Fuzzy set theory to establish resilient production systems

In a global market, companies must deal with a high rate of changes in business environment. Most manufacturing systems fail to sustain productivity when process disturbances occur. Therefore, it is necessary to create resilient production systems with the ability to return, rapidly, to the initial stage or to an improved one. The parameters, variables and restrictions of the production system are inherently vagueness. This situation suggests that there are dependencies between relevant variables which are not possible to know with precision. The fuzzy logic theory has the ability to describe, in a quantitative and qualitative way, problems that involve vagueness and imprecision. This paper provides an overview for the use of fuzzy logic to establish resilient production systems. Fuzzy concepts are reviewed, in particular its applications to production systems, trying to classify related fuzzy parameters and variables. In addition, the concept of resilient systems are reviewed and extended to production. Finally, it is proposed an approach to establish resilient production systems using the fuzzy set theory.

[1]  Mitsuo Gen,et al.  Hybrid genetic algorithm for multi-time period production/distribution planning , 2005, Comput. Ind. Eng..

[2]  Freerk A. Lootsma,et al.  Fuzzy set theory and its applications, 3rd edition , 1997 .

[3]  Tien-Fu Liang,et al.  Application of fuzzy multi-objective linear programming to aggregate production planning , 2004, Comput. Ind. Eng..

[4]  Denis Gien,et al.  Design and simulation of manufacturing systems facing imperfectly defined information , 2005, Simul. Model. Pract. Theory.

[5]  A. W. Labib,et al.  Optimal control of an unreliable machine using fuzzy-logic control: from design to implementation , 2005 .

[6]  Osman Taylan,et al.  Neural and fuzzy model performance evaluation of a dynamic production system , 2006 .

[7]  Nikolaos V. Sahinidis,et al.  Optimization under uncertainty: state-of-the-art and opportunities , 2004, Comput. Chem. Eng..

[8]  Bjorn Egil Asbjornslett,et al.  Assess the vulnerability of your production system , 1999 .

[9]  Emin Gundogar,et al.  Fuzzy priority rule for job shop scheduling , 2004, J. Intell. Manuf..

[10]  Chi-Bin Cheng,et al.  Fuzzy process control: construction of control charts with fuzzy numbers , 2005, Fuzzy Sets Syst..

[11]  Robert K. L. Gay,et al.  Dynamic scheduling I: simulation-based scheduling for dynamic discrete manufacturing , 2003, WSC '03.

[12]  Da Ruan,et al.  Fuzzy group decision-making for facility location selection , 2003, Inf. Sci..

[13]  D. Watts,et al.  Designing Resilient , Sustainable Systems , 2022 .

[14]  Soroosh Saghiri,et al.  Supply Chain: Crisp and Fuzzy Aspects , 2002 .

[15]  Manoranjan Maiti,et al.  Multi-objective fuzzy inventory model with three constraints: a geometric programming approach , 2005, Fuzzy Sets Syst..

[16]  Wolfgang Eiden.  Scheduling with Fuzzy Methods , 2004, OR.

[17]  Hong Zhang,et al.  Fuzzy discrete‐event simulation for modeling uncertain activity duration , 2004 .

[18]  B. Bhattacharyya,et al.  Fuzzy decision support system for manufacturing facilities layout planning , 2005, Decis. Support Syst..

[19]  Tien-Fu Liang,et al.  Project management decisions with multiple fuzzy goals , 2004 .

[20]  Kostas S. Metaxiotis,et al.  Integrating fuzzy logic into decision suppport systems: current research and future prospects , 2003, Inf. Manag. Comput. Secur..

[21]  Raul Poler Escoto,et al.  MRP with flexible constraints: A fuzzy mathematical programming approach , 2006, Fuzzy Sets Syst..

[22]  P. Hines,et al.  Learning to evolve: A review of contemporary lean thinking , 2004 .

[23]  Hassen Taleb,et al.  On fuzzy and probabilistic control charts , 2002 .

[24]  Antonio Rizzi,et al.  A fuzzy logic based methodology to rank shop floor dispatching rules , 2002 .

[25]  Pavel V. Sevastjanov,et al.  Fuzzy modeling of manufacturing and logistic systems , 2003, Math. Comput. Simul..

[26]  Richard Y. K. Fung,et al.  Fuzzy modelling and simulation for aggregate production planning , 2003, Int. J. Syst. Sci..

[27]  Manoranjan Maiti,et al.  Fuzzy inventory model with two warehouses under possibility constraints , 2006, Fuzzy Sets Syst..

[28]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[29]  Siegfried Gottwald,et al.  Fuzzy Sets and Fuzzy Logic , 1993 .

[30]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[31]  Samir Allet,et al.  Handling flexibility in a "generalised job shop" with a fuzzy approach , 2003, Eur. J. Oper. Res..

[32]  H. Peck.  Drivers of supply chain vulnerability: an integrated framework , 2005 .