Fuzzy multiple objective fractional optimization in rough approximation and its aptness to the fixed-charge transportation problem
RAIRO. Operations Research, Tome 55 (2021) no. 3, pp. 1715-1741

This article presents a multiple objective fractional fixed-charge transportation problem (MFFTP) in a rough decision-making framework. A transformation procedure is modified to convert non-linear multi-objective transportation problem to its linear version. The parameters of the designed model are considered to be fuzzy. We employ separate kinds of fuzzy scale, i.e., possibility, credibility and necessity measures, to deal with the fuzzy parameters. Using the fuzzy chance-constrained rough approximation (FCRA) technique, we extract the more preferable optimal solution from our suggested MFFTP. The initial result is compared with that of the robust ranking (RR) technique. We also use the theory of rough sets for expanding as well as dividing the feasible domain of the MFFTP to accommodate more information by considering two approximations. Employing these approximations, we introduce two variants, namely, the lower approximation (LA) and the upper approximation (UA), of the suggested MFFTP. Finally, by using these models, we provide the optimal solutions for our proposed problem. We also associate our MFFTP with a real-world example to showcase its applicability as well as performance. Our core concept of this article is that it tackles an MFFTP using two separate kinds of uncertainty and expands its feasible domain for optimal solutions. Optimal solutions of the designed model (obtained from FCRA technique) belong to two separate regions, namely, “surely region” and “possible region”. The optimal solution which belongs to the “surely region” is better (as these are minimum values) than the one in the “possible region” and other cases. An interpretation of our approach along with offers about the intended future research work are provided at last.

DOI : 10.1051/ro/2021078
Classification : 90C32, 90C29, 90C08, 90C70
Keywords: Fractional programming, fixed-charge transportation problem, rough programming, fuzzy programming, robust ranking technique, fuzzy chance-constrained rough technique
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     journal = {RAIRO. Operations Research},
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Midya, Sudipta; Kumar Roy, Sankar; Wilhelm Weber, Gerhard. Fuzzy multiple objective fractional optimization in rough approximation and its aptness to the fixed-charge transportation problem. RAIRO. Operations Research, Tome 55 (2021) no. 3, pp. 1715-1741. doi: 10.1051/ro/2021078

[1] Y. Almogy and O. Levin, The fractional fixed charge problem. Nav. Res. Logist. Q. 18 (1971) 307–315. | MR | Zbl | DOI

[2] P. Anukokila, B. Radhakrishnan and A. Anju, Goal programming approach for solving multi-objective fractional transportation problem with fuzzy parameters. RAIRO-Oper. Res. 53 (2019) 157–178. | MR | Zbl | Numdam | DOI

[3] R. Arya, P. Singh, S. Kumari and M. S. Obaidat, An approach for solving fully fuzzy multi-objective linear fractional optimization problems. Soft Comput. 24 (2020) 9105–9119. | DOI

[4] R. E. Bellman and L. A. Zadeh, Decision making in fuzzy environment. Manage. Sci. 17 (1970) 141–164. | MR | Zbl | DOI

[5] D. Bhati and P. Singh, Branch and bound computational method for multi-objective linear fractional optimization problem. Neural Comput. Appl. 28 (2017) 3341–3351. | DOI

[6] A. Charnes and W. W. Cooper, Programming with linear fractional functionals. Nav. Res. Logist. Q. 9 (1962) 181–186. | MR | Zbl | DOI

[7] M. Chakraborty and S. Gupta, Fuzzy mathematical programming for multi objective linear fractional programming problem. Fuzzy Set. Syst. 125 (2002) 335–342. | MR | Zbl | DOI

[8] C.-T. Chang, Fuzzy linearization strategy for multiple objective linear fractional programming with binary utility functions. Comput. Ind. Eng. 112 (2017) 437–446. | DOI

[9] S. K. Das, S. A. Edalatpanah and T. Mandal, A proposed model for solving fuzzy linear fractional programming problem: Numerical point of view. J. Comput. Sci. 25 (2018) 367–375. | MR | DOI

[10] D. Dubois, H. Prade, H. Farreny, R. Martin-Clouaire, C. Testemale and E. Harding, Possibility theory. Plenum Press, New York (1988). | MR

[11] A. Ebrahimnejad, S. J. Ghomi and S. M. Mirhosseini-Alizamini, A revisit of numerical approach for solving linear fractional programming problem in a fuzzy environment. Appl. Math. Model. 57 (2018) 459–473. | MR | DOI

[12] S. Ghosh, S. K. Roy, A. Ebrahimnejad and J.-L. Verdegay, Multi-objective fully intuitionistic fuzzy fixed-charge solid transportation problem. Complex Intell. Syst. 7 (2021) 1009–1023. | DOI

[13] S. Ghosh and S. K. Roy, Fuzzy-rough multi-objective product blending fixed-charge transportation problem with truck load constraints through transfer station. RAIRO-Oper. Res. 55 (2021) S2923–S2952. | MR | DOI

[14] A. Goli, H. K. Zara, T. R. Moghaddam and A. Sadegheih, Multiobjective fuzzy mathematical model for a financially constrained closed-loop supply chain with labor employment. Comput. Intell. 36 (2020) 4–34. | MR | DOI

[15] A. Goli, H. K. Zare, T. R. Moghaddam and A. Sadegheih, Hybrid artificial intelligence and robust optimization for a multiobjective product portfolio problem Case study: The dairy products industry. Comput. Ind. Eng. 137 (2019) 106090. | DOI

[16] A. Goli and B. Malmir, A covering tour approach for disaster relief locating and routing with fuzzy demand. Int. J. Intell. Transp. Syst. Res. 18 (2020) 140–152.

[17] A. Goli, E. B. Tirkolaee and N. S. Aydin, Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors. IEEE Trans. Fuzzy Syst. (2020). | DOI

[18] W. M. Hirsch and G. B. Dantzig, The fixed charge problem. Nav. Res. Logist. Q. 15 (1968) 413–424. | MR | Zbl | DOI

[19] H. Jiao and S. Liu, A new linearization technique for minmax linear fractional programming. Int. J. Comput. Math. 91 (2014) 1730–1743. | MR | Zbl | DOI

[20] P. Kaur, V. Verma and K. Dahiya, Capacitated two-stage time minimization transportation problem with restricted flow. RAIRO-Oper. Res. 51 (2017) 447–467. | MR | Zbl | Numdam | DOI

[21] L. Li and K. K. Lai, A fuzzy approach to the multi-objective transportation problem. Comput. Oper. Res. 27 (2000) 43–57. | MR | Zbl | DOI

[22] B. Liu, Theory and practice of uncertain programming. Physica-Verlag, Heidelberg (2002). | Zbl | DOI

[23] B. Liu and K. Iwamura, Chance constrained programming with fuzzy parameters. Fuzzy Set. Syst. 94 (1998) 227–237. | MR | Zbl | DOI

[24] B. Liu and Y. K. Liu, Expected value of fuzzy variable and fuzzy expected value model. IEEE Trans. Fuzzy Syst. 10 (2002) 445–450. | DOI

[25] A. Mahmoodirad, R. Dehghan and S. Niroomand, Modelling linear fractional transportation problem in belief degree based uncertain environment. J. Exp. Theor. Artif. Intell. 31 (2018) 1–16.

[26] G. Maity and S. K. Roy, Solving a multi-objective transportation problem with nonlinear cost and multi-choice demand. Int. J. Manag. Sci. Eng. Manag. 11 (2016) 62–70.

[27] G. Maity, D. Mardanya, S. K. Roy and G. W. Weber, A new approach for solving dual-hesitant fuzzy transportation problem with restrictions. Sadhana 44 (2019) 1–11. | DOI | MR

[28] S. Midya and S. K. Roy, Solving single-sink fixed-charge multi-objective multi-index stochastic transportation problem. Am. J. Math. Manag. Sci. 33 (2014) 300–314.

[29] S. Midya and S. K. Roy, Analysis of interval programming in different environments and its application to fixed-charge transportation problem. Discrete Math. Algorithm Appl. 9 (2017) 750040. | MR | DOI

[30] S. Midya and S. K. Roy, Multi-objective fixed-charge transportation problem using rough programming. Int. J. Oper. Res. 37 (2020) 377–395. | MR | DOI

[31] S. Midya, S. K. Roy and V. F. Yu, Intuitionistic fuzzy multi-stage multi-objective fixed-charge solid transportation problem in a green supply chain. Int. J. Mach. Learn. Cybern. 12 (2021) 699–717. | DOI

[32] S. Mishra, Weighting method for bi-level fractional programming problems. Eur. J. Oper. Res. 183 (2007) 296–302. | Zbl | DOI

[33] B. Mishra, K. A. Nishad and S. R. Singh, Fuzzy multi-fractional programming for land use planning in agricultural production system. Fuzzy Info. Eng. 6 (2014) 245–262. | MR | DOI

[34] T. Paksoy, N. Y. Pehlivan, E. Özceylan, Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer. Appl. Math. Model. 36 (2012) 2762–2776. | MR | Zbl | DOI

[35] A. Paul, M. Pervin, S. K. Roy, G. W. Weber and A. Mirzazadeh, Effect of price-sensitive demand and default risk on optimal credit period and cycle time for a deteriorating inventory model. RAIRO-Oper. Res. 55 (2021) S2575–S2592. | MR | DOI

[36] Z. Pawlak, Rough sets. Int. J. Info. Comput. Sci. 11 (1982) 341–356. | MR | Zbl | DOI

[37] S. K. Roy, G. Maity, G. W. Weber and S. Z. A. Gök, Conic scalarization approach to solve multi-choice multi-objective transportation problem with interval Goal. Ann. Oper. Res. 253 (2017) 599–620. | MR | DOI

[38] S. K. Roy, S. Midya and V. F. Yu, Multi-objective fixed-charge transportation problem with random rough variables. Int. J. Uncertain. Fuzziness Knowledge-Based Syst. 26 (2018) 971–996. | MR | DOI

[39] S. K. Roy and S. Midya, Multi-objective fixed-charge solid transportation problem with product blending under intuitionistic fuzzy environment. Appl. Intell. 49 (2019) 3524–3538. | DOI

[40] S. K. Roy, S. Midya and G. W. Weber, Multi-objective multi-item fixed-charge solid transportation problem under twofold uncertainty. Neural Comput. Appl. 31 (2019) 8593–8613. | DOI

[41] S. Sagratella, M. Schmidt and N. Sudermann-Merx, The noncooperative fixed charge transportation problem. Eur. J. Oper. Res. 284 (2019) 373–382. | MR | DOI

[42] S. Schaible, Fractional programming I: Duality. Manag. Sci. 22 (1976) 858–867. | MR | Zbl | DOI

[43] M. Sivri, I. Emiroglu, C. Guler and F. Tasci, A solution proposal to the transportation problem with the linear fractional objective function. In: Proc. of the 4th IEEE International Conference on Modeling, Simulation and Applied Optimization, Kuala Lumpur, Malaysia (2011).

[44] B. Stanojević and M. Stanojević, Comment on “Fuzzy mathematical programming for multi-objective linear fractional programming problem”. Fuzzy Set. Syst. 246 (2014) 156–159. | MR | DOI

[45] Z. Tao and J. Xu, A class of rough multiple objective programming and its application to solid transportation problem. Info. Sci. 188 (2012) 215–235. | MR | Zbl | DOI

[46] M. D. Toksari, Taylor series approach to fuzzy multi-objective linear fractional programming. Info. Sci. 178 (2008) 1189–1204. | MR | Zbl | DOI

[47] M. Upmanyu and R. R. Saxena, On solving a multi-objective fixed charge problem with imprecise fractional objectives. Appl. Soft Comput. 40 (2016) 64–69. | DOI

[48] E. B. Tirkolaee, A. Goli, A. Faridnia, M. Soltani and G. W. Weber, Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms. J. Clean. Prod. 276 (2020). | DOI

[49] P. Vasant, G. W. Weber and V. N. Dieu, Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics. IGI Global, Hershey, PA (2016). | DOI | MR

[50] C. Veeramani and M. Sumathi, Fuzzy mathematical programming approach for solving fuzzy linear fractional programming problem. RAIRO-Oper. Res. 48 (2014) 109–122. | MR | Zbl | Numdam | DOI

[51] F. Xie and R. Jia, Nonlinear fixed charge transportation problem by minimum cost flow-based genetic algorithm. Comput. Ind. Eng. 63 (2012) 763–778. | DOI

[52] J. Xu and Z. Tao, Rough multiple objective decision making. Taylor and Francis Group, CRC Press, USA (2012). | MR

[53] J. Xu and L. Zhao, A class of fuzzy rough expected value multi-objective decision making model and its application to inventory problems. Comput. Math. Appl. 56 (2008) 2107–2119. | MR | Zbl | DOI

[54] R. R. Yager, A procedure for ordering fuzzy subsets of the unit interval. Info. Sci. 24 (1981) 143–161. | MR | Zbl | DOI

[55] L. A. Zadeh, Fuzzy sets. Info. Control 8 (1965) 338–353. | MR | Zbl | DOI

[56] L. A. Zadeh, Fuzzy sets as a basic for theory of possibility. Fuzzy Set. Syst. 1 (1978) 3–28. | MR | Zbl | DOI

[57] H. J. Zimmermann, Fuzzy programming and linear programming with several objective functions. Fuzzy Set. Syst. 1 (1978) 45–55. | MR | Zbl | DOI

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