Geometric and harmonic means based priority dispatching rules for single machine scheduling problems

[EN] This work proposes two new prority dispatching rules (PDRs) for solving single machine scheduling problems. These rules are based on the geometric mean (GM) and harmonic mean (HM) of the processing time (PT) and the due date (DD) and they are referred to as GMPD and HMPD respectively. Performance of the proposed PDRs is evaluated on the basis of five measures/criteria i.e. Total Flow Time (TFT), Total Lateness (TL), Number of Late Jobs (TNL), Total Earliness (TE) and Number of Early Parts (TNE). It is found that GMPD performs better than other PDRs in achieving optimal values of multiple performance measures. Further, effect of variation in the weight assigned to PT and DD on the combined performance of TFT and TL is also examined which reveals that for deriving optimal values of TFT and TL, weighted harmonic mean (WHMPD) rule with a weight of 0.105 outperforms other PDRs. The weighted geometric mean (WGMPD) rule with a weight of 0.37 is found to be the next after WHMPD followed by the weighted PDT i.e. WPDT rule with a weight of 0.76.Ahmad, S.; Khan, ZA.; Ali, M.; Asjad, M. (2021). Geometric and harmonic means based priority dispatching rules for single machine scheduling problems. International Journal of Production Management and Engineering. 9(2):93-102. https://doi.org/10.4995/ijpme.2021.15217OJS9310292Baharom, M. Z., Nazdah, W., &Hussin, W. (2015). Scheduling Analysis for Job Sequencing in Veneer Lamination Line. Journal of Industrial and Intelligent Information, 3(3). https://doi.org/10.12720/jiii.3.3.181-185Chan, F. T. S., Chan, H. K., Lau, H. C. W., & Ip, R. W. L. (2003). Analysis of dynamic dispatching rules for a flexible manufacturing system. Journal of Materials Processing Technology, 138(1), 325-331. https://doi.org/10.1016/S0924-0136(03)00093-1Cheng, T. C. E., &Kahlbacher, H. G. (1993). Single-machine scheduling to minimize earliness and number of tardy jobs. Journal of Optimization Theory and Applications, 77(3), 563-573. https://doi.org/10.1007/BF00940450da Silva, N. C. O., Scarpin, C. T., Pécora, J. E., & Ruiz, A. (2019). Online single machine scheduling with setup times depending on the jobs sequence. Computers & Industrial Engineering, 129, 251-258. https://doi.org/10.1016/j.cie.2019.01.038Doh, H.H., Yu, J.M., Kim, J.S., Lee, D.H., & Nam, S.H. (2013). A priority scheduling approach for flexible job shops with multiple process plans. International Journal of Production Research, 51(12), 3748-3764. https://doi.org/10.1080/00207543.2013.765074Dominic, Panneer D. D., Kaliyamoorthy, S., & Kumar, M. S. (2004). Efficient dispatching rules for dynamic job shop scheduling. The International Journal of Advanced Manufacturing Technology, 24(1), 70-75.Ðurasević, M., &Jakobović, D. (2018). A survey of dispatching rules for the dynamic unrelated machines environment. Expert Systems with Applications, 113, 555-569. https://doi.org/10.1016/j.eswa.2018.06.053Forrester, P. (2006). Operations Management: An Integrated Approach. International Journal of Operations & Production Management.Geiger, C. D., &Uzsoy, R. (2008). Learning effective dispatching rules for batch processor scheduling. International Journal of Production Research, 46(6), 1431-1454. https://doi.org/10.1080/00207540600993360Hamidi, M. (2016). Two new sequencing rules for the non-preemptive single machine scheduling problem. The Journal of Business Inquiry, 15(2), 116-127.Holthaus, O., & Rajendran, C. (1997). New dispatching rules for scheduling in a job shop-An experimental study. The International Journal of Advanced Manufacturing Technology, 13(2), 148-153. https://doi.org/10.1007/BF01225761Hussain, M. S., & Ali, M. (2019). A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems. Global Journal of Flexible Systems Management, 20(3), 267-290. https://doi.org/10.1007/s40171-019-00214-9Jayamohan, M. S., & Rajendran, C. (2000). New dispatching rules for shop scheduling: A step forward. International Journal of Production Research, 38(3), 563-586. https://doi.org/10.1080/002075400189301Kadipasaoglu, S. N., Xiang, W., &Khumawala, B. M. (1997). A comparison of sequencing rules in static and dynamic hybrid flow systems. International Journal of Production Research, 35(5), 1359-1384. https://doi.org/10.1080/002075497195371Kanet, J. J., & Li, X. (2004). A Weighted Modified Due Date Rule for Sequencing to Minimize Weighted Tardiness. Journal of Scheduling, 7(4), 261-276. https://doi.org/10.1023/B:JOSH.0000031421.64487.95Lee, D.K., Shin, J.H., & Lee, D.H. (2020). Operations scheduling for an advanced flexible manufacturing system with multi-fixturing pallets. Computers & Industrial Engineering, 144, 106496. https://doi.org/10.1016/j.cie.2020.106496Lu, C.C., Lin, S.W., & Ying, K.C. (2012). Robust scheduling on a single machine to minimize total flow time. Computers & Operations Research, 39(7), 1682-1691. https://doi.org/10.1016/j.cor.2011.10.003Krishnan, M., Chinnusamy, T. R., & Karthikeyan, T. (2012). Performance Study of Flexible Manufacturing System Scheduling Using Dispatching Rules in Dynamic Environment. Procedia Engineering, 38, 2793-2798. https://doi.org/10.1016/j.proeng.2012.06.327Munir, E. U., Li, J., Shi, S., Zou, Z., & Yang, D. (2008). MaxStd: A task scheduling heuristic for heterogeneous computing environment. Information Technology Journal, 7(4), 679-683. https://doi.org/10.3923/itj.2008.679.683Oyetunji, E. O. (2009). Some common performance measures in scheduling problems. Research Journal of Applied Sciences, Engineering and Technology, 1(2), 6-9.Pinedo, M. L. (2009). Planning and Scheduling in Manufacturing and Services (2nd ed.). Springer-Verlag. https://doi.org/10.1007/978-1-4419-0910-7Prakash, A., Chan, F. T. S., & Deshmukh, S. G. (2011). FMS scheduling with knowledge based genetic algorithm approach. Expert Systems with Applications, 38(4), 3161-3171. https://doi.org/10.1016/j.eswa.2010.09.002Rafsanjani, M. K., &Bardsiri, A. K. (2012). A New Heuristic Approach for Scheduling Independent Tasks on Heterogeneous Computing Systems. International Journal of Machine Learning and Computing, 371-376. https://doi.org/10.7763/IJMLC.2012.V2.147Tyagi, N., Tripathi, R. P., &Chandramouli, A. B. (2016). Single Machine Scheduling Model with Total Tardiness Problem. Indian Journal of Science and Technology, 9(37). https://doi.org/10.17485/ijst/2016/v9i37/97527Vinod, V., & Sridharan, R. (2008). Dynamic job-shop scheduling with sequence-dependent setup times: Simulation modeling and analysis. The International Journal of Advanced Manufacturing Technology, 36(3), 355-372. https://doi.org/10.1007/s00170-006-0836-4Waikar, A. M., Sarker, B. R., & Lal, A. M. (1995). A comparative study of some priority dispatching rules under different shop loads. Production Planning & Control, 6(4), 301-310. https://doi.org/10.1080/0953728950893028

Learning-based scheduling of flexible manufacturing systems using ensemble methods

Dispatching rules are commonly applied to schedule jobs in Flexible Manufacturing Systems (FMSs). However, the suitability of these rules relies heavily on the state of the system; hence, there is no single rule that always outperforms the others. In this scenario, machine learning techniques, such as support vector machines (SVMs), inductive learning-based decision trees (DTs), backpropagation neural networks (BPNs), and case based-reasoning (CBR), offer a powerful approach for dynamic scheduling, as they help managers identify the most appropriate rule in each moment. Nonetheless, different machine learning algorithms may provide different recommendations. In this research, we take the analysis one step further by employing ensemble methods, which are designed to select the most reliable recommendations over time. Specifically, we compare the behaviour of the bagging, boosting, and stacking methods. Building on the aforementioned machine learning algorithms, our results reveal that ensemble methods enhance the dynamic performance of the FMS. Through a simulation study, we show that this new approach results in an improvement of key performance metrics (namely, mean tardiness and mean flow time) over existing dispatching rules and the individual use of each machine learning algorithm

Artificial immune system for static and dynamic production scheduling problems

Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in production scheduling is to allocate the machines to perform the tasks. Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP) are two of the areas in production scheduling problems for these machines. One of the main objectives in solving JSSP and FJSSP is to obtain the best solution with minimum total completion processing time. Thus, this thesis developed algorithms for single and hybrid methods to solve JSSP and FJSSP in static and dynamic environments. In a static environment, no change is needed for the produced solution but changes to the solution are needed. On the other hand, in a dynamic environment, there are many real time events such as random arrival of jobs or machine breakdown requiring solutions. To solve these problems for static and dynamic environments, the single and hybrid methods were introduced. Single method utilizes Artificial Immune System (AIS), whereas AIS and Variable Neighbourhood Descent (VND) are used in the hybrid method. Clonal Selection Principle (CSP) algorithm in the AIS was used in the proposed single and hybrid methods. In addition, to evaluate the significance of the proposed methods, experiments and One-Way ANOVA tests were conducted. The findings showed that the hybrid method was proven to give better performance compared to single method in producing optimized solution and reduced solution generating time. The main contribution of this thesis is the development of an algorithm used in the single and hybrid methods to solve JSSP and FJSSP in static and dynamic environment

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

Impact of Preventive Maintenance and Machine Breakdown on Performance of Stochastic Flexible Job Shop Scheduling with Setup Time

Real-time scheduling problems increase the practical implementation of the manufacturing system. In this study, using a single objective performance measure i.e., Number of Tardy Jobs (NTJ), the influence of 5 input constraints, i.e., reliability level (R_L), percentage of machine failure (%McF), mean time to repair for random machine breakdown (MTR_RMcB), due date tightness factor (Ғ), and routing flexibility level (R_FL) were evaluated for considered stochastic Flexible Job Shop Scheduling Problem (FJSSP). The study integrated reliability-centered preventive maintenance (PMRC) and random machine breakdown (RMcB) environment with sequence-dependent setup time in the considered problem. A statistical response surface methodology was used to assesses NTJ. A second-order regression model was obtained to compute correlation between input constraints and NOTJ at 95% confidence level. The results demonstrate that main effects of R_L, %McF, Ғ, and R_FL; the interaction effects of R_L and Ғ, %McF and R_FL, MTR_RMcB and R­_FL, and Ғ and R_FL; and quadratic effects of Ғ and R_FL, have significant impact on NTJ performance measure. Ғ has emerged as the major factor affecting NTJ. The confirmatory data demonstrate that error is less than 5%, confirming model can be used for future computations. Further, the novelties of the work are shown by the fact that it takes into account the uncertainties in the scheduling issue, as well as the dynamic tasks arrival environment. The aforementioned findings will assist production managers in planning and scheduling flexible job shops in order to satisfy customer demand on time

Scheduling Jobs in Flowshops with the Introduction of Additional Machines in the Future

This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/expert-systems-with-applications/.The problem of scheduling jobs to minimize total weighted tardiness in flowshops,\ud with the possibility of evolving into hybrid flowshops in the future, is investigated in\ud this paper. As this research is guided by a real problem in industry, the flowshop\ud considered has considerable flexibility, which stimulated the development of an\ud innovative methodology for this research. Each stage of the flowshop currently has\ud one or several identical machines. However, the manufacturing company is planning\ud to introduce additional machines with different capabilities in different stages in the\ud near future. Thus, the algorithm proposed and developed for the problem is not only\ud capable of solving the current flow line configuration but also the potential new\ud configurations that may result in the future. A meta-heuristic search algorithm based\ud on Tabu search is developed to solve this NP-hard, industry-guided problem. Six\ud different initial solution finding mechanisms are proposed. A carefully planned\ud nested split-plot design is performed to test the significance of different factors and\ud their impact on the performance of the different algorithms. To the best of our\ud knowledge, this research is the first of its kind that attempts to solve an industry-guided\ud problem with the concern for future developments