
Industries face many challenges when emergencies arise. In emergency, there is an increasing demand for self-administered products that are easy to use. The decay rate of these products decreases with time. Moreover, the lack of disposal of used products increases waste and carbon emissions. By observing the scenario, this study develops a closed-loop supply chain management that considers the collection and remanufacturing of used products. The manufacturing rate is linear and the demand is ramp-type and carbon emissions dependent. The model is solved by a classical optimization and calculates the optimal total cost. The results show that the retailer can handle a shortage situation when the demand becomes stable (Case 2) and the total cost increases with the production rate. A sensitivity analysis shows the changes in the total cost with respect to the parameters.
Citation: Subhash Kumar, Ashok Kumar, Rekha Guchhait, Biswajit Sarkar. An environmental decision support system for manufacturer-retailer within a closed-loop supply chain management using remanufacturing[J]. AIMS Environmental Science, 2023, 10(5): 644-676. doi: 10.3934/environsci.2023036
[1] | Rajish Kumar P, Sunil Jacob John . On redundancy, separation and connectedness in multiset topological spaces. AIMS Mathematics, 2020, 5(3): 2484-2499. doi: 10.3934/math.2020164 |
[2] | Seçil Çeken, Cem Yüksel . Generalizations of strongly hollow ideals and a corresponding topology. AIMS Mathematics, 2021, 6(12): 12986-13003. doi: 10.3934/math.2021751 |
[3] | Saad Ihsan Butt, Ahmet Ocak Akdemir, Muhammad Nadeem, Nabil Mlaiki, İşcan İmdat, Thabet Abdeljawad . (m,n)-Harmonically polynomial convex functions and some Hadamard type inequalities on the co-ordinates. AIMS Mathematics, 2021, 6(5): 4677-4690. doi: 10.3934/math.2021275 |
[4] | Qian Liu, Jianrui Xie, Ximeng Liu, Jian Zou . Further results on permutation polynomials and complete permutation polynomials over finite fields. AIMS Mathematics, 2021, 6(12): 13503-13514. doi: 10.3934/math.2021783 |
[5] | Usman Babar, Haidar Ali, Shahid Hussain Arshad, Umber Sheikh . Multiplicative topological properties of graphs derived from honeycomb structure. AIMS Mathematics, 2020, 5(2): 1562-1587. doi: 10.3934/math.2020107 |
[6] | Ninghe Yang . Exact wave patterns and chaotic dynamical behaviors of the extended (3+1)-dimensional NLSE. AIMS Mathematics, 2024, 9(11): 31274-31294. doi: 10.3934/math.20241508 |
[7] | Ali N. A. Koam, Ali Ahmad, Yasir Ahmad . Computation of reverse degree-based topological indices of hex-derived networks. AIMS Mathematics, 2021, 6(10): 11330-11345. doi: 10.3934/math.2021658 |
[8] | R. Aguilar-Sánchez, J. A. Mendez-Bermudez, José M. Rodríguez, José M. Sigarreta . Multiplicative topological indices: Analytical properties and application to random networks. AIMS Mathematics, 2024, 9(2): 3646-3670. doi: 10.3934/math.2024179 |
[9] | Muhammad Riaz, Khadija Akmal, Yahya Almalki, S. A. Alblowi . Cubic m-polar fuzzy topology with multi-criteria group decision-making. AIMS Mathematics, 2022, 7(7): 13019-13052. doi: 10.3934/math.2022721 |
[10] | Maryam Salem Alatawi, Ali Ahmad, Ali N. A. Koam, Sadia Husain, Muhammad Azeem . Computing vertex resolvability of benzenoid tripod structure. AIMS Mathematics, 2022, 7(4): 6971-6983. doi: 10.3934/math.2022387 |
Industries face many challenges when emergencies arise. In emergency, there is an increasing demand for self-administered products that are easy to use. The decay rate of these products decreases with time. Moreover, the lack of disposal of used products increases waste and carbon emissions. By observing the scenario, this study develops a closed-loop supply chain management that considers the collection and remanufacturing of used products. The manufacturing rate is linear and the demand is ramp-type and carbon emissions dependent. The model is solved by a classical optimization and calculates the optimal total cost. The results show that the retailer can handle a shortage situation when the demand becomes stable (Case 2) and the total cost increases with the production rate. A sensitivity analysis shows the changes in the total cost with respect to the parameters.
In today's world mathematics is necessary in all fields. It is an essential instrument for comprehension around us. It covers all the facts of life. Mathematics is the branch of science that deals with the reasoning of figures, numbers and order. In our daily life, we use mathematics in our routine work in various forms. There are many branches of mathematics like algebra, geometry, arithmetic, trigonometry, analysis and many other theories. Graph theory is the study of mathematical objects known as graph, which consist of vertices connected by edges. It is the mathematical theory which deals with the properties and applications of graphs. When we apply graph theory on chemistry then it is called chemical graph theory. In mathematical models graph theory is used to get a deep understanding of the physical properties of these chemical compounds. Some physical properties such as boiling point, melting point, density are associated to geometrical structure of the compound. Now a days, several ways are used in mathematical chemistry to understand chemical structure which are existing behind the chemical concepts, to create and inquire novel mathematical representation. In complete history of chemistry certain scientist, usually contemplates connections between mathematics with chemistry and the possibility of using mathematics to analyze and predict new chemical concepts. Mufti defined sanskruti and harmonic indices of certain graph structure [25]. Babujee calculated topological indices and new graph structures in 2012 [5]. Farahani worked on a new version of zagreb index of circumcoronene series of benzenoid in 2013 [7]. Hayat defined some degree based topological indices of certain nanotubes [8]. Imran worked on topological indices of certain interconnection networks in 2014 [14]. In 2016, Siddiqui computed zagreb indices and zagreb polynomials of some nanostars dendrimers [27]. Saleem computed retractions and homomorphisms on some operations of graphs [28]. Iqbal calculated eccentricity based topological indices of some benzenoid structures [12]. Yang examined two-point resistances and random walks on stellated regular graphs [29]. Islam defined M-polynomial and entropy of paraline graph of Napthalene in 2019 [11]. Iqbal worked on topological indices of subdivided and line graph of subdivided friendship graph [13]. In 2020, Afzal examined M-polynomial and topological indices of zigzag edge coronoid fused by starphene [1]. Archdeacon defined the medial graph and voltage-current duality [2]. Munir computed M-polynomial and degree-based topological indices of polyhex nanotubes [22]. Iqbal calculated ve topological indices of tickysim spinnaker model [15]. Azhar examined a note on valency dependence invariants of L(G(K)) Graph [4]. Jamil defined the first general zagreb eccentricity index [18]. Iqbal defined ABC4 and GA5 index of subdivided and line graph of subdivided dutch windmill graph [16]. Maji worked on the first entire zagreb index of various corona products and their bounds [23]. Imran describe computation of topological indices of NEPS of graphs [17]. Hayat computed topological indices for networks derived by applying graph operations from honeycomb structures [9], based on this idea we have computed the topological indices for transformed structures. Next we have few definitions [3,9]:
Definition 1:
Let K be a simple connected graph its M-polynomial is defined as;
M(K;x,y)=∑S≤a≤b≤Tmab(K)xayb, (i) |
where: S = Min{dβ|β∈V(K)}, T = Max{dβ|β∈V(K)}, and mab(K) is the number of edges γβ∈E(K) such that {dγ, dβ} = {a,b}.
Definition 2:
When we place a new vertex in each face of a planar graph G, and attach that vertex with all the vertices of the respective face of G, we get the stellation of G and denote it as St(G).
Definition 3:
We introduce a node in each edge of the graph and join the nodes if their corresponding edges are adjacent and is denoted by Md(G).
Definition 4:
We introduce a node in each bounded faces of the graph and join the nodes by an edge if the faces share an edge in graph it is called bounded dual. It is represented as Bdu(G).
T. Réti introduced First and Second Zagreb indices are [26],
M1(K)=∑γβ∈E(K)(dγ+dβ), | (1.1) |
M2(K)=∑γβ∈E(K)(dγdβ). | (1.2) |
Second modified Zagreb index is defined as [10],
mM2(k)=∑γβ∈E(K)(1dγdβ). | (1.3) |
Symmetric division and reciprocal general Randic index is [19],
SDD(K)=∑γβ∈E(K) {min(dγ,dβ)max(dγ,dβ)+max(dγ,dβ)min(dγ,dβ)}, | (1.4) |
RRα(K)=∑γβ∈E(K)1(dγdβ)α. | (1.5) |
Whereas, the General Randic index defined as [24],
Rα(K)=∑γβ∈E(K)(dγdβ)α, | (1.6) |
where α is an arbitrary real number.
In 1998, the Atomic Bond Connectivity Index is defined as [6],
ABC(K)=∑γβ∈E(K)√dγ+dβ−2dγdβ. | (1.7) |
Geometric-Arithmetic index is [30],
GA(K)=∑γβ∈E(K)2√dγdβdγ+dβ. | (1.8) |
In 2015, the General Version of Harmonic Index is defined [31],
Hk(K)=∑γβ∈E(K)(2dγ+dβ)k. | (1.9) |
First and Second Gourava Indices were introduced by Kalli which is defined as, respectively [20],
GO1(K)=∑γβ∈E(K)[(dγ+dβ)+(dγdβ)], | (1.10) |
GO2(K)=∑γβ∈E(K)[(dγ+dβ)(dγdβ)]. | (1.11) |
Kalli introduced the First and Second Hyper-Gourava Indices which is defined as, respectively [21],
HGO1(K)=∑γβ∈E(K)[(dγ+dβ)+(dγdβ)]2, | (1.12) |
HGO2(K)=∑γβ∈E(K)[(dγ+dβ)(dγdβ)]2. | (1.13) |
At first, we obtain transformed pattern of molecular structures zigzag and triangular benzenoid named as T1 and T2. Next we define M-polynomial and topological indices of these networks by applying the stellation, medial and bounded dual. Further we define edge partition depending upon degree based vertices of T1 and T2 and then calculate the indices.
Now we will construct the stellation, medial and bounded dual operations on simple and undirected zigzag benzenoid structure. We get a new transformed network (say) T1, shown in Figure 1 for n = 2. After this we will compute M-polynomial and degree based topological indices of our newly obtained network. Where, blue is stellation, red is medial, green is bounded dual operations applied on T1.
Following Table 3 shows the types of edges and their count for network T1; n≥2. Now, we will calculate the M-polynomial and vertex degree based topological indices namely first Zagreb index, second Zagreb index, second modified index, symmetric division index, general Randic index, reciprocal general Randic index, atomic bond connectivity index, geometric arithmetic index, general version of harmonic index, first and second gourava indices, first and second hyper gourava indices.
Let T1, be the transformed network then its M-polynomial is
M(T1;x,y)=(8n+8)x3y4+8x3y7+(4n−4)x3y8+(4n+4)x4y4+(8n−4)x4y5+(8n−4)x4y6+(4n−2)x5y6+4x5y7+(8n−8)x5y8+2x7y8+(2n−3)x8y8. |
Proof:
Using definition of M-polynomial and information from Table 3, we have
M(T1;x,y)=∑a≤bmabxayb=∑3≤4m34x3y4+∑3≤7m37x3y7+∑3≤8m38x3y8+∑4≤4m44x4y4+∑4≤5m45x4y5+∑4≤6m46x4y6+∑5≤6m56x5y6+∑5≤7m57x5y7+∑5≤8m58x5y8+∑7≤8m78x7y8+∑8≤8m88x8y8=|E3,4|x3y4+|E3,7|x3y7+|E3,8|x3y8+|E4,4|x4y4+|E4,5|x4y5+|E4,6|x4y6+|E5,6|x5y6+|E5,7|x5y7+|E5,8|x5y8+|E7,8|x7y8+|E8,8|x8y8=(8n+8)x3y4+8x3y7+(4n−4)x3y8+(4n+4)x4y4+(8n−4)x4y5+(8n−4)x4y6+(4n−2)x5y6+4x5y7+(8n−8)x5y8+2x7y8+(2n−3)x8y8. |
Let T1, be the transformed network and
M(T1;x,y)=(8n+8)x3y4+8x3y7+(4n−4)x3y8+(4n+4)x4y4+(8n−4)x4y5+(8n−4)x4y6+(4n−2)x5y6+4x5y7+(8n−8)x5y8+2x7y8+(2n−3)x8y8, |
be its M-polynomial. Then, the first Zagreb index M1(T1), the second Zagreb index M2(T1), the second modified Zagreb index mM2(T1), the general Randic index Rα(T1), where α ϵ N, reciprocal general Randic index RRα(T1), where α ϵ N, and the symmetric division degree index SDD(T1) obtained from M-polynomial are as follows:
M1(T1)=464n−48,M2(T1)=1176n−264,mM2(T1)=1112(n+1)+1130(n−1)+1330(2n−1)+164(2n−3)+223420,Rα(T1)=12α(8n+8)+21α(8)+24α(4n−4)+16α(4n+4)+20α(8n−4)+24α(8n−4)+30α(4n−2)+35α(4)+40α(8n−8)+56α(2)+64α(2n−3),RRα(T1)=812α(n+1)+821α+424α(n−1)+442α(n+1)+420α(2n−1)+424α(2n−1)+230α(2n−1)+435α+840α(n−1)+256α+164α(2n−3),SDD(T1)=743(n+1)+89930(n−1)+31415(2n−1)+2(2n−3)+14527420. |
Proof: Let f(x,y) = M(T1;x,y) be the M-polynomial of the transformed network T1. Then
M(T1;x,y)=(8n+8)x3y4+8x3y7+(4n−4)x3y8+(4n+4)x4y4+(8n−4)x4y5+(8n−4)x4y6+(4n−2)x5y6+4x5y7+(8n−8)x5y8+2x7y8+(2n−3)x8y8. |
Now, the required partial derivatives and integrals are obtained as:
By using the information given in Table 3 and formulas for Table 1;
Dxf(x,y)=3(8n+8)x3y4+24x3y7+3(4n−4)x3y8+4(4n+4)x4y4+4(8n−4)x4y5+4(8n−4)x4y6+5(4n−2)x5y6+20x5y7+5(8n−8)x5y8+14x7y8+8(2n−3)x8y8,Dyf(x,y)=4(8n+8)x3y4+56x3y7+8(4n−4)x3y8+4(4n+4)x4y4+5(8n−4)x4y5+6(8n−4)x4y6+6(4n−2)x5y6+28x5y7+8(8n−8)x5y8+16x7y8+8(2n−3)x8y8,DxDyf(x,y)=12(8n+8)x3y4+168x3y7+24(4n−4)x3y8+16(4n+4)x4y4+20(8n−4)x4y5+24(8n−4)x4y6+30(4n−2)x5y6+140x5y7+40(8n−8)x5y8+112x7y8+64(2n−3)x8y8,SxSyf(x,y)=23(n+1)x3y4+821x3y7+16(n−1)x3y8+14(n+1)x4y4+15(2n−1)x4y5+16(2n−1)x4y6+115(2n−1)x5y6+435x5y7+15(n−1)x5y8+128x7y8+164(2n−3)x8y8,DαxDαyf(x,y)=12α(8n+8)x3y4+21α(8)x3y7+24α(4n−4)x3y8+42α(4n+4)x4y4+20α(8n−4)x4y5+24α(8n−4)x4y6+30α(4n−2)x5y6+35α(4)x5y7+40α(8n−8)x5y8+56α(2)x7y8+46α(2n−3)x8y8,SαxSαyf(x,y)=812α(n+1)x3y4+821αx3y7+424α(n−1)x3y8+442α(n+1)x4y4+420α(2n−1)x4y5+424α(2n−1)x4y6+230α(2n−1)x5y6+435αx5y7+840α(n−1)x5y8+256αx7y8+164α(2n−3)x8y8,SyDxf(x,y)=6(n+1)x3y4+247x3y7+32(n−1)x3y8+4(n+1)x4y4+165(2n−1)x4y5+83(2n−1)x4y6+53(2n−1)x5y6+207x5y7+5(n−1)x5y8+74x7y8+(2n−3)x8y8,SxDyf(x,y)=323(n+1)x3y4+563x3y7+323(n−1)x3y8+4(n+1)x4y4+5(2n−1)x4y5+6(2n−1)x4y6+125(2n−1)x5y6+285x5y7+645(n−1)x5y8+167x7y8+(2n−3)x8y8. |
Topological Index | Derivation from M (K; x, y) | |
First Zagreb | (Dx+Dy)(M(K;x,y))|x=1=y | (ii) |
Second Zagreb | (DxDy)(M(K;x,y))|x=1=y | (iii) |
Second Modified Zagreb | (SxSy)(M(K;x,y))|x=1=y | (iv) |
general Randic | (DαxDαy)(M(K;x,y))|x=1=y | (v) |
Reciprocal general Randic | (SαxSαy)(M(K;x,y))|x=1=y | (vi) |
Symmetric Division Index | (SyDx)+(SxDy)(M(K;x,y))|x=1=y | (vii) |
Graph | Planar/ Non−Planar |
St(G1,G2) | Planar |
Md(G1,G2) | Planar |
Bdu(G1,G2) | Non−Planar |
T1 | Non−Planar |
T2 | Non−Planar |
Consequently,
M1(T1)=(Dx+Dy)f(x,y)|x=1=y=464n−48,M2(T1)=DxDyf(x,y)|x=1=y=1176n−264,mM2(T1)=SxSyf(x,y)|x=1=y=1112(n+1)+1130(n−1)+1330(2n−1)+164(2n−3)+223420,Rα(T1)=DαxDαyf(x,y)|x=1=y=12α(8n+8)+(21)α8+24α(4n−4)+16α(4n+4)+20α(8n−4)+24α(8n−4)+30α(4n−2)+(35)α4+40α(8n−8)+(56)α2+64α(2n−3),RRα(T1)=SαxSαyf(x,y)|x=1=y=812α(n+1)+821α+424α(n−1)+442α(n+1)+420α(2n−1)+424α(2n−1)+230α(2n−1)+435α+840α(n−1)+256α+164α(2n−3),SDD(T1)=(SyDx+SxDy)(x,y)|x=1=y=743(n+1)+89930(n−1)+31415(2n−1)+2(2n−3)+14527420. |
For T1, the Atomic Bond Connectivity, Geometric Arithematic Index and General Harmonic Index are as follows, respectively.
1) ABC(T1)=2n√6+(n+1)4√153+(14n+11)√1456+(n−1)2√1105+(2n−1)(2√75+4√3+√65)+16√4221+√18214,2) GA(T1)=(n+1)(32√37+4)+(n−1)32√1013+(32n−21)8√655+(2n−1)(16√59+4√3011)+(2n−3)+8√215+2√353+8√1415,3) Hk(T1)=(8n+8)(27)k+(8n+4)(15)k+(4n−4)(211)k+(4n+4)(14)k+(8n−4)(29)k+(4n−2)(211)k+4(16)k+(8n−8)(213)k+2(215)k+(2n−3)(18)k. |
Proof:
1). According to Eq (1.7)
ABC(T1)=∑γβ∈E(K)√dγ+dβ−2dγdβ. |
By using the information given in Table 3.
=(8n+8)(√3+4−23×4)+8(√3+7−23×7)+(4n−4)(√3+8−23×8)+(4n+4)(√4+4−24×4)+(8n−4)(√4+5−24×5)+(8n−4)(√4+6−24×6)+(4n−2)(√5+6−25×6)+4(√5+7−25×7)+(8n−8)(√5+8−25×8)+2(√7+8−27×8)+(2n−3)(√8+8−28×8)=2n√6+(n+1)4√153+(14n+11)√1456+(n−1)2√1105+(2n−1)(2√75+4√3+√65)+16√4221+√18214. |
Types of edges | Count of edges |
(3,4) | 8n+8 |
(3,7) | 8 |
(3,8) | 4n−4 |
(4,4) | 4n+4 |
(4,5) | 8n−4 |
(4,6) | 8n−4 |
(5,6) | 4n−2 |
(5,7) | 4 |
(5,8) | 8n−8 |
(7,8) | 2 |
(8,8) | 2n−3 |
2). According to Eq (1.8)
GA(T1)=∑γβ∈E(K)2√dγdβdγ+dβ. |
By using the information given in Table 3.
=(8n+8)(2√3×43+4)+8(2√3×73+7)+(4n−4)(2√3×83+8)+(4n+4)(2√4×44+4)+(8n−4)(2√4×54+5)+(8n−4)(2√4×64+6)+(4n−2)(2√5×65+6)+4(2√5×75+7)+(8n−8)(2√5×85+8)+2(2√7×87+8)+(2n−3)(2√8×88+8)=(n+1)(32√37+4)+(n−1)32√1013+(32n−21)8√655+(2n−1)(16√59+4√3011)+(2n−3)+8√215+2√353+8√1415. |
3). According to Eq (1.9)
Hk(T1)=∑γβ∈E(K)(2dγ+dβ)k. |
By using the information given in Table 3.
=(8n+8)(23+4)k+8(23+7)k+(4n−4)(23+8)k+(4n+4)(24+4)k+(8n−4)(24+5)k+(8n−4)(24+6)k+(4n−2)(25+6)k+4(25+7)k+(8n−8)(25+8)k+2(27+8)k+(2n−3)(28+8)k=(8n+8)(27)k+(8n+4)(15)k+(4n−4)(211)k+(4n+4)(14)k+(8n−4)(29)k+(4n−2)(211)k+4(16)k+(8n−8)(213)k+2(215)k+(2n−3)(18)k. |
For T1, the First, Second Gourava Indices and the First, Second Hyper Gourava Indices are as follows, respectively.
1) GO1(T1)=1640n−312,2) GO2(T1)=13128n−4404,3) HGO1(T1)=68064n−26124,4) HGO2(T1)=5816720n−3573928. |
Proof:
1). According to Eq (1.10)
GO1(T1)=∑γβ∈E(K)[(dγ+dβ)+(dγdβ)]. |
By using the information given in Table 3.
=(8n+8)[(3+4)+(3×4)]+8[(3+7)+(3×7)]+(4n−4)[(3+8)+(3×8)]+(4n+4)[(4+4)+(4×4)]+(8n−4)[(4+5)+(4×5)]+(8n−4)[(4+6)+(4×6)]+(4n−2)[(5+6)+(5×6)]+4[(5+7)+(5×7)]+(8n−8)[(5+8)+(5×8)]+2[(7+8)+(7×8)]+(2n−3)[(8+8)+(8×8)]=1640n−312. |
2). According to Eq (1.11)
GO2(T1)=∑γβ∈E(K)[(dγ+dβ)(dγdβ)]. |
By using the information given in Table 3.
=(8n+8)[(3+4)(3×4)]+8[(3+7)(3×7)]+(4n−4)[(3+8)(3×8)]+(4n+4)[(4+4)(4×4)]+(8n−4)[(4+5)(4×5)]+(8n−4)[(4+6)(4×6)]+(4n−2)[(5+6)(5×6)]+4[(5+7)(5×7)]+(8n−8)[(5+8)(5×8)]+2[(7+8)(7×8)]+(2n−3)[(8+8)(8×8)]=13128n−4404. |
3). According to Eq (1.12)
HGO1(T1)=∑γβ∈E(K)[(dγ+dβ)+(dγdβ)]2. |
By using the information given in Table 3.
=(8n+8)[(3+4)+(3×4)]2+8[(3+7)+(3×7)]2+(4n−4)[(3+8)+(3×8)]2+(4n+4)[(4+4)+(4×4)]2+(8n−4)[(4+5)+(4×5)]2+(8n−4)[(4+6)+(4×6)]2+(4n−2)[(5+6)+(5×6)]2+4[(5+7)+(5×7)]2+(8n−8)[(5+8)+(5×8)]2+2[(7+8)+(7×8)]2+(2n−3)[(8+8)+(8×8)]2=68064n−26124. |
4). According to Eq (1.13)
HGO2(T1)=∑γβ∈E(K)[(dγ+dβ)(dγdβ)]2. |
By using the information given in Table 3.
=(8n+8)[(3+4)(3×4)]2+8[(3+7)(3×7)]2+(4n−4)[(3+8)(3×8)]2+(4n+4)[(4+4)(4×4)]2+(8n−4)[(4+5)(4×5)]2+(8n−4)[(4+6)(4×6)]2+(4n−2)[(5+6)(5×6)]2+4[(5+7)(5×7)]2+(8n−8)[(5+8)(5×8)]2+2[(7+8)(7×8)]2+(2n−3)[(8+8)(8×8)]2=5816720n−3573928. |
Now we will construct the stellation, medial and bounded dual operations on simple and undirected triangular benzenoid structure. We get a new transformed network (say) T2, shown in Figure 3 for n = 5. After this we will compute M-polynomial and degree based topological indices of our newly obtained network. Where, blue is stellation, red is medial, green is bounded dual operations applied on T2.
Following Table 4 shows the types of edges and their count for network T2; n≥5. Now, we will calculate the different M-polynomial and vertex degree based topological indices namely first Zagreb index, second Zagreb index, second modified index, symmetric division index, general Randic index, reciprocal general Randic index, atomic bond connectivity index, geometric arithmetic index, general version of harmonic index, first and second gourava indices, first and second hyper gourava indices.
Types of edges | Count of edges |
(3,4) | 6n+6 |
(3,8) | 9 |
(3,10) | 3n−6 |
(4,4) | 3n+3 |
(4,5) | 6n−6 |
(4,6) | 6n−6 |
(5,6) | 3n−3 |
(5,8) | 6 |
(5,10) | 6n−12 |
(6,6) | 6(n−1)2 |
(6,8) | 3 |
(6,10) | 9n−18 |
(6,12) | 6[(n−(n−1))+(n−(n−2))+... |
+(n−4)+(n−3)] | |
(8,10) | 6 |
(10,10) | 3n−6 |
(10,12) | 6n−18 |
(12,12) | 3[(n−4)+(n−5)+... |
+(n−(n−1))] |
Let T2, be the transformed network then its M-polynomial is
M(T2;x,y)=(6n+6)x3y4+9x3y8+(3n−6)x3y10+(3n+3)x4y4+(6n−6)x4y5+(6n−6)x4y6+(3n−3)x5y6+6x5y8+(6n−12)x5y10+6(n−1)2x6y6+3x6y8+(9n−18)x6y10+6[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+6x8y10+(3n−6)x10y10+(6n−18)x10y12+3[(n−4)+(n−5)+...+(n−(n−1))]x12y12. |
Proof: Using definition of M-polynomial and information from Table 4, we have
M(T2;x,y)=∑a≤bmabxayb=∑3≤4m34x3y4+∑3≤8m38x3y8+∑3≤10m3 10x3y10+∑4≤4m44x4y4+∑4≤5m45x4y5+∑4≤6m46x4y6+∑5≤6m56x5y6+∑5≤8m58x5y8+∑5≤10m5 10x5y10+∑6≤6m66x6y6+∑6≤8m68x6y8+∑6≤10m6 10x6y10+∑6≤12m6 12x6y12+∑8≤10m8 10x8y10+∑10≤10m10 10x10y10+∑10≤12m10 12x10y12+∑12≤12m12 12x12y12=|E3,4|x3y4+|E3,8|x3y8+|E3,10|x3y10+|E4,4|x4y4+|E4,5|x4y5+|E4,6|x4y6+|E5,6|x5y6+|E5,8|x5y8+|E5,10|x5y10+|E6,6|x6y6+|E6,8|x6y8+|E6,10|x6y10+|E6,12|x6y12+|E8,10|x8y10+|E10,10|x10y10+|E10,12|x10y12+|E12,12|x12y12=(6n+6)x3y4+9x3y8+(3n−6)x3y10+(3n+3)x4y4+(6n−6)x4y5+(6n−6)x4y6+(3n−3)x5y6+6x5y8+(6n−12)x5y10+6(n−1)2x6y6+3x6y8+(9n−18)x6y10+6[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+6x8y10+(3n−6)x10y10+(6n−18)x10y12+3[(n−4)+(n−5)+...+(n−(n−1))]x12y12. |
Let T2, be the transformed network and
M(T2;x,y)=(6n+6)x3y4+9x3y8+(3n−6)x3y10+(3n+3)x4y4+(6n−6)x4y5+(6n−6)x4y6+(3n−3)x5y6+6x5y8+(6n−12)x5y10+6(n−1)2x6y6+3x6y8+(9n−18)x6y10+6[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+6x8y10+(3n−6)x10y10+(6n−18)x10y12+3[(n−4)+(n−5)+...+(n−(n−1))]x12y12, |
be its M-polynomial. Then, the first Zagreb index M1(T2), the second Zagreb index M2(T2), the second modified Zagreb mM2(T2), the general Randic index Rα(T2), where α ϵ N, reciprocal general Randic index RRα(T2), where α ϵ N, and the symmetric division degree index SDD(T2) obtained from M-polynomial are as follows:
M1(T2)=678n−816+72(n−1)2+108[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+72[(n−4)+(n−5)+...+(n−(n−1))],M2(T2)=2424n−3774+216(n−1)2+432[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+432[(n−4)+(n−5)+...+(n−(n−1))],mM2(T2)=1116(n+1)+25(n−2)+1320(n−1)+120(n−3)+5380+16(n−1)2+112[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+148[(n−4)+(n−5)+...+(n−(n−1))],Rα(T2)=12α(6n+6)+24α(9)+30α(3n−6)+42α(3n+3)+20α(6n−6)+24α(6n−6)+30α(3n−3)+40α(6)+50α(6n−12)+6(n−1)2(36)α+48α(3)+60α(9n−18)+72α[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]6+80α(6)+100α(3n−6)+120α(6n−18)+144α[(n−4)+(n−5)+...+(n−(n−1))]3,RRα(T2)=612α(n+1)+924α+330α(n−2)+342α(n+1)+620α(n−1)+624α(n−1)+330α(n−1)+640α+650α(n−2)+636α(n−1)2+348α+960α(n−2)+680α+3100α(n−2)+172α[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]6+6120α(n−3)+1144α[(n−4)+(n−5)+...+(n−(n−1))]3,SDD(T2)=372(n+1)+1575(n−1)+52310(n−2)+615(n−3)+237140+12(n−1)2+15[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+6[(n−4)+(n−5)+...+(n−(n−1))]. |
Proof: Let f(x,y) = M(T2;x,y) be the M-polynomial of the transformed network T2. Then
M(T2;x,y)=(6n+6)x3y4+9x3y8+(3n−6)x3y10+(3n+3)x4y4+(6n−6)x4y5+(6n−6)x4y6+(3n−3)x5y6+6x5y8+(6n−12)x5y10+6(n−1)2x6y6+3x6y8+(9n−18)x6y10+6[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+6x8y10+(3n−6)x10y10+(6n−18)x10y12+3[(n−4)+(n−5)+...+(n−(n−1))]x12y12. |
Now, the required partial derivatives and integrals are obtained.
By using the information given in Table 4 and formulas for Table 1:
Dxf(x,y)=3(6n+6)x3y4+27x3y8+3(3n−6)x3y10+4(3n+3)x4y4+4(6n−6)x4y5+4(6n−6)x4y6+5(3n−3)x5y6+30x5y8+5(6n−12)x5y10+36(n−1)2x6y6+18x6y8+6(9n−18)x6y10+36[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+48x8y10+10(3n−6)x10y10+10(6n−18)x10y12+36[(n−4)+(n−5)+...+(n−(n−1))]x12y12,Dyf(x,y)=4(6n+6)x3y4+72x3y8+10(3n−6)x3y10+4(3n+3)x4y4+5(6n−6)x4y5+6(6n−6)x4y6+6(3n−3)x5y6+48x5y8+10(6n−12)x5y10+36(n−1)2x6y6+24x6y8+10(9n−18)x6y10+72[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+60x8y10+10(3n−6)x10y10+12(6n−18)x10y12+36[(n−4)+(n−5)+...+(n−(n−1))]x12y12,DxDyf(x,y)=12(6n+6)x3y4+216x3y8+30(3n−6)x3y10+16(3n+3)x4y4+20(6n−6)x4y5+24(6n−6)x4y6+30(3n−3)x5y6+240x5y8+50(6n−12)x5y10+216(n−1)2x6y6+144x6y8+60(9n−18)x6y10+432[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+480x8y10+100(3n−6)x10y10+120(6n−18)x10y12+432[(n−4)+(n−5)+...+(n−(n−1))]x12y12,SxSyf(x,y)=12(n+1)x3y4+38x3y8+110(n−2)x3y10+316(n+1)x4y4+310(n−1)x4y5+14(n−1)x4y6+110(n−1)x5y6+320x5y8+325(n−2)x5y10+16(n−1)2x6y6+116x6y8+320(n−2)x6y10+112[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+340x8y10+3100(n−2)x10y10+120(n−3)x10y12+148[(n−4)+(n−5)+...+(n−(n−1))]x12y12,DαxDαyf(x,y)=12α(6n+6)x3y4+24α(9)x3y8+30α(3n−6)x3y10+42α(3n+3)x4y4+20α(6n−6)x4y5+24α(6n−6)x4y6+30α(3n−3)x5y6+40α(6)x5y8+50α(6n−12)x5y10+(36)α(n−1)26x6y6+48α(3)x6y8+60α(9n−18)x6y10+72α[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]6x6y12+80α(6)x8y10+100α(3n−6)x10y10+120α(6n−18)x10y12+144α[(n−4)+(n−5)+...+(n−(n−1))]3x12y12,SαxSαyf(x,y)=612α(n+1)x3y4+924αx3y8+330α(n−2)x3y10+342α(n+1)x4y4+620α(n−1)x4y5+624α(n−1)x4y6+330α(n−1(n−1)x5y6+640αx5y8+650α(n−2)x5y10+636α(n−1)2x6y6+348αx6y8+960α(n−2)x6y10+172α[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]6x6y12+680αx8y10+3100α(n−2)x10y10+6120α(n−3)x10y12+1144α[(n−4)+(n−5)+...+(n−(n−1))]3x12y12,SyDxf(x,y)=92(n+1)x3y4+278x3y8+910(n−2)x3y10+3(n+1)x4y4+245(n−1)x4y5+4(n−1)x4y6+52(n−1)x5y6+154x5y8+3(n−2)x5y10+6(n−1)2x6y6+94x6y8+275(n−2)x6y10+3[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+245x8y10+3(n−2)x10y10+5(n−3)x10y12+3[(n−4)+(n−5)+...+(n−(n−1))]x12y12,SxDyf(x,y)=8(n+1)x3y4+24x3y8+10(n−2)x3y10+3(n+1)x4y4+152(n−1)x4y5+9(n−1)x4y6+185(n−1)x5y6+485x5y8+12(n−2)x5y10+6(n−1)2x6y6+4x6y8+15(n−2)x6y10+12[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]x6y12+152x8y10+3(n−2)x10y10+365(n−3)x10y12+3[(n−4)+(n−5)+...+(n−(n−1))]x12y12. |
Consequently,
M1(T2)=(Dx+Dy)f(x,y)|x=1=y=678n−816+72(n−1)2+108[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+72[(n−4)+(n−5)+...+(n−(n−1))],M2(T2)=DxDyf(x,y)|x=1=y=2424n−3774+216(n−1)2+432[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+432[(n−4)+(n−5)+...+(n−(n−1))],mM2(T2)=SxSyf(x,y)|x=1=y=1116(n+1)+25(n−2)+1320(n−1)+120(n−3)+710+16(n−1)2+112[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+148[(n−4)+(n−5)+...+(n−(n−1))],Rα(T2)=DαxDαyf(x,y)|x=1=y=12α(6n+6)+24α(9)+30α(3n−6)+42α(3n+3)+20α(6n−6)+24α(6n−6)+30α(3n−3)+40α(6)+50α(6n−12)+6(n−1)2(36)α+48α(3)+60α(9n−18)+72α[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]6+80α(6)+100α(3n−6)+120α(6n−18)+144α[(n−4)+(n−5)+...+(n−(n−1))]3,RRα(T2)=SαxSαyf(x,y)|x=1=y=612α(n+1)+924α+330α(n−2)+342α(n+1)+620α(n−1)+624α(n−1)+330α(n−1)+640α+650α(n−2)+636α(n−1)2+348α+960α(n−2)+680α+3100α(n−2)+172α[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]6+6120α(n−3)+1144α[(n−4)+(n−5)+...+(n−(n−1))]3,SDD(T2)=(SyDx+SxDy)(x,y)|x=1=y=372(n+1)+1575(n−1)+52310(n−2)+615(n−3)+237140+12(n−1)2+15[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+6[(n−4)+(n−5)+...+(n−(n−1))]. |
For T2, the Atomic Bond Connectivity, Geometric Arithmetic Index and General Harmonic Index are as follows, respectively.
1) ABC(T2)=(7n)√64+(n+1)√15+(n−1)(3√75+2√3+3√310)(n−2)(√33010+3√265+3√21010+9√210)+3√11010+32+6√5+(n−1)2√10+[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]2√2+[(n−4)+(n−5)+...+(n−(n−1))]√224,2) GA(T2)=(24n+36)√37+(132n+48)√655+(50n−113)6√30143+3(n+1)+(8n)√53+(n−2)(4√2+9√154+3)+24√1013+6(n−1)2+[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]4√2+3[(n−4)+(n−5)+...+(n−(n−1))],3) Hk(T2)=(6n+6)(27)k+9(211)k+(3n−6)(213)k+(3n+3)(14)k+(6n−6)(29)k+(6n−6)(15)k+(3n−3)(211)k+6(213)k+(6n−12)(215)k+6(n−1)2)(16)k+3(17)k+(9n−18)(18)k+6[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)](19)k+6(19)k+(3n−6)(110)k+(6n−18)(111)k+3[(n−4)+(n−5)+...+(n−(n−1))](112)k. |
Proof: Using Eqs 1.7–1.9 and Table 4, we get the results 1–3.
For T2, the First, Second Gourava Indices and the First, Second Hyper Gourava Indices are as follows, respectively.
1) GO1(T2)=3102n−4590+288(n−1)2+540[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+504[(n−4)+(n−5)+...+(n−(n−1))],2) GO2(T2)=40548n−74610+2592(n−1)2+7776[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+10368[(n−4)+(n−5)+...+(n−(n−1))],3) HGO1(T2)=267984n−531210+13824(n−1)2+48600[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+84672[(n−4)+(n−5)+...+(n−(n−1))],4) HGO2(T2)=66901488n−158433396+1119744(n−1)2+10077696[(n−(n−1))+(n−(n−2))+...+(n−4)+(n−3)]+35831808[(n−4)+(n−5)+...+(n−(n−1))]. |
Proof: Using Eqs 1.10–1.13 and Table 4 we get the above results 1–4.
In this paper, we defined M-polynomial of the transformed zigzag benzenoid and transformed triangular benzenoid structures by applying stellation, medial and bounded dual operations to get networks named as T1 and T2. With the help of M-polynomial, we computed certain degree-based topological indices such as first Zagreb index, second Zagreb index, second modified Zagreb index, general Randic index, reciprocal general Randic index, symmetric division degree index. We also computed atomic bound connectivity index, geometric arithmetic index, general harmonic index, first and second gourava indices, first and second hyper gourava indices. M-polynomial is used to calculate the certain degree based topological indices as a latest developed instrument in the chemical graph theory. In future, we can compute other indices on these structures and some additional transformed structures can be studied for a variety of topological indices to have an insight about their properties.
This work was supported by the Key teaching research project of quality engineering in Colleges and universities in Anhui Province. Anhui Vocational college of Electronics and Information Technology Guangzhou Zhuoya Education Investment Co., Ltd. Practical Education Base (subject No: 2020sjjd093). The authors are very thankful for this research funding. We are also very grateful to the reviewers for their valuable suggestions.
The authors declare no conflict of interest.
[1] |
Rani S, Ali R, Agarwal A (2022) Fuzzy inventory model for new and refurbished deteriorating items with cannibalisation in green supply chain. Int J Syst Sci Operat Logist 9: 22–38. https://doi.org/10.1080/23302674.2020.1803434 doi: 10.1080/23302674.2020.1803434
![]() |
[2] |
Sarkar B, Sarkar M, Ganguly B, et al. (2020) Combined effects of carbon emission and production quality improvement for fixed lifetime products in a sustainable supply chain management. Int J Prod Econ 231: 107867. https://doi.org/10.1016/j.ijpe.2020.107867 doi: 10.1016/j.ijpe.2020.107867
![]() |
[3] |
Sarkar B, Guchhait R (2023) Ramification of information asymmetry on a green supply chain management with the cap-trade, service, and vendor-managed inventory strategies. Elect Commer Res App 60: 101274. https://doi.org/10.1016/j.elerap.2023.101274 doi: 10.1016/j.elerap.2023.101274
![]() |
[4] |
Saxena N, Sarkar B, Wee H M, et al. (2023) A reverse logistics model with eco-design under the Stackelberg-Nash equilibrium and centralized framework. J Clean Prod 387: 135789. https://doi.org/10.1016/j.jclepro.2022.135789 doi: 10.1016/j.jclepro.2022.135789
![]() |
[5] |
Ullah M, Asghar I, Zahid M, et al. (2021) Ramification of remanufacturing in a sustainable three-echelon closed-loop supply chain management for returnable products. J Clean Prod 290: 125609. https://doi.org/10.1016/j.jclepro.2020.125609 doi: 10.1016/j.jclepro.2020.125609
![]() |
[6] |
Sarkar B, Bhuniya S (2022) A sustainable flexible manufacturing–remanufacturing model with improved service and green investment under variable demand, Exp Syst with Appli 202: 117154. https://doi.org/10.1016/j.eswa.2022.117154 doi: 10.1016/j.eswa.2022.117154
![]() |
[7] |
Cheng S, Zhang F, Chen X (2024) Optimal contract design for a supply chain with information asymmetry under dual environmental responsibility constraints. Exp Syst App 237: 121466. https://doi.org/10.1016/j.eswa.2023.121466 doi: 10.1016/j.eswa.2023.121466
![]() |
[8] |
Kumar S, Kumar A, Jain M (2020) Learning effect on an optimal policy for mathematical inventory model for decaying items under preservation technology with the environment of COVID-19 pandemic. Malaya J Matemat 8: 1694–1702. https://doi.org/10.26637/MJM0804/0063 doi: 10.26637/MJM0804/0063
![]() |
[9] |
Dey B K, Bhuniya S, Sarkar B (2021) Involvement of controllable lead time and variable demand for a smart manufacturing system under a supply chain management. Exp Syst App 184: 115464. https://doi.org/10.1016/j.eswa.2021.115464 doi: 10.1016/j.eswa.2021.115464
![]() |
[10] |
Ullah M, Sarkar B (2020) Recovery-channel selection in a hybrid manufacturing-remanufacturing production model with RFID and product quality. Int J Prod Econ 219: 360–374. https://doi.org/10.1016/j.ijpe.2019.07.017 doi: 10.1016/j.ijpe.2019.07.017
![]() |
[11] |
Cheng M, Zhang B, Wang G (2011) Optimal policy for deteriorating items with trapezoidal type demand and partial backlogging. App Math Model 35: 3552–3560. https://doi.org/10.1016/j.apm.2011.01.001 doi: 10.1016/j.apm.2011.01.001
![]() |
[12] |
Rini, Priyamvada, Jaggi C K (2021) Sustainable and flexible production system for a deteriorating item with quality consideration. Int J Syst Assuran Eng Manag 12: 951–960. https://doi.org/10.1007/s13198-021-01169-w doi: 10.1007/s13198-021-01169-w
![]() |
[13] | Kawakatsu H (2010) Optimal retailer's replenishment policy for seasonal products with ramp-type demand rate. Int J App Math 40: 1–7. |
[14] |
Panda S, Senapati S, Basu M (2008) Optimal replenishment policy for perishable seasonal products in a season with ramp-type time dependent demand. Comput Indust Eng 54: 301–314. https://doi.org/10.1016/j.cie.2007.07.011 doi: 10.1016/j.cie.2007.07.011
![]() |
[15] |
Sarkar B, Ullah M, Sarkar M (2022) Environmental and economic sustainability through innovative green products by remanufacturing. J Clean Prod 332: 129813. https://doi.org/10.1016/j.jclepro.2021.129813 doi: 10.1016/j.jclepro.2021.129813
![]() |
[16] |
Saha S, Sarkar B, Sarkar M (2023) Application of improved meta-heuristic algorithms for green preservation technology management to optimize dynamical investments and replenishment strategies. Math Comp Simul 209: 426–450. https://doi.org/10.1016/j.matcom.2023.02.005 doi: 10.1016/j.matcom.2023.02.005
![]() |
[17] | Skouri K, Konstantaras I, Papachristos S, et al. (2009) Inventory models with ramp type demand rate, partial backlogging and Weibull deterioration rate. Euro J Operat Res 192: 79–92. ttps://doi.org/10.1016/j.ejor.2007.09.003 |
[18] |
Wu J, Skouri K, Teng J T, et al. (2016) Two inventory systems with trapezoidal-type demand rate and time-dependent deterioration and backlogging. Exp Syst App 46: 367–379. https://doi.org/10.1016/j.eswa.2015.10.048 doi: 10.1016/j.eswa.2015.10.048
![]() |
[19] |
Wee H M, Chung c C J (2009) Optimizing replenishment policy for an integrated production inventory deteriorating model considering green component-value design and remanufacturing. Int J Prod Res 47: 1343–1368. https://doi.org/10.1080/00207540701570182 doi: 10.1080/00207540701570182
![]() |
[20] |
Miramontes-Viña V, Romero-Castro N, López-Cabarcos M Á (2023) Advancing towards a sustainable energy model. Uncovering the untapped potential of rural areas. AIMS Env Sci 10: 287–312. https://doi.org/10.3934/environsci.2023017 doi: 10.3934/environsci.2023017
![]() |
[21] |
Monteiro S, Amor-Esteban V, Lemos K, et al. (2023) Are we doing the same? A worldwide analysis of business commitment to the SDGs. AIMS Env Sci 10: 446–466. https://doi.org/10.3934/environsci.2023025 doi: 10.3934/environsci.2023025
![]() |
[22] |
Osabuohien-Irabor O, Drapkin I M (2023) Toward achieving zero-emissions in European Union countries: The contributions of trade and overseas direct investments in consumptionbased carbon emissions. AIMS Env Sci 10: 129–156. https://doi.org/10.3934/environsci.2023008 doi: 10.3934/environsci.2023008
![]() |
[23] |
Zhou X, Zhu Q, Xu L, et al. (2024) The effect of carbon tariffs and the associated coping strategies: A global supply chain perspective. Omega 122: 102960. https://doi.org/10.1016/j.omega.2023.102960 doi: 10.1016/j.omega.2023.102960
![]() |
[24] |
Rossit D, Guggeri E M, Ham C, et al. (2023) Goal programming and multi-criteria methods in remanufacturing and reverse logistics: Systematic literature review and survey. Comp Indust Eng 109587. https://doi.org/10.1016/j.cie.2023.109587 doi: 10.1016/j.cie.2023.109587
![]() |
[25] |
Jaber M Y, El Saadany A M A (2009) The production, remanufacture and waste disposal model with lost sales. Int J Prod Econ 115–124. https://doi.org/10.1016/j.ijpe.2008.07.016 doi: 10.1016/j.ijpe.2008.07.016
![]() |
[26] |
Konstantaras I, Skouri K, Jaber M Y (2010) Lot sizing for a recoverable product with inspection and sorting, Comput Indust Eng 58: 452–462. https://doi.org/10.1016/j.cie.2009.11.004 doi: 10.1016/j.cie.2009.11.004
![]() |
[27] |
Koh S G, Hwang H, Sohn K I, et al. (2002) An optimal ordering and recovery policy for reusable items. Comput Indust Eng 43: 59–73. https://doi.org/10.1016/S0360-8352(02)00062-1 doi: 10.1016/S0360-8352(02)00062-1
![]() |
[28] |
Liao T Y (2018) Reverse logistics network design for product recovery and remanufacturing. App Math Model 60: 145–163. https://doi.org/10.1016/j.apm.2018.03.003 doi: 10.1016/j.apm.2018.03.003
![]() |
[29] |
Marić J, Opazo-Basáez M (2019) Green servitization for flexible and sustainable supply chain operations: A review of reverse logistics services in manufacturing. Global J Flex Syst Manag 20: S65-S80. https://doi.org/10.1007/s40171-019-00225-6 doi: 10.1007/s40171-019-00225-6
![]() |
[30] |
Weng Z K, McClurg T (2003) Coordinated ordering decisions for short life cycle products with uncertainty in delivery time and demand. Euro J Operat Res 151: 12–24. https://doi.org/10.1016/S0377-2217(02)00577-5 doi: 10.1016/S0377-2217(02)00577-5
![]() |
[31] |
Ahmadi S, Shokouhyar S, Amerioun M, et al. (2024) A social media analytics-based approach to customer-centric reverse logistics management of electronic devices: A case study on notebooks. J Retail Consum Serv 76: 103540. https://doi.org/10.1016/j.jretconser.2023.103540 doi: 10.1016/j.jretconser.2023.103540
![]() |
[32] |
Ahmed W, Sarkar B (2019) Management of next-generation energy using a triple bottom line approach under a supply chain framework. Resour Conserv Recyc 150: 104431. https://doi.org/10.1016/j.resconrec.2019.104431 doi: 10.1016/j.resconrec.2019.104431
![]() |
[33] |
Goodarzian F, Ghasemi P, Gonzalez E D R S, et al. (2023) A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms. J Environ Manag 328: 116892. https://doi.org/10.1016/j.jenvman.2022.116892 doi: 10.1016/j.jenvman.2022.116892
![]() |
[34] |
Momenitabar M, Dehdari Ebrahimi Z, Arani M, et al. (2022) Designing a sustainable closed-loop supply chain network considering lateral resupply and backup suppliers using fuzzy inference system. Environ Dev Sustain https://doi.org/10.1007/s10668-022-02332-4. doi: 10.1007/s10668-022-02332-4
![]() |
[35] |
Mashud A H M, Pervin M, Mishra U, et al. (2021) A sustainable inventory model with controllable carbon emissions in green-warehouse farms. J Clean Prod 298: 126777. https://doi.org/10.1016/j.jclepro.2021.126777 doi: 10.1016/j.jclepro.2021.126777
![]() |
[36] |
Sarkar B, Omair M, Kim N (2020) A cooperative advertising collaboration policy in supply chain management under uncertain conditions. App Soft Comput 88: 105948. https://doi.org/10.1016/j.asoc.2019.105948 doi: 10.1016/j.asoc.2019.105948
![]() |
[37] |
Mishra U, Wu J Z, Sarkar B (2021) Optimum sustainable inventory management with backorder and deterioration under controllable carbon emissions. J Clean Prod 279: 123699. https://doi.org/10.1016/j.jclepro.2020.123699 doi: 10.1016/j.jclepro.2020.123699
![]() |
[38] |
Kugele A S H, Sarkar B (2023) Reducing carbon emissions of a multi-stage smart production for biofuel towards sustainable development. Alex Eng J 70: 93-113. https://doi.org/10.1016/j.aej.2023.01.003 doi: 10.1016/j.aej.2023.01.003
![]() |
[39] |
Mridha B, Pareek S, Goswami A, et al. (2023) Joint effects of production quality improvement of biofuel and carbon emissions towards a smart sustainable supply chain management. J Clean Prod 386: 135629. https://doi.org/10.1016/j.jclepro.2022.135629 doi: 10.1016/j.jclepro.2022.135629
![]() |
[40] |
Teng Y, Feng B (2021) Optimal channel structure for remanufacturing under cap-and-trade regulation. Process 9: 370. https://doi.org/10.3390/pr9020370 doi: 10.3390/pr9020370
![]() |
[41] |
Tiwari S, Daryanto Y, Wee H M (2018) Sustainable inventory management with deteriorating and imperfect quality items considering carbon emission. J Clean Prod 192: 281–292. https://doi.org/10.1016/j.jclepro.2018.04.261 doi: 10.1016/j.jclepro.2018.04.261
![]() |
[42] |
Yadav S, Khanna A (2021) Sustainable inventory model for perishable products with expiration date and price reliant demand under carbon tax policy. Proces Int Opt Sust 5: 475–486. https://doi.org/10.1007/s41660-021-00157-8 doi: 10.1007/s41660-021-00157-8
![]() |
[43] |
Mishra U, Wu J Z, Sarkar B (2020) A sustainable production-inventory model for a controllable carbon emissions rate under shortages. J Clean Prod 256: 120268. https://doi.org/10.1016/j.jclepro.2020.120268 doi: 10.1016/j.jclepro.2020.120268
![]() |
[44] |
Babaeinesami A, Tohidi H, Ghasemi P, et al. (2022) A closed-loop supply chain configuration considering environmental impacts: A self-adaptive NSGA-Ⅱ algorithm. App Intell 52: 13478–13496. https://doi.org/10.1007/s10489-021-02944-9 doi: 10.1007/s10489-021-02944-9
![]() |
[45] |
Goodarzian F, Kumar V, Ghasemi P (2022) Investigating a citrus fruit supply chain network considering CO2 emissions using meta-heuristic algorithms. Ann Oper Res https://doi.org/10.1007/s10479-022-05005-7. doi: 10.1007/s10479-022-05005-7
![]() |
[46] |
Alamri A A (2010) Theory and methodology on the global optimal solution to a general reverse logistics inventory model for deteriorating items and time-varying rates. Comput Indust Eng 60: 236–247. https://doi.org/10.1016/j.cie.2010.11.005 doi: 10.1016/j.cie.2010.11.005
![]() |
[47] |
Chung C J, Wee H M Short (2011) lifecycle deteriorating product remanufacturing in a green supply chain inventory control system. Int J Prod Econ 129: 195–203. https://doi.org/10.1016/j.ijpe.2010.09.033 doi: 10.1016/j.ijpe.2010.09.033
![]() |
[48] |
Motla R, Kumar A, Singh S R, et al. (2021) A fuzzy integrated inventory system with end of life treatment: A possibility in sport industry. Opsearch 58: 869–888. https://doi.org/10.1007/s12597-020-00492-3 doi: 10.1007/s12597-020-00492-3
![]() |
[49] |
Rani S, Ali R, Agarwal A (2019) Fuzzy inventory model for deteriorating items in a green supply chain with carbon concerned demand. Opsearch 56: 91–122. https://doi.org/10.1007/s12597-019-00361-8 doi: 10.1007/s12597-019-00361-8
![]() |
[50] | Rani S, Ali R, Agarwal A (2020) Inventory model for deteriorating items in green supply chain with credit period dependent demand. Int J App Eng Res 15: 157–172. |
[51] |
Garai A, Sarkar B (2022) Economically independent reverse logistics of customer-centric closed-loop supply chain for herbal medicines and biofuel J Clean Prod 334: 129977. https://doi.org/10.1016/j.jclepro.2021.129977 doi: 10.1016/j.jclepro.2021.129977
![]() |
[52] |
Safdar N, Khalid R, Ahmed W, et al. (2020) Reverse logistics network design of e-waste management under the triple bottom line approach. J Clean Prod 272: 122662. https://doi.org/10.1016/j.jclepro.2020.122662 doi: 10.1016/j.jclepro.2020.122662
![]() |
[53] |
Sarkar B, Debnath A, Chiu A S F, et al. (2022) Circular economy-driven two-stage supply chain management for nullifying waste. J Clean Prod 339: 130513. https://doi.org/10.1016/j.jclepro.2022.130513 doi: 10.1016/j.jclepro.2022.130513
![]() |
[54] |
Singh S R, Sharma S (2019) A partially backlogged supply chain model for deteriorating items under reverse logistics, imperfect production/remanufacturing and inflation. Int J Logist Syst Manag 33: 221. https://doi.org/10.1504/IJLSM.2019.100113 doi: 10.1504/IJLSM.2019.100113
![]() |
[55] |
Wang C, Huang R (2014) Pricing for seasonal deteriorating products with price-and ramp-type time-dependent demand. Comput Indust Eng 77: 29–34. https://doi.org/10.1016/j.cie.2014.09.005 doi: 10.1016/j.cie.2014.09.005
![]() |
[56] |
Yang P C, Chung S L, Wee H M, et al. (2013) Collaboration for a closed-loop deteriorating inventory supply chain with multi-retailer and price-sensitive demand. Int J Prod Econ 143: 557–566. https://doi.org/10.1016/j.ijpe.2012.07.020 doi: 10.1016/j.ijpe.2012.07.020
![]() |
[57] |
Sarkar M, Sarkar B (2020) How does an industry reduce waste and consumed energy within a multi-stage smart sustainable biofuel production system? J Clean Prod 262: 121200. https://doi.org/10.1016/j.jclepro.2020.121200 doi: 10.1016/j.jclepro.2020.121200
![]() |
[58] |
Dey B K, Pareek S, Tayyab M, et al. (2021) Autonomation policy to control work-in-process inventory in a smart production system. Int J Prod Res 59: 1258–1280. https://doi.org/10.1080/00207543.2020.1722325 doi: 10.1080/00207543.2020.1722325
![]() |
[59] |
Li P, Chen B, Cui Q (2023) A probabilistic life-cycle assessment of carbon emission from magnesium phosphate cementitious material with uncertainty analysis. J Clean Prod 139164. https://doi.org/10.1016/j.jclepro.2023.139164 doi: 10.1016/j.jclepro.2023.139164
![]() |
[60] |
Habib M S, Asghar O, Hussain A, et al. (2021) A robust possibilistic programming approach toward animal fat-based biodiesel supply chain network design under uncertain environment. J Clean Prod 278: 122403. https://doi.org/10.1016/j.jclepro.2020.122403 doi: 10.1016/j.jclepro.2020.122403
![]() |
[61] |
Sarkar B, Tayyab M, Kim N, et al. (2019) Optimal production delivery policies for supplier and manufacturer in a constrained closed-loop supply chain for returnable transport packaging through metaheuristic approach. Comp Indust Eng 135: 987–1003. https://doi.org/10.1016/j.cie.2019.05.035 doi: 10.1016/j.cie.2019.05.035
![]() |
[62] |
Singh S K, Chauhan A, Sarkar B (2023) Sustainable biodiesel supply chain model based on waste animal fat with subsidy and advertisement. J Clean Prod 382: 134806. https://doi.org/10.1016/j.jclepro.2022.134806 doi: 10.1016/j.jclepro.2022.134806
![]() |
[63] |
Ke C, Pan X, Wan P, et al. (2023) An integrated design method for used product remanufacturing scheme considering carbon emission. Sustain Prod Consum 41: 348–361. https://doi.org/10.1016/j.spc.2023.08.018 doi: 10.1016/j.spc.2023.08.018
![]() |
[64] |
Huang J, Yan Y, Kang J, et al. (2023) Driving technology factors of carbon emissions: Theoretical framework and its policy implications for China. Sci Total Environ 904: 166858. https://doi.org/10.1016/j.scitotenv.2023.166858 doi: 10.1016/j.scitotenv.2023.166858
![]() |
[65] |
Tiwari S, Ahmed W, Sarkar B (2019) Sustainable ordering policies for non-instantaneous deteriorating items under carbon emission and multi-trade-credit-policies. J Clean Prod 240: 118183. https://doi.org/10.1016/j.jclepro.2019.118183 doi: 10.1016/j.jclepro.2019.118183
![]() |
[66] |
Sepehri A, Mishra U, Sarkar B (2021) A sustainable production-inventory model with imperfect quality under preservation technology and quality improvement investment. J Clean Prod 310: 127332. https://doi.org/10.1016/j.jclepro.2021.127332 doi: 10.1016/j.jclepro.2021.127332
![]() |
[67] |
Yadav D, Kumari R, Kumar N, et al. (2021) Reduction of waste and carbon emission through the selection of items with cross-price elasticity of demand to form a sustainable supply chain with preservation technology. J Clean Prod 297: 126298. https://doi.org/10.1016/j.jclepro.2021.126298 doi: 10.1016/j.jclepro.2021.126298
![]() |
[68] |
Tayyab M, Jemai J, Lim H, et al. (2020)A sustainable development framework for a cleaner multi-item multi-stage textile production system with a process improvement initiative. J Clean Prod 246: 119055. https://doi.org/10.1016/j.jclepro.2019.119055 doi: 10.1016/j.jclepro.2019.119055
![]() |
[69] |
Mao H, Wang W, Liu C, et al. (2023) Effects of the carbon emission quota policy on the quality and sales of manufactured and remanufactured products. Int J Prod Econ 266: 109058. https://doi.org/10.1016/j.ijpe.2023.109058 doi: 10.1016/j.ijpe.2023.109058
![]() |
[70] | LI Y, YI F, YUAN C Influences of large-scale farming on carbon emissions from cropping: Evidence from China. J Integr Agricul https://doi.org/10.1016/j.jia.2023.08.006 |
![]() |
![]() |
1. | Deeba Afzal, Farkhanda Afzal, Sabir Hussain, Faryal Chaudhry, Dhana Kumari Thapa, Gohar Ali, Investigation on Boron Alpha Nanotube by Studying Their M-Polynomial and Topological Indices, 2022, 2022, 2314-4785, 1, 10.1155/2022/6136168 | |
2. | Jia-Bao Liu, Hifza Iqbal, Khurram Shahzad, Topological Properties of Concealed Non-Kekulean Benzenoid Hydrocarbon, 2023, 43, 1040-6638, 1776, 10.1080/10406638.2022.2039230 | |
3. | Kamel Jebreen, Hifza Iqbal, Muhammad Haroon Aftab, Iram Yaqoob, Mohammed Issa Sowaity, Amjad Barham, Study of eccentricity based topological indices for benzenoid structure, 2023, 45, 10269185, 221, 10.1016/j.sajce.2023.05.010 | |
4. | Claudio Rocco, Jose A. Moronta, A Novel Perspective for Hosoya and M Polynomials for Analyzing Electric Power Network Topology, 2024, 1556-5068, 10.2139/ssrn.4806323 | |
5. | Hifza Iqbal, Muhammad Haroon Aftab, Ali Akgul, Zeeshan Saleem Mufti, Iram Yaqoob, Mustafa Bayram, Muhammad Bilal Riaz, Further study of eccentricity based indices for benzenoid hourglass network, 2023, 9, 24058440, e16956, 10.1016/j.heliyon.2023.e16956 |
Topological Index | Derivation from M (K; x, y) | |
First Zagreb | (Dx+Dy)(M(K;x,y))|x=1=y | (ii) |
Second Zagreb | (DxDy)(M(K;x,y))|x=1=y | (iii) |
Second Modified Zagreb | (SxSy)(M(K;x,y))|x=1=y | (iv) |
general Randic | (DαxDαy)(M(K;x,y))|x=1=y | (v) |
Reciprocal general Randic | (SαxSαy)(M(K;x,y))|x=1=y | (vi) |
Symmetric Division Index | (SyDx)+(SxDy)(M(K;x,y))|x=1=y | (vii) |
Graph | Planar/ Non−Planar |
St(G1,G2) | Planar |
Md(G1,G2) | Planar |
Bdu(G1,G2) | Non−Planar |
T1 | Non−Planar |
T2 | Non−Planar |
Types of edges | Count of edges |
(3,4) | 8n+8 |
(3,7) | 8 |
(3,8) | 4n−4 |
(4,4) | 4n+4 |
(4,5) | 8n−4 |
(4,6) | 8n−4 |
(5,6) | 4n−2 |
(5,7) | 4 |
(5,8) | 8n−8 |
(7,8) | 2 |
(8,8) | 2n−3 |
Types of edges | Count of edges |
(3,4) | 6n+6 |
(3,8) | 9 |
(3,10) | 3n−6 |
(4,4) | 3n+3 |
(4,5) | 6n−6 |
(4,6) | 6n−6 |
(5,6) | 3n−3 |
(5,8) | 6 |
(5,10) | 6n−12 |
(6,6) | 6(n−1)2 |
(6,8) | 3 |
(6,10) | 9n−18 |
(6,12) | 6[(n−(n−1))+(n−(n−2))+... |
+(n−4)+(n−3)] | |
(8,10) | 6 |
(10,10) | 3n−6 |
(10,12) | 6n−18 |
(12,12) | 3[(n−4)+(n−5)+... |
+(n−(n−1))] |
Topological Index | Derivation from M (K; x, y) | |
First Zagreb | (Dx+Dy)(M(K;x,y))|x=1=y | (ii) |
Second Zagreb | (DxDy)(M(K;x,y))|x=1=y | (iii) |
Second Modified Zagreb | (SxSy)(M(K;x,y))|x=1=y | (iv) |
general Randic | (DαxDαy)(M(K;x,y))|x=1=y | (v) |
Reciprocal general Randic | (SαxSαy)(M(K;x,y))|x=1=y | (vi) |
Symmetric Division Index | (SyDx)+(SxDy)(M(K;x,y))|x=1=y | (vii) |
Graph | Planar/ Non−Planar |
St(G1,G2) | Planar |
Md(G1,G2) | Planar |
Bdu(G1,G2) | Non−Planar |
T1 | Non−Planar |
T2 | Non−Planar |
Types of edges | Count of edges |
(3,4) | 8n+8 |
(3,7) | 8 |
(3,8) | 4n−4 |
(4,4) | 4n+4 |
(4,5) | 8n−4 |
(4,6) | 8n−4 |
(5,6) | 4n−2 |
(5,7) | 4 |
(5,8) | 8n−8 |
(7,8) | 2 |
(8,8) | 2n−3 |
Types of edges | Count of edges |
(3,4) | 6n+6 |
(3,8) | 9 |
(3,10) | 3n−6 |
(4,4) | 3n+3 |
(4,5) | 6n−6 |
(4,6) | 6n−6 |
(5,6) | 3n−3 |
(5,8) | 6 |
(5,10) | 6n−12 |
(6,6) | 6(n−1)2 |
(6,8) | 3 |
(6,10) | 9n−18 |
(6,12) | 6[(n−(n−1))+(n−(n−2))+... |
+(n−4)+(n−3)] | |
(8,10) | 6 |
(10,10) | 3n−6 |
(10,12) | 6n−18 |
(12,12) | 3[(n−4)+(n−5)+... |
+(n−(n−1))] |