Relevant bibliographies by topics / Scheduling objectives

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Scheduling objectives'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Scheduling objectives"

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Castillo, Ignacio, Tarja Joro, and Yong Yue Li. "Workforce scheduling with multiple objectives." European Journal of Operational Research 196, no. 1 (July 2009): 162–70. http://dx.doi.org/10.1016/j.ejor.2008.02.038.

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Epstein, Sheldon, Yonah Wilamowsky, and Bernard Dickman. "Deterministic multiprocessor scheduling with multiple objectives." Computers & Operations Research 19, no. 8 (November 1992): 743–49. http://dx.doi.org/10.1016/0305-0548(92)90013-u.

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Franke, Carsten, Joachim Lepping, and Uwe Schwiegelshohn. "Greedy scheduling with custom-made objectives." Annals of Operations Research 180, no. 1 (December 6, 2008): 145–64. http://dx.doi.org/10.1007/s10479-008-0491-2.

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Važan, Pavel, Michal Škamla, Dominika Jurovatá, and Vladimír Ľupták. "Effect of Selected Priority Rules on Manufacturing Objectives in Scheduling." Advanced Materials Research 488-489 (March 2012): 1125–29. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1125.

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In this paper, the effect of selected priority rules of scheduling is presented. This effect has been studied for chosen scheduling objectives by simulation method. The authors used the simulator Witness to experimentation with priority rules in a designed manufacturing system. The results of experiments have been processed into the synthesis of knowledge. The implementation of scheduling procedure is described in this paper.
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Brezoňáková, Andrea. "Realistic scheduling agreement: Defining principles and objectives." Transportation Research Procedia 43 (2019): 156–64. http://dx.doi.org/10.1016/j.trpro.2019.12.030.

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Monteiro, Thibaud, Nadine Meskens, and Tao Wang. "Surgical scheduling with antagonistic human resource objectives." International Journal of Production Research 53, no. 24 (September 11, 2015): 7434–49. http://dx.doi.org/10.1080/00207543.2015.1082040.

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Ozturkoglu, Yucel. "An efficient time algorithm for makespan objectives." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 5, no. 2 (July 1, 2015): 75–80. http://dx.doi.org/10.11121/ijocta.01.2015.00260.

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This paper focuses on a single machine scheduling subject to machine deterioration with rate-modifying activities (RMA). The motivation for this study stems from the automatic-production line problem with one machine. The main question is to find the sequence in which jobs should be scheduled, how many maintenance activity (RMA) to use, if any, and where to insert them in the schedule during the time interval with optimal makespan objective. This problem is known to be NP-hard and we give concise analyses of the problem and provide polynomial time algorithms to solve the makespan problem. We also propose an algorithm which can be applied to some scheduling problems with the actual processing time of job nonlinearly based on its position.This paper focuses on a single machine scheduling subject to machine deterioration with rate-modifying activities (RMA). The motivation for this study stems from the automatic-production line problem with one machine. The main question is to find the sequence in which jobs should be scheduled, how many maintenance activity (RMA) to use, if any, and where to insert them in the schedule during the time interval with optimal makespan objective. This problem is known to be NP-hard and we give concise analyses of the problem and provide polynomial time algorithms to solve the makespan problem. We also propose an algorithm which can be applied to some scheduling problems with the actual processing time of job nonlinearly based on its position.
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Bhattacharyya, Siddhartha, and Gary J. Koehler. "Learning by Objectives for Adaptive Shop-Floor Scheduling." Decision Sciences 29, no. 2 (March 1998): 347–75. http://dx.doi.org/10.1111/j.1540-5915.1998.tb01580.x.

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Salman, Saad Mohsin. "Scheduling Critical Activities on Multi-objectives Stochastic Projects." Journal of Al-Nahrain University Science 16, no. 3 (September 1, 2013): 246–51. http://dx.doi.org/10.22401/jnus.16.3.34.

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Hellerstein, J. L. "Achieving service rate objectives with decay usage scheduling." IEEE Transactions on Software Engineering 19, no. 8 (1993): 813–25. http://dx.doi.org/10.1109/32.238584.

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Dissertations / Theses on the topic "Scheduling objectives"

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Yang, Jaehwan. "Scheduling with batch objectives /." The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487952208107258.

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Huaccho, Huatuco Luisa Delfa. "The role of rescheduling in managing manufacturing systems' complexity." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275307.

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Mundt, Andreas, and Thomas Wich. "Single Machine Scheduling with Tardiness Involved Objectives : A Survey." Thesis, Linköping University, Department of Mathematics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8628.

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This thesis contributes to theoretical and quantitative aspects of machine scheduling. In fact, it is dedicated to the issue of scheduling n jobs on one single machine. The scope is limited to deterministic problems - i.e. those with all data available and known with certainty in advance - with tardiness involved objectives; hence, the common denominator of all problems addressed are jobs with a predetermined due date assigned to. A job is finished on time as long as it is completed before its due date, otherwise it is said to be tardy. Since the single machine utilized is assumed to be restricted to process at most one job at a time, the aim is to find a proper sequence - a schedule - of how to process the jobs in order to best fulfill a certain objective. The contribution of this thesis aims at giving a state of the art survey and detailed review of research effort considering the objectives "minimizing the number of tardy jobs" and "minimizing the weighted number of tardy jobs". Further, the objectives of "minimizing the total tardiness", "minimizing the total weighted tardiness" and "minimizing the maximum tardiness" are adumbrated but reduced to a rough overview of research effort made.

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Berry, Pauline M. "A predictive model for satisfying conflicting objectives in scheduling problems." Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332064.

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Aguilar-Soto, Armando. "Fixed-priority scheduling algorithms with multiple objectives in hard real-time systems." Thesis, University of York, 2006. http://etheses.whiterose.ac.uk/11057/.

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In the context ofFixed-Priority Scheduling in Real-Time Systems, we investigate scheduling mechanisms for supporting systems where, in addition to timing constraints, their performance with respect to additional QoS requirements must be improved. This'type of situation may occur when the worst-case res~urce requirements of all or some running tasks cannot be simultaneously met due to task contention. . Solutions to these problems have been proposed in the context of both fixed-priority and dynamic-priority scheduling. In fixed-priority scheduling, the typical approach is to artificially modify the attributes or structure of tasks, and/or usually require non-standard run-time support. In dynamic-priority scheduling approaches, utility functions are employed to make scheduling decisions with the objective of maximising the utility. The main difficulties with these approaches are the inability to formulate and model appropriately utility functions for each task, and the inability to guarantee hard deadlines without executing computationally costly algorithms. In this thesis we propose a different approach. Firstly, we introduce the concept of relative importance among tasks as a new metric for expressing QoS requirements. The meaning of this importance relationship is to express that in a schedule it i~ desirable to run a task in preference to other ones. This model is more intuitive and less restrictive than traditional utility-based app~oaches. Secondly, we formulate a scheduling problem in terms of finding a feasible assignment of fixed priorities that maximises the new QoS metric, and propose the DI and DI+ algorithms that find optimal solutions. By extensive simulation, we show that the new QoS metric combined with the DI algorithm outperforms the rate monotonic priority algorithm in several practical problems such as minimising jitter, minimising the number of preemptions or minimising the latency. In addition, our approach outperforms EDF in several scenarios.
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Xie, Wenbin. "Metaheuristics for single and multiple objectives production scheduling for the capital goods industry." Thesis, University of Newcastle Upon Tyne, 2011. http://hdl.handle.net/10443/1261.

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In the capital goods industry, companies produce plant and machinery that is used to produce consumer products or commodities such as electricity or gas. Typical products produced in these companies include steam turbines, large boilers and oil rigs. Scheduling of these products is difficult due to the complexity of the product structure, which involves many levels of assembly and long complex routings of many operations which are operated in multiple machines. There are also many scheduling constraints such as machine capacity as well as operation and assembly precedence relationships. Products manufactured in the capital goods industry are usually highly customised in order to meet specific customer requirements. Delivery performance is a particularly important aspect of customer service and it is common for contracts to include severe penalties for late deliveries. Holding costs are incurred if items are completed before the due date. Effective planning and inventory control are important to ensure that products are delivered on time and that inventory costs are minimised. Capital goods companies also give priority to resource utilisation to ensure production efficiency. In practice there are tradeoffs between achieving on time delivery, minimising inventory costs whilst simultaneously maximising resource utilisation. Most production scheduling research has focused on job-shops or flow-shops which ignored assembly relationships. There is a limited literature that has focused on assembly production. However, production scheduling in capital goods industry is a combination of component manufacturing (using jobbing, batch and flow processes), assembly and construction. Some components have complex operations and routings. The product structures for major products are usually complex and deep. A practical scheduling tool not only needs to solve some extremely large scheduling problems, but also needs to solve these problems within a realistic time. Multiple objectives are usually encountered in production scheduling in the capital goods industry. Most literature has focused on minimisation of total flow time, or makespan and earliness and tardiness of jobs. In the capital goods industry, inventory costs, delivery performance and machine utilisation are crucial competitive. This research develops a scheduling tool that can successfully optimise these criteria simultaneously within a realistic time. ii The aim of this research was firstly to develop the Enhanced Single-Objective Genetic Algorithm Scheduling Tool (ESOGAST) to make it suitable for solving very large production scheduling problems in capital goods industry within a realistic time. This tool aimed to minimise the combination of earliness and lateness penalties caused by early or late completion of items. The tool was compared with previous approaches in literature and was proved superior in terms of the solution quality and the computational time. Secondly, this research developed a Multi-Objective Genetic Algorithm Scheduling Tool (MOGAST) that was based upon the development of ESOGAST but was able to solve scheduling problems with multiple objectives. The objectives of this tool were to optimise delivery performance, minimise inventory costs, and maximise resource utilisation simultaneously. Thirdly, this research developed an Artificial Immune System Scheduling Tool (AISST) that achieved the same objective of the ESOGAST. The performances of both tools were compared and analysed. Results showed that AISST performs better than ESOGAST on relatively small scheduling problems, but the computation time required by the AISST was several times longer. However ESOGAST performed better than the AISST for larger problems. Optimum configurations were identified in a series of experiments that conducted for each tool. The most efficient configuration was also successfully applied for each tool to solve the full size problem and all three tools achieved satisfactory results.
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Gama, Pinheiro Vinicius. "The management of multiple submissions in parallel systems : the fair scheduling approach." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM042/document.

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Le problème étudié est celui de l'ordonnancement d'applications dans lessystèmes parallèles et distribués avec plusieurs utilisateurs. Les nouvellesplates-formes de calcul parallèle et distribué offrent des puissances trèsgrandes qui permettent d'envisager la résolution d'applications complexesinteractives. Aujourd'hui, il reste encore difficile d'utiliser efficacementcette puissance par manque d'outils de gestion de ressources. Le travaileffectué dans cette thèse se place dans cette perspective d'analyser etdévelopper des algorithmes efficaces pour gérer efficacement des ressources decalcul partagées entre plusieurs utilisateurs. On analyse les scénarios avecplusieurs soumissions lancées par multiples utilisateurs au cours du temps. Cessoumissions ont un ou plus de processus et l'ensemble de soumissions estorganisé en successifs campagnes. Les processus d'une seule campagnesont séquentiels et indépendants, mais les processus d'une campagne ne peuventpas commencer leur exécution avant que tous les processus provenant de ladernière campagne sont completés. Chaque utilisateur est intéressé à minimiserla somme des temps de réponses des campagnes. On définit un modèle théorique pour l'ordonnancement des campagnes et on montreque, dans le cas général, c'est NP-difficile. Pour le cas avec un utilisateur,on démontre qu'un algorithme d'ordonnancement $ho$-approximation pour le(classique) problème d'ordonnancement de tâches parallèles est aussi un$ho$-approximation pour le problème d'ordonnancement de campagnes. Pour lecas général avec $k$ utilisateurs, on établis un critère de emph{fairness}inspiré par partage de temps. On propose FairCamp, un algorithmed'ordonnancement qu'utilise dates limite pour réaliser emph{fairness} parmiles utilisateurs entre consécutifes campagnes. On prouve que FairCamp augmentele temps de réponse de chaque utilisateur par a facteur maximum de $kho$ parrapport un processeur dédiée à l'utilisateur. On prouve aussi que FairCamp estun algorithme $ho$-approximation pour le maximum emph{stretch}.On compare FairCamp contre emph{First-Come-First-Served} (FCFS) parsimulation. On démontre que, comparativement à FCFS, FairCamp réduit le maximal{em stretch} a la limite de $3.4$ fois. La différence est significative dansles systèmes utilisé pour plusieurs ($k>5$) utilisateurs.Les résultats montrent que, plutôt que juste des tâches individuelle etindépendants, campagnes de tâches peuvent être manipulées d'une manièreefficace et équitable
We study the problem of scheduling in parallel and distributedsystems with multiple users. New platforms for parallel and distributedcomputing offers very large power which allows to contemplate the resolution ofcomplex interactive applications. Nowadays, it is still difficult to use thispower efficiently due to lack of resource management tools. The work done inthis thesis lies in this context: to analyse and develop efficient algorithmsfor manage computing resources shared among multiple users. We analyzescenarios with many submissions issued from multiple users over time. Thesesubmissions contain one or more jobs and the set of submissions are organizedin successive campaigns. Any job from a campaign can not start until allthe jobs from the previous campaign are completed. Each user is interested inminimizing the sum of flow times of the campaigns.In the first part of this work, we define a theoretical model for Campaign Scheduling under restrictive assumptions andwe show that, in the general case, it is NP-hard. For the single-user case, we show that an$ho$-approximation scheduling algorithm for the (classic) parallel jobscheduling problem is also an $ho$-approximation for the Campaign Schedulingproblem. For the general case with $k$ users, we establish a fairness criteriainspired by time sharing. Then, we propose FairCamp, a scheduling algorithm whichuses campaign deadlines to achieve fairness among users between consecutivecampaigns. We prove that FairCamp increases the flow time of each user by afactor of at most $kho$ compared with a machine dedicated to the user. Wealso prove that FairCamp is an $ho$-approximation algorithm for the maximumstretch.We compare FairCamp to {em First-Come-First-Served} (FCFS) by simulation. We showthat, compared with FCFS, FairCamp reduces the maximum stretch by up to $3.4$times. The difference is significant in systems used by many ($k>5$) users.Our results show that, rather than just individual, independent jobs, campaignsof jobs can be handled by the scheduler efficiently and fairly
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Trivedi, Manas. "Multi-objective generation scheduling with hybrid energy resources." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1202498690/.

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Shaw, Katherine Jane. "Using genetic algorithms for practical multi-objective production schedule optimisation." Thesis, University of Sheffield, 1997. http://etheses.whiterose.ac.uk/14767/.

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Production scheduling is a notoriously difficult problem. Manufacturing environments contain complex, time-critical processes, which create highly constrained scheduling problems. Genetic algorithms (GAs) are optimisation tools based on the principles of evolution. They can tackle problems that are mathematically complex, or even impossible to solve by traditional methods. They allow problem-specific implementation, so that the user can develop a technique that suits the situation, whilst still providing satisfactory schedule optimisation performance. This work tests GA optimisation on a real-life scheduling application, a chilled ready-meal factory. A schedule optimisation system is required to adapt to changing problem circumstances and to include uncertain or incomplete information. A GA was designed to allow successive improvements to its effectiveness at scheduling. Three objectives were chosen for minimisation. The GA proved capable of finding a solution that attempted to minimise the sum of the three costs. The GA performance was improved after experiments showed the effects of rules and preference modelling upon the optimisation process, allowing 'uncertain' data to be included. Multi-objective GAs (MOGAs) minimise each cost as a separate objective, rather than as part of a single-objective sum. Combining Pareto-optimality with varying emphasis on the conflicting objectives, a set of possible solutions can be found from one run of MOGA. Each MOGA solution represents a different situation within the factory, thus being well suited to a constantly changing manufacturing problem. Three MOGA implementations are applied to the problem; a standard weighted sum, two versions of a Pareto-optimal method and a parallel populations method. Techniques are developed to allow suitable comparison of MOGAs. Performance comparisons indicate which method is most effective for meeting the factory's requirements. Graphical and statistical methods indicate that the Pareto-based MOGA is most effective for this problem. The MOGA is demonstrated as being a highly applicable technique for production schedule optimisation.
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Metta, Haritha. "ADAPTIVE, MULTI-OBJECTIVE JOB SHOP SCHEDULING USING GENETIC ALGORITHMS." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_theses/518.

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This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time.
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Books on the topic "Scheduling objectives"

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Mathematical programming and financial objectives for scheduling projects. Boston: Kluwer Academic Publishers, 2001.

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Kimms, Alf. Mathematical Programming and Financial Objectives for Scheduling Projects. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1453-4.

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Kimms, Alf. Mathematical Programming and Financial Objectives for Scheduling Projects. Boston, MA: Springer US, 2001.

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Miller, Dennis P. Building a project work breakdown structure: Visualizing objectives, deliverables, activities, and schedules. Boca Raton: Auerbach Publications, 2008.

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1968-, Schwindt Christoph, and Zimmermann Jürgen 1963-, eds. Project scheduling with time windows and scarce resources: Temporal and resource-constrained project scheduling with regular and nonregular objective functions. Berlin: Springer, 2002.

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Garbett, K. S. Multi-objective scheduling and control of a nonlinear automotive powertrain. [s.l.]: typescript, 1991.

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Shaw, Katharine Jane. Initial study of multi-objective genetic algorithms for scheduling the production of chilled ready meals. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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Building a Project Work Breakdown Structure: Visualizing Objectives, Deliverables, Activities, and Schedules. AUERBACH, 2008.

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Kimms, Alf. Mathematical Programming and Financial Objectives for Scheduling Products (International Series in Operations Research and Management Science, Volume 38) ... in Operations Research & Management Science). Springer, 2001.

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Project Scheduling with Time Windows and Scarce Resources: Temporal and Resource-Constrained Project Scheduling with Regular and Nonregular Objective Functions. 2nd ed. Springer, 2003.

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Book chapters on the topic "Scheduling objectives"

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Framinan, Jose M., Rainer Leisten, and Rubén Ruiz García. "Objectives." In Manufacturing Scheduling Systems, 101–25. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6272-8_5.

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Hapke, Maciej, Andrzej Jaszkiewicz, and Roman Słowiński. "Fuzzy Multi-Mode Resource-Constrained Project Scheduling with multiple Objectives." In Project Scheduling, 353–80. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5533-9_16.

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Topcuoglu, Haluk, and Can Sevilmis. "Task Scheduling with Conflicting Objectives." In Advances in Information Systems, 346–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36077-8_36.

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Pan, Hongqi, and Chung-Hsing Yeh. "Fuzzy Project Scheduling with Multiple Objectives." In PRICAI 2004: Trends in Artificial Intelligence, 1011–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28633-2_139.

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Menouer, Tarek, Christophe Cérin, and Étienne Leclercq. "New Multi-objectives Scheduling Strategies in Docker SwarmKit." In Algorithms and Architectures for Parallel Processing, 103–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05057-3_8.

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Kendall, Graham, Barry McCollum, Frederico R. B. Cruz, Paul McMullan, and Lyndon While. "Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives." In Hybrid Metaheuristics, 369–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30671-6_14.

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Wang, Binggang, Yunqing Rao, Xinyu Shao, and Mengchang Wang. "Scheduling Mixed-Model Assembly Lines with Cost Objectives by a Hybrid Algorithm." In Intelligent Robotics and Applications, 378–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88518-4_41.

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Yu, Calvin K., and Pei-Fang Lee. "A Weighting Approach for Scheduling Multi-Product Assembly Line with Multiple Objectives." In Proceedings of the Institute of Industrial Engineers Asian Conference 2013, 415–22. Singapore: Springer Singapore, 2013. http://dx.doi.org/10.1007/978-981-4451-98-7_50.

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Albers, Susanne, and Alexander Eckl. "Explorable Uncertainty in Scheduling with Non-uniform Testing Times." In Approximation and Online Algorithms, 127–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2_9.

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AbstractThe problem of scheduling with testing in the framework of explorable uncertainty models environments where some preliminary action can influence the duration of a task. In the model, each job has an unknown processing time that can be revealed by running a test. Alternatively, jobs may be run untested for the duration of a given upper limit. Recently, Dürr et al. [4] have studied the setting where all testing times are of unit size and have given lower and upper bounds for the objectives of minimizing the sum of completion times and the makespan on a single machine. In this paper, we extend the problem to non-uniform testing times and present the first competitive algorithms. The general setting is motivated for example by online user surveys for market prediction or querying centralized databases in distributed computing. Introducing general testing times gives the problem a new flavor and requires updated methods with new techniques in the analysis. We present constant competitive ratios for the objective of minimizing the sum of completion times in the deterministic case, both in the non-preemptive and preemptive setting. For the preemptive setting, we additionally give a first lower bound. We also present a randomized algorithm with improved competitive ratio. Furthermore, we give tight competitive ratios for the objective of minimizing the makespan, both in the deterministic and the randomized setting.
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Hammami, Hayfa, and Thomas Stützle. "A Computational Study of Neighborhood Operators for Job-Shop Scheduling Problems with Regular Objectives." In Evolutionary Computation in Combinatorial Optimization, 1–17. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55453-2_1.

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Conference papers on the topic "Scheduling objectives"

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Shafransky, Yakov M., T.-C. Edwin Cheng, and C.-T. Daniel Ng. "An approach for proving the NP-hardness of optimization problems with hard computable objectives." In Workshop on dynamic scheduling problems. Polish Mathematical Society, 2016. http://dx.doi.org/10.14708/isbn.978-83-937220-7-5p75-78.

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Maigha and Mariesa L. Crow. "Multi-objective electric vehicle scheduling considering customer and system objectives." In 2017 IEEE Manchester PowerTech. IEEE, 2017. http://dx.doi.org/10.1109/ptc.2017.7981275.

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Guo, Chengzhi, Ming Li, and Deming Lei. "Multi-objective flexible job shop scheduling problem with key objectives." In 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 2019. http://dx.doi.org/10.1109/yac.2019.8787585.

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Im, Sungjin, and Benjamin Moseley. "Online batch scheduling for flow objectives." In SPAA '13: 25th ACM Symposium on Parallelism in Algorithms and Architectures. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2486159.2486161.

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Hreinsson, Egill Benedikt. "Efficiencies and objectives in short term hydro scheduling." In 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE, 2016. http://dx.doi.org/10.1109/pesgm.2016.7741694.

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Xu, Ke, and Souran Manoochehri. "Job Shop Scheduling Optimization Using Genetic Algorithm With Global Criterion Technique." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98076.

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Abstract The Job Shop Scheduling Problem (JSSP) is a method which assigns multiple jobs to various machines. The large dimension of JSSP and the dynamic manufacturing environment have always been a difficult problem to optimize due to its size and complexity. In this study, three objective functions are selected namely, minimizing makespan, minimizing total cost and maximizing machine utilization. Genetic Algorithm (GA) is used to solve this scheduling problem. Lot size optimization technique is investigated for the potential of optimizing the makespan, total cost, and machine utilization objectives. Global Criterion (GC) Technique is implemented which can optimize multiple objectives all at once and obtain the best schedule. Finally, a case study is presented.
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Zhang, Heng, and Utpal Roy. "A Semantic Similarity Based Dispatching Rule Selection System for Job Shop Scheduling With Multiple Production Objectives." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47822.

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Job shop scheduling is an important activity which properly assigns production jobs to different manufacturing resources before production starts. Compared to other scheduling approaches that use optimal branch and bound algorithms, meta-heuristics, etc., the dispatching rule based approach has been widely used in the industry because it is easier to implement, and it yields reasonable solutions within a very short computation time. The dispatching rule based approach uses a selected single dispatching rule (e.g. Shortest Processing Time or Earliest Due Date) or a rule combination depending on the current production objective like maximizing productivity, minimizing makespan or meeting the due dates. However, a dispatching rule or a pre-set rule combination always pursues a single and fixed production objective. This characteristic confines the flexibility of the scheduling system in practice. In order to address this issue, this paper proposes a semantic similarity based dispatching rule selection system that can achieve the intelligent selection of dispatching rules based on the user selected one or more production objectives for job shop scheduling. The intelligent selection is addressed by measuring the semantic similarities (based on ontology) between the user selected production objectives and the characteristics of the dispatching rules. The rule combinations will then be constructed by combining individual dispatching rules with similarity value based weights. A proof-of-concept demo has also been provided as a case study in this paper.
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Wang, Zuntong, Fei Qiao, and Qidi Wu. "Scheduling Semiconductor Wafer Fabrication with Optimization of Multiple objectives." In 2006 IEEE International Conference on Automation Science and Engineering. IEEE, 2006. http://dx.doi.org/10.1109/coase.2006.326889.

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Nouri, Nouha, and Talel Ladhari. "Minimizing Regular Objectives for Blocking Permutation Flow Shop Scheduling." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739480.2754638.

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Krompass, Stefan, Harumi Kuno, Kevin Wilkinson, Umeshwar Dayal, and Alfons Kemper. "Adaptive query scheduling for mixed database workloads with multiple objectives." In the Third International Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1838126.1838127.

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Reports on the topic "Scheduling objectives"

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Bellabai, Robert, Jeen Robert, and Ramasubbu Rajkumar. Multi-objective Optimization Using Hybrid Algorithm and Its Application to Scheduling in Flow Shops. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2019. http://dx.doi.org/10.7546/crabs.2019.01.14.

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Amela, R., R. Badia, S. Böhm, R. Tosi, C. Soriano, and R. Rossi. D4.2 Profiling report of the partner’s tools, complete with performance suggestions. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.023.

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This deliverable focuses on the proling activities developed in the project with the partner's applications. To perform this proling activities, a couple of benchmarks were dened in collaboration with WP5. The rst benchmark is an embarrassingly parallel benchmark that performs a read and then multiple writes of the same object, with the objective of stressing the memory and storage systems and evaluate the overhead when these reads and writes are performed in parallel. A second benchmark is dened based on the Continuation Multi Level Monte Carlo (C-MLMC) algorithm. While this algorithm is normally executed using multiple levels, for the proling and performance analysis objectives, the execution of a single level was enough since the forthcoming levels have similar performance characteristics. Additionally, while the simulation tasks can be executed as parallel (multi-threaded tasks), in the benchmark, single threaded tasks were executed to increase the number of simulations to be scheduled and stress the scheduling engines. A set of experiments based on these two benchmarks have been executed in the MareNostrum 4 supercomputer and using PyCOMPSs as underlying programming model and dynamic scheduler of the tasks involved in the executions. While the rst benchmark was executed several times in a single iteration, the second benchmark was executed in an iterative manner, with cycles of 1) Execution and trace generation; 2) Performance analysis; 3) Improvements. This had enabled to perform several improvements in the benchmark and in the scheduler of PyCOMPSs. The initial iterations focused on the C-MLMC structure itself, performing re-factors of the code to remove ne grain and sequential tasks and merging them in larger granularity tasks. The next iterations focused on improving the PyCOMPSs scheduler, removing existent bottlenecks and increasing its performance by making the scheduler a multithreaded engine. While the results can still be improved, we are satised with the results since the granularity of the simulations run in this evaluation step are much ner than the one that will be used for the real scenarios. The deliverable nishes with some recommendations that should be followed along the project in order to obtain good performance in the execution of the project codes.
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Brown, Willie, and Jonathan Alt. Investigating the USACE Operational Condition Assessment process current and future. Engineer Research and Development Center (U.S.), March 2021. http://dx.doi.org/10.21079/11681/39999.

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The US Army Corps of Engineers operates, maintains, and manages more than $232 billion worth of the Nation’s water resource infrastructure and relies on the Operational Condition Assessment (OCA) process to determine the condition of the assets and their components. The sheer number of components, all of equal OCA scheduling priority, creates challenges in ensuring that assessments are conducted in a timely manner and that data generated are of sufficient quality to inform resource allocation decisions. This research applied methods from systems design to determine the OCA system “as-is” state and create a stakeholder-informed vision of a “to-be” state that addresses current system challenges. To meet its objective of providing current assessments of asset condition, the OCA system must provide four high-level functions: provide access to asset data, conduct assessments, determine asset risk, and prioritize and schedule assessments. The development of capabilities to provide these functions will facilitate the achievement of the OCA system to-be vision: a consistent view of asset condition and risk across the enterprise.
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