Research article Special Issues

Improved artificial bee colony algorithm for air freight station scheduling


  • Received: 13 June 2022 Revised: 26 July 2022 Accepted: 30 August 2022 Published: 05 September 2022
  • Aiming at improving the operating efficiency of air freight station, the problem of optimizing the sequence of inbound/outbound tasks meanwhile scheduling the actions of elevating transfer vehicles (ETVs) is discussed in this paper. First of all, the scheduling model in airport container storage area, which considers not only the influence of picking sequence, optimal ETVs routing without collision, but also the assignment of input and output ports, is established. Then artificial bee colony (ABC) is proposed to solve the above scheduling issue. For further balancing the abilities of exploration and exploitation, improved multi-dimensional search (IMABC) algorithm is proposed where more dimensions will be covered, and the best dimension of the current optimal solution is used to guide the evolutionary direction in the following exploitation processes. Numerical experiments show that the proposed method can generate optimal solution for the complex scheduling problem, and the proposed IMABC outperforms original ABC and other improved algorithms.

    Citation: Haiquan Wang, Hans-Dietrich Haasis, Menghao Su, Jianhua Wei, Xiaobin Xu, Shengjun Wen, Juntao Li, Wenxuan Yue. Improved artificial bee colony algorithm for air freight station scheduling[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13007-13027. doi: 10.3934/mbe.2022607

    Related Papers:

    [1] Aliya Fahmi, Fazli Amin, Sayed M Eldin, Meshal Shutaywi, Wejdan Deebani, Saleh Al Sulaie . Multiple attribute decision-making based on Fermatean fuzzy number. AIMS Mathematics, 2023, 8(5): 10835-10863. doi: 10.3934/math.2023550
    [2] Shichao Li, Zeeshan Ali, Peide Liu . Prioritized Hamy mean operators based on Dombi t-norm and t-conorm for the complex interval-valued Atanassov-Intuitionistic fuzzy sets and their applications in strategic decision-making problems. AIMS Mathematics, 2025, 10(3): 6589-6635. doi: 10.3934/math.2025302
    [3] Muhammad Qiyas, Muhammad Naeem, Saleem Abdullah, Neelam Khan . Decision support system based on complex T-Spherical fuzzy power aggregation operators. AIMS Mathematics, 2022, 7(9): 16171-16207. doi: 10.3934/math.2022884
    [4] Aliya Fahmi, Rehan Ahmed, Muhammad Aslam, Thabet Abdeljawad, Aziz Khan . Disaster decision-making with a mixing regret philosophy DDAS method in Fermatean fuzzy number. AIMS Mathematics, 2023, 8(2): 3860-3884. doi: 10.3934/math.2023192
    [5] Muhammad Akram, Sumera Naz, Feng Feng, Ghada Ali, Aqsa Shafiq . Extended MABAC method based on 2-tuple linguistic T-spherical fuzzy sets and Heronian mean operators: An application to alternative fuel selection. AIMS Mathematics, 2023, 8(5): 10619-10653. doi: 10.3934/math.2023539
    [6] Muhammad Naeem, Muhammad Qiyas, Lazim Abdullah, Neelam Khan, Salman Khan . Spherical fuzzy rough Hamacher aggregation operators and their application in decision making problem. AIMS Mathematics, 2023, 8(7): 17112-17141. doi: 10.3934/math.2023874
    [7] Aziz Khan, Shahzaib Ashraf, Saleem Abdullah, Muhammad Ayaz, Thongchai Botmart . A novel decision aid approach based on spherical hesitant fuzzy Aczel-Alsina geometric aggregation information. AIMS Mathematics, 2023, 8(3): 5148-5174. doi: 10.3934/math.2023258
    [8] Tahir Mahmood, Azam, Ubaid ur Rehman, Jabbar Ahmmad . Prioritization and selection of operating system by employing geometric aggregation operators based on Aczel-Alsina t-norm and t-conorm in the environment of bipolar complex fuzzy set. AIMS Mathematics, 2023, 8(10): 25220-25248. doi: 10.3934/math.20231286
    [9] Shahid Hussain Gurmani, Zhao Zhang, Rana Muhammad Zulqarnain . An integrated group decision-making technique under interval-valued probabilistic linguistic T-spherical fuzzy information and its application to the selection of cloud storage provider. AIMS Mathematics, 2023, 8(9): 20223-20253. doi: 10.3934/math.20231031
    [10] Muhammad Naeem, Aziz Khan, Shahzaib Ashraf, Saleem Abdullah, Muhammad Ayaz, Nejib Ghanmi . A novel decision making technique based on spherical hesitant fuzzy Yager aggregation information: application to treat Parkinson's disease. AIMS Mathematics, 2022, 7(2): 1678-1706. doi: 10.3934/math.2022097
  • Aiming at improving the operating efficiency of air freight station, the problem of optimizing the sequence of inbound/outbound tasks meanwhile scheduling the actions of elevating transfer vehicles (ETVs) is discussed in this paper. First of all, the scheduling model in airport container storage area, which considers not only the influence of picking sequence, optimal ETVs routing without collision, but also the assignment of input and output ports, is established. Then artificial bee colony (ABC) is proposed to solve the above scheduling issue. For further balancing the abilities of exploration and exploitation, improved multi-dimensional search (IMABC) algorithm is proposed where more dimensions will be covered, and the best dimension of the current optimal solution is used to guide the evolutionary direction in the following exploitation processes. Numerical experiments show that the proposed method can generate optimal solution for the complex scheduling problem, and the proposed IMABC outperforms original ABC and other improved algorithms.



    Let ϕ:IRR be a convex function and α,βI with α<β, then the following inequality holds,

    ϕ(α+β2)1βαβαϕ(x)dxϕ(α)+ϕ(β)2, (1.1)

    which is well known as Hermite-Hadamard's inequality [1] for convex functions. Both inequalities hold in the reversed direction if ϕ is concave.

    Convex function is an important function in mathematical analysis and has been applied in many aspects [2,3]. With the extension of the definition of convex function, Hermite-Hadamard's inequality has been deeply studied. Some improvement and generalizations for Hermite-Hadamard's inequality (1.1) can been found in the references [4,5,6,7,8,9,10,11,12].

    In [11], İşcan gave the definition of harmonically convexity as follows:

    Definition 1. Let IR{0} be a real interval. A function ϕ:IR is said to be harmonically convex, if

    ϕ(xytx+(1t)y)tϕ(y)+(1t)ϕ(x) (1.2)

    for all x,yI and t[0,1]. If the inequality in (1.2) is reversed, then ϕ is said to be harmonically concave.

    In recent years, many researchers presented many kinds of fractional calculus by different methods and explored their applications. For example, Riemann-Liouville fractional integrals and its applications in inequalities [13,14,15,16]. Recently, Yang stated the theory of local fractional calculus on Yang's fractal sets systematically in [17,18,19]. Local fractional calculus can explain the behavior of continuous but nowhere differentiable function. In view of the special advantages of local fractional calculus, more and more researchers extended their studies to Yang's fractal space, see [20,21,22,23,24,25,26,27,28,29].

    In [22], Sun introduced the definition of the generalized harmonically convex function on Yang's fractal sets as follows:

    Definition 2. Let IR{0} be a real interval. A function ϕ:IRϵ(0<ϵ1) is said to be generalized harmonically convex, if

    ϕ(xytx+(1t)y)tϵϕ(y)+(1t)ϵϕ(x) (1.3)

    for all x,yI and t[0,1]. If the inequality in (1.3) is reversed, then ϕ is said to be generalized harmonically concave. The sign ϵ represents the fractal dimension.

    Example 1. Let ϕ:(0,)Rϵ and ψ:(,0)Rϵ, then ϕ(x)=xϵ is a generalized harmonically convex function and ψ(x)=xϵ is a generalized harmonically concave function.

    The following result related to Hermite-Hadamard's inequalities holds.

    Theorem 1. [22] Let ϕ:IR{0}Rϵ be a generalized harmonically convex function on fractal space and α,βI with α<β. If ϕ(x)I(ϵ)x[α,β], then

    1Γ(1+ϵ)ϕ(2αβα+β)αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵΓ(1+ϵ)Γ(1+2ϵ)[ϕ(α)+ϕ(β)]. (1.4)

    Based on the theory of local fractional calculus and the definition of the generalized harmonically convex function on Yang's fractal sets, the main aim of this paper is using a new integral identity and monotonicity of functions to establish some new Hermite-Hadamard type inequalities involving local fractional calculus.

    Let Rϵ(0<ϵ1) be ϵ-type set of the real line numbers on Yang's fractal sets, and give the following operation rules, see[17,18]. The sign ϵ represents the fractal dimension, not the exponential sign.

    If αϵ,βϵ,γϵRϵ, then addition and multiplication operations satisfy

    (a) αϵ+βϵRϵ, αϵβϵRϵ,

    (b) αϵ+βϵ=βϵ+αϵ=(α+β)ϵ=(β+α)ϵ,

    (c) αϵ+(βϵ+γϵ)=(α+β)ϵ+γϵ,

    (d) αϵβϵ=βϵαϵ=(αβ)ϵ=(βα)ϵ,

    (e) αϵ(βϵγϵ)=(αϵβϵ)γϵ,

    (f) αϵ(βϵ+γϵ)=αϵβϵ+αϵγϵ,

    (g) αϵ+0ϵ=αϵ, αϵ+(α)ϵ=0ϵ and αϵ1ϵ=1ϵαϵ=αϵ,

    (h) (αβ)ϵ=αϵβϵ.

    Definition 3. [18,19] If there exists the relation

    |ϕ(x)ϕ(x0)|<εϵ

    with |xx0|<δ, for ε,δ>0 and ε,δR.Then the function ϕ(x) is called local fractional continuous at x0. If ϕ(x) is local fractional continuous on (α,β), we denote by ϕ(x)Cϵ(α,β).

    Definition 4. [17,19] Supposing that ϕ(x)Cϵ(α,β), the local fractional derivative of ϕ(x) of order ϵ at x=x0 is defined by

    ϕ(ϵ)(x0)=dϵϕ(x)dxϵ|x=x0=limxx0Γ(ϵ+1)(ϕ(x)ϕ(x0))(xx0)ϵ.

    For any x(α,β), there exists ϕ(ϵ)(x)=D(ϵ)x, denoted by ϕ(ϵ)(x)D(ϵ)x(α,β). Dϵ(α,β) is called ϵ-local fractional derivative set. If there exits ϕ((n+1)ϵ)(x)=(n+1)timesDϵxDϵxϕ(x) for any xIR, then we denote ϕD(n+1)ϵ(I), where n=0,1,2,

    Definition 5. [17,19] Let ϕ(x)Cϵ[α,β]. The local fractional integral of function ϕ(x) of order ϵ is defined by

    αI(ϵ)βϕ(x)=1Γ(ϵ+1)βαϕ(t)(dt)ϵ=1Γ(ϵ+1)limΔt0N1j=0f(tj)(Δtj)ϵ,

    where α=t0<t1<<tN1<tN=β,[tj,tj+1] is a partition of the interval [α,β], Δtj=tj+1tj,Δt=max{Δt0,Δt1ΔtN1}.

    Note that αI(ϵ)αϕ(x)=0, and αI(ϵ)βϕ(x)=βI(ϵ)αϕ(x) if α<β. We denote ϕ(x)I(ϵ)x[α,β] if there exits αI(ϵ)xϕ(x) for any x(α,β).

    Lemma 1. [17]

    (1) Suppose that ϕ(x)=φ(ϵ)(x)Cϵ[α,β], then

    αI(ϵ)βϕ(x)=φ(β)φ(α).

    (2) (Local fractional integration by parts)

    Suppose that ϕ(x),φ(x)Dϵ(α,β), and ϕ(ϵ)(x),φ(ϵ)(x)Cϵ[α,β], then

    αI(ϵ)βϕ(x)φ(ϵ)(x)=[ϕ(x)φ(x)]|βααI(ϵ)βϕ(ϵ)(x)φ(x).

    Lemma 2. [17] Suppose that ϕ(x)Cϵ[α,β] and α<γ<β, then

    αI(ϵ)βϕ(x)=αI(ϵ)γϕ(x)+γI(ϵ)βϕ(x).

    Lemma 3. [17]

    dϵxkϵdxϵ=Γ(1+kϵ)Γ(1+(k1)ϵ)x(k1)ϵ;
    1Γ(ϵ+1)βαxkϵ(dx)ϵ=Γ(1+kϵ)Γ(1+(k+1)ϵ)(β(k+1)ϵα(k+1)ϵ),k>0.

    Lemma 4. [18,30] (Generalized Hölder's inequality) Let ϕ,φCϵ[α,β],p,q>1, with 1p+1q=1, then

    1Γ(ϵ+1)βα|ϕ(x)φ(x)|(dx)ϵ(1Γ(ϵ+1)βα|ϕ(x)|p(dx)ϵ)1/p(1Γ(ϵ+1)βα|φ(x)|q(dx)ϵ)1/q.

    Lemma 5. [17]

    αI(ϵ)β1ϵ=(βα)ϵΓ(1+ϵ).

    Let us introduce the special functions on Yang's fractal sets as follows:

    (1) The generalized Beta function is given by

    Bϵ(x,y)=1Γ(1+ϵ)10t(x1)ϵ(1t)(y1)ϵ(dt)ϵ,x>0,y>0,

    (2) The generalized hypergeometric function is given by

    2Fϵ1(α,β;γ;z)=1Bϵ(β,γβ)1Γ(1+ϵ)10t(β1)ϵ(1t)(γβ1)ϵ(1zt)αϵ(dt)ϵ,γ>β>0,|z|<1.

    For convenience, we use the symbol At to denote tα+(1t)β in the following sections.

    Lemma 6. Let I(0,) be an interval, ϕ:IRϵ (I is the interior of I) such that ϕDϵ(I) and ϕ(ϵ)Cϵ(α,β) for α,βI with α<β. Then the following equality holds

    ϕ(2αβα+β)Γ(1+ϵ)αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵ=I1+I2+I3, (3.1)

    where

    I1=αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)1/20ϕ(ϵ)(αβAt)(dt)ϵ(At)2ϵ,I2=αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)11/2ϕ(ϵ)(αβAt)(dt)ϵ(At)2ϵ,I3=αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)10(12t)ϵϕ(ϵ)(αβAt)(dt)ϵ(At)2ϵ.

    Proof. Calculating I1,I2, from Lemma 1(1), we get

    I1=αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)1/20ϕ(ϵ)(αβAt)(dt)ϵ(At)2ϵ=1ϵ2ϵϕ(αβAt)|1/20=1ϵ2ϵ[ϕ(2αβα+β)ϕ(α)]

    and

    I2=αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)11/2ϕ(ϵ)(αβAt)(dt)ϵ(At)2ϵ=1ϵ2ϵϕ(αβAt)|11/2=1ϵ2ϵ[ϕ(2αβα+β)ϕ(β)].

    Calculating I3, by the local fractional integration by parts, we have

    I3=αϵβϵ(αβ)ϵ2ϵ1Γ(1+ϵ)10(12t)ϵ(At)2ϵϕ(ϵ)(αβAt)(dt)ϵ=(2t1)ϵ2ϵϕ(αβAt)|101Γ(1+ϵ)10Γ(1+ϵ)ϕ(αβAt)(dt)ϵ=ϕ(α)+ϕ(β)2ϵΓ(1+ϵ)Γ(1+ϵ)10ϕ(αβtα+(1t)β)(dt)ϵ.

    Using changing variable with x=αβAt, we have

    I3=ϕ(α)+ϕ(β)2ϵΓ(1+ϵ)(αββα)ϵ1Γ(1+ϵ)βαϕ(x)x2ϵ(dx)ϵ=ϕ(α)+ϕ(β)2ϵΓ(1+ϵ)αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵ.

    Adding I1I3, the desired result is obtained. This completes the proof.

    Theorem 2. Let I(0,) be an interval, ϕ:IRϵ (I is the interior of I) is an increasing function on I such that ϕDϵ(I) and ϕ(ϵ)Cϵ[α,β] for α,βI with α<β. If |ϕ(ϵ)|q is generalized harmonically convex on [α,β] for some fixed q>1, then for all x[α,β], the following local fractional integrals inequality holds,

    |ϕ(2αβα+β)Γ(1+ϵ)αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵ|ϕ(β)ϕ(α)2ϵ+αϵβϵ(βα)ϵ2ϵ(Kϵ1(α,β))11q(Kϵ2(α,β)|ϕ(ϵ)(α)|q+Kϵ3(α,β)|ϕ(ϵ)(β)|q)1q, (3.2)

    where

    Kϵ1(α,β)=β2ϵ[2Fϵ1(2,1;3;12(1αβ))Γ(1+ϵ)Γ(1+2ϵ)+2Fϵ1(2,2;3;1αβ)2ϵΓ(1+ϵ)Γ(1+2ϵ)2Fϵ1(2,1;2;1αβ)Γ(1+ϵ)],Kα2(α,β)=β2ϵ[12ϵ2Fϵ1(2,2;4;12(1αβ))(Γ(1+ϵ)Γ(1+2ϵ)Γ(1+2ϵ)Γ(1+3ϵ))+2ϵ2Fϵ1(2,3;4;1αβ)Γ(1+2ϵ)Γ(1+3ϵ)2Fϵ1(2,2;3;1αβ)Γ(1+ϵ)Γ(1+2ϵ)],Kϵ3(α,β)=β2ϵ[2Fϵ1(2,1;3;12(1αβ))Γ(1+ϵ)Γ(1+2ϵ)+2ϵ2Fϵ1(2,2;4;1αβ)(Γ(1+ϵ)Γ(1+2ϵ)Γ(1+2ϵ)Γ(1+3ϵ))2Fϵ1(2,1;3;1αβ)Γ(1+ϵ)Γ(1+2ϵ)].

    Proof. Since ϕ is an increasing function on I, and 0<α<2αβα+β<β, we can obtain

    ϕ(α)<ϕ(2αβα+β)<ϕ(β).

    From the proof of Lemma 6, we have

    |I1|+|I2|=ϕ(β)ϕ(α)2ϵ. (3.3)

    Taking modulus in equality (3.1), we obtain

    |ϕ(2αβα+β)Γ(1+ϵ)αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵ||I1|+|I2|+|I3|=ϕ(β)ϕ(α)2ϵ+|I3|. (3.4)

    From Lemma 6, using the property of the modulus and the generalized Hölder's inequality, we have

    |I3|=|αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)10(12t)ϵϕ(ϵ)(αβAt)(dt)ϵ(At)2ϵ|αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)10|(12t)ϵA2ϵt||ϕ(ϵ)(αβAt)|(dt)ϵ=αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)10|(12t)ϵA2ϵt|(11q)+1q|ϕ(ϵ)(αβAt)|(dt)ϵαϵβϵ(βα)ϵ2ϵ[1Γ(1+ϵ)10|(12t)ϵA2ϵt|(dt)ϵ]11q[1Γ(1+ϵ)10|(12t)ϵA2ϵt||ϕ(ϵ)(αβAt)|q(dt)ϵ]1q. (3.5)

    Since |ϕ(ϵ)|q is generalized harmonically convex on [α,β], thus

    1Γ(1+ϵ)10|(12t)ϵA2ϵt||ϕ(ϵ)(αβAt)|q(dt)ϵ1Γ(1+ϵ)10|(12t)ϵA2ϵt|(tϵ|ϕ(ϵ)(β)|q+(1t)ϵ|ϕ(ϵ)(α)|q)(dt)ϵ=(1Γ(1+ϵ)10|12t|ϵtϵA2ϵt(dt)ϵ)|ϕ(ϵ)(α)|q+(1Γ(1+ϵ)10|12t|ϵ(1t)ϵA2ϵt(dt)ϵ)|ϕ(ϵ)(β)|q. (3.6)

    By calculating, we get

    1Γ(1+ϵ)10|12t|ϵA2ϵt(dt)ϵ=1Γ(1+ϵ)120(12t)ϵA2ϵt(dt)ϵ+1Γ(1+ϵ)112(2t1)ϵA2ϵt(dt)ϵ=2ϵΓ(1+ϵ)120(12t)ϵA2ϵt(dt)ϵ+1Γ(1+ϵ)10(2t)ϵA2ϵt(dt)ϵ1Γ(1+ϵ)101ϵA2ϵt(dt)ϵ=β2ϵ[1Γ(1+ϵ)10(1u)ϵ(1u2(1αβ))2ϵ(du)ϵ+2ϵΓ(1+ϵ)10tϵ(1(1αβ)t)2ϵ(dt)ϵ1Γ(1+ϵ)10(1(1αβ)t)2ϵ(dt)ϵ]=β2ϵ[2Fϵ1(2,1;3;12(1αβ))Bϵ(1,2)+2ϵ2Fϵ1(2,2;3;1αβ)Bϵ(2,1)2Fϵ1(2,1;2;1αβ)Bϵ(1,1)]=β2ϵ[2Fϵ1(2,1;3;12(1αβ))Γ(1+ϵ)Γ(1+2ϵ)+2Fϵ1(2,2;3;1αβ)2ϵΓ(1+ϵ)Γ(1+2ϵ)2Fϵ1(2,1;2;1αβ)Γ(1+ϵ)]=Kϵ1(α,β). (3.7)

    Similarly, we get

    1Γ(1+ϵ)10|12t|ϵtϵA2ϵt(dt)ϵ=1Γ(1+ϵ)120(12t)ϵtϵA2ϵt(dt)ϵ+1Γ(1+ϵ)112(2t1)ϵtϵA2ϵt(dt)ϵ=2ϵΓ(1+ϵ)120tϵ(12t)ϵA2ϵt(dt)ϵ+1Γ(1+ϵ)102ϵt2ϵA2ϵt(dt)ϵ1Γ(1+ϵ)10tϵA2ϵt(dt)ϵ=β2ϵ[12ϵ1Γ(1+ϵ)10uϵ(1u)ϵ(1u2(1αβ))2ϵ(du)ϵ+2ϵΓ(1+ϵ)10t2ϵ(1(1αβ)t)2ϵ(dt)ϵ1Γ(1+ϵ)10tϵ(1(1αβ)t)2ϵ(dt)ϵ]=β2ϵ[12ϵ2Fϵ1(2,2;4;12(1αβ))(Γ(1+ϵ)Γ(1+2ϵ)Γ(1+2ϵ)Γ(1+3ϵ))+2ϵ2Fϵ1(2,3;4;1αβ)Γ(1+2ϵ)Γ(1+3ϵ)2Fϵ1(2,2;3;1αβ)Γ(1+ϵ)Γ(1+2ϵ)]=Kϵ2(α,β), (3.8)

    and

    1Γ(1+ϵ)10|12t|ϵ(1t)ϵA2ϵt(dt)ϵ=2ϵΓ(1+ϵ)120(12t)ϵ(1t)ϵA2ϵt(dt)ϵ+1Γ(1+ϵ)10(2t1)ϵ(1t)ϵA2ϵt(dt)ϵ2ϵΓ(1+ϵ)120(12t)ϵA2ϵt(dt)ϵ+1Γ(1+ϵ)102ϵtϵ(1t)ϵA2ϵt(dt)ϵ1Γ(1+ϵ)10(1t)ϵA2ϵt(dt)ϵ=β2ϵ[1Γ(1+ϵ)10(1u)ϵ(1u2(1αβ))2ϵ(du)ϵ+2ϵΓ(1+ϵ)10tϵ(1t)ϵ(1(1αβ)t)2ϵ(dt)ϵ1Γ(1+ϵ)10(1t)ϵ(1(1αβ)t)2ϵ(dt)ϵ]=β2ϵ[2Fϵ1(2,1;3;12(1αβ))Γ(1+ϵ)Γ(1+2ϵ)+2ϵ2Fϵ1(2,2;4;1αβ)(Γ(1+ϵ)Γ(1+2ϵ)Γ(1+2ϵ)Γ(1+3ϵ))2Fϵ1(2,1;3;1αβ)Γ(1+ϵ)Γ(1+2ϵ)]=Kϵ3(α,β). (3.9)

    From (3.4)–(3.9), we get inequality (3.2). This completes the proof.

    Theorem 3. Let I(0,) be an interval, ϕ:IRϵ is an increasing function on I such that ϕDϵ(I) and ϕ(ϵ)Cϵ[α,β] for α,βI with α<β. If |ϕ(ϵ)|q is generalized harmonically convex on [α,β], q>1,1p+1q=1, then for all x[α,β], the following local fractional integrals inequality holds.

    |ϕ(2αβα+β)Γ(1+ϵ)αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵ|ϕ(β)ϕ(α)2ϵ+αϵ(βα)ϵ2ϵβϵ[Γ(1+pϵ)Γ(1+(p+1)ϵ)]1p(Γ(1+ϵ)Γ(1+2ϵ))1q×[2Fϵ1(2q,2;3;1αβ)|ϕ(ϵ)(α)|q+2Fϵ1(2q,1;3;1αβ)|ϕ(ϵ)(β)|q]1q. (3.10)

    Proof. From inequality (3.4), we have

    |ϕ(2αβα+β)Γ(1+ϵ)αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵ|ϕ(β)ϕ(α)2ϵ+|I3|. (3.11)

    From Lemma 6, using the property of the modulus and the generalized Hölder's inequality, we have

    |I3|αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)10|12t|ϵ|1A2ϵtϕ(ϵ)(αβAt)|(dt)ϵαϵβϵ(βα)ϵ2ϵ[1Γ(1+ϵ)10|12t|ϵp(dt)ϵ]1p[1Γ(1+ϵ)101A2ϵqt|ϕ(ϵ)(αβAt)|q(dt)ϵ]1q. (3.12)

    Since |ϕ(ϵ)|q is generalized harmonically convex on [α,β], we can get

    1Γ(1+ϵ)101A2ϵqt|ϕ(ϵ)(αβAt)|q(dt)ϵ1Γ(1+ϵ)101A2qϵt(tϵ|ϕ(ϵ)(β)|q+(1t)ϵ|ϕ(ϵ)(α)|q)(dt)ϵ=(1Γ(1+ϵ)10tϵA2qϵt(dt)ϵ)|ϕ(ϵ)(β)|q+(1Γ(1+ϵ)10(1t)ϵA2qϵt(dt)ϵ)|ϕ(ϵ)(α)|q. (3.13)

    By calculating, we have

    1Γ(1+ϵ)10tϵA2qϵt(dt)ϵ=β2qϵ1Γ(1+ϵ)10tϵ(1(1αβ)t)2qϵ(dt)ϵ=β2qϵ2Fϵ1(2q,2;3;1αβ)Bϵ(2,1),=Γ(1+ϵ)β2qϵΓ(1+2ϵ)2Fϵ1(2q,2;3;1αβ), (3.14)
    1Γ(1+ϵ)10(1t)ϵA2qϵt(dt)ϵ=β2qϵ1Γ(1+ϵ)10(1t)ϵ(1(1αβ)t)2qϵ(dt)ϵ=β2qϵ2Fϵ1(2q,1;3;1αβ)Bϵ(1,2),=Γ(1+ϵ)β2qϵΓ(1+2ϵ)2Fϵ1(2q,1;3;1αβ), (3.15)

    and

    1Γ(1+ϵ)10|12t|ϵp(dt)ϵ=Γ(1+pϵ)Γ(1+(p+1)ϵ). (3.16)

    Thus, combining (3.11)–(3.16), we obtain the required inequality. The proof is completed.

    Theorem 4. Let I(0,) be an interval, ϕ:IRϵ is an increasing function on I such that ϕDϵ(I) and ϕ(ϵ)Cϵ[α,β] for α,βI with α<β. If |ϕ(ϵ)|q is generalized harmonically convex on [α,β], q>1,1p+1q=1, then for all x[α,β], the following local fractional integrals inequality holds.

    |ϕ(2αβα+β)Γ(1+ϵ)αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵ|ϕ(β)ϕ(α)2ϵ+αϵ(βα)ϵ2ϵβϵ[2Fϵ1(2p,1;2;1αβ)Γ(1+ϵ)]1p(Γ(1+qϵ)2ϵΓ(1+(q+1)ϵ))1q×[|ϕ(ϵ)(α)|q+|ϕ(ϵ)(β)|q]1q. (3.17)

    Proof. From Lemma 6, using the generalized Hölder's inequality and the generalized harmonically convexity of |ϕ(ϵ)|q, we have

    |I3|αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)10|12t|ϵ1A2ϵt|ϕ(ϵ)(αβAt)|(dt)ϵαϵβϵ(βα)ϵ2ϵ[1Γ(1+ϵ)101A2ϵpt(dt)ϵ]1p[1Γ(1+ϵ)10|12t|ϵq|ϕ(ϵ)(αβAt)|q(dt)ϵ]1qαϵβϵ(βα)ϵ2ϵ[1Γ(1+ϵ)101A2ϵpt(dt)ϵ]1p×[1Γ(1+ϵ)10|12t|ϵq(tϵ|ϕ(ϵ)(β)|q+(1t)ϵ|ϕ(ϵ)(α)|q)(dt)ϵ]1q. (3.18)

    By calculating, we have

    1Γ(1+ϵ)101A2pϵt(dt)ϵ=β2pϵ1Γ(1+ϵ)10(1(1αβ)t)2pϵ(dt)ϵ=β2pϵ2Fϵ1(2p,1;2;1αβ)Bϵ(1,1),=2Fϵ1(2p,1;2;1αβ)β2pϵΓ(1+ϵ), (3.19)
    1Γ(1+ϵ)10|12t|ϵqtϵ(dt)ϵ=1Γ(1+ϵ)1/20(12t)ϵqtϵ(dt)ϵ+1Γ(1+ϵ)11/2(2t1)ϵqtϵ(dt)ϵ=Γ(1+qϵ)2ϵΓ(1+(q+1)ϵ), (3.20)

    and

    1Γ(1+ϵ)10|12t|ϵq(1t)ϵ(dt)ϵ=1Γ(1+ϵ)1/20(12t)ϵq(1t)ϵ(dt)ϵ+1Γ(1+ϵ)11/2(2t1)ϵq(1t)ϵ(dt)ϵ=Γ(1+qϵ)2ϵΓ(1+(q+1)ϵ). (3.21)

    From (3.11) in Theorem 3, combining (3.18)–(3.21), we obtain the required inequality. The proof is completed.

    Theorem 5. Let I(0,) be an interval, ϕ:IRϵ is an increasing function on I such that ϕDϵ(I) and ϕ(ϵ)Cϵ[α,β] for α,βI with α<β. If |ϕ(ϵ)|q is generalized harmonically convex on [α,β], q>1,1p+1q=1, then for all x[α,β], the following local fractional integrals inequality holds.

    |ϕ(2αβα+β)Γ(1+ϵ)αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵ|ϕ(β)ϕ(α)2ϵ+αϵ(βα)ϵ2ϵβϵ[Γ(1+pϵ)Γ(1+(p+1)ϵ)]1p[(2Fϵ1(2p,1;p+2;1αβ))1p+(2Fϵ1(2p,p+1;p+2;1αβ))1p](Γ(1+ϵ)Γ(1+2ϵ))1q[|ϕ(ϵ)(α)|q+|ϕ(ϵ)(β)|q]1q. (3.22)

    Proof. Note that (αβ)ϵ=αϵβϵ. From Lemma 6, using the generalized Hölder's inequality and the generalized harmonically convexity of |ϕ(ϵ)|q, we have

    |I3|αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)10|12t|ϵA2ϵt|ϕ(ϵ)(αβAt)|(dt)ϵ=αϵβϵ(βα)ϵ2ϵ1Γ(1+ϵ)10|(1t)ϵtϵ|A2ϵt|ϕ(ϵ)(αβAt)|(dt)ϵαϵβϵ(βα)ϵ2ϵ[1Γ(1+ϵ)10(1t)ϵA2ϵt|ϕ(ϵ)(αβAt)|(dt)ϵ+1Γ(1+ϵ)10tϵA2ϵt|ϕ(ϵ)(αβAt)|(dt)ϵ]αϵβϵ(βα)ϵ2ϵ[(1Γ(1+ϵ)10(1t)ϵpA2ϵpt(dt)ϵ)1/p(1Γ(1+ϵ)10|ϕ(ϵ)(αβAt)|q(dt)ϵ)1/q+(1Γ(1+ϵ)10tϵpA2ϵpt(dt)ϵ)1/p(1Γ(1+ϵ)10|ϕ(ϵ)(αβAt)|q(dt)ϵ)1/q]=αϵβϵ(βα)ϵ2ϵ[(1Γ(1+ϵ)10(1t)ϵpA2ϵpt(dt)ϵ)1/p+(1Γ(1+ϵ)10tϵpA2ϵpt(dt)ϵ)1/p]×(1Γ(1+ϵ)10|ϕ(ϵ)(αβAt)|q(dt)ϵ)1/qαϵβϵ(βα)ϵ2ϵ[(1Γ(1+ϵ)10(1t)ϵpA2ϵpt(dt)ϵ)1/p+(1Γ(1+ϵ)10tϵpA2ϵpt(dt)ϵ)1/p]×(1Γ(1+ϵ)10[tϵ|ϕ(ϵ)(β)|q+(1t)ϵ|ϕ(ϵ)(α)|q](dt)ϵ)1/q. (3.23)

    By calculating, we have

    1Γ(1+ϵ)10(1t)ϵpA2ϵpt(dt)ϵ=β2pϵ1Γ(1+ϵ)10(1t)ϵp[1(1αβ)t]2pϵ(dt)ϵ=β2pϵ2Fϵ1(2p,1;p+2;1αβ)Bϵ(1,p+1)=β2pϵΓ(1+pϵ)Γ(1+(p+1)ϵ)2Fϵ1(2p,1;p+2;1αβ). (3.24)

    Similarly,

    1Γ(1+ϵ)10tϵpA2ϵpt(dt)ϵ=β2pϵ2Fϵ1(2p,p+1;p+2;1αβ)Bϵ(p+1,1)=β2pϵΓ(1+pϵ)Γ(1+(p+1)ϵ)2Fϵ1(2p,p+1;p+2;1αβ). (3.25)

    And

    1Γ(1+ϵ)10tϵ(dt)ϵ=1Γ(1+ϵ)10(1t)ϵ(dt)ϵ=Γ(1+ϵ)Γ(1+2ϵ). (3.26)

    From (3.11) in Theorem 3, combining (3.23)–(3.26), we obtain the required inequality. The proof is completed.

    We consider the following ϵ-type generalized special means of the real line numbers αϵ,βϵ with α<β on Yang's fractal sets.

    (1) The generalized arithmetic mean

    Aϵ(α,β)=αϵ+βϵ2ϵ;

    (2) The generalized p-logarithmic mean

    Lpϵ(α,β)=[Γ(1+pϵ)Γ(1+(p+1)ϵ)β(p+1)ϵα(p+1)ϵ(βα)ϵ]1/p,pR{1,0};

    (3) The generalized geometric mean

    Gϵ(α,β)=(αϵβϵ)12;

    (4) The generalized harmonic mean

    Hϵ(α,β)=(2αβ)ϵαϵ+βϵ.

    Consider the function ϕ:(0,)Rϵ, ϕ(x)=Γ(1+kϵ)Γ(1+(k+1)ϵ)x(k+1)ϵ, x>0,k1 and q1. Because the function φ(x)=|ϕ(ϵ)(x)|q=xkqϵ is generalized convex and nondecreasing on (0,), by Proposition 3.3 in [22], the function φ(x) is generalized harmonically convex on (0,).

    Let ϕ(x)=Γ(1+kϵ)Γ(1+(k+1)ϵ)x(k+1)ϵ, x>0,k>1 and q>1. Then

    ϕ(2αβα+β)=Γ(1+kϵ)Γ(1+(k+1)ϵ)Hk+1ϵ(α,β),
    αϵβϵ(βα)ϵαI(ϵ)βϕ(x)x2ϵ=Γ(1+kϵ)Γ(1+(k+1)ϵ)Lk1(k1)ϵ(α,β)G2ϵ(α,β),
    ϕ(β)ϕ(α)2ϵ=(βα)ϵ2ϵLkkϵ(α,β).

    Proposition 1. From Theorem 2, we obtain the following inequality

    |Hk+1ϵ(α,β)Γ(1+ϵ)Lk1(k1)ϵ(α,β)G2ϵ(α,β)|(βα)ϵΓ(1+(k+1)ϵ)2ϵΓ(1+kϵ)[Lkkϵ(α,β)+αϵβϵ(Kϵ1(α,β))11q(Kϵ2(α,β)αkqϵ+Kϵ3(α,β)βkqϵ)1q],

    where Kϵ1(α,β),Kϵ2(α,β) and Kϵ3(α,β) as in Theorem 2.

    Proposition 2. From Theorem 3, we obtain the following inequality

    |Hk+1ϵ(α,β)Γ(1+ϵ)Lk1(k1)ϵ(α,β)G2ϵ(α,β)|(βα)ϵΓ(1+(k+1)ϵ)2ϵΓ(1+kϵ)[Lkkϵ(α,β)+αϵβϵ(Γ(1+pϵ)Γ(1+(p+1)ϵ))1p(Γ(1+ϵ)Γ(1+2ϵ))1q×(2Fϵ1(2q,2;3;1αβ)αkqϵ+2Fϵ1(2q,1;3;1αβ)βkqϵ)1q],

    where 1p+1q=1,q>1.

    Proposition 3. From Theorem 4, we obtain the following inequality

    |Hk+1ϵ(α,β)Γ(1+ϵ)Lk1(k1)ϵ(α,β)G2ϵ(α,β)|(βα)ϵΓ(1+(k+1)ϵ)2ϵΓ(1+kϵ)[Lkkϵ(α,β)+αϵβϵ(2Fϵ1(2p,1;2;1αβ)Γ(1+ϵ))1p(Γ(1+qϵ)2ϵΓ(1+(q+1)ϵ))1q×(αkqϵ+βkqϵ)1q],

    where 1p+1q=1,q>1.

    Proposition 4. From Theorem 5, we obtain the following inequality

    |Hk+1α(α,β)Γ(1+ϵ)Lk1(k1)ϵ(α,β)G2ϵ(α,β)|(βα)ϵΓ(1+(k+1)ϵ)2ϵΓ(1+kϵ){Lkkϵ(α,β)+αϵβϵ(Γ(1+pϵ)Γ(1+(p+1)ϵ))1p[(2Fϵ1(2p,1;p+2;1αβ))1p+(2Fϵ1(2p,p+1;p+2;1αβ))1p](Γ(1+ϵ)Γ(1+2ϵ))1q(αkqϵ+βkqϵ)1q},

    where 1p+1q=1,q>1.

    In this paper, the research on Hermite-Hadamard type inequalities is extended to Yang's fractal space. By using the definitions of generalized harmonically convex function and the theory of local fractional calculus, we construct some new Hermite-Hadamard type integral inequalities for monotonically increasing functions with generalized harmonically convexity. Some applications related to the special mean are established by using the obtained inequalities, which shows that our results have certain application significance. Our research may inspire more scholars to further explore Hermite-Hadamard type integral inequalities on Yang's fractal sets.

    This work is supported by the Natural Science Foundation of Hunan Province (No. 2020JJ4554) and Scientific Research Project of Hunan Provincial Education Department (No. 18B433).

    This work does not have any conflicts of interest.



    [1] D. W. Alexander, R. Merkert, Challenges to domestic air freight in Australia: evaluating air traffic markets with gravity modelling, J. Air Transp. Manage., 61 (2017), 41–55. https://doi.org/10.1016/j.jairtraman.2016.11.008 doi: 10.1016/j.jairtraman.2016.11.008
    [2] C. H. Guo, Research on application of scheduling optimization of ETV based on improved genetic algorithm, Logist. Sci-Tech, 38 (2015), 61–69. https://doi.org/10.13714/j.cnki.1002-3100.2015.10.019 doi: 10.13714/j.cnki.1002-3100.2015.10.019
    [3] J. D. Qiu, Z. Y. Jiang, M. N. Tang, Research and application of NLAPSO algorithm to ETV scheduling optimization in airport cargo terminal, J. Lanzhou Jiaotong Univ., 34 (2015), 65–70. https://doi.org/10.3969/j.issn.1001-4373.2015.01.013 doi: 10.3969/j.issn.1001-4373.2015.01.013
    [4] B. Lei, Study on two-ETV task scheduling of airport cargo terminal based on expert system, Logist. Sci-Tech, 38 (2015), 13–16. https://doi.org/10.13714/j.cnki.1002-3100.2015.03.004 doi: 10.13714/j.cnki.1002-3100.2015.03.004
    [5] F. Ding, X. J. Song, Application of shared fitness particle swarm in double ETV system, Comput. Meas. Control, 26 (2018), 228–247. https://doi.org/10.16526/j.cnki.11-4762/tp.2018.11.050 doi: 10.16526/j.cnki.11-4762/tp.2018.11.050
    [6] H. Q. Wang, J. H. Wei, S. J. Wen, H. N. Yu, X. G. Zhang, Improved artificial bee colony algorithm and its application in classification, J. Rob. Mechatron., 30 (2018), 921–926. https://doi.org/10.20965/jrm.2018.p0921 doi: 10.20965/jrm.2018.p0921
    [7] L. Z. Cui, G. H. Li, Y. L. Luo, F. Chen, Z. Ming, N. Lu, et al., An enhanced artificial bee colony algorithm with dual-population framework, Swarm Evol. Comput., 43 (2018), 184–206. https://doi.org/10.1016/j.swevo.2018.05.002 doi: 10.1016/j.swevo.2018.05.002
    [8] L. Z. Cui, G. H. Li, Z. X. Zhu, Q. Z. Lin, Z. K. Wen, N. Lu, et al., A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization, Inf. Sci., 414 (2017), 53–67. https://doi.org/10.1016/j.ins.2017.05.044 doi: 10.1016/j.ins.2017.05.044
    [9] D. Karaboga, B. Basturk, Artificial bee colony optimization algorithm for solving constrained optimization problems, in Foundations of Fuzzy Logic and Soft Computing, (2007), 789–798. https://doi.org/10.1007/978-3-540-72950-1_77
    [10] Y. C. Li, J. Wang, L. B. Liu, J. Zhao, Improved artificial bee algorithm for reliability-based optimization of truss structures, Open Civ. Eng. J., 11 (2017), 235–243. https://doi.org/10.2174/1874149501711010235 doi: 10.2174/1874149501711010235
    [11] K. P. Luo, A hybrid binary artificial bee colony algorithm for the satellite photograph scheduling problem, Eng. Optim., 52 (2019), 1421–1440. https://doi.org/10.1080/0305215X.2019.1657113 doi: 10.1080/0305215X.2019.1657113
    [12] A. K. Alazzawi, H. Rais, S. Basri, Y. A. Alsariera, PhABC: A hybrid artificial bee colony strategy for t-way test set generation with constraints support, in 2019 IEEE Student Conference on Research and Development, (2019), 106–111. https://doi.org/10.1109/scored.2019.8896324
    [13] F. Weidinger, Picker routing in rectangular mixed shelves warehouses, Comput. Oper. Res., 95 (2018), 139–150. https://doi.org/10.1016/j.cor.2018.03.012 doi: 10.1016/j.cor.2018.03.012
    [14] J. J. Zhou, X. F. Yao, A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition, Int. J. Adv. Manuf. Technol., 88 (2017), 3371–3387. https://doi.org/10.1007/s00170-016-9034-1 doi: 10.1007/s00170-016-9034-1
    [15] G. Chen, P. Sun, J. Zhang, Repair strategy of military communication network based on discrete artificial bee colony algorithm, IEEE Access, 8 (2020), 73051–73060. https://doi.org/10.1109/ACCESS.2020.2987860 doi: 10.1109/ACCESS.2020.2987860
    [16] M. Ghanem, A. Jantan, A novel hybrid artificial bee colony with monarch butterfly optimization for global optimization problems, in First EAI International Conference on Computer Science and Engineering, (2017), 27–38. http://dx.doi.org/10.4108/eai.27-2-2017.152257
    [17] X. Chen, X. Wei, G. X. Yang, W. L. Du, Fireworks explosion based artificial bee colony for numerical optimization, Knowledge-Based Syst., 188 (2020), 105002. https://doi.org/10.1016/j.knosys.2019.105002 doi: 10.1016/j.knosys.2019.105002
    [18] P. J. Gaidhane, M. J. Nigam, A hybrid grey wolf optimizer and artificial bee colony algorithm for enhancing the performance of complex systems, J. Comput. Sci., 27 (2018), 284–302. https://doi.org/10.1016/j.jocs.2018.06.008 doi: 10.1016/j.jocs.2018.06.008
    [19] Z. P. Liang, K. F. Hu, Q. X. Zhu, Z. X. Zhu, An enhanced artificial bee colony algorithm with adaptive differential operators, Appl. Soft Comput., 58 (2017), 480–494. https://doi.org/10.1016/j.asoc.2017.05.005 doi: 10.1016/j.asoc.2017.05.005
    [20] F. Y. Xu, H. L. Li, C. M. Pun, H. D. Hu, Y. J. Li, Y. R. Song, et al., A new global best guided artificial bee colony algorithm with application in robot path planning, Appl. Soft Comput., 88 (2020), 106037. https://doi.org/10.1016/j.asoc.2019.106037 doi: 10.1016/j.asoc.2019.106037
    [21] X. Y. Song, M. Zhao, Q. F. Yan, S. G. Xing, A high-efficiency adaptive artificial bee colony algorithm using two strategies for continuous optimization, Swarm Evol. Comput., 50 (2019), 100549. https://doi.org/10.1016/j.swevo.2019.06.006 doi: 10.1016/j.swevo.2019.06.006
    [22] W. F. Gao, Z. F. Wei, Y. T. Luo, J. Cao, Artificial bee colony algorithm based on parzen window method, Appl. Soft Comput., 74 (2019), 679–692. https://doi.org/10.1016/j.asoc.2018.10.024 doi: 10.1016/j.asoc.2018.10.024
    [23] H. Wang, W. J. Wang, S. Y. Xiao, Z. H. Cui, M. Y. Xu, X. Y. Zhou, Improving artificial bee colony algorithm using a new neighborhood selection mechanism, Inf. Sci., 527 (2020), 227–240. https://doi.org/10.1016/j.ins.2020.03.064 doi: 10.1016/j.ins.2020.03.064
    [24] S. Q. Zhang, J. F. Teng, J. H. Gu, Artificial bee algorithm based on multi-dimensional greedy search, Comput. Eng., 40 (2014), 189–193. https://doi.org/10.3969/j.issn.1000-3428.2014.11.037 doi: 10.3969/j.issn.1000-3428.2014.11.037
    [25] W. L. Xiang, X. L. Meng, Y. Z. Li, R. C. He, M. Q. An, An improved artificial bee colony algorithm based on the gravity model, Inf. Sci., 429 (2018), 49–71. https://doi.org/10.1016/j.ins.2017.11.007 doi: 10.1016/j.ins.2017.11.007
    [26] H. Q. Wang, M. H. Su, R. Zhao, X. B. Xu, H. D. Haasis, J. H. Wei, et al., Improved multi-dimensional bee colony algorithm for airport freight station scheduling, preprint, arXiv: 2207.11651.
    [27] H. Q. Wang, J. H. Wei, M. H. Su, Z. Dong, S. S. Zhang, Task set scheduling of airport freight station based on parallel artificial bee colony algorithm, in Bio-inspired Computing: Theories and Applications, (2019), 484–492. https://doi.org/10.1007/978-981-15-3425-6_37
    [28] J. C. Bansal, A. Gopal, A. K. Nagar, Stability analysis of artificial bee colony optimization algorithm, Swarm Evol. Comput., 41 (2018), 9–19. https://doi.org/10.1016/j.swevo.2018.01.003 doi: 10.1016/j.swevo.2018.01.003
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3168) PDF downloads(77) Cited by(4)

Figures and Tables

Figures(8)  /  Tables(10)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog