Flowshop scheduling problems with due date related objectives: A review of the literature

3rd International Conference on Industrial Engineering and Industrial Management XIII Congreso de Ingeniería de Organización Barcelona-Terrassa, September 2nd-4th 200

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Polynomial-time approximation schemes for scheduling problems with time lags

We identify two classes of machine scheduling problems with time lags that possess Polynomial-Time Approximation Schemes (PTASs). These classes together, one for minimizing makespan and one for minimizing total completion time, include many well-studied time lag scheduling problems. The running times of these approximation schemes are polynomial in the number of jobs, but exponential in the number of machines and the ratio between the largest time lag and the smallest positive operation time. These classes constitute the first PTAS results for scheduling problems with time lags

Branch and Bound Method to Solve The Sum of Two Objective Functions

In this paper, the problem of sequencing a set of n jobs on single machine was considered to minimize multiple objectives function (MOF). The objective  is to find the optimal  solution (scheduling) for n independent jobs to minimize the objective function consists of a sum  of  weighted number  of  early  jobs  and  total  weighted of completion time. This problem is strongly NP-hard and to resolve it we derived two lower bounds (LB1, LB2) and heuristic method to get an upper bound which are used in root node of branch and bound tree. Some special cases  and dominance rule were proposed and proved. Results of extensive computational tests show that the proposed (BAB) algorithm effective in solving problems with up to (30)  jobs in time less than or equal to (30) minutes

Integrating Capacitated Lot-Sizing and Lot Streaming in Flowshop Schedules with Time Varying Demand

Any reasonable production planning contains three important decisions on lot size, lead time, and capacity. The common approach in the literature is to divide the planning problem into lot sizing, lot sequencing, and lot splitting sub-problems. Very few studies, to the best of our knowledge, have been conducted on the interdependencies and three- way interaction of lead-time, lot size, and actual capacity usage. A particular lot size calculated by the sub-problem method, however, will likely yield an infeasible solution or at least result in schedule instability (nervousness). This is just because in most capacitated lot sizing models, the capacity constraints in the model only take into consideration the available time on each work station, ignoring the sequencing of lots, sublot sizes, and their effect on makespan and lead times. In this thesis we bridge the gap between lot sizing and scheduling in flowshops, and examine the use of the lot splitting and sequencing techniques to reduce schedule instability. A mixed integer programming formulation is presented, which enables us to simultaneously find the optimal lot sizes as well as the corresponding sublot sizes and sequence of jobs. With this model, small size problems can be solved within a reasonable time. The computational results confirm that this model can be advantageous in dampening the scheduling nervousness. For larger size instances, a Genetic algorithm is proposed

Flow shop rescheduling under different types of disruption

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 2013, available online:http://www.tandfonline.com/10.1080/00207543.2012.666856Almost all manufacturing facilities need to use production planning and scheduling systems to increase productivity and to reduce production costs. Real-life production operations are subject to a large number of unexpected disruptions that may invalidate the original schedules. In these cases, rescheduling is essential to minimise the impact on the performance of the system. In this work we consider flow shop layouts that have seldom been studied in the rescheduling literature. We generate and employ three types of disruption that interrupt the original schedules simultaneously. We develop rescheduling algorithms to finally accomplish the twofold objective of establishing a standard framework on the one hand, and proposing rescheduling methods that seek a good trade-off between schedule quality and stability on the other.The authors would like to thank the anonymous referees for their careful and detailed comments that helped to improve the paper considerably. This work is partially financed by the Small and Medium Industry of the Generalitat Valenciana (IMPIVA) and by the European Union through the European Regional Development Fund (FEDER) inside the R + D program "Ayudas dirigidas a Institutos tecnologicos de la Red IMPIVA" during the year 2011, with project number IMDEEA/2011/142.Katragjini Prifti, K.; Vallada Regalado, E.; Ruiz García, R. (2013). Flow shop rescheduling under different types of disruption. International Journal of Production Research. 51(3):780-797. https://doi.org/10.1080/00207543.2012.666856S780797513Abumaizar, R. J., & Svestka, J. A. (1997). Rescheduling job shops under random disruptions. International Journal of Production Research, 35(7), 2065-2082. doi:10.1080/002075497195074Adiri, I., Frostig, E., & Kan, A. H. G. R. (1991). Scheduling on a single machine with a single breakdown to minimize stochastically the number of tardy jobs. Naval Research Logistics, 38(2), 261-271. doi:10.1002/1520-6750(199104)38:23.0.co;2-iAkturk, M. S., & Gorgulu, E. (1999). Match-up scheduling under a machine breakdown. European Journal of Operational Research, 112(1), 81-97. doi:10.1016/s0377-2217(97)00396-2Allahverdi, A. (1996). Two-machine proportionate flowshop scheduling with breakdowns to minimize maximum lateness. Computers & Operations Research, 23(10), 909-916. doi:10.1016/0305-0548(96)00012-3Arnaout, J. P., & Rabadi, G. (2008). Rescheduling of unrelated parallel machines under machine breakdowns. International Journal of Applied Management Science, 1(1), 75. doi:10.1504/ijams.2008.020040Artigues, C., Billaut, J.-C., & Esswein, C. (2005). Maximization of solution flexibility for robust shop scheduling. European Journal of Operational Research, 165(2), 314-328. doi:10.1016/j.ejor.2004.04.004Azizoglu, M., & Alagöz, O. (2005). Parallel-machine rescheduling with machine disruptions. IIE Transactions, 37(12), 1113-1118. doi:10.1080/07408170500288133Bean, J. C., Birge, J. R., Mittenthal, J., & Noon, C. E. (1991). Matchup Scheduling with Multiple Resources, Release Dates and Disruptions. Operations Research, 39(3), 470-483. doi:10.1287/opre.39.3.470Caricato, P., & Grieco, A. (2008). An online approach to dynamic rescheduling for production planning applications. International Journal of Production Research, 46(16), 4597-4617. doi:10.1080/00207540601136225CHURCH, L. K., & UZSOY, R. (1992). Analysis of periodic and event-driven rescheduling policies in dynamic shops. International Journal of Computer Integrated Manufacturing, 5(3), 153-163. doi:10.1080/09511929208944524Cowling, P., & Johansson, M. (2002). Using real time information for effective dynamic scheduling. European Journal of Operational Research, 139(2), 230-244. doi:10.1016/s0377-2217(01)00355-1Curry, J., & Peters *, B. (2005). Rescheduling parallel machines with stepwise increasing tardiness and machine assignment stability objectives. International Journal of Production Research, 43(15), 3231-3246. doi:10.1080/00207540500103953DUTTA, A. (1990). Reacting to Scheduling Exceptions in FMS Environments. IIE Transactions, 22(4), 300-314. doi:10.1080/07408179008964185Ghezail, F., Pierreval, H., & Hajri-Gabouj, S. (2010). Analysis of robustness in proactive scheduling: A graphical approach. Computers & Industrial Engineering, 58(2), 193-198. doi:10.1016/j.cie.2009.03.004Goren, S., & Sabuncuoglu, I. (2008). Robustness and stability measures for scheduling: single-machine environment. IIE Transactions, 40(1), 66-83. doi:10.1080/07408170701283198Hall, N. G., & Potts, C. N. (2004). Rescheduling for New Orders. Operations Research, 52(3), 440-453. doi:10.1287/opre.1030.0101Herrmann, J. W., Lee, C.-Y., & Snowdon, J. L. (1993). A Classification of Static Scheduling Problems. Complexity in Numerical Optimization, 203-253. doi:10.1142/9789814354363_0011Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289-306. doi:10.1016/j.ejor.2004.04.002Hozak, K., & Hill, J. A. (2009). Issues and opportunities regarding replanning and rescheduling frequencies. International Journal of Production Research, 47(18), 4955-4970. doi:10.1080/00207540802047106Huaccho Huatuco, L., Efstathiou, J., Calinescu, A., Sivadasan, S., & Kariuki, S. (2009). Comparing the impact of different rescheduling strategies on the entropic-related complexity of manufacturing systems. International Journal of Production Research, 47(15), 4305-4325. doi:10.1080/00207540701871036Jensen, M. T. (2003). Generating robust and flexible job shop schedules using genetic algorithms. IEEE Transactions on Evolutionary Computation, 7(3), 275-288. doi:10.1109/tevc.2003.810067King, J. R. (1976). The theory-practice gap in job-shop scheduling. Production Engineer, 55(3), 137. doi:10.1049/tpe.1976.0044Kopanos, G. M., Capón-García, E., Espuña,, A., & Puigjaner, L. (2008). Costs for Rescheduling Actions: A Critical Issue for Reducing the Gap between Scheduling Theory and Practice. Industrial & Engineering Chemistry Research, 47(22), 8785-8795. doi:10.1021/ie8005676Lee, C.-Y., Leung, J. Y.-T., & Yu, G. (2006). Two Machine Scheduling under Disruptions with Transportation Considerations. Journal of Scheduling, 9(1), 35-48. doi:10.1007/s10951-006-5592-7Li, Z., & Ierapetritou, M. (2008). Process scheduling under uncertainty: Review and challenges. Computers & Chemical Engineering, 32(4-5), 715-727. doi:10.1016/j.compchemeng.2007.03.001Liao, C. J., & Chen, W. J. (2004). Scheduling under machine breakdown in a continuous process industry. Computers & Operations Research, 31(3), 415-428. doi:10.1016/s0305-0548(02)00224-1Mehta, S. V. (1999). Predictable scheduling of a single machine subject to breakdowns. International Journal of Computer Integrated Manufacturing, 12(1), 15-38. doi:10.1080/095119299130443MUHLEMANN, A. P., LOCKETT, A. G., & FARN, C.-K. (1982). Job shop scheduling heuristics and frequency of scheduling. International Journal of Production Research, 20(2), 227-241. doi:10.1080/00207548208947763Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95. doi:10.1016/0305-0483(83)90088-9O’Donovan, R., Uzsoy, R., & McKay, K. N. (1999). Predictable scheduling of a single machine with breakdowns and sensitive jobs. International Journal of Production Research, 37(18), 4217-4233. doi:10.1080/002075499189745Özlen, M., & Azizoğlu, M. (2009). Generating all efficient solutions of a rescheduling problem on unrelated parallel machines. International Journal of Production Research, 47(19), 5245-5270. doi:10.1080/00207540802043998Pfeiffer, A., Kádár, B., & Monostori, L. (2007). Stability-oriented evaluation of rescheduling strategies, by using simulation. Computers in Industry, 58(7), 630-643. doi:10.1016/j.compind.2007.05.009Pierreval, H., & Durieux-Paris, S. (2007). Robust simulation with a base environmental scenario. European Journal of Operational Research, 182(2), 783-793. doi:10.1016/j.ejor.2006.07.045Damodaran, P., Hirani, N. S., & Gallego, M. C. V. (2009). Scheduling identical parallel batch processing machines to minimise makespan using genetic algorithms. European J. of Industrial Engineering, 3(2), 187. doi:10.1504/ejie.2009.023605Qi, X., Bard, J. F., & Yu, G. (2006). Disruption management for machine scheduling: The case of SPT schedules. International Journal of Production Economics, 103(1), 166-184. doi:10.1016/j.ijpe.2005.05.021Rangsaritratsamee, R., Ferrell, W. G., & Kurz, M. B. (2004). Dynamic rescheduling that simultaneously considers efficiency and stability. Computers & Industrial Engineering, 46(1), 1-15. doi:10.1016/j.cie.2003.09.007Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-2049. doi:10.1016/j.ejor.2005.12.009Sabuncuoglu, I., & Goren, S. (2009). Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. International Journal of Computer Integrated Manufacturing, 22(2), 138-157. doi:10.1080/09511920802209033Sabuncuoglu, I., & Kizilisik, O. B. (2003). Reactive scheduling in a dynamic and stochastic FMS environment. International Journal of Production Research, 41(17), 4211-4231. doi:10.1080/0020754031000149202Salveson, M. E. (1952). On a Quantitative Method in Production Planning and Scheduling. Econometrica, 20(4), 554. doi:10.2307/1907643Samarghandi, H., & ElMekkawy, T. Y. (2011). An efficient hybrid algorithm for the two-machine no-wait flow shop problem with separable setup times and single server. European J. of Industrial Engineering, 5(2), 111. doi:10.1504/ejie.2011.039869Subramaniam *, V., Raheja, A. S., & Rama Bhupal Reddy, K. (2005). Reactive repair tool for job shop schedules. International Journal of Production Research, 43(1), 1-23. doi:10.1080/0020754042000270412Taillard, E. (1990). Some efficient heuristic methods for the flow shop sequencing problem. European Journal of Operational Research, 47(1), 65-74. doi:10.1016/0377-2217(90)90090-xTaillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278-285. doi:10.1016/0377-2217(93)90182-mValente, J. M. S., & Schaller, J. E. (2010). Improved heuristics for the single machine scheduling problem with linear early and quadratic tardy penalties. European J. of Industrial Engineering, 4(1), 99. doi:10.1504/ejie.2010.029572Vallada, E., & Ruiz, R. (2010). Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem☆. Omega, 38(1-2), 57-67. doi:10.1016/j.omega.2009.04.002Vieira, G. E., Herrmann, J. W., & Lin, E. (2000). Predicting the performance of rescheduling strategies for parallel machine systems. Journal of Manufacturing Systems, 19(4), 256-266. doi:10.1016/s0278-6125(01)80005-4Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Journal of Scheduling, 6(1), 39-62. doi:10.1023/a:1022235519958Yang, J., & Yu, G. (2002). Journal of Combinatorial Optimization, 6(1), 17-33. doi:10.1023/a:1013333232691Zandieh, M., & Gholami, M. (2009). An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. International Journal of Production Research, 47(24), 6999-7027. doi:10.1080/0020754080240063

A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems

Flow shop scheduling deals with the determination of the optimal sequence of jobs processing on machines in a fixed order with the main objective consisting of minimizing the completion time of all jobs (makespan). This type of scheduling problem appears in many industrial and production planning applications. This study proposes a new bi-objective mixed-integer programming model for solving the synchronous flow shop scheduling problems with completion time. The objective functions are the total makespan and the sum of tardiness and earliness cost of blocks. At the same time, jobs are moved among machines through a synchronous transportation system with synchronized processing cycles. In each cycle, the existing jobs begin simultaneously, each on one of the machines, and after completion, wait until the last job is completed. Subsequently, all the jobs are moved concurrently to the next machine. Four algorithms, including non-dominated sorting genetic algorithm (NSGA II), multi-objective simulated annealing (MOSA), multi-objective particle swarm optimization (MOPSO), and multi-objective hybrid vibration-damping optimization (MOHVDO), are used to find a near-optimal solution for this NP-hard problem. In particular, the proposed hybrid VDO algorithm is based on the imperialist competitive algorithm (ICA) and the integration of a neighborhood creation technique. MOHVDO and MOSA show the best performance among the other algorithms regarding objective functions and CPU Time, respectively. Thus, the results from running small-scale and medium-scale problems in MOHVDO and MOSA are compared with the solutions obtained from the epsilon-constraint method. In particular, the error percentage of MOHVDO’s objective functions is less than 2% compared to the epsilon-constraint method for all solved problems. Besides the specific results obtained in terms of performance and, hence, practical applicability, the proposed approach fills a considerable gap in the literature. Indeed, even though variants of the aforementioned meta-heuristic algorithms have been largely introduced in multi-objective environments, a simultaneous implementation of these algorithms as well as a compared study of their performance when solving flow shop scheduling problems has been so far overlooked

New Solution Approaches for Scheduling Problems in Production and Logistics

The current cumulative PhD thesis consists of six papers published in/submitted to scientific journals. The focus of the thesis is to develop new solution approaches for scheduling problems encountering in manufacturing as well as in logistics. The thesis is divided into two parts: “ma-chine scheduling in production” and “scheduling problems in logistics” each of them consisting three papers. To have most comprehensive overview of the topic of machine scheduling, the first part of the thesis starts with two systematic review papers, which were conducted on tertiary level (i.e., re-viewing literature reviews). Both of these papers analyze a sample of around 130 literature re-views on machine scheduling problems. The first paper use a subjective quantitative approach to evaluate the sample, while the second papers uses content analysis which is an objective quanti-tative approach to extract meaningful information from massive data. Based on the analysis, main attributes of scheduling problems in production are identified and are classified into sever-al categories. Although the focus of both these papers are set to review scheduling problems in manufacturing, the results are not restricted to machine scheduling problem and the results can be extended to the second part of the thesis. General drawbacks of literature reviews are identi-fied and several suggestions for future researches are also provided in both papers. The third paper in the first part of the thesis presents the results of 105 new heuristic algorithms developed to minimize total flow time of a set of jobs in a flowshop manufacturing environ-ment. The computational experiments confirm that the best heuristic proposed in this paper im-proves the average error of best existing algorithm by around 25 percent. The first paper in second part is focused on minimizing number of electric tow-trains responsi-ble to deliver spare parts from warehouse to the production lines. Together with minimizing number of these electric vehicles the paper is also focused to maximize the work load balance among the drivers of the vehicles. For this problem, after analyzing the complexity of the prob-lem, an opening heuristic, a mixed integer linear programing (MILP) model and a taboo-search neighborhood search approach are proposed. Several managerial insights, such as the effect of battery capacity on the number of required vehicles, are also discussed. The second paper of the second part addresses the problem of preparing unit loaded devices (ULDs) at air cargos to be loaded latter on in planes. The objective of this problem is to mini-mize number of workers required in a way that all existing flight departure times are met and number of available places for building ULDs is not violated. For this problem, first, a MILP model is proposed and then it is boosted with a couple of heuristics which enabled the model to find near optimum solutions in a matter of 10 seconds. The paper also investigates the inherent tradeoff between labor and space utilization as well as the uncertainty about the volume of cargo to be processed. The last paper of the second part proposes an integrated model to improve both ergonomic and economic performance of manual order picking process by rotating pallets in the warehouse. For the problem under consideration in this paper, we first present and MILP model and then pro-pose a neighborhood search based on simulated annealing. The results of numerical experiment indicate that selectively rotating pallets may reduce both order picking time as well as the load on order picker, which leads to a quicker and less risky order picking process

Sessenta anos de Shop Scheduling : uma revisão sistemática da literatura

Orientador : Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa: Curitiba, 09/02/2017Inclui referências : f. 449-492Resumo: Desde o seminal artigo de Johnson em 1954, a Programação da Produção em Shop Scheduling tem se tornado uma área relevante dentro da Pesquisa Operacional e, atualmente, duzentos trabalhos tangentes à temática são publicados anualmente. Dentre os artigos aqui citados tem-se aqueles que se dedicam à apresentação e síntese do estado da arte desse assunto, intitulados artigos de revisão. Quando tais artigos são elaborados a partir de um conjunto objetivo de critérios, relativos à categorização dos artigos selecionados, tem-se a Revisão Sistemática da Literatura (RSL). O presente trabalho realiza uma RSL em Shop Scheduling, a partir da análise de cada ambiente fabril que o compõe. Fez-se o escrutínio de 560 artigos, à luz de um conjunto de métricas, que constitui a estrutura basilar da proposta de nova taxonomia do Shop Scheduling, complementar à notação de Graham, objetivo fulcral do presente trabalho. Além disso, utilizou-se uma representação em redes dos resultados obtidos em algumas das métricas empregadas, como a característica dos itens, algo outrora inaudito em estudos de revisão desse assunto. Ademais, outro ponto relevante desse estudo repousa na identificação de campos pouco explorados, de modo a colaborar com a pesquisa futura neste tomo. Palavras-chave: Shop Scheduling. Revisão Sistemática da Literatura. Taxonomia. Representação em Redes.Abstract: Since Johnson's seminal article in 1954, Shop Scheduling in Production Scheduling has become a relevant area within Operational Research, and currently hundreds of tangential works on the subject are published annually. Among the articles cited here are those dedicated to the presentation and synthesis of the state of the art of this subject, which are entitled review articles. When these articles are elaborated from an objective set of criteria, regarding the categorization of the selected articles, we have the Systematic Review of Literature (SLR). The present work performs a SLR in Shop Scheduling, based on the analysis of each manufacturing environment that composes it. There were 560 articles scrutinized based on a set of metrics, which is the basic structure of the proposed new Taxonomy of Shop Scheduling, complementary to Graham's notation, the main objective of this work. In addition to that a network representation of the results was obtained in some of the metrics used, such as the job characteristics, something previously unheard of in review studies of this subject. Moreover, another relevant point of this study lies in the identification of less explored fields in order to collaborate with future research in this matter. Keywords: Shop Scheduling. Systematic Literature Review. Taxonomy. Network Representation