16,828 research outputs found
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., PĂŠcora, 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.ĂuraseviÄ, M., &JakoboviÄ, 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
An investigation into minimising total energy consumption and total completion time in a flexible job shop for recycling carbon fiber reinforced polymer
The increased use of carbon fiber reinforced polymer (CFRP) in industry coupled with European Union restrictions on landfill disposal has
resulted in a need to develop relevant recycling technologies. Several methods, such as mechanical grinding, thermolysis and solvolysis, have
been tried to recover the carbon fibers. Optimisation techniques for reducing energy consumed by above processes have also been developed.
However, the energy efficiency of recycling CFRP at the workshop level has never been considered before. An approach to incorporate energy
reduction into consideration while making the scheduling plans for a CFRP recycling workshop is presented in this paper. This research sets in
a flexible job shop circumstance, model for the bi-objective problem that minimise total processing energy consumption and makespan is developed.
A modified Genetic Algorithm for solving the raw material lot splitting problem is developed. A case study of the lot sizing problem
in the flexible job shop for recycling CFRP is presented to show how scheduling plans affect energy consumption, and to prove the feasibility
of the model and the developed algorithm
High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
DisertaÄnĂ prĂĄce je zamÄĹena na optimalizaci prĹŻbÄhu pracovnĂch operacĂ v logistickĂ˝ch skladech a distribuÄnĂch centrech. HlavnĂm cĂlem je optimalizovat procesy plĂĄnovĂĄnĂ, rozvrhovĂĄnĂ a odbavovĂĄnĂ. JelikoĹž jde o problĂŠm patĹĂcĂ do tĹĂdy sloĹžitosti NP-teĹžkĂ˝, je vĂ˝poÄetnÄ velmi nĂĄroÄnĂŠ nalĂŠzt optimĂĄlnĂ ĹeĹĄenĂ. MotivacĂ pro ĹeĹĄenĂ tĂŠto prĂĄce je vyplnÄnĂ pomyslnĂŠ mezery mezi metodami zkoumanĂ˝mi na vÄdeckĂŠ a akademickĂŠ pĹŻdÄ a metodami pouĹžĂvanĂ˝mi v produkÄnĂch komerÄnĂch prostĹedĂch. JĂĄdro optimalizaÄnĂho algoritmu je zaloĹženo na zĂĄkladÄ genetickĂŠho programovĂĄnĂ ĹĂzenĂŠho bezkontextovou gramatikou. HlavnĂm pĹĂnosem tĂŠto prĂĄce je a) navrhnout novĂ˝ optimalizaÄnĂ algoritmus, kterĂ˝ respektuje nĂĄsledujĂcĂ optimalizaÄnĂ podmĂnky: celkovĂ˝ Äas zpracovĂĄnĂ, vyuĹžitĂ zdrojĹŻ, a zahlcenĂ skladovĂ˝ch uliÄek, kterĂŠ mĹŻĹže nastat bÄhem zpracovĂĄnĂ ĂşkolĹŻ, b) analyzovat historickĂĄ data z provozu skladu a vyvinout sadu testovacĂch pĹĂkladĹŻ, kterĂŠ mohou slouĹžit jako referenÄnĂ vĂ˝sledky pro dalĹĄĂ vĂ˝zkum, a dĂĄle c) pokusit se pĹedÄit stanovenĂŠ referenÄnĂ vĂ˝sledky dosaĹženĂŠ kvalifikovanĂ˝m a trĂŠnovanĂ˝m operaÄnĂm manaĹžerem jednoho z nejvÄtĹĄĂch skladĹŻ ve stĹednĂ EvropÄ.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.
A generic method for energy-efficient and energy-cost-effective production at the unit process level
Evaluation of Material Shortage Effect on Assembly Systems Considering Flexibility Levels
The global pandemic caused delays in global supply chains, and numerous manufacturing companies are experiencing a lack of materials and
components. This material shortage affects assembly systems at various levels: process level (decreasing of the resource efficiency), system level
(blocking or s tarvation of production entities), and company level (breaking the deadlines for the supplying of the products to customers or
retailers). Flexible assembly systems allow dynamic reactions in such uncertain environments. However, online scheduling algorithms of current
research are not considering reactions to material shortages.
In the present research, we aim to evaluate the influence of material shortage on the assembly system performance. The paper presents a discrete
event simulation of an assembly system. The system architecture, its behavior, the resources, their capacities, and product specific operations are
included. The material shortage effect on the assembly system is compensated utilizing different system flexibility levels, characterized by
operational and routing flexibility. An online control algorithm determines optimal production operation under material shortage uncertain
conditions. With industrial data, different simulation scenarios evaluate the benefits of assembly systems with varying flexibility levels.
Consideration of flexibility levels might facilitate exploration of the optimal flexibility level with the lowest production makespan that influence
further supply chain, as makespan minimization cause reducing of delays for following supply chain entities
From supply chains to demand networks. Agents in retailing: the electrical bazaar
A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version
Scheduling Algorithms: Challenges Towards Smart Manufacturing
Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario
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