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Research article

Extended DEA method for solving multi-objective transportation problem with Fermatean fuzzy sets

  • Received: 26 July 2022 Revised: 12 September 2022 Accepted: 15 September 2022 Published: 13 October 2022
  • MSC : 90C32, 90C70

  • Data envelopment analysis (DEA) is a linear programming approach used to determine the relative efficiencies of multiple decision-making units (DMUs). A transportation problem (TP) is a special type of linear programming problem (LPP) which is used to minimize the total transportation cost or maximize the total transportation profit of transporting a product from multiple sources to multiple destinations. Because of the connection between the multi-objective TP (MOTP) and DEA, DEA-based techniques are more often used to handle practical TPs. The objective of this work is to investigate the TP with Fermatean fuzzy costs in the presence of numerous conflicting objectives. In particular, a Fermatean fuzzy DEA (FFDEA) method is proposed to solve the Fermatean fuzzy MOTP (FFMOTP). In this regard, every arc in FFMOTP is considered a DMU. Additionally, those objective functions that should be maximized will be used to define the outputs of DMUs, while those that should be minimized will be used to define the inputs of DMUs. As a consequence, two different Fermatean fuzzy effciency scores (FFESs) will be obtained for every arc by solving the FFDEA models. Therefore, unique FFESs will be obtained for every arc by finding the mean of these FFESs. Finally, the FFMOTP will be transformed into a single objective Fermatean fuzzy TP (FFTP) that can be solved by applying standard algorithms. A numerical example is illustrated to support the proposed method, and the results obtained by using the proposed method are compared to those of existing techniques. Moreover, the advantages of the proposed method are also discussed.

    Citation: Muhammad Akram, Syed Muhammad Umer Shah, Mohammed M. Ali Al-Shamiri, S. A. Edalatpanah. Extended DEA method for solving multi-objective transportation problem with Fermatean fuzzy sets[J]. AIMS Mathematics, 2023, 8(1): 924-961. doi: 10.3934/math.2023045

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  • Data envelopment analysis (DEA) is a linear programming approach used to determine the relative efficiencies of multiple decision-making units (DMUs). A transportation problem (TP) is a special type of linear programming problem (LPP) which is used to minimize the total transportation cost or maximize the total transportation profit of transporting a product from multiple sources to multiple destinations. Because of the connection between the multi-objective TP (MOTP) and DEA, DEA-based techniques are more often used to handle practical TPs. The objective of this work is to investigate the TP with Fermatean fuzzy costs in the presence of numerous conflicting objectives. In particular, a Fermatean fuzzy DEA (FFDEA) method is proposed to solve the Fermatean fuzzy MOTP (FFMOTP). In this regard, every arc in FFMOTP is considered a DMU. Additionally, those objective functions that should be maximized will be used to define the outputs of DMUs, while those that should be minimized will be used to define the inputs of DMUs. As a consequence, two different Fermatean fuzzy effciency scores (FFESs) will be obtained for every arc by solving the FFDEA models. Therefore, unique FFESs will be obtained for every arc by finding the mean of these FFESs. Finally, the FFMOTP will be transformed into a single objective Fermatean fuzzy TP (FFTP) that can be solved by applying standard algorithms. A numerical example is illustrated to support the proposed method, and the results obtained by using the proposed method are compared to those of existing techniques. Moreover, the advantages of the proposed method are also discussed.



    In 1922, S. Banach [15] provided the concept of Contraction theorem in the context of metric space. After, Nadler [28] introduced the concept of set-valued mapping in the module of Hausdroff metric space which is one of the potential generalizations of a Contraction theorem. Let (X,d) is a complete metric space and a mapping T:XCB(X) satisfying

    H(T(x),T(y))γd(x,y)

    for all x,yX, where 0γ<1, H is a Hausdorff with respect to metric d and CB(X)={SX:S is closed and bounded subset of X equipped with a metric d}. Then T has a fixed point in X.

    In the recent past, Matthews [26] initiate the concept of partial metric spaces which is the classical extension of a metric space. After that, many researchers generalized some related results in the frame of partial metric spaces. Recently, Asadi et al. [4] introduced the notion of an M-metric space which is the one of interesting generalizations of a partial metric space. Later on, Samet et al. [33] introduced the class of mappings which known as (α,ψ)-contractive mapping. The notion of (α,ψ) -contractive mapping has been generalized in metric spaces (see more [10,12,14,17,19,25,29,30,32]).

    Throughout this manuscript, we denote the set of all positive integers by N and the set of real numbers by R. Let us recall some basic concept of an M-metric space as follows:

    Definition 1.1. [4] Let m:X×XR+be a mapping on nonempty set X is said to be an M-metric if for any x,y,z in X, the following conditions hold:

    (i) m(x,x)=m(y,y)=m(x,y) if and only if x=y;

    (ii) mxym(x,y);

    (iii) m(x,y)=m(y,x);

    (iv) m(x,y)mxy(m(x,z)mxz)+(m(z,y)mz,y) for all x,y,zX. Then a pair (X,m) is called M-metric space. Where

    mxy=min{m(x,x),m(y,y)}

    and

    Mxy=max{m(x,x),m(y,y)}.

    Remark 1.2. [4] For any x,y,z in M-metric space X, we have

    (i) 0Mxy+mxy=m(x,x)+m(y,y);

    (ii) 0Mxymxy=|m(x,x)m(y,y)|;

    (iii) Mxymxy(Mxzmxz)+(Mzymzy).

    Example 1.3. [4] Let (X,m) be an M-metric space. Define mw, ms:X×XR+ by:

    (i)

    mw(x,y)=m(x,y)2mx,y+Mx,y,

    (ii)

    ms={m(x,y)mx,y, if xy0, if x=y.

    Then mw and ms are ordinary metrics. Note that, every metric is a partial metric and every partial metric is an M-metric. However, the converse does not hold in general. Clearly every M-metric on X generates a T0 topology τm on X whose base is the family of open M -balls

    {Bm(x,ϵ):xX, ϵ>0},

    where

    Bm(x,ϵ)={yX:m(x,y)<mxy+ϵ}

    for all xX, ε>0. (see more [3,4,23]).

    Definition 1.4. [4] Let (X,m) be an M-metric space. Then,

    (i) A sequence {xn} in (X,m) is said to be converges to a point x in X with respect to τm if and only if

    limn(m(xn,x)mxnx)=0.

    (ii) Furthermore, {xn} is said to be an M-Cauchy sequence in (X,m) if and only if

    limn,m(m(xn,xm)mxnxm), and limn,m(Mxn,xmmxnxm)

    exist (and are finite).

    (iii) An M-metric space (X,m) is said to be complete if every M-Cauchy sequence {xn} in (X,m) converges with respect to τm to a point xX such that

    limnm(xn,x)mxnx=0, and limn(Mxn,xmxnx)=0.

    Lemma 1.5. [4] Let (X,m) be an M-metric space. Then:

    (i) {xn} is an M-Cauchy sequence in (X,m) if and only if {xn} is a Cauchy sequence in a metric space (X,mw).

    (ii) An M-metric space (X,m) is complete if and only if the metric space (X,mw) is complete. Moreover,

    limnmw(xn,x)=0 if and only if (limn(m(xn,x)mxnx)=0, limn(Mxnxmxnx)=0).

    Lemma 1.6. [4] Suppose that {xn} convergesto x and {yn} converges to y as n approaches to in M-metric space (X,m). Then we have

    limn(m(xn,yn)mxnyn)=m(x,y)mxy.

    Lemma 1.7. [4] Suppose that {xn} converges to xas n approaches to in M-metric space (X,m).Then we have

    limn(m(xn,y)mxny)=m(x,y)mxy for all yX.

    Lemma 1.8. [4] Suppose that {xn} converges to xand {xn} converges to y as n approaches to in M-metric space (X,m). Then m(x,y)=mxymoreover if m(x,x)= m(y,y), then x=y.

    Definition 1.9. Let α:X×X[0,). A mapping T:XX is said to be an α-admissible mapping if for all x,yX

    α(x,y)1α(T(x),T(y))1.

    Let Ψ be the family of the (c)-comparison functions ψ:R+{0}R+{0} which satisfy the following properties:

    (i) ψ is nondecreasing,

    (ii) n=0ψn(t)< for all t>0, where ψn is the n-iterate of ψ (see [7,8,10,11]).

    Definition 1.10. [33] Let (X,d) be a metric space and α:X×X[0,). A mapping T:XX is called (α,ψ)-contractive mapping if for all x,yX, we have

    α(x,y)d(T(x),T(x))ψ(d(x,y)),

    where ψΨ.

    A subset K of an M-metric space X is called bounded if for all xK, there exist yX and r>0 such that xBm(y,r). Let ¯K denote the closure of K. The set K is closed in X if and only if ¯K=K.

    Definition 1.11. [31] Define Hm:CBm(X)×CBm(X)[0,) by

    Hm(K,L)=max{m(K,L),m(L,K)},

    where

    m(x,L)=inf{m(x,y):yL} andm(L,K)=sup{m(x,L):xK}

    Lemma 1.12. [31] Let F be any nonempty set in M-metric space (X,m), then

    x¯F if and only if m(x,F)=supaF{mxa}.

    Proposition 1.13. [31] Let A,B,CCBm(X), then

    (i) m(A,A)=supxA{supyAmxy},

    (ii) (m(A,B)supxAsupyBmxy)(m(A,C)infxAinfzCmxz)+

    (m(C,B)infzCinfyBmzy).

    Proposition 1.14. [31] Let A,B,CCBm(X) followingare hold

    (i) Hm(A,A)=m(A,A)=supxA{supyAmxy},

    (ii) Hm(A,B)=Hm(B,A),

    (iii) Hm(A,B)supxAsupyAmxy)Hm(A,C)+Hm(B,C)infxAinfzCmxzinfzCinfyBmzy.

    Lemma 1.15. [31] Let A,BCBm(X) and h>1.Then for each xA, there exist at the least one yB such that

    m(x,y)hHm(A,B).

    Lemma 1.16. [31] Let A,BCBm(X) and l>0.Then for each xA, there exist at least one yB such that

    m(x,y)Hm(A,B)+l.

    Theorem 1.17. [31] Let (X,m) be a complete M-metric space and T:XCBm(X). Assume that there exist h(0,1) such that

    Hm(T(x),T(y))hm(x,y), (1.1)

    for all x,yX. Then T has a fixed point.

    Proposition 1.18. [31] Let T:XCBm(X) be a set-valued mapping satisfying (1.1) for all x,y inan M-metric space X. If zT(z) for some z in Xsuch that m(x,x)=0 for xT(z).

    We start with the following definition:

    Definition 2.1. Assume that Ψ is a family of non-decreasing functions ϕM:R+R+ such that

    (i) +nϕnM(x)< for every x>0 where ϕnM is a nth-iterate of ϕM,

    (ii) ϕM(x+y)ϕM(x)+ϕM(y) for all x,yR+,

    (iii) ϕM(x)<x, for each x>0.

    Remark 2.2. If αn|n= =0 is a convergent series with positive terms then there exists a monotonic sequence (βn)|n= such that βn|n== and αnβn|n==0 converges.

    Definition 2.3. Let (X,m) be an M-metric pace. A self mapping T:XX is called (α,ϕM)-contraction if there exist two functions α:X×X[0,) and ϕMΨ such that

    α(x,y)m(T(x),T(y))ϕM(m(x,y)),

    for all x,yX.

    Definition 2.4. Let (X,m) be an M-metric space. A set-valued mapping T:XCBm(X) is said to be (α,ϕM)-contraction if for all x,yX, we have

    α(x,y)Hm(T(x),T(x))ϕM(m(x,y)), (2.1)

    where ϕMΨ and α:X×X[0,).

    A mapping T is called α-admissible if

    α(x,y)1α(a1,b1)1

    for each a1T(x) and b1T(y).

    Theorem 2.5. Let (X,m) be a complete M-metric space.Suppose that (α,ϕM) contraction and α-admissible mapping T:XCBm(X)satisfies the following conditions:

    (i) there exist x0X such that α(x0,a1)1 for each a1T(x0),

    (ii) if {xn}X is a sequence such that α(xn,xn+1)1 for all n and {xn}xX as n, then α(xn,x)1 for all nN. Then T has a fixed point.

    Proof. Let x1T(x0) then by the hypothesis (i) α(x0,x1)1. From Lemma 1.16, there exist x2T(x1) such that

    m(x1,x2)Hm(T(x0),T(x1))+ϕM(m(x0,x1)).

    Similarly, there exist x3T(x2) such that

    m(x2,x3)Hm(T(x1),T(x2))+ϕ2M(m(x0,x1)).

    Following the similar arguments, we obtain a sequence {xn}X such that there exist xn+1T(xn) satisfying the following inequality

    m(xn,xn+1)Hm(T(xn1),T(xn))+ϕnM(m(x0,x1)).

    Since T is α-admissible, therefore α(x0,x1)1α(x1,x2)1. Using mathematical induction, we get

    α(xn,xn+1)1. (2.2)

    By (2.1) and (2.2), we have

    m(xn,xn+1)Hm(T(xn1),T(xn))+ϕnM(m(x0,x1))α(xn,xn+1)Hm(T(xn1),T(xn))+ϕnM(m(x0,x1))ϕM(m(xn1,xn))+ϕnM(m(x0,x1))=ϕM[(m(xn1,xn))+ϕn1M(m(x0,x1))]ϕM[Hm(T(xn2),T(xn1))+ϕn1M(m(x0,x1))]ϕM[α(xn1,xn)Hm(T(xn1),T(xn))+ϕn1M(m(x0,x1))]ϕM[ϕM(m(xn2,xn1))+ϕn1M(m(x0,x1))+ϕn1M(m(x0,x1))]ϕ2M(m(xn2,xn1))+2ϕnM(m(x0,x1))....
    m(xn,xn+1)ϕnM(m(x0,x1))+nϕnM(m(x0,x1))m(xn,xn+1)(n+1)ϕnM(m(x0,x1)).

    Let us assume that ϵ>0, then there exist n0N such that

    nn0(n+1)ϕnM(m(x0,x1))<ϵ.

    By the Remarks (1.2) and (2.2), we get

    limnm(xn,xn+1)=0.

    Using the above inequality and (m2), we deduce that

    limnm(xn,xn)=limnmin{m(xn,xn),m(xn+1,xn+1)}=limnmxnxn+1limnm(xn,xn+1)=0.

    Owing to limit, we have limnm(xn,xn)=0,

    limn,mmxnxm=0.

    Now, we prove that {xn} is M-Cauchy in X. For m,n in N with m>n and using the triangle inequality of an M-metric we get

    m(xn,xm)mxnxmm(xn,xn+1)mxnxn+1+m(xn+1,xm)mxn+1xmm(xn,xn+1)mxnxn+1+m(xn+1,xn+2)mxn+1xn+1+m(xn+2,xm)mxn+2xmm(xn,xn+1)mxnxn+1+m(xn+1,xn+2)mxn+1xn+2++m(xm1,xm)mxm1xmm(xn,xn+1)+m(xn+1,xn+2)++m(xm1,xm)=m1r=nm(xr,xr+1)m1r=n(r+1)ϕrM(m(x0,x1))m1rn0(r+1)ϕrM(m(x0,x1))m1rn0(r+1)ϕrM(m(x0,x1))<ϵ.

    m(xn,xm)mxnxm0, as n, we obtain limm,n(Mxnxmmxnxm)=0. Thus {xn} is a M-Cauchy sequence in X. Since (X,m) is M-complete, there exist xX such that

    limn(m(xn,x)mxnx)=0 andlimn(Mxnxmxnx)=0.

    Also, limnm(xn,xn)=0 gives that

    limnm(xn,x)=0 and limnMxnx=0, (2.3)
    limn{max(m(xn,x),m(x,x))}=0,

    which implies that m(x,x)=0 and hence we obtain mxT(x)=0. By using (2.1) and (2.3) with

    limnα(xn,x)1.

    Thus,

    limnHm(T(xn),T(x))limnϕM(m(xn,x))limnm(xn,x).
    limnHm(T(xn),T(x))=0. (2.4)

    Now from (2.3), (2.4), and xn+1T(xn), we have

    m(xn+1,T(x))Hm(T(xn),T(x))=0.

    Taking limit as n and using (2.4), we obtain that

    limnm(xn+1,T(x))=0. (2.5)

    Since mxn+1T(x)m(xn+1,T(x)) which gives

    limnmxn+1T(x)=0. (2.6)

    Using the condition (m4), we obtain

    m(x,T(x))supyT(x)mxym(x,T(x))mx,T(x)m(x,xn+1)mxxn+1+m(xn+1,T((x))mxn+1T(x).

    Applying limit as n and using (2.3) and (2.6), we have

    m(x,T(x))supyT(x)mxy. (2.7)

    From (m2), mxym(xy) for each yT(x) which implies that

    mxym(x,y)0.

    Hence,

    sup{mxym(x,y):yT(x)}0.

    Then

    supyT(x)mxyinfyT(x)m(x,y)0.

    Thus

    supyT(x)mxym(x,T(x)). (2.8)

    Now, from (2.7) and (2.8), we obtain

    m(T(x),x)=supyT(x)mxy.

    Consequently, owing to Lemma (1.12), we have x¯T(x)=T(x).

    Corollary 2.6. Let (X,m) be a complete M-metric space and anself mapping T:XX an α-admissible and (α,ϕM)-contraction mapping. Assume that thefollowing properties hold:

    (i) there exists x0X such that α(x0,T(x0))1,

    (ii) either T is continuous or for any sequence {xn}X with α(xn,xn+1)1 for all nN and {xn}x as n , we have α(xn,x)1 for all nN. Then T has a fixed point.

    Some fixed point results in ordered M-metric space.

    Definition 2.7. Let (X,) be a partially ordered set. A sequence {xn}X is said to be non-decreasing if xnxn+1 for all n.

    Definition 2.8. [16] Let F and G be two nonempty subsets of partially ordered set (X,). The relation between F and G is defined as follows: F1G if for every xF, there exists yG such that xy.

    Definition 2.9. Let (X,m,) be a partially ordered set on M-metric. A set-valued mapping T:XCBm(X) is said to be ordered (α,ϕM)-contraction if for all x,yX, with xy we have

    Hm(T(x),T(y))ϕM(m(x,y))

    where ϕMΨ. Suppose that α:X×X[0,) is defined by

    α(x,y)={1     if Tx1Ty0       otherwise.

    A mapping T is called α-admissible if

    α(x,y)1α(a1,b1)1,

    for each a1T(x) and b1T(y).

    Theorem 2.10. Let (X,m,) be a partially orderedcomplete M-metric space and T:XCBm(X) an α-admissible ordered (α,ϕM)-contraction mapping satisfying the following conditions:

    (i) there exist x0X such that {x0}1{T(x0)}, α(x0,a1)1 for each a1T(x0),

    (ii) for every x,yX, xy implies T(x)1T(y),

    (iii) If {xn}X is a non-decreasing sequence such that xnxn+1 for all n and {xn}xX as n gives xnx for all nN. Then T has a fixed point.

    Proof. By assumption (i) there exist x1T(x0) such that x0x1 and α(x0,x1)1. By hypothesis (ii), T(x0)1T(x1). Let us assume that there exist x2T(x1) such that x1x2 and we have the following

    m(x1,x2)Hm(T(x0),T(x1))+ϕM(m(x0,x1)).

    In the same way, there exist x3T(x2) such that x2x3 and

    m(x2,x3)Hm(T(x1),T(x2))+ϕ2M(m(x0,x1)).

    Following the similar arguments, we have a sequence {xn}X  and xn+1T(xn) for all n0 satisfying x0x1x2x3...xnxn+1. The proof is complete follows the arguments given in Theorem 2.5.

    Example 2.11. Let X=[16,1] be endowed with an M -metric given by m(x,y)=x+y2. Define T:XCBm(X) by

    T(x)={{12x+16,14}, if x=16{x2,x3},  if 14x13{23,56},  if 12x1.

    Define a mapping α:X×X[0,) by

    α(x,y)={1     if x,y[14,13]0       otherwise.

    Let ϕM:R+R+ be given by ϕM(t)=1710 where ϕMΨ, for x,yX. If x=16, y=14 then m(x,y)=524, and

    Hm(T(x),T(y))=Hm({312,14},{18,112})=max(m({312,14},{18,112}),m({18,112},{312,14}))=max{316,212}=316ϕM(t)m(x,y).

    If x=13, y=12 then m(x,y)=512, and

    Hm(T(x),T(y))=Hm({16,19},{23,1})=max(m({16,19},{23,1}),m({23,1},{16,19}))=max{1736,718}=1736ϕM(t)m(x,y).

    If x=16, y=1, then m(x,y)=712 and

    Hm(T(x),T(y))=Hm({312,14},{23,56})=max(m({312,14},{23,56}),m({23,56},{312,14}))=max{1124,1324}=1324ϕM(t)m(x,y).

    In all cases, T is (α,ϕM)-contraction mapping. If x0=13, then T(x0)={x2,x3}.Therefore α(x0,a1)1 for every a1T(x0). Let x,yX be such that α(x,y)1, then x,y[x2,x3] and T(x)={x2,x3} and T(y)= {x2,x3} which implies that α(a1,b1)1 for every a1T(x) and b1T(x). Hence T is α-admissble.

    Let {xn}X be a sequence such that α(xn,xn+1)1 for all n in N and xn converges to x as n converges to , then xn[x2,x3]. By definition of α -admissblity, therefore x[x2,x3] and hence α(xn,x)1. Thus all the conditions of Theorem 2.3 are satisfied. Moreover, T has a fixed point.

    Example 2.12. Let X={(0,0),(0,15),(18,0)} be the subset of R2 with order defined as: For (x1,y1),(x2,y2)X, (x1,y1)(x2,y2) if and only if x1x2, y1y2. Let m:X×XR+ be defined by

    m((x1,y1),(x2,y2))=|x1+x22|+|y1+y22|, for x=(x1,y1), y=(x2,y2)X.

    Then (X,m) is a complete M-metric space. Let T:XCBm(X) be defined by

    T(x)={{(0,0)}, if x=(0,0),{(0,0),(18,0)},  if x(0,15){(0,0)},  if x(18,0).

    Define a mapping α:X×X[0,) by

    α(x,y)={1     if x,yX0       otherwise.

    Let ϕM:R+R+ be given by ϕM(t)=12. Obviously, ϕMΨ. For x,yX,

    if x=(0,15) and y=(0,0), then Hm(T(x),T(y))=0 and m(x,y)=110 gives that

    Hm(T(x),T(y))=Hm({(0,0),(18,0)},{(0,0)})=max(m({(0,0),(18,0)},{(0,0)}),m({(0,0)},{(0,0),(18,0)}))=max{0,0}=0ϕM(t)m(x,y).

    If x=(18,0) and y=(0,0) then Hm(T(x),T(y))=0, and m(x,y)=116 implies that

    Hm(T(x),T(y))ϕM(t)m(x,y).

    If x=(0,0) and y=(0,0) then Hm(T(x),T(y))=0, and m(x,y)=0 gives

    Hm(T(x),T(y))ϕM(t)m(x,y).

    If x=(0,15) and y=(0,15) then Hm(T(x),T(y))=0, and m(x,y)=15 implies that

    Hm(T(x),T(y))ϕM(t)m(x,y).

    If x=(0,18) and y=(0,18) then Hm(T(x),T(y))=0, and m(x,y)=18 gives that

    Hm(T(x),T(y))ϕM(t)m(x,y).

    Thus all the condition of Theorem 2.10 satisfied. Moreover, (0,0) is the fixed point of T.

    In this section, we present an application of our result in homotopy theory. We use the fixed point theorem proved for set-valued (α,ϕM)-contraction mapping in the previous section, to establish the result in homotopy theory. For further study in this direction, we refer to [6,35].

    Theorem 3.1. Suppose that (X,m) is a complete M-metricspace and A and B are closed and open subsets of X respectively, suchthat AB. For a,bR, let T:B×[a,b]CBm(X) be aset-valued mapping satisfying the following conditions:

    (i) xT(y,t) for each yB/Aand t[a,b],

    (ii) there exist ϕMΨ and α:X×X[0,) such that

    α(x,y)Hm(T(x,t),T(y,t))ϕM(m(x,y)),

    for each pair (x,y)B×B and t[a,b],

    (iii) there exist a continuous function Ω:[a,b]R such that for each s,t[a,b] and xB, we get

    Hm(T(x,s),T(y,t))ϕM|Ω(s)Ω(t)|,

    (iv) if xT(x,t),then T(x,t)={x},

    (v) there exist x0 in X such that x0T(x0,t),

    (vi) a function :[0,)[0,) defined by (x)=xϕM(x) is strictly increasing and continuous if T(.,t) has a fixed point in B for some t[a,b], then T(.,t) has afixed point in A for all t[a,b]. Moreover, for a fixed t[a,b], fixed point is unique provided that ϕM(t)=12t where t>0.

    Proof. Define a mapping α:X×X[0,) by

    α(x,y)={1     if xT(x,t), yT(y,t) 0       otherwise.

    We show that T is α-admissible. Note that α(x,y)1 implies that xT(x,t) and yT(y,t) for all t[a,b]. By hypothesis (iv), T(x,t)={x} and T(y,t)={y}. It follows that T is α -admissible. By hypothesis (v), there exist x0X such that x0(x0,t) for all t, that is α(x0,x0)1. Suppose that α(xn,xn+1)1 for all n and xn converges to q as n approaches to and xnT(xn,t) and xn+1T(xn+1,t) for all n and t[a,b] which implies that qT(q,t) and thus α(xn,q)1. Set

    D={t[a,b]: xT(x,t) for xA}.

    So T(.,t) has a fixed point in B for some t[a,b], there exist xB such that xT(x,t). By hypothesis (i) xT(x,t) for t[a,b] and xA so Dϕ. Now we now prove that D is open and close in [a,b]. Let t0D and x0A with x0T(x0,t0). Since A is open subset of X, ¯Bm(x0,r)A for some r>0. For ϵ=r+mxx0ϕ(r+mxx0) and a continuous function Ω on [a,b], there exist δ>0 such that

    ϕM|Ω(t)Ω(t0)|<ϵ for all t(t0δ,t0+δ).

    If t(t0δ,t0+δ) for xBm(x0,r)={xX:m(x0,x)mx0x+r} and lT(x,t), we obtain

    m(l,x0)=m(T(x,t),x0)=Hm(T(x,t),T(x0,t0)).

    Using the condition (iii) of Proposition 1.13 and Proposition 1.18, we have

    m(l,x0)Hm(T(x,t),T(x0,t0))+Hm(T(x,t),T(x0,t0)) (2.9)

    as xT(x0,t0) and xBm(x0,r)AB, t0[a,b] with α(x0,x0)1. By hypothesis (ii), (iii) and (2.9)

    m(l,x0)ϕM|Ω(t)Ω(t0)|+α(x0,x0)Hm(T(x,t),T(x0,t0))ϕM|Ω(t)Ω(t0)|+ϕM(m(x,x0))ϕM(ϵ)+ϕM(mxx0+r)ϕM(r+mxx0ϕM(r+mxx0))+ϕM(mxx0+r)<r+mxx0ϕM(r+mxx0)+ϕM(mxx0+r)=r+mxx0.

    Hence l¯Bm(x0,r) and thus for each fixed t(t0δ,t0+δ), we obtain T(x,t)¯Bm(x0,r) therefore T:¯Bm(x0,r)CBm(¯Bm(x0,r)) satisfies all the assumption of Theorem (3.1) and T(.,t) has a fixed point ¯Bm(x0,r)=Bm(x0,r)B. But by assumption of (i) this fixed point belongs to A. So (t0δ,t0+δ)D, thus D is open in [a,b]. Next we prove that D is closed. Let a sequence {tn}D with tn converges to t0[a,b] as n approaches to . We will prove that t0 is in D.

    Using the definition of D, there exist {tn} in A such that xnT(xn,tn) for all n. Using Assumption (iii)(v), and the condition (iii) of Proposition 1.13, and an outcome of the Proposition 1.18, we have

    m(xn,xm)Hm(T(xn,tn),T(xm,tm))Hm(T(xn,tn),T(xn,tm))+Hm(T(xn,tm),T(xm,tm))ϕM|Ω(tn)Ω(tm)|+α(xn,xm)Hm(T(xn,tm),T(xm,tm))ϕM|Ω(tn)Ω(tm)|+ϕM(m(xn,xm))m(xn,xm)ϕM(m(xn,xm))ϕM|Ω(tn)Ω(tm)|(m(xn,xm))ϕM|Ω(tn)Ω(tm)|(m(xn,xm))<|Ω(tn)Ω(tm)|m(xn,xm)<1|Ω(tn)Ω(tm)|.

    So, continuity of 1, and convergence of {tn}, taking the limit as m,n in the last inequality, we obtain that

    limm,nm(xn,xm)=0.

    Sine mxnxmm(xn,xm), therefore

    limm,nmxnxm=0.

    Thus, we have limnm(xn,xn)=0=limmm(xm,xm). Also,

    limm,n(m(xn,xm)mxnxm)=0, limm,n(Mxnxmmxnxm).

    Hence {xn} is an M-Cauchy sequence. Using Definition 1.4, there exist x in X such that

    limn(m(xn,x)mxnx)=0 and limn(Mxnxmxnx)=0.

    As limnm(xn,xn)=0, therefore

    limnm(xn,x)=0 and limnMxnx=0.

    Thus, we have m(x,x)=0. We now show that xT(x,t). Note that

    m(xn,T(x,t))Hm(T(xn,tn),T(x,t))Hm(T(xn,tn),T(xn,t))+Hm(T(xn,t),T(x,t))ϕM|Ω(tn)Ω(t)|+α(xn,t)Hm(T(xn,t),T(x,t))ϕM|Ω(tn)Ω(t)|+ϕM(m(xn,t)).

    Applying the limit n in the above inequality, we have

    limnm(xn,T(x,t))=0.

    Hence

    limnm(xn,T(x,t))=0. (2.10)

    Since m(x,x)=0, we obtain

    supyT(x,t)mxy=supyT(x,t)min{m(x,x),m(y,y)}=0. (2.11)

    From above two inequalities, we get

    m(x,T(x,t))=supyT(x,t)mxy.

    Thus using Lemma 1.12 we get xT(x,t). Hence xA. Thus xD and D is closed in [a,b], D=[a,b] and D is open and close in [a,b]. Thus T(.,t) has a fixed point in A for all t[a,b]. For uniqueness, t[a,b] is arbitrary fixed point, then there exist xA such that xT(x,t). Assume that y is an other point of T(x,t), then by applying condition 4, we obtain

    m(x,y)=Hm(T(x,t),T(y,t))αM(x,y)Hm(T(x,t),T(y,t))ϕM(m(x,y)).

    ForϕM(t)=12t, where t>0, the uniqueness follows.

    In this section we will apply the previous theoretical results to show the existence of solution for some integral equation. For related results (see [13,20]). We see for non-negative solution of (3.1) in X=C([0,δ],R). Let X=C([0,δ],R) be a set of continuous real valued functions defined on [0,δ] which is endowed with a complete M-metric given by

    m(x,y)=supt[0,δ](|x(t)+x(t)2|) for all x,yX.

    Consider an integral equation

    v1(t)=ρ(t)+δ0h(t,s)J(s,v1(s))ds for all 0tδ. (3.1)

    Define g:XX by

    g(x)(t)=ρ(t)+δ0h(t,s)J(s,x(s))ds

    where

    (i) for δ>0,  (a) J:[0,δ]×RR, (b) h:[0,δ]×[0,δ][0,), (c) ρ:[0,δ]R are all continuous functions

    (ii) Assume that σ:X×XR is a function with the following properties,

    (iii) σ(x,y)0 implies that σ(T(x),T(y))0,

    (iv) there exist x0X such that σ(x0,T(x0))0,

    (v) if {xn}X is a sequence such that σ(xn,xn+1)0 for all nN and xnx as n, then σ(x,T(x))0

    (vi)

    supt[0,δ]δ0h(t,s)ds1

    where t[0,δ], sR,

    (vii) there exist ϕMΨ, σ(y,T(y))1 and σ(x,T(x))1 such that for each t[0,δ], we have

    |J(s,x(t))+J(s,y(t))|ϕM(|x+y|). (3.3)

    Theorem 4.1. Under the assumptions (i)(vii) theintegral Eq (3.1) has a solution in {X=C([0,δ],R) for all t[0,δ]}.

    Proof. Using the condition (vii), we obtain that

    m(g(x),g(y))=|g(x)(t)+g(y)(t)2|=|δ0h(t,s)[J(s,x(s))+J(s,y(s))2]ds|δ0h(t,s)|J(s,x(s))+J(s,y(s))2|dsδ0h(t,s)[ϕM|x(s)+y(s)2|]ds(supt[0,δ]δ0h(t,s)ds)(ϕM|x(s)+y(s)2|)ϕM(|x(s)+y(s)2|)
    m(g(x),g(y))ϕ(m(x,y))

    Define α:X×X[0,+) by

    α(x,y)={1     if σ(x,y)0 0       otherwise

    which implies that

    m(g(x),g(y))ϕM(m(x,y)).

    Hence all the assumption of the Corollary 2.6 are satisfied, the mapping g has a fixed point in X=C([0,δ],R) which is the solution of integral Eq (3.1).

    In this study we develop some set-valued fixed point results based on (α,ϕM)-contraction mappings in the context of M-metric space and ordered M-metric space. Also, we give examples and applications to the existence of solution of functional equations and homotopy theory.

    The authors declare that they have no competing interests.



    [1] A. Charnes, W. W. Cooper, E. Rhodes, Measuring the efficiency of decision making units, Eur. J. Oper. Res., 2 (1978), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8 doi: 10.1016/0377-2217(78)90138-8
    [2] A. Charnes, W. W. Cooper, B. Golany, L. Seiford, J. Stutz, Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions, J. Econometrics, 30 (1985), 91–107. https://doi.org/10.1016/0304-4076(85)90133-2 doi: 10.1016/0304-4076(85)90133-2
    [3] L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
    [4] L. Sahoo, An approach for solving fuzzy matrix games using signed distance method, J. Inf. Comput. Sci., 12 (2017), 73–80.
    [5] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Set. Syst., 20 (1986), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3 doi: 10.1016/S0165-0114(86)80034-3
    [6] R. R. Yager, Pythagorean membership grades in multi-criteria decision making, IEEE T. Fuzzy Syst., 22 (2014), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989 doi: 10.1109/TFUZZ.2013.2278989
    [7] R. R. Yager, Pythagorean fuzzy subsets, In: 2013 Joint IFSA world congress and NAFIPS annual meeting, 2013, 57–61. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
    [8] T. Senapati, R. R. Yager, Fermatean fuzzy sets, J. Amb. Intel. Hum. Comp., 11 (2020), 663–674. https://doi.org/10.1007/s12652-019-01377-0 doi: 10.1007/s12652-019-01377-0
    [9] T. Senapati, R. R. Yager, Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision making methods, Eng. Appl. Artif. Intel., 85 (2019), 112–121. https://doi.org/10.1016/j.engappai.2019.05.012 doi: 10.1016/j.engappai.2019.05.012
    [10] T. Senapati, R. R. Yager, Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making, Informatica, 30 (2019), 391–412.
    [11] L. Sahoo, Some score functions on Fermatean fuzzy sets and its application to bride selection based on TOPSIS method, Int. J. Fuzzy Syst. Appl., 10 (2021), 18–29. https://doi.org/10.4018/IJFSA.2021070102 doi: 10.4018/IJFSA.2021070102
    [12] L. Sahoo, Similarity measures for Fermatean fuzzy sets and its applications in group decision-making, Decis. Sci. Lett., 11 (2022), 167–180. https://doi.org/10.5267/j.dsl.2021.11.003 doi: 10.5267/j.dsl.2021.11.003
    [13] R. E. Bellman, L. A. Zadeh, Decision making in a fuzzy environment, Manage. Sci., 17 (1970), 141–164. https://doi.org/10.1287/mnsc.17.4.B141 doi: 10.1287/mnsc.17.4.B141
    [14] H. J. Zimmerman, Fuzzy programming and linear programming with several objective functions, Fuzzy Set. Syst., 1 (1978), 45–55. https://doi.org/10.1016/0165-0114(78)90031-3 doi: 10.1016/0165-0114(78)90031-3
    [15] T. Allahviranloo, F. H. Lotfi, M. L. Kiasary, N. A. Kiani, L. A. Zadeh, Solving fully fuzzy linear programming problem by the ranking function, Appl. Math. Sci., 2 (2008), 19–32.
    [16] M. Akram, I. Ullah, S. A. Edalatpanah, T. Allahviranloo, Fully Pythagorean fuzzy linear programming problems with equality constraints, Comput. Appl. Math., 40 (2021), 120. https://doi.org/ 10.1007/s40314-021-01503-9 doi: 10.1007/s40314-021-01503-9
    [17] M. Akram, I. Ullah, T. Allahviranloo, S. A. Edalatpanah, LR-type fully Pythagorean fuzzy linear programming problems with equality constraints, J. Inte. Fuzzy Syst., 41 (2021), 1975–1992. https://doi.org/ 10.3233/JIFS-210655 doi: 10.3233/JIFS-210655
    [18] M. Akram, G. Shahzadi, A. A. H. Ahmadini, Decision-making framework for an effective sanitizer to reduce COVID-19 under Fermatean fuzzy environment, J. Math., 2020 (2020), 3263407. https://doi.org/10.1155/2020/3263407 doi: 10.1155/2020/3263407
    [19] M. Akram, I. Ullah, M. G. Alharbi, Methods for solving LR-type Pythagorean fuzzy linear programming problems with mixed constraints, Math. Probl. Eng., 2021 (2021), 4306058. https://doi.org/10.1155/2021/4306058 doi: 10.1155/2021/4306058
    [20] M. Akram, S. M. U. Shah, M. A. Al-Shamiri, S. A. Edalatpanah, Fractional transportation problem under interval-valued Fermatean fuzzy sets, AIMS Mathematics, 7 (2022), 17327–17348. https://doi.org/ 10.3934/math.2022954 doi: 10.3934/math.2022954
    [21] M. A. Mehmood, M. Akram, M. G. Alharbi, S. Bashir, Solution of fully bipolar fuzzy linear programming models, Math. Probl. Eng., 2021 (2021), 9961891. https://doi.org/10.1155/2021/9961891 doi: 10.1155/2021/9961891
    [22] M. A. Mehmood, M. Akram, M. G. Alharbi, S. Bashir, Optimization of LR-type fully bipolar fuzzy linear programming problems, Math. Probl. Eng., 2021 (2021), 1199336. https://doi.org/10.1155/2021/1199336 doi: 10.1155/2021/1199336
    [23] J. Ahmed, M. G. Alharbi, M. Akram, S. Bashir, A new method to evaluate linear programming problem in bipolar single-valued neutrosophic environment, Comp. Model. Eng., 129 (2021), 881–906. https://doi.org/10.32604/cmes.2021.017222 doi: 10.32604/cmes.2021.017222
    [24] F. L. Hitchcock, The distribution of product from several resources to numerous localities, J. Math. Phys., 20 (1941), 224–230. https://doi.org/10.1002/sapm1941201224 doi: 10.1002/sapm1941201224
    [25] R. D. Banker, A. Charnes, W. W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Manage. Sci., 30 (1984), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078 doi: 10.1287/mnsc.30.9.1078
    [26] T. Ahn, A. Charnes, W. W. Cooper, Some statistical and DEA evaluations of relative efficiencies of public and private institutions of higher learning, Socio-Econ. Plan. Sci., 22 (1988), 259–269. https://doi.org/10.1016/0038-0121(88)90008-0 doi: 10.1016/0038-0121(88)90008-0
    [27] Y. Roll, W. D. Cook, B. Golany, Controlling factor weights in data envelopment analysis, IIE Trans., 23 (1991), 2–9. https://doi.org/10.1080/07408179108963835 doi: 10.1080/07408179108963835
    [28] J. K. Sengupta, A fuzzy systems approach in data envelopment analysis, Comput. Math. Appl., 24 (1992), 259–266. https://doi.org/10.1016/0898-1221(92)90203-T doi: 10.1016/0898-1221(92)90203-T
    [29] C. Kao, S. T. Liu, Fuzzy efficiency measures in data envelopment analysis, Fuzzy Set. Syst., 113 (2000), 427–437. https://doi.org/10.1016/S0165-0114(98)00137-7 doi: 10.1016/S0165-0114(98)00137-7
    [30] S. Saati, M. Memariani, G. R. Jahanshahloo, Efficiency analysis and ranking of DMUs with fuzzy data, Fuzzy Optim. Decis. Ma., 1 (2002), 255–267. https://doi.org/10.1023/A:1019648512614 doi: 10.1023/A:1019648512614
    [31] S. Lertworasirikul, S. C. Fang, J. A. Joines, H. L. Nuttle, Fuzzy data envelopment analysis (DEA): A possibility approach, Fuzzy Set. Syst., 139 (2003), 379–394. https://doi.org/10.1016/S0165-0114(02)00484-0 doi: 10.1016/S0165-0114(02)00484-0
    [32] A. L. M. Zerafat, S. M. Saati, M. Mokhtaran, An alternative approach to assignment problem with nonhomogeneous costs using common set of weights in DEA, Far East J. Math. Sci., 10 (2003), 29–39.
    [33] W. W. Cooper, L. M. Seiford, K. Tone, Introduction to data envelopment analysis and its uses: With DEA-solver software and references, New York: Springer, 2006.
    [34] P. Zhou, B. W. Ang, K. L. Poh, A survey of data envelopment analysis in energy and environmental studies, Eur. J. Oper. Res., 189 (2008), 1–18. https://doi.org/10.1016/j.ejor.2007.04.042 doi: 10.1016/j.ejor.2007.04.042
    [35] P. Guo, Fuzzy data envelopment analysis and its applications to location problems, Inform. Sci., 179 (2009), 820–829. https://doi.org/10.1016/j.ins.2008.11.003 doi: 10.1016/j.ins.2008.11.003
    [36] F. H. Lotfi, G. R. Jahanshahloo, A. R. Vahidi, A. Dalirian, Efficiency and effectiveness in multi-activity network DEA model with fuzzy data, Appl. Math. Sci., 3 (2009), 2603–2618.
    [37] F. H. Lotfi, G. R. Jahanshahloo, M. Soltanifar, A. Ebrahimnejad, S. M. Mansourzadeh, Relationship between MOLP and DEA based on output-orientated CCR dual model, Expert Syst. Appl., 37 (2010), 4331–4336. https://doi.org/10.1016/j.eswa.2009.11.066 doi: 10.1016/j.eswa.2009.11.066
    [38] S. H. Mousavi-Avval, S. Rafiee, A. Mohammadi, Optimization of energy consumption and input costs for apple production in Iran using data envelopment analysis, Energy, 36 (2011), 909–916. https://doi.org/10.1016/j.energy.2010.12.020 doi: 10.1016/j.energy.2010.12.020
    [39] A. Amirteimoori, An extended transportation problem: A DEA-based approach, Cent. Eur. J. Oper. Res., 19 (2011), 513–521. https://doi.org/10.1007/s10100-010-0140-0 doi: 10.1007/s10100-010-0140-0
    [40] A. Amirteimoori, An extended shortest path problem: A data envelopment analysis approach, Appl. Math. Lett., 25 (2012), 1839–1843. https://doi.org/10.1016/j.aml.2012.02.042 doi: 10.1016/j.aml.2012.02.042
    [41] A. Nabavi-Pelesaraei, R. Abdi, S. Rafiee, H. G. Mobtaker, Optimization of energy required and greenhouse gas emissions analysis for orange producers using data envelopment analysis approach, J. Clean. Prod., 65 (2014), 311–317. https://doi.org/10.1016/j.jclepro.2013.08.019 doi: 10.1016/j.jclepro.2013.08.019
    [42] Z. Zhu, K. Wang, B. Zhang, Applying a network data envelopment analysis model to quantify the eco-efficiency of products: A case study of pesticides, J. Clean. Prod., 69 (2014), 67–73. https://doi.org/10.1016/j.jclepro.2014.01.064 doi: 10.1016/j.jclepro.2014.01.064
    [43] M. Azadi, M. Jafarian, S. R. Farzipoor, S. M. Mirhedayatian, A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context, Comput. Oper. Res., 54 (2015), 274–285. https://doi.org/10.1016/j.cor.2014.03.002 doi: 10.1016/j.cor.2014.03.002
    [44] G. H. Shirdel, A. Mortezaee, A DEA-based approach for the multi-criteria assignment problem, Croat. Oper. Res. Rev., 6 (2015), 145–154. https://doi.org/10.17535/crorr.2015.0012 doi: 10.17535/crorr.2015.0012
    [45] A. Azar, M. Z. Mahmoudabadi, A. Emrouznejad, A new fuzzy additive model for determining the common set of weights in data envelopment analysis, J. Inte. Fuzzy Syst., 30 (2016), 61–69. https://doi.org/10.3233/IFS-151710 doi: 10.3233/IFS-151710
    [46] A. Mardania, E. Kazimieras, Zavadskasb, Streimikienec, A. Jusoha, M. Khoshnoudia, A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency, Renew. Sust. Energ. Rev., 70 (2017), 1298–1322. https://doi.org/10.1016/j.rser.2016.12.030 doi: 10.1016/j.rser.2016.12.030
    [47] A. Hatami-Marbini, A. Ebrahimnejad, S. Lozano, Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach, Comput. Ind. Eng., 105 (2017), 362–376. https://doi.org/10.1016/j.cie.2017.01.009 doi: 10.1016/j.cie.2017.01.009
    [48] A. Hatami-Marbini, S. Saati, Efficiency evaluation in two-stage data envelopment analysis under a fuzzy environment: A common weights approach, Appl. Soft Comput., 72 (2018), 156–165. https://doi.org/10.1016/j.asoc.2018.07.057 doi: 10.1016/j.asoc.2018.07.057
    [49] R. M. Rizk-Allaha A. E. Hassanienb, M. Elhoseny, A multi-objective transportation model under neutrosophic environment, Comput. Electr. Eng., 69 (2018), 705–719. https://doi.org/10.1016/j.compeleceng.2018.02.024 doi: 10.1016/j.compeleceng.2018.02.024
    [50] M. Tavana, K. Khalili-Damghani, A new two-stage Stackelberg fuzzy data envelopment analysis model, Measurement, 53 (2014), 277–296. https://doi.org/10.1016/j.measurement.2014.03.030 doi: 10.1016/j.measurement.2014.03.030
    [51] S. A. Edalatpanah, F. Smarandache, Data envelopment analysis for simplified neutrosophic sets, Neutrosophic Sets Sy., 29 (2019), 215–226. https://doi.org/10.5281/zenodo.3514433 doi: 10.5281/zenodo.3514433
    [52] J. Liu, J. Song, Q. Xu, Z. Tao, H. Chen, Group decision making based on DEA cross-efficiency with intuitionistic fuzzy preference relations, Fuzzy Optim. Decis. Ma., 18 (2019), 345–370. https://doi.org/10.1007/s10700-018-9297-0 doi: 10.1007/s10700-018-9297-0
    [53] S. A. Edalatpanah, Data envelopment analysis based on triangular neutrosophic numbers, CAAI T. Intell. Techno., 5 (2020), 94–98. https://doi.org/10.1049/trit.2020.0016 doi: 10.1049/trit.2020.0016
    [54] M. Bagheri, A. Ebrahimnejad, S. Razavyan, F. H. Lotfi, N. Malekmohammadi, Solving the fully fuzzy multi-objective transportation problem based on the common set of weights in DEA, J. Inte. Fuzzy Syst., 39 (2020), 3099–3124. https://doi.org/10.3233/JIFS-191560 doi: 10.3233/JIFS-191560
    [55] M. R. Soltani, S. A. Edalatpanah, F. M. Sobhani, S. E. Najafi, A novel two-stage DEA model in fuzzy environment: Application to industrial workshops performance measurement, Int. J. Comput. Int. Sys., 13 (2020), 1134–1152. https://doi.org/10.2991/ijcis.d.200731.002 doi: 10.2991/ijcis.d.200731.002
    [56] L. Sahoo, A new score function based Fermatean fuzzy transportation problem, Results Control Optim., 1 (2021), 100040. https://doi.org/10.1016/j.rico.2021.100040 doi: 10.1016/j.rico.2021.100040
    [57] S. Ghosh, S. K. Roy, A. Ebrahimnejad, J. L. Verdegay, Multi-objective fully intuitionistic fuzzy fixed-charge solid transportation problem, Complex Intell. Syst., 7 (2021), 1009–1023. https://doi.org/10.1007/s40747-020-00251-3 doi: 10.1007/s40747-020-00251-3
    [58] A. Mondal, S. K. Roy, S. Midya, Intuitionistic fuzzy sustainable multi-objective multi-item multi-choice step fixed-charge solid transportation problem, J. Amb. Intel. Hum. Comp., 2021, 1–25. https://doi.org/10.1007/s12652-021-03554-6 doi: 10.1007/s12652-021-03554-6
    [59] B. K. Giri, S. K. Roy, Neutrosophic multi-objective green four-dimensional fixed-charge transportation problem, Int. J. Mach. Learn. Cyb., 13 (2022), 3089–3112. https://doi.org/10.1007/s13042-022-01582-y doi: 10.1007/s13042-022-01582-y
    [60] S. Ghosh, K-H. Kufer, S. K. Roy, G-W. Weber, Carbon mechanism on sustainable multi-objective solid transportation problem for waste management in Pythagorean hesitant fuzzy environment, Complex Intell. Syst., 8 (2022). https://doi.org/10.1007/s40747-022-00686-w doi: 10.1007/s40747-022-00686-w
    [61] M. Akram, S. M. U. Shah, T. Allahviranloo, A new method to determine the Fermatean fuzzy optimal solution of transportation problems, J. Intell. Fuzzy Syst., 2022. https://doi.org/10.3233/JIFS-221959 doi: 10.3233/JIFS-221959
    [62] M. Bagheri, A. Ebrahimnejad, S. Razavyan, F. H. Lotfi, N. Malekmohammadi, Fuzzy arithmetic DEA approach for fuzzy multi-objective transportation problem, Oper. Res., 22 (2022), 1479–1509. https://doi.org/10.1007/s12351-020-00592-4 doi: 10.1007/s12351-020-00592-4
    [63] Y. M. Wang, Y. Luo, L. Liang, Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises, Expert Syst. Appl., 36 (2009), 5205–5211. https://doi.org/10.1016/j.eswa.2008.06.102 doi: 10.1016/j.eswa.2008.06.102
    [64] A. Mahmoodirad, T. Allahviranloo, S. Niroomand, A new effective solution method for fully fuzzy transportation problem, Soft Comput., 23 (2019), 4521–4530. https://doi.org/10.1007/s00500-018-3115-z doi: 10.1007/s00500-018-3115-z
    [65] M. Ehrgott, Multi-criteria optimization, Berlin, Heidelberg: Springer, 2005.
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