an expert system which activates the network according to the prevailing shop environment. The effectiveness of the approach is compared with the traditional dispatch rule approach as well as a composite rule expert system. Results of scheduling with a neural network show that the network is able to perform well against its component factors for job lots with varying arrival rates.

Implementation of a decision support system for scheduling semiconductor test operations

Journal of Electronics ManufacturingVol. 03, No. 03, pp. 121-131 (1993) PAPERSNo AccessImplementation of a decision support system for scheduling semiconductor test operationsCHUNG-YEE LEE, LOUIS A. MARTIN-VEGA, REHA UZSOY and JIM HINCHMANCHUNG-YEE LEEDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA Search for more papers by this author , LOUIS A. MARTIN-VEGACollege of Engineering, Florida Institute of Technology, Melbourne, FL 32901-6988, USA Search for more papers by this author , REHA UZSOYSchool of Industrial Engineering, 1287 Grissom Hall, Purdue University, West Lafayette, IN 47907-1287, USA Search for more papers by this author and JIM HINCHMANHarris Semiconductor Corporation, Melbourne, FL 32902, USA Search for more papers by this author https://doi.org/10.1142/S0960313193000140Cited by:22 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractThe problem of scheduling semiconductor test operations is a dynamic job shop scheduling problem complicated by the presence of machines with different scheduling characteristics and sequence-dependent setup times. The well-known intractability of job shop scheduling problems and the need to address multiple, conflicting management goals motivates the use of a decision support system (DSS) combining scheduling heuristics and existing information systems to support the scheduling function. The DSS is currently in use in two separate testing facilities, where management feels it has significantly improved shop performance and reduced production planning workload.Keywords:Semiconductor testingschedulingdecision support systems FiguresReferencesRelatedDetailsCited By 22Modeling and Solving Scheduling Problems in PracticeMichael L. Pinedo11 February 2016Optimal test capacity allocation in automated high-frequency testing environmentsYong-Hee Han and Sung-Seok Ko2 December 2011 | The International Journal of Advanced Manufacturing Technology, Vol. 62, No. 1-4Constructing a production exception handling system for the semiconductor manufacturing processTsu‐Ming Yeh18 Oct 2011 | Kybernetes, Vol. 40, No. 9/10A two-phase dynamic dispatching approach to semiconductor wafer testingYin-Yann Chen, James T. Lin and Tzu-Li Chen1 Oct 2011 | Robotics and Computer-Integrated Manufacturing, Vol. 27, No. 5Product–process interface for manufacturing data management as a support for DFM and virtual manufacturingJawhar Elgueder, Florent Cochennec, Lionel Roucoules and Emmanuelle Rouhaud23 October 2010 | International Journal on Interactive Design and Manufacturing (IJIDeM), Vol. 4, No. 4Scheduling semiconductor final testing a DBR based simulation modelWu-Der Jeng and Meng-Shiun Tsai1 Jul 2010The Construction of a Real-Time WIP Exception Monitoring System for Semiconductor IndustryTsu-Ming Yeh, Fan-Yun Pai and Ching-Shih Tsou1 Oct 2008Improved customer satisfaction with a hybrid dispatching rule in semiconductor back-end factoriesDavid M. Chiang, Ruey-Shan Guo and Fan-Yun Pai1 Sep 2008 | International Journal of Production Research, Vol. 46, No. 17Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in designAchraf Skander, Lionel Roucoules and Jean Sébastien Klein Meyer21 June 2007 | The International Journal of Advanced Manufacturing Technology, Vol. 37, No. 5-6Solution strategies for multi-stage wafer probing scheduling problem with reentryW L Pearn, S H Chung, M H Yang and K P Shiao21 December 2017 | Journal of the Operational Research Society, Vol. 59, No. 5In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operationsT. Freed, K. H. Doerr and T. Chang26 September 2007 | International Journal of Production Research, Vol. 45, No. 21A WIP-based exception-management model for integrated circuit back-end production processesRuey-Shan Guo, David M. Chiang and Fan-Yun Pai6 May 2006 | The International Journal of Advanced Manufacturing Technology, Vol. 33, No. 11-12Multi-objectives exception management model for semiconductor back-end environment under turnkey serviceR.-S. Guo, D. M. Chiang and F.-Y. Pai22 March 2007 | Production Planning & Control, Vol. 18, No. 3A parameterized-dispatching rule for a Logic IC sort in a wafer fabricationJ. T. Lin, F. K. Wang * and P. C. Kuo21 February 2007 | Production Planning & Control, Vol. 16, No. 5PRACTICE-FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*KENNETH MCKAY, MICHAEL PINEDO and SCOTT WEBSTER5 January 2009 | Production and Operations Management, Vol. 11, No. 2Scheduling semiconductor device test operations on multihead testersT. Freed and R.C. Leachman1 Nov 1999 | IEEE Transactions on Semiconductor Manufacturing, Vol. 12, No. 4A framework for decision support systems for scheduling problemsKlaus Ecker, Jatinder N.D. Gupta and Günter Schmidt1 Sep 1997 | European Journal of Operational Research, Vol. 101, No. 3A Generic Decomposition Procedure for Semiconductor Testing FacilitiesIrfan M. Ovacik and Reha Uzsoy1 Jan 1997An iterative heuristic for the single machine dynamic total completion time scheduling problemSuresh Chand, Rodney Traub and Reha Uzsoy1 Jul 1996 | Computers & Operations Research, Vol. 23, No. 7GLOBAL JOB SHOP SCHEDULING WITH A GENETIC ALGORITHMJEFFREY W. HERRMANN, CHUNG-YEE LEE and JIM HINCHMAN5 January 2009 | Production and Operations Management, Vol. 4, No. 1A REVIEW OF PRODUCTION PLANNING AND SCHEDULING MODELS IN THE SEMICONDUCTOR INDUSTRY PART II: SHOP-FLOOR CONTROLREHA UZSOY, CHUNG-YEE LEE and LOUIS A. MARTIN-VEGA1 Sep 1994 | IIE Transactions, Vol. 26, No. 5Decomposition algorithms for scheduling semiconductor testing facilitiesE. Demirkol, R. Uzsoy and I.M. Ovacik Recommended Vol. 03, No. 03 Metrics History Received 1 June 1993 Accepted 1 July 1993 KeywordsSemiconductor testingschedulingdecision support systemsPDF download

The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling

The goal of this paper is to determine if the results for dynamic job-shop scheduling problems are affected by the assumptions made with regard to the processing time distributions and the scheduler's knowledge of the processing times. Three dynamic job-shop scheduling problems (including a two-station version of Conway et al.'s 1967 nine-station symmetric shop) are tested under seven different scenarios, one deterministic and six stochastic, using computer simulation. The deterministic scenario, where the processing times are exponential and observed by the scheduler, has been considered in many simulation studies, including Conway et al.'s. The six stochastic scenarios include the case where the processing times are exponential and only the mean is known by the scheduler, and five different cases where the machines are subject to unpredictable failures. Two policies were tested, the shortest expected processing time (SEPT) rule, and a rule derived from a Brownian analysis of the corresponding queueing network scheduling problem. Although the SEPT rule performed well in the deterministic scenario, it was easily outperformed by the Brownian policies in the six stochastic scenarios for all three problems. Thus, the results from simulation studies of dynamic, deterministic job-shop scheduling problems may not carry over to the more realistic setting where there is unpredictable variability.

Dynamic Job-Shop Scheduling by Hopfield-Type Neural Metwork

This paper deals with an approach to the dynamic job-shop scheduling problem. In this approach, the Hopfield-type neural network is introduced for solving the problem. The idea is based on the mapping between the scheduling problem and the neural network. That is, the energy function of the network is set for the problem so that a job assignment corresponds to the equilibrium of the network. The solution of the scheduling problem is obtained when the network is in equilibrium. In this paper, the method of constructing the energy function with due date and in-process inventory as criterions is described, and reasonable results of several numerical experiments are shown.

Simulation studies in job shop sheduling—I a survey

The dynamic job shop scheduling problem has been studied extensively during the last two decades. Because of the complexity of the dynamic job shop scheduling problem, numerous simulation studies have been conducted and published in the area. These studies fall into one of the following categories: the studies comparing and/or developing scheduling rules which will give good shop performance under a given set of job and shop parameters, and the studies investigating sensitivity of shop performance to job and shop parameters under a given set of scheduling rules. In the literature, shop performance has been evaluated in terms of (1) criteria based on job completion times, (2) criteria based on due dates, (3) criteria based on costs. This paper discusses approaches taken in major simulation studies of dynamic job shop scheduling problem according to the above classification.

Simulation studies in job shop scheduling—II: Performance of priority rules

Major simulation studies of dynamic job shop scheduling problem and approaches taken to model dynamic job shops have been considered in Part I[25] of this paper. In Part II we focus our attention on basic results on relative effectiveness of priority rules in job shop simulation literature. Information on surveyed articles also is provided in the Appendix.