A Dynamic Selection of Dispatching Rules Based on the Kano Model Satisfaction Scheduling Tool

Production scheduling is a function that can contribute strongly to the competitive capacity of companies producing goods and services. Failure to stagger tasks properly causes enormous waste of time and resources, with a clear decrease in productivity and high monetary losses. The efficient use of internal resources in organizations becomes a competitive advantage and can thus dictate their survival and sustainability. In that sense, it becomes crucial to analyze and develop production scheduling models, which can be simplified as the function of affecting tasks to means of production over time. This report is part of a project to develop a dynamic scheduling tool for decision support in a single machine environment. The system created has the ability, after a first solution has been generated, to trigger a new solution as some tasks leave the system and new ones arrive, allowing the user, at each instant of time, to determine new scheduling solutions, in order to minimize a certain measure of performance. The proposed tool was validated in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model.

[1]  Cecilia E. Nugraheni,et al.  On the Development of Hyper heuristics based Framework for Scheduling Problems in Textile Industry , 2016 .

[2]  Marc G. Caron,et al.  Partially Purified and Reconstituted G-Protein Coupled Receptors as Substrates of Specific Receptor Kinases , 1994 .

[3]  J. Christopher Beck,et al.  Queueing-theoretic approaches for dynamic scheduling: A survey , 2014 .

[4]  Artiba,et al.  The Planning and Scheduling of Production Systems: Methodologies and applications , 2011 .

[5]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[6]  Dvir Shabtay,et al.  The single-machine earliness-tardiness scheduling problem with due date assignment and resource-dependent processing times , 2008, Ann. Oper. Res..

[7]  Rajeev Agrawal,et al.  Investigation of reconfiguration effect on makespan with social network method for flexible job shop scheduling problem , 2017, Comput. Ind. Eng..

[8]  Ivo Pereira,et al.  Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties , 2010, 2010 10th International Conference on Hybrid Intelligent Systems.

[9]  Anders Gustafsson,et al.  How to create attractive and unique customer experiences: An application of Kano's theory of attractive quality to recreational tourism , 2010 .

[10]  Krzysztof Kalinowski,et al.  Preparatory Stages of the Production Scheduling of Complex and Multivariant Products Structures , 2015, SOCO.

[11]  John W. Fowler,et al.  A SURVEY OF ALGORITHMS FOR SINGLE AND MULTI-OBJECTIVE UNRELATED PARALLEL-MACHINE DETERMINISTIC SCHEDULING PROBLEMS , 2004 .

[12]  Ivo Pereira,et al.  Negotiation mechanism for self-organized scheduling system with collective intelligence , 2014, Neurocomputing.

[13]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[14]  Abdellah Salhi,et al.  A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling , 2007, Evolutionary Scheduling.

[15]  Jacek Blazewicz,et al.  Handbook on Scheduling: From Theory to Applications , 2014 .

[16]  Maria Leonilde Rocha Varela,et al.  Comparative Simulation Study of Production Scheduling in the Hybrid and the Parallel Flow , 2017 .

[17]  S. E. Elmaghraby,et al.  The Planning and Scheduling of Production Systems , 1996 .

[18]  Lars Witell,et al.  Theory of attractive quality and life cycles of quality attributes , 2011 .

[19]  Kenneth R. Baker,et al.  Principles of Sequencing and Scheduling. New York: John Wiley & Sons , 2009 .

[20]  Damian Krenczyk,et al.  Computer aided production planning - SWZ system of order verification , 2015 .

[21]  Sanja Petrovic,et al.  A new dispatching rule based genetic algorithm for the multi-objective job shop problem , 2010, J. Heuristics.

[22]  André Borges Guimarães Serra e Santos Análise do Desempenho de Técnicas de Otimização no Problema de Escalonamento , 2015 .

[23]  Maria Leonilde Rocha Varela,et al.  Comparing extended neighborhood search techniques applied to production scheduling , 2010 .

[24]  Maria Leonilde Rocha Varela Uma contribuição para o escalonamento da produção baseado em métodos globalmente distribuídos , 2007 .

[25]  Maria Leonilde Rocha Varela,et al.  Distributed Manufacturing Scheduling Based on a Dynamic Multi-criteria Decision Model , 2014 .

[26]  José Machado,et al.  The Tool Supporting Decision Making Process in Area of Job-Shop Scheduling , 2017, WorldCIST.