Research Article
BibTex RIS Cite
Year 2019, Volume: 48 Issue: 1, 112 - 139, 01.02.2019

Abstract

References

  • S.M. Aljazzar, A. Gurtu and M.Y. Jaber, Delay in payments- A strategy to reduce carbon emissions from supply chains, J. Clean. Prod. 170, 636-644, 2018.
  • T. Allahviranloo and M. Afsher Kermani, Numerical methods for fuzzy linear partial differential equations under new definition for derivative, Iran. J. Fuzzy Syst. 7 (3), 33-50, 2010.
  • D. Battini, A. Persona and F. Sgarbossa, A Sustainable EOQ model: Theoretical formulation and applications, Int. J. Prod. Econ. 149, 145-153, 2014.
  • B. Bede, A note on two-point boundary value problems associated with non-linear fuzzy differential equations, Fuzzy Sets and Systems 157, 986-989, 2006.
  • B. Bede and S.G. Gal, Generalizations of the differentiability of fuzzy-number-valued functions with applications to fuzzy differential equations, Fuzzy Sets and Systems 151, 581-599, 2005.
  • S.C. Chang, Fuzzy production inventory for fuzzy product quantity with triangular fuzzy number, Fuzzy Sets and Systems 107, 37-57, 1999.
  • Y. Chen and L. Zhang, Some new results about arithmetic of type-2 fuzzy variables, Journal of Uncertain System 5 (3), 227-240, 2011.
  • D. Dubois and H. Prade, Operations on fuzzy numbers, Int. J. Syst. Sci. 9 (6), 613-626, 1978.
  • R. Ezzati, K. Maleknejad and S. Khezertoo, Convergence, Consistency and stability in fuzzy differential equations, Iran. J. Fuzzy Syst. 12 (3), 95-112, 2015.
  • P. Guchhait, M.K. Maiti and M. Maiti, A production inventory model with fuzzy production and demand using fuzzy differential equation: An interval compared genetic algorithm approach, Eng. Appl. Artif. Intell. 26, 766-778, 2013.
  • V. Hovelaque and L. Bironneau, The carbon-constrained EOQ model with carbon emission dependent demand, Int. J. Prod. Econ. 164, 285-291, 2015.
  • M. Jonas, M. Marland, W. Winiwarter, T. White, Z. Nahorski, R. Bun and S. Nilsson, Benefits of dealing with uncertainty in greenhouse gas inventories: introduction, Climatic Change 103 (1-2), 175-213, 2010b.
  • A. Kandel and W.J. Byatt, Fuzzy differential equations, In Proceedings of the International Conference on Cybernetics and Society, Tokyo, 1213-1216, 1978.
  • N. Kazemi, S.H. Abdul-Rashid, R.A.R. Ghazila, E. Shekarian and S. Zanoni, Economic order quantity models for items with imperfect quality and emission considerations, Int. J. Syst. Sci.: Oper. & Logist. 5 (2), 99-115, 2018.
  • P. Kundu, S. Kar and M. Maiti, Fixed charge transportation problem with type-2 fuzzy variables, Inf. Sci. 255, 170-184, 2014.
  • P. Kundu, S. Kar and M. Maiti, Multi-item solid transportation problem with type-2 fuzzy parameters, Appl. Soft Comput. 31, 61-80, 2015.
  • H.M. Lee and J.S. Yao, Economic production quantity for fuzzy demand quantity and fuzzy production quantity, European J. Oper. Res. 109, 203-211, 1998.
  • D.C. Lin and J.S. Yao, Fuzzy economic production for production inventory, Fuzzy Sets and Systems 111, 465-495, 2000.
  • Z.Q. Liu and Y.K. Liu, Type-2 fuzzy variables and their arithmetic, Soft Computing 14, 729-747, 2010.
  • P. Majumder, S.P. Mondal, U.K. Bera and M. Maiti, Application of Generalized Hukuhara derivative approach in an economic production quantity model with partial trade credit policy under fuzzy environment, Operations Research Perspective 3, 77- 91, 2016.
  • S. Miller, M. Gongora and R. John, Interval type-2 fuzzy modeling and simulated annealing for real world inventory management, International Conference on Hybrid Artificial Intelligence Systems, 231-238, Springer-Verlag Bertin, Heidelberg, 2011.
  • M. Mizumoto and K. Tanaka, Fuzzy sets of type-2 under algebraic product and algebraic sum, Fuzzy Sets and Systems 5 (3), 277-280, 1981.
  • R. Qin, Y.K. Liu and Z.Q. Liu, Methods of critical value reduction for type-2 fuzzy variables and their applications, J. Comput. Appl. Math. 235, 1454-1481, 2011.
  • P. Rajarajeswari, A.S. Sudha and R. Karthika, A New Operation on Hexagonal Fuzzy Number, Int. J. Fuzzy Log. Syst. 3 (3), 15-26, 2013.
  • J. Sadeghi, S.T.A. Niaki, M. Malekian and Y. Wang, A Lagrangian relaxation for a fuzzy random EPQ problem with shortages and redundancy allocation: two tuned meta-heuristics, Int. J. Fuzzy Syst. 20 (2), 515-533, 2018.
  • S. Sharan, S.P. Tiwary and V.K. Yadav, Interval type-2 fuzzy rough sets and interval type-2 fuzzy closure spaces, Iran. J. Fuzzy Syst. 12 (3), 113-125, 2015.
  • E. Shekarian, C.H. Glock, S.M.P. Amiri and K. Schwindl, Optimal manufacturing lot size for a single-stage production system with rework in a fuzzy environment, J. Intell. Fuzzy Syst. 27 (6), 3067-3080, 2014.
  • E. Shekarian, M.Y. Jaber, N. Kazemi and E. Ehsani, A fuzzified version of the economic production quantity (EPQ) model with backorders and rework for a single stage system, Eur. J. Ind. Eng. 8 (3), 291-324, 2014.
  • E. Shekarian, N. Kazemi, S.H. Abdul-Rashid and E.U. Olugu, Fuzzy inventory models: A comprehensive review, Appl. Soft Comput. 55, 588-621, 2017.
  • E. Shekarian, E.U. Olugu, S.H. Abdul-Rashid and E. Bottani, A fuzzy reverse logistics inventory system integrating economic order/production quantity models, Int. J. Fuzzy Syst. 18 (6), 1141-1161, 2016.
  • E. Shekarian, E.U. Olugu, S.H. Abdul-Rashid and N. Kazemi, An economic order quantity model considering different holding costs for imperfect quality items subject to fuzziness and learning, J. Intell. Fuzzy Syst. 30 (5), 1985-2997, 2016.
  • H.N. Soni, B. Sarkar and M. Joshi, Demand uncertainty and learning in fuzziness in a continuous review inventory model, J. Intell. Fuzzy Syst. 33 (4), 2595-2608, 2017.
  • L. Stefanini and B. Bede, Generalized Hukuhara differentiability of interval-valued functions and interval differential equations, Nonlinear Anal. 71, 1311-1328, 2009.
  • J.R. Stock, S.L. Boyer and T. Harmon, Research opportunities in supply chain management, J. Acad. Mark. Sci. 38 (1), 32-41, 2010.
  • Z. Takac, Inclusion and subsethood measure for interval-valued fuzzy sets and for continuous type-2 fuzzy sets, Fuzzy Sets and Systems 224, 106-120, 2013.
  • E.J. Villamizar-Roa, V. Angulo-Castilo and Y. Chaleo-Cano, Existence of solutions to fuzzy differential equation with generalized Hukuhara derivative via contractive-like mapping principles, Fuzzy Sets and Systems 265, 24-38, 2015.
  • H.C. Wu, The central limit theorems for fuzzy random variables, Inf. Sci. 120, 239- 256, 1999.
  • L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning - I, Inf. Sci. 8, 199-249, 1975.
  • L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning - II, Inf. Sci. 8, 301-357, 1975.

Multi-objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model with Demand as Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach

Year 2019, Volume: 48 Issue: 1, 112 - 139, 01.02.2019

Abstract

A sustainable fuzzy economic production quantity (SFEPQ) inventory model is formulated by introducing the concept of fuzzy differential equation (FDE) due to dynamic behavior of the production-demand system. Generalized Hukuhara (gH) differentiability proceedure is applied to solve FDE. Since the demand parameter is taken as trapezoidal type-2 fuzzy number, to get corresponding defuzzified values, first critical value (CV)-based reduction method is applied on demand function to transfer into type-1 fuzzy variable which turns to hexagonal fuzzy number in form. After that $\alpha$-cut of a hexagonal fuzzy number is used to find the upper and lower value of demand. To apply the $\alpha$-cut operation on FDE, we divided the interval [0,1] into two sub-intervals [0,0.5] and [0.5,1] and gH-differentiation is applied on this sub-intervals. The objective of this paper is to maximize the profit and simultaneously minimize the carbon emission cost occurring due to the process of inventory management. Finally, the non-linear objective functions are solved by using of multi-objective genetic algorithm and sensitivity analyses on various parameters are also performed in numerically and graphically.

References

  • S.M. Aljazzar, A. Gurtu and M.Y. Jaber, Delay in payments- A strategy to reduce carbon emissions from supply chains, J. Clean. Prod. 170, 636-644, 2018.
  • T. Allahviranloo and M. Afsher Kermani, Numerical methods for fuzzy linear partial differential equations under new definition for derivative, Iran. J. Fuzzy Syst. 7 (3), 33-50, 2010.
  • D. Battini, A. Persona and F. Sgarbossa, A Sustainable EOQ model: Theoretical formulation and applications, Int. J. Prod. Econ. 149, 145-153, 2014.
  • B. Bede, A note on two-point boundary value problems associated with non-linear fuzzy differential equations, Fuzzy Sets and Systems 157, 986-989, 2006.
  • B. Bede and S.G. Gal, Generalizations of the differentiability of fuzzy-number-valued functions with applications to fuzzy differential equations, Fuzzy Sets and Systems 151, 581-599, 2005.
  • S.C. Chang, Fuzzy production inventory for fuzzy product quantity with triangular fuzzy number, Fuzzy Sets and Systems 107, 37-57, 1999.
  • Y. Chen and L. Zhang, Some new results about arithmetic of type-2 fuzzy variables, Journal of Uncertain System 5 (3), 227-240, 2011.
  • D. Dubois and H. Prade, Operations on fuzzy numbers, Int. J. Syst. Sci. 9 (6), 613-626, 1978.
  • R. Ezzati, K. Maleknejad and S. Khezertoo, Convergence, Consistency and stability in fuzzy differential equations, Iran. J. Fuzzy Syst. 12 (3), 95-112, 2015.
  • P. Guchhait, M.K. Maiti and M. Maiti, A production inventory model with fuzzy production and demand using fuzzy differential equation: An interval compared genetic algorithm approach, Eng. Appl. Artif. Intell. 26, 766-778, 2013.
  • V. Hovelaque and L. Bironneau, The carbon-constrained EOQ model with carbon emission dependent demand, Int. J. Prod. Econ. 164, 285-291, 2015.
  • M. Jonas, M. Marland, W. Winiwarter, T. White, Z. Nahorski, R. Bun and S. Nilsson, Benefits of dealing with uncertainty in greenhouse gas inventories: introduction, Climatic Change 103 (1-2), 175-213, 2010b.
  • A. Kandel and W.J. Byatt, Fuzzy differential equations, In Proceedings of the International Conference on Cybernetics and Society, Tokyo, 1213-1216, 1978.
  • N. Kazemi, S.H. Abdul-Rashid, R.A.R. Ghazila, E. Shekarian and S. Zanoni, Economic order quantity models for items with imperfect quality and emission considerations, Int. J. Syst. Sci.: Oper. & Logist. 5 (2), 99-115, 2018.
  • P. Kundu, S. Kar and M. Maiti, Fixed charge transportation problem with type-2 fuzzy variables, Inf. Sci. 255, 170-184, 2014.
  • P. Kundu, S. Kar and M. Maiti, Multi-item solid transportation problem with type-2 fuzzy parameters, Appl. Soft Comput. 31, 61-80, 2015.
  • H.M. Lee and J.S. Yao, Economic production quantity for fuzzy demand quantity and fuzzy production quantity, European J. Oper. Res. 109, 203-211, 1998.
  • D.C. Lin and J.S. Yao, Fuzzy economic production for production inventory, Fuzzy Sets and Systems 111, 465-495, 2000.
  • Z.Q. Liu and Y.K. Liu, Type-2 fuzzy variables and their arithmetic, Soft Computing 14, 729-747, 2010.
  • P. Majumder, S.P. Mondal, U.K. Bera and M. Maiti, Application of Generalized Hukuhara derivative approach in an economic production quantity model with partial trade credit policy under fuzzy environment, Operations Research Perspective 3, 77- 91, 2016.
  • S. Miller, M. Gongora and R. John, Interval type-2 fuzzy modeling and simulated annealing for real world inventory management, International Conference on Hybrid Artificial Intelligence Systems, 231-238, Springer-Verlag Bertin, Heidelberg, 2011.
  • M. Mizumoto and K. Tanaka, Fuzzy sets of type-2 under algebraic product and algebraic sum, Fuzzy Sets and Systems 5 (3), 277-280, 1981.
  • R. Qin, Y.K. Liu and Z.Q. Liu, Methods of critical value reduction for type-2 fuzzy variables and their applications, J. Comput. Appl. Math. 235, 1454-1481, 2011.
  • P. Rajarajeswari, A.S. Sudha and R. Karthika, A New Operation on Hexagonal Fuzzy Number, Int. J. Fuzzy Log. Syst. 3 (3), 15-26, 2013.
  • J. Sadeghi, S.T.A. Niaki, M. Malekian and Y. Wang, A Lagrangian relaxation for a fuzzy random EPQ problem with shortages and redundancy allocation: two tuned meta-heuristics, Int. J. Fuzzy Syst. 20 (2), 515-533, 2018.
  • S. Sharan, S.P. Tiwary and V.K. Yadav, Interval type-2 fuzzy rough sets and interval type-2 fuzzy closure spaces, Iran. J. Fuzzy Syst. 12 (3), 113-125, 2015.
  • E. Shekarian, C.H. Glock, S.M.P. Amiri and K. Schwindl, Optimal manufacturing lot size for a single-stage production system with rework in a fuzzy environment, J. Intell. Fuzzy Syst. 27 (6), 3067-3080, 2014.
  • E. Shekarian, M.Y. Jaber, N. Kazemi and E. Ehsani, A fuzzified version of the economic production quantity (EPQ) model with backorders and rework for a single stage system, Eur. J. Ind. Eng. 8 (3), 291-324, 2014.
  • E. Shekarian, N. Kazemi, S.H. Abdul-Rashid and E.U. Olugu, Fuzzy inventory models: A comprehensive review, Appl. Soft Comput. 55, 588-621, 2017.
  • E. Shekarian, E.U. Olugu, S.H. Abdul-Rashid and E. Bottani, A fuzzy reverse logistics inventory system integrating economic order/production quantity models, Int. J. Fuzzy Syst. 18 (6), 1141-1161, 2016.
  • E. Shekarian, E.U. Olugu, S.H. Abdul-Rashid and N. Kazemi, An economic order quantity model considering different holding costs for imperfect quality items subject to fuzziness and learning, J. Intell. Fuzzy Syst. 30 (5), 1985-2997, 2016.
  • H.N. Soni, B. Sarkar and M. Joshi, Demand uncertainty and learning in fuzziness in a continuous review inventory model, J. Intell. Fuzzy Syst. 33 (4), 2595-2608, 2017.
  • L. Stefanini and B. Bede, Generalized Hukuhara differentiability of interval-valued functions and interval differential equations, Nonlinear Anal. 71, 1311-1328, 2009.
  • J.R. Stock, S.L. Boyer and T. Harmon, Research opportunities in supply chain management, J. Acad. Mark. Sci. 38 (1), 32-41, 2010.
  • Z. Takac, Inclusion and subsethood measure for interval-valued fuzzy sets and for continuous type-2 fuzzy sets, Fuzzy Sets and Systems 224, 106-120, 2013.
  • E.J. Villamizar-Roa, V. Angulo-Castilo and Y. Chaleo-Cano, Existence of solutions to fuzzy differential equation with generalized Hukuhara derivative via contractive-like mapping principles, Fuzzy Sets and Systems 265, 24-38, 2015.
  • H.C. Wu, The central limit theorems for fuzzy random variables, Inf. Sci. 120, 239- 256, 1999.
  • L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning - I, Inf. Sci. 8, 199-249, 1975.
  • L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning - II, Inf. Sci. 8, 301-357, 1975.
There are 39 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Mathematics
Authors

B. K. Debnath This is me

P. Majumder

U. K. Bera This is me

Publication Date February 1, 2019
Published in Issue Year 2019 Volume: 48 Issue: 1

Cite

APA Debnath, B. K., Majumder, P., & Bera, U. K. (2019). Multi-objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model with Demand as Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach. Hacettepe Journal of Mathematics and Statistics, 48(1), 112-139.
AMA Debnath BK, Majumder P, Bera UK. Multi-objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model with Demand as Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach. Hacettepe Journal of Mathematics and Statistics. February 2019;48(1):112-139.
Chicago Debnath, B. K., P. Majumder, and U. K. Bera. “Multi-Objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model With Demand As Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach”. Hacettepe Journal of Mathematics and Statistics 48, no. 1 (February 2019): 112-39.
EndNote Debnath BK, Majumder P, Bera UK (February 1, 2019) Multi-objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model with Demand as Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach. Hacettepe Journal of Mathematics and Statistics 48 1 112–139.
IEEE B. K. Debnath, P. Majumder, and U. K. Bera, “Multi-objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model with Demand as Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach”, Hacettepe Journal of Mathematics and Statistics, vol. 48, no. 1, pp. 112–139, 2019.
ISNAD Debnath, B. K. et al. “Multi-Objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model With Demand As Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach”. Hacettepe Journal of Mathematics and Statistics 48/1 (February 2019), 112-139.
JAMA Debnath BK, Majumder P, Bera UK. Multi-objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model with Demand as Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach. Hacettepe Journal of Mathematics and Statistics. 2019;48:112–139.
MLA Debnath, B. K. et al. “Multi-Objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model With Demand As Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach”. Hacettepe Journal of Mathematics and Statistics, vol. 48, no. 1, 2019, pp. 112-39.
Vancouver Debnath BK, Majumder P, Bera UK. Multi-objective Sustainable Fuzzy Economic Production Quantity (SFEPQ) Model with Demand as Type-2 Fuzzy Number: A Fuzzy Differential Equation Approach. Hacettepe Journal of Mathematics and Statistics. 2019;48(1):112-39.