
We focus on the two-stage stochastic programming (SP) with information update, and study how to evaluate and acquire information, especially when the information is imperfect. The scarce-data setting in which the probabilistic interdependent relationship within the updating process is unavailable, and thus, the classic Bayes' theorem is inapplicable. To address this issue, a robust approach is proposed to identify the worst probabilistic relationship of information update within the two-stage SP, and the robust Expected Value of Imperfect Information (EVII) is evaluated by developing a scenario-based max-min-min model with the bi-level structure. Three ways are developed to find the optimal solution for different settings. Furthermore, we study a costly information acquisition game between a two-stage SP decision-maker and an exogenous information provider. A linear compensation contract is designed to realize the global optimum. Finally, the proposed approach is applied to address a two-stage production and shipment problem to validate the effectiveness of our work. This paper enriches the interactions between uncertain optimization and information management and enables decision-makers to evaluate and manage imperfect information in a scarce-data setting.
Citation: Chang-Jun Wang, Zi-Jian Gao. Two-stage stochastic programming with imperfect information update: Value evaluation and information acquisition game[J]. AIMS Mathematics, 2023, 8(2): 4524-4550. doi: 10.3934/math.2023224
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We focus on the two-stage stochastic programming (SP) with information update, and study how to evaluate and acquire information, especially when the information is imperfect. The scarce-data setting in which the probabilistic interdependent relationship within the updating process is unavailable, and thus, the classic Bayes' theorem is inapplicable. To address this issue, a robust approach is proposed to identify the worst probabilistic relationship of information update within the two-stage SP, and the robust Expected Value of Imperfect Information (EVII) is evaluated by developing a scenario-based max-min-min model with the bi-level structure. Three ways are developed to find the optimal solution for different settings. Furthermore, we study a costly information acquisition game between a two-stage SP decision-maker and an exogenous information provider. A linear compensation contract is designed to realize the global optimum. Finally, the proposed approach is applied to address a two-stage production and shipment problem to validate the effectiveness of our work. This paper enriches the interactions between uncertain optimization and information management and enables decision-makers to evaluate and manage imperfect information in a scarce-data setting.
The field of interval analysis is a subfield of set-valued analysis, which focuses on sets in mathematics and topology. Historically, Archimede's method included calculating the circumference of a circle, which is an example of interval enclosure. By focusing on interval variables instead of point variables, and expressing computation results as intervals, this method eliminates errors that cause misleading conclusions. An initial objective of the interval-valued analysis was to estimate error estimates for numerical solutions to finite state machines. In 1966, Moore [2], published the first book on interval analysis, which is credited with being the first to use intervals in computer mathematics in order to improve calculation results. There are many situations where the interval analysis can be used to solve uncertain problems because it can be expressed in terms of uncertain variables. In spite of this, interval analysis remains one of the best approaches to solving interval uncertain structural systems and has been used for over fifty years in mathematical modeling such as computer graphics [3], decision-making analysis [4], multi-objective optimization, [5], error analysis [40]. In summary, interval analysis research has yielded numerous excellent results, and readers can consult Refs. [7,8,9], for additional information.
Convexity has been recognized for many years as a significant factor in such fields as probability theory, economics, optimal control theory, and fuzzy analysis. On the other hand, generalized convexity of mappings is a powerful tool for solving numerous nonlinear analysis and applied analysis problems, including a wide range of mathematical physics problems. A number of rigorous generalizations of convex functions have recently been investigated, see Refs. [10,11,12,13]. An interesting topic in mathematical analysis is integral inequalities. Convexity plays a significant role in inequality theory. During the last few decades, generalized convexity has played a prominent role in many disciplines and applications of IVFS, see Refs. [14,15,16,17,18,19]. Several recent applications have addressed these inequalities, see Refs. [20,21,22]. First, Breckner describes the idea of continuity for IVFS, see Ref. [23]. Using the generalized Hukuhara derivative, Chalco-Cano et al. [24], and Costa et al. [25], derived some Ostrowski and Opial type inequality for IVFS, respectively. Bai et al. [26], formulated an interval-based Jensen inequality. First, Zhao [27], and co-authors established (H.H) and Jensen inequality using h-convexity for IVFS. In general, the traditional (H.H) inequality has the following definition:
Θ(g)+Θ(h)2≥1h−g∫hgΘ(γ)dγ≥Θ(g+h2). | (1.1) |
Because of the nature of its definition, it is the first geometrical interpretation of convex mappings in elementary mathematics, and has attracted a large amount of attention. Several generalizations of this inequality are presented here, see Refs. [28,29,30,31]. Initially, Awan et al. explored (h1,h2)-convex functions and proved the following inequality [32]. Several authors have developed H.H and Jensen-type inequalities utilizing (h1,h2)-convexity. Ruonan Liu [33] developed H.H inequalities for harmonically (h1,h2)-convex functions. Wengui Yang [34] developed H.H inequalities on the coordinates for (p1,h1)-(p2,h2)-convex functions. Shi et al. [35] developed H.H inequalities for (m,h1,h2)-convex functions via Riemann Liouville fractional integrals. Sahoo et al. [36] established H.H and Jensen-type inequalities for harmonically (h1,h2)-Godunova-Levin functions. Afzal et al. [37] developed these inequalities for a generalized class of Godunova-Levin functions using inclusion relation. An et al. [38] developed H.H type inequalities for interval-valued (h1,h2)-convex functions. Results are now influenced by less accurate inclusion relation and interval LU-order relation. For some recent developments using the inclusion relation for the generalized class of Godunova-Levin functions, see Refs. [39,40,44]. It is clear from comparing the examples presented in this literature that the inequalities obtained using these old partial order relations are not as precise as those obtained by using CR-order relation. As a result, it is critically important that we are able to study inequalities and convexity by using a total order relation. Therefore, we use Bhunia's [41], CR-order, which is total interval order relation. The notions of CR-convexity and CR-order relation were used by several authors in 2022, in an attempt to prove a number of recent developments in these inequalities, see Refs. [42,43]. Afzal et al. using the notion of the h-GL function, proves the following result [45].
Theorem 1.1. (See [45]) Consider Θ:[g,h]→RI+. Define h:(0,1)→R+ and h(12)≠0. If Θ∈SX(CR-h,[g,h],RI+) and Θ∈ IR[g,h], then
h(12)2Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR[Θ(g)+Θ(h)]∫10dxh(x). | (1.2) |
Also, by using the notion of the h-GL function Jensen-type inequality was also developed.
Theorem 1.2. (See [45]) Let ui∈R+, ji∈[g,h]. If h is non-negative super multiplicative function and Θ∈SX(CR-h,[g,h],RI+), then this holds :
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1Θ(ji)h(uiUk). | (1.3) |
In addition, it introduces a new concept of interval-valued GL-functions pertaining to a total order relation, the Center-Radius order, which is unique as far as the literature goes. With the example presented in this article, we are able to show how CR-IVFS can be used to analyze various integral inequalities. In contrast to classical interval-valued analysis, CR-order interval-valued analysis differs from it. Using the concept of Centre and Radius, we calculate intervals as follows: MC=M_+¯M2 and MR=¯M−M_2, respectively, where M=[M_,ˉM]. Inspired by the concepts of interval valued analysis and the strong literature that has been discussed above with particular articles, see e.g., Zhang et al. [39], Bhunia and Samanta [41], Shi et al. [42], Liu et al. [43] and Afzal et al. [44,45], we introduced the idea of CR-(h1,h2)-GL function. By using this new concept we developed H.H and Jensen-type inequalities. The study also includes useful examples to back up its findings.
Finally, the article is designed as follows: In Section 2, preliminary is provided. The main problems and applications are provided in Section 3 and 4. Finally, Section 5 provides the conclusion.
As for the notions used in this paper but not defined, see Refs. [42,43,45]. It is a good idea to familiarize yourself with some basic arithmetic related to interval analysis in this section since it will prove very helpful throughout the paper.
[M]=[M_,¯M](x∈R, M_≦x≦¯M;x∈R) |
[N]=[N_,¯N](x∈R, N_≦x≦¯N;x∈R) |
[M]+[N]=[M_,¯M]+[N_,¯N]=[M_+N_,¯M+¯N] |
ηM=η[M_,¯M]={[ηM_,η¯M](η>0){0}(η=0)[η¯M,ηM_](η<0), |
where η∈R.
Let RI and R+I be the set of all closed and all positive compact intervals of R, respectively. Several algebraic properties of interval arithmetic will now be discussed.
Consider M=[M_,ˉM]∈RI, then Mc=¯M+M_2 and Mr=¯M−M_2 are the center and radius of interval M respectively. The CR form of interval M can be defined as:
M=⟨Mc,Mr⟩=⟨¯M+M_2,¯M−M_2⟩. |
Following are the order relations for the center and radius of intervals:
Definition 2.1. The CR-order relation for M=[M_,¯M]=⟨Mc,Mr⟩, N=[N_,¯N]=⟨Nc,Nr⟩∈RI represented as:
M⪯crN⟺{Mc<Nc,ifMc≠Nc;Mr≤Nr,ifMc=Nc. |
Note: For arbitrary two intervals M,N∈RI, we have either M⪯crN or N⪯crM.
Riemann integral operators for IVFS are presented here.
Definition 2.2 (See [45]) Let D:[g,h] be an IVF such that D=[D_,¯D]. Then D is Riemann integrable (IR) on [g,h] if D_ and ¯D are IR on [g,h], that is,
(IR)∫hgD(s)ds=[(R)∫hgD_(s)ds,(R)∫hg¯D(s)ds]. |
The collection of all (IR) IVFS on [g,h] is represented by IR([g,h]).
Shi et al. [42] proved that the based on CR-order relations, the integral preserves order.
Theorem 2.1. Let D,F:[g,h] be IVFS given by D=[D_,¯D] and F=[F_,¯F]. If D(s)⪯CRF(s), ∀ i∈[g,h], then
∫hgD(s)ds⪯CR∫hgF(s)ds. |
We'll now provide an illustration to support the aforementioned Theorem.
Example 2.1. Let D=[s,2s] and F=[s2,s2+2], then for s∈[0,1].
DC=3s2,DR=s2,FC=s2+1 and FR=1. |
From Definition 2.1, we have D(s)⪯CRF(s), s∈[0,1].
Since,
∫10[s,2s]ds=[12,1] |
and
∫10[s2,s2+2]ds=[13,73]. |
Also, from above Theorem 2.1, we have
∫10D(s)ds⪯CR∫10F(s)ds. |
Definition 2.3. (See [42]) Define h1,h2:[0,1]→R+. We say that Θ:[g,h]→R+ is called (h1,h2)-convex function, or that Θ∈SX((h1,h2),[g,h],R+), if ∀ g1,h1∈[g,h] and γ∈[0,1], we have
Θ(γg1+(1−γ)h1)≤ h1(γ)h2(1−γ)Θ(g1)+h1(1−γ)h2(γ)Θ(h1). | (2.1) |
If in (2.1) "≤" replaced with "≥" it is called (h1,h2)-concave function or Θ∈SV((h1,h2),[g,h],R+).
Definition 2.4. (See [42]) Define h1,h2:(0,1)→R+. We say that Θ:[g,h]→R+ is called (h1,h2)-GL convex function, or that Θ∈SGX((h1,h2),[g,h],R+), if ∀ g1,h1∈[g,h] and γ∈[0,1], we have
Θ(γg1+(1−γ)h1)≤Θ(g1)h1(γ)h2(1−γ)+Θ(h1)h1(1−γ)h2(γ). | (2.2) |
If in (2.2) "≤" replaced with "≥" it is called (h1,h2)-GL concave function or Θ∈SGV((h1,h2),[g,h],R+).
Now let's introduce the concept for CR-order form of convexity.
Definition 2.5. (See [42]) Define h1,h2:[0,1]→R+. We say that Θ:[g,h]→R+ is called CR−(h1,h2)-convex function, or that Θ∈SX(CR-(h1,h2),[g,h],R+), if ∀ g1,h1∈[g,h] and γ∈[0,1], we have
Θ(γg1+(1−γ)h1)⪯CR h1(γ)h2(1−γ)Θ(g1)+h1(1−γ)h2(γ)Θ(h1). | (2.3) |
If in (2.3) "⪯CR" replaced with "⪰CR" it is called CR-(h1,h2)-concave function or Θ∈SV(CR-(h1,h2),[g,h],R+).
Definition 2.6. (See [42]) Define h1,h2:(0,1)→R+. We say that Θ:[g,h]→R+ is called CR-(h1,h2)-GL convex function, or that Θ∈SGX(CR-(h1,h2),[g,h],R+), if ∀ g1,h1∈[g,h] and γ∈[0,1], we have
Θ(γg1+(1−γ)h1)⪯CRΘ(g1)h1(γ)h2(1−γ)+Θ(h1)h1(1−γ)h2(γ). | (2.4) |
If in (2.4) "⪯CR" replaced with "⪰CR" it is called CR-(h1,h2)-GL concave function or Θ∈SGV(CR-(h1,h2),[g,h],R+).
Remark 2.1. ● If h1=h2=1, Definition 2.6 becomes a CR-P-function [45].
● If h1(γ)=1h1(γ), h2=1 Definition 2.6 becomes a CR-h-convex function [45].
● If h1(γ)=h1(γ), h2=1 Definition 2.6 becomes a CR-h-GL function [45].
● If h1(γ)=1γs, h2=1 Definition 2.6 becomes a CR-s-convex function [45].
● If h(γ)=γs, Definition 2.6 becomes a CR-s-GL function [45].
Proposition 3.1. Consider Θ:[g,h]→RI given by [Θ_,¯Θ]=(ΘC,ΘR). If ΘC and ΘR are (h1,h2)-GL over [g,h], then Θ is called CR-(h1,h2)-GL function over [g,h].
Proof. Since ΘC and ΘR are (h1,h2)-GL over [g,h], then for each γ∈(0,1) and for all g1,h1∈[g,h], we have
ΘC(γg1+(1−γ)h1)⪯CRΘC(g1)h1(γ)h2(1−γ)+ΘC(h1)h1(1−γ)h2(γ), |
and
ΘR(γg1+(1−γ)h1)⪯CRΘR(g1)h1(γ)h2(1−γ)+ΘR(h1)h1(1−γ)h2(γ). |
Now, if
ΘC(γg1+(1−γ)h1)≠ΘC(g1)h1(γ)h2(1−γ)+ΘC(h1)h1(1−γ)h2(γ), |
then for each γ∈(0,1) and for all g1,h1∈[g,h],
ΘC(γg1+(1−γ)h1)<ΘC(g1)h1(γ)h2(1−γ)+ΘC(h1)h1(1−γ)h2(γ). |
Accordingly,
ΘC(γg1+(1−γ)h1)⪯CRΘC(g1)h1(γ)h2(1−γ)+ΘC(h1)h1(1−γ)h2(γ). |
Otherwise, for each γ∈(0,1) and for all g1,h1∈[g,h],
ΘR(νg1+(1−γ)h1)≤ΘR(g1)h1(γ)h2(1−γ)+ΘR(h1)h1(1−γ)h2(γ) |
⇒Θ(γg1+(1−γ)h1)⪯CRΘ(g1)h1(γ)h2(1−γ)+Θ(h1)h1(1−γ)h2(γ). |
Taking all of the above into account, and Definition 2.6 this can be written as
Θ(γg1+(1−γ)h1)⪯CRΘ(g1)h1(γ)h2(1−γ)+Θ(h1)h1(1−γ)h2(γ) |
for each γ∈(0,1) and for all g1,h1∈[g,h].
This completes the proof.
The next step is to establish the H.H inequality for the CR-(h1,h2)-GL function.
Theorem 3.1. Define h1,h2:(0,1)→R+ and h1(12)h2(12)≠0. Let Θ:[g,h]→RI+, if Θ∈SGX(CR-(h1,h2),[t,u],RI+) and Θ∈ IR[t,u], we have
[H(12,12)]2Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR[Θ(g)+Θ(h)]∫10dxH(x,1−x). |
Proof. Since Θ∈SGX(CR-(h1.h2),[g,h],RI+), we have
[H(12,12)]Θ(g+h2)⪯CRΘ(xg+(1−x)h)+Θ((1−x)g+xh). |
Integration over (0, 1), we have
[H(12,12)]Θ(g+h2)⪯CR[∫10Θ(xg+(1−x)h)dx+∫10Θ((1−x)g+xh)dx]=[∫10Θ_(xg+(1−x)h)dx+∫10Θ_((1−x)g+xh)dx,∫10¯Θ(xg+(1−x)h)dx+∫10¯Θ((1−x)g+xh)dx]=[2h−g∫hgΘ_(γ)dγ,2h−g∫hg¯Θ(γ)dγ]=2h−g∫hgΘ(γ)dγ. | (3.1) |
By Definition 2.6, we have
Θ(xg+(1−x)h)⪯CRΘ(g)h1(x)h2(1−x)+Θ(h)h1(1−x)h2(x). |
Integration over (0, 1), we have
∫10Θ(xg+(1−x)h)dx⪯CRΘ(g)∫10dxh1(x)h2(1−x)+Θ(h)∫10dxh1(1−x)h2(x). |
Accordingly,
1h−g∫hgΘ(γ)dγ⪯CR[Θ(g)+Θ(h)]∫10dxH(x,1−x). | (3.2) |
Now combining (3.1) and (3.2), we get required result
[H(12,12)]2Θ(t+u2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR[Θ(g)+Θ(h)]∫10dxH(x,1−x). |
Remark 3.1. ● If h1(x)=h2(x)=1, Theorem 3.1 becomes result for CR- P-function:
12Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR [Θ(g)+Θ(h)]. |
● If h1(x)=h(x), h2(x)=1 Theorem 3.1 becomes result for CR-h-GL-function:
h(12)2Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR ∫10dxh(x). |
● If h1(x)=1h(x), h2(x)=1 Theorem 3.1 becomes result for CR-h-convex function:
12h(12)Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR ∫10h(x)dx. |
● If h1(x)=1h1(x), h2(x)=1h2(x) Theorem 3.1 becomes result for CR-(h1,h2)-convex function:
12[H(12,12)]Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR ∫10dxH(x,1−x). |
Example 3.1. Consider [t,u]=[0,1], h1(x)=1x, h2(x)=1, ∀ x∈ (0,1). Θ:[g,h]→RI+ is defined as
Θ(γ)=[−γ2,2γ2+1]. |
where
[H(12,12)]2Θ(g+h2)=Θ(12)=[−14,32], |
1h−g∫hgΘ(γ)dγ=[∫10(−γ2)dγ,∫10(2γ2+1)dγ]=[−13,53], |
[Θ(g)+Θ(h)]∫10dxH(x,1−x)=[−12,2]. |
As a result,
[−14,32]⪯CR[−13,53]⪯CR[−12,2]. |
This proves the above theorem.
Theorem 3.2. Define h1,h2:(0,1)→R+ and h1(12)h2(12)≠0. Let Θ:[g,h]→RI+, if Θ∈SGX(CR-(h1,h2),[t,u],RI+) and Θ∈ IR[g,h], we have
[H(12,12)]24Θ(g+h2)⪯CR△1⪯CR1h−g∫hgΘ(γ)dγ⪯CR△2 |
⪯CR{[Θ(g)+Θ(h)][12+1H(12,12)]}∫10dxH(x,1−x), |
where
△1=H(12,12)4[Θ(3g+h4)+Θ(3h+g4)], |
△2=[Θ(g+h2)+Θ(g)+Θ(h)2]∫10dxH(x,1−x). |
Proof. Take [g,g+h2], we have
Θ(g+g+h22)=Θ(3g+h2)⪯CRΘ(xg+(1−x)g+h2)H(12,12)+Θ((1−x)g+xg+h2)H(12,12). |
Integration over (0, 1), we have
Θ(3g+h2)⪯CR1H(12,12)[∫10Θ(xg+(1−x)g+h2)dx+∫10Θ(xg+h2+(1−x)h)dx] |
=1H(12,12)[2h−g∫g+h2gη(γ)dγ+2h−g∫g+h2gΘ(γ)dγ] |
=4H(12,12)[1h−g∫g+h2gΘ(γ)dγ]. |
Accordingly,
H(12,12)4Θ(3g+h2)⪯CR1h−g∫g+h2gΘ(γ)dγ. | (3.3) |
Similarly for interval [g+h2,h], we have
H(12,12)4Θ(3h+g2)⪯CR1h−g∫g+h2gΘ(γ)dγ. | (3.4) |
Adding inequalities (3.3) and (3.4), we get
△1=H(12,12)4[Θ(3g+h4)+Θ(3h+g4)]⪯CR[1h−g∫hgΘ(γ)dγ]. |
Now
[H(12,12)]24Θ(g+h2) |
=[H(12,12)]24Θ(12(3g+h4)+12(3h+g4)) |
⪯CR[H(12,12)]24[Θ(3g+h4)h(12)+Θ(3h+g4)h(12)] |
=H(12,12)4[Θ(3g+h4)+Θ(3h+g4)] |
=△1 |
⪯CRH(12,12)4{1H(12,12)[Θ(g)+Θ(g+h2)]+1H(12,12)[Θ(h)+Θ(g+h2)]} |
=12[Θ(g)+Θ(h)2+Θ(g+h2)] |
⪯CR[Θ(g)+Θ(h)2+Θ(g+h2)]∫10dxH(x,1−x) |
=△2 |
⪯CR[Θ(g)+Θ(h)2+Θ(g)H(12,12)+Θ(h)H(12,12)]∫10dxH(x,1−x) |
⪯CR[Θ(g)+Θ(h)2+1H(12,12)[Θ(g)+Θ(h)]]∫10dxH(x,1−x) |
⪯CR{[Θ(g)+Θ(h)][12+1H(12,12)]}∫10dxH(x,1−x). |
Example 3.2. Thanks to Example 3.1, we have
[H(12,12)]24Θ(g+h2)=Θ(12)=[−14,32], |
△1=12[Θ(14)+Θ(34)]=[−516,138], |
△2=[Θ(0)+Θ(1)2+Θ(12)]∫10dxH(x,1−x), |
△2=12([−14,32]+[−12,2]), |
△2=[−38,74], |
{[Θ(g)+Θ(h)][12+1H(12,12)]}∫10dxH(x,1−x)=[−12,2]. |
Thus we obtain
[−14,32]⪯CR[−516,138]⪯CR[−13,53]⪯CR[−38,74]⪯cr[−12,2]. |
This proves the above theorem.
Theorem 3.3. Let Θ,θ:[g,h]→RI+,h1,h2:(0,1)→R+ such that h1,h2≠0. If Θ∈SGX(CR-h1,[g,h],RI+), θ∈SGX(CR-h2,[g,h],RI+) and Θ,θ∈ IR[g,h] then, we have
1h−g∫hgΘ(γ)θ(γ)dγ⪯CRM(g,h)∫10dxH2(x,1−x)+N(g,h)∫10dxH(x,x)H(1−x,1−x), |
where
M(g,h)=Θ(g)θ(g)+Θ(h)θ(h),N(g,h)=Θ(g)θ(h)+Θ(h)θ(g). |
Proof. Conider Θ∈SGX(CR-h1,[g,h],RI+), θ∈SGX(CR-h2,[g,h],RI+) then, we have
Θ(gx+(1−x)h)⪯CRΘ(g)h1(x)h2(1−x)+Θ(h)h1(1−x)h2(x), |
θ(gx+(1−x)h)⪯CRθ(g)h1(x)h2(1−x)+θ(h)h1(1−x)h2(x). |
Then,
Θ(gx+(1−x)h)θ(tx+(1−x)u) |
⪯CRΘ(g)θ(g)H2(x,1−x)+Θ(g)θ(h)+Θ(g)θ(g)H2(1−x,x)+Θ(h)θ(h)H(x,x)H(1−x,1−x). |
Integration over (0, 1), we have
∫10Θ(gx+(1−x)h)θ(gx+(1−x)h)dx |
=[∫10Θ_(gx+(1−x)h)θ_(gx+(1−x)h)dx,∫10¯Θ(gx+(1−x)h)¯θ(gx+(1−x)h)dx] |
=[1h−g∫hgΘ_(γ)θ_(γ)dγ,1h−g∫hg¯Θ(γ)¯θ(γdγ]=1h−g∫hgΘ(γ)θ(γ)dγ |
⪯CR∫10[Θ(g)θ(g)+Θ(h)θ(h)]H2(x,1−x)dx+∫10[Θ(g)θ(h)+Θ(h)θ(g)]H(x,x)H(1−x,1−x)dx. |
It follows that
1h−g∫hgΘ(γ)θ(γ)dγ⪯CRM(g,h)∫10dxH2(x,1−x)+N(g,h)∫10dxH(x,x)H(1−x,1−x). |
Theorem is proved.
Example 3.3. Consider [g,h]=[1,2], h1(x)=1x, h2(x)=1 ∀ x∈ (0,1). Θ,θ:[g,h]→RI+ be defined as
Θ(γ)=[−γ2,2γ2+1],θ(γ)=[−γ,γ]. |
Then,
1h−g∫hgΘ(γ)θ(γ)dγ=[154,9], |
M(g,h)∫101H2(x,1−x)dx=M(1,2)∫10x2dx=[−7,7], |
N(g,h)∫101H(x,x)H(1−x,1−x)dx=N(1,2)∫10x(1−x)dx=[−156,156]. |
It follows that
[154,9]⪯CR[−7,7]+[−156,156]=[−192,192]. |
It follows that the theorem above is true.
Theorem 3.4. Let Θ,θ:[g,h]→RI+,h1,h2:(0,1)→R+ such that h1,h2≠0. If Θ∈SGX(CR-h1,[g,h],RI+), θ∈SGX(CR-h2,[g,h],RI+) and Θ,θ∈ IR[g,h] then, we have
[H(12,12)]22Θ(g+h2)θ(g+h2)⪯CR1h−g∫hgΘ(γ)θ(γ)dγ+M(g,h)∫10dxH(x,x)H(1−x,1−x)+N(g,h)∫10dxH2(x,1−x). |
Proof. Since Θ∈SGX(CR-h1,[g,h],RI+), θ∈SGX(CR-h2,[g,h],RI+), we have
Θ(g+h2)⪯CRΘ(gx+(1−x)h)H(12,12)+Θ(g(1−x)+xh)H(12,12), |
θ(g+h2)⪯CRθ(gx+(1−x)h)H(12,12)+θ(g(1−x)+xh)H(12,12). |
Then,
Θ(g+h2)θ(g+h2)⪯CR1[H(12,12)]2[Θ(gx+(1−x)h)θ(gx+(1−x)h)+Θ(g(1−x)+xh)θ(g(1−x)+xh)]+1[H(12,12)]2[Θ(gx+(1−x)h)θ(g(1−x)+xh)+Θ(g(1−x)+xh)θ(gx+(1−x)h)]+⪯CR1[H(12,12)]2[Θ(gx+(1−x)h)θ(gx+(1−x)h)+Θ(g(1−x)+(xh)θ(g(1−x)+xh)]+1[H(12,12)]2[(Θ(g)H(x,1−x)+θ(h)H(1−x,x))(θ(h)H(1−x,x)+θ(h)H(x,1−x))]+[(Θ(g)H(1−x,x)+Θ(h)H(x,1−x))(θ(g)H(x,1−x)+θ(h)H(1−x,x))]⪯CR1[H(12,12)]2[Θ(gx+(1−x)h)θ(gx+(1−x)h)+Θ(g(1−x)+xh)θ(g(1−x)+xh)]+1[H(12,12)]2[(2H(x,x)H(1−x,1−x))M(g,h)+(1H2(x,1−x)+1H2(1−x,x))N(g,h)]. |
Integration over (0,1), we have
∫10Θ(g+h2)θ(g+h2)dx=[∫10Θ_(g+h2)θ_(g+h2)dx,∫10¯Θ(g+h2)¯θ(g+h2)dx]=Θ(g+h2)θ(g+h2)dx⪯CR2[H(12,12)]2[1h−g∫hgΘ(γ)θ(γ)dγ]+2[H(12,12)]2[M(g,h)∫101H(x,x)H(1−x,1−x)dx+N(g,h)∫101H2(x,1−x)dx]. |
Multiply both sides by [H(12,12)]22 above equation, we get the required result
[H(12,12)]22Θ(g+h2)θ(g+h2)⪯CR1h−g∫hgΘ(γ)θ(γ)dγ+M(g,h)∫10dxH(x,x)H(1−x,1−x)+N(g,h)∫10dxH2(x,1−x). |
As a result, the proof is complete.
Example 3.4. Consider [g,h]=[1,2], h1(x)=1x, h2(x)=14, ∀ x∈ (0,1). Θ,θ:[g,h]→RI+ be defined as
Θ(γ)=[−γ2,2γ2+1],θ(γ)=[−γ,γ]. |
Then,
[H(12,12)]22Θ(g+h2)θ(g+h2)=18Θ(32)θ(32)=[−3332,3332], |
1h−g∫hgΘ(γ)θ(γ)dγ=[154,9], |
M(g,h)∫10dxH(x,x)H(1−x,1−x)=(16)M(1,2)∫10x(1−x)dx=[−56,56], |
N(g,h)∫10dxH2(x,1−x)=(16)N(1,2)∫10x2dx=[−80,80]. |
It follows that
[−3332,3332]⪯CR[154,9]+[−56,56]+[−80,80]=[−5294,145]. |
This proves the above theorem. Next, we will develop the Jensen-type inequality for CR-(h1,h2)-GL functions.
Theorem 4.1. Let ui∈R+, ji∈[g,h]. If h1,h2 is super multiplicative non-negative functions and if Θ∈SGX(CR-(h1,h2),[g,h],RI+). Then the inequality become as :
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1[Θ(ji)H(uiUk,Uk−1Ek)], | (4.1) |
where Uk=∑ki=1ui
Proof. When k=2, then (4.1) holds. Suppose that (4.1) is also valid for k−1, then
Θ(1Ukk∑i=1uiji)=Θ(ukUkvk+k−1∑i=1uiUkji) |
⪯CRΘ(jk)h1(ukUk)h2(Uk−1Uk)+Θ(k−1∑i=1uiUkji)h1(Uk−1Uk)h2(ukUk) |
⪯CRΘ(jk)h1(ukUk)h2(Uk−1Uk)+k−1∑i=1[Θ(ji)H(uiUk,Uk−2Uk−1)]1h1(Uk−1Uk)h2(ukUk) |
⪯CRΘ(jk)h1(ukUk)h2(Uk−1Uk)+k−1∑i=1[Θ(ji)H(uiUk,Uk−2Uk−1)] |
⪯CRk∑i=1[Θ(ji)H(uiUk,Uk−1Uk)]. |
It follows from mathematical induction that the conclusion is correct.
Remark 4.1. ● If h1(x)=h2(x)=1, Theorem 4.1 becomes result for CR- P-function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1Θ(ji). |
● If h1(x)=1h1(x), h2(x)=1h2(x) Theorem 4.1 becomes result for CR-(h1,h2)-convex function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1H(uiUk,Uk−1Uk)Θ(ji). |
● If h1(x)=1x, h2(x)=1 Theorem 4.1 becomes result for CR-convex function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1uiUkΘ(ji). |
● If h1(x)=1h(x), h2(x)=1 Theorem 4.1 becomes result for CR-h-convex function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1h(uiUk)Θ(ji). |
● If h1(x)=h(x), h2(x)=1 Theorem 4.1 becomes result for CR-h-GL-function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1[Θ(ji)h(uiUk)]. |
● If h1(x)=1(x)s, h2(x)=1 Theorem 4.1 becomes result for CR-s-convex function:
η(1Ukk∑i=1uiji)⪯CRk∑i=1(uiUk)sΘ(ji). |
A useful alternative for incorporating uncertainty into prediction processes is IVFS. The present study introduces the (h1,h2)-GL concept for IVFS using the CR-order relation. As a result of utilizing this new concept, we observe that the inequality terms derived from this class of convexity and pertaining to Cr-order relations give much more precise results than other partial order relations. These findings are generalized from the very recent results described in [37,42,43,45]. There are many new findings in this study that extend those already known. In addition, we provide some numerical examples to demonstrate the validity of our main conclusions. Future research could include determining equivalent inequalities for different types of convexity utilizing various fractional integral operators, including Katugampola, Riemann-Liouville and generalized K-fractional operators. The fact that these are the most active areas of study for integral inequalities will encourage many mathematicians to examine how different types of interval-valued analysis can be applied. We anticipate that other researchers working in a number of scientific fields will find this idea useful.
The authors declare that there is no conflict of interest in publishing this paper.
[1] |
M. Brito, E. Laan, Inventory control with product returns: the impact of imperfect information, Eur. J. Oper. Res., 194 (2009), 85–101. https://doi.org/10.1016/j.ejor.2007.11.063 doi: 10.1016/j.ejor.2007.11.063
![]() |
[2] |
A. Heath, I. Manolopoulou, G. Baio, A review of methods for the analysis of the expected value of information, Med. Decis. Making, 37 (2017), 747–758. https://doi.org/10.1177/0272989X17697692 doi: 10.1177/0272989X17697692
![]() |
[3] |
R. A. Howard, Information value theory, IEEE T. Syst. Man Cy., 2 (1966), 22–26. https://doi.org/10.1109/TSSC.1966.300074 doi: 10.1109/TSSC.1966.300074
![]() |
[4] | H. Raiffa, Decision analysis: Introductory lectures on choices under uncertainty, Reading, MA: Addison-Wesley, 1968. |
[5] | R. L. Winkler, An introduction to Bayesian inference and decision, Gainesville, FL: Probabilistic Publishing, 2003. |
[6] |
R. B. Bratvold, J. E. Bickel, H. P. Lohne, Value of information in the oil and gas industry: Past, present, and future, SPE Reserv. Eval. Eng., 12 (2007), 630–638. https://doi.org/10.2118/110378-MS doi: 10.2118/110378-MS
![]() |
[7] | D. Koller, N. Friedman, Probabilistic graphical models: Principles and techniques, Cambridge University Press, 2009. |
[8] |
E. K. Hussain, P. R. Thies, J. Hardwick, P. M. Connor, M. Abusara, Grid Island energy transition scenarios assessment through network reliability and power flow analysis, Front. Energy Res., 8 (2021), 584440. https://doi.org/10.3389/fenrg.2020.584440 doi: 10.3389/fenrg.2020.584440
![]() |
[9] |
Q. Wang, A. Farahat, C. Gupta, S. Zheng, Deep time series models for scarce data, Neurocomputing, 456 (2021), 504–518. https://doi.org/10.1016/j.neucom.2020.12.132 doi: 10.1016/j.neucom.2020.12.132
![]() |
[10] |
A. Maxhuni, P. Hernandez-Leal, L. E. Sucar, V. Osmani, E. F. Morales, O. Mayora, Stress modelling and prediction in presence of scarce data, J. Bio. Info., 63 (2016), 344–356. https://doi.org/10.1016/j.jbi.2016.08.023 doi: 10.1016/j.jbi.2016.08.023
![]() |
[11] |
C. J. Wang, S. T. Chen, A distributionally robust optimization for blood supply network considering disasters, Transport Res. E-Log., 134 (2020), 1–30. https://doi.org/10.1016/j.tre.2020.101840 doi: 10.1016/j.tre.2020.101840
![]() |
[12] | R. A. Howard, A. E. Abbas, Foundations of decision analysis, Boston, MA: Pearson Education Limited, 2016. |
[13] | K. Szaniawski, The value of perfect information, Synthese, 17 (1967), 408–424. |
[14] |
D. Samson, A. Wirth, J. Rickard, The value of information from multiple sources of uncertainty in decision analysis, Eur. J. Oper. Res., 39 (1989), 254–260. https://doi.org/10.1016/0377-2217(89)90163-X doi: 10.1016/0377-2217(89)90163-X
![]() |
[15] |
S. H. Azondékon, J. M. Martel, "Value" of additional information in multicriterion analysis under uncertainty, Eur. J. Oper. Res., 117 (1999), 45–62. https://doi.org/10.1016/S0377-2217(98)00102-7 doi: 10.1016/S0377-2217(98)00102-7
![]() |
[16] |
S. Ben Amor, K. Zaras, E. A. Aguayo, The value of additional information in multicriteria decision making choice problems with information imperfections, Ann. Oper. Res., 253 (2017), 61–76. https://doi.org/10.1007/s10479-016-2318-x doi: 10.1007/s10479-016-2318-x
![]() |
[17] |
M. E. Dakins, The value of the value of information, Hum. Ecol. Risk. Assess., 5(1999), 281–289. https://doi.org/10.1080/10807039991289437 doi: 10.1080/10807039991289437
![]() |
[18] |
I. Yanikoglu, B. L. Gorissen, D. den Hertog, A survey of adjustable robust optimization, Eur. J. Oper. Res., 277 (2019), 799–813. https://doi.org/10.1016/j.ejor.2018.08.031 doi: 10.1016/j.ejor.2018.08.031
![]() |
[19] |
G. Dutta, N. Gupta, J. Mandal, M. K. Tiwari, New decision support system for strategic planning in process industries: computational results, Comput. Ind. Eng., 124 (2018), 36–47. https://doi.org/10.1016/j.cie.2018.07.016 doi: 10.1016/j.cie.2018.07.016
![]() |
[20] |
S. Khalilabadi, S. H. Zegordi, E. Nikbakhsh, A multi-stage stochastic programming approach for supply chain risk mitigation via product substitution, Comput. Ind. Eng., 149 (2020), 106786. https://doi.org/10.1016/j.cie.2020.106786 doi: 10.1016/j.cie.2020.106786
![]() |
[21] |
J. C. López, J. Contreras, J. I. Munoz, J. Mantovani, A multi-stage stochastic non-linear model for reactive power planning under contingencies, IEEE T. Power Syst., 28 (2013), 1503–1514. https://doi.org/10.1109/TPWRS.2012.2226250 doi: 10.1109/TPWRS.2012.2226250
![]() |
[22] |
D. Bhattacharjya, J. Eidsvik, T. Mukerji, The value of information in portfolio problems with dependent projects, Decis. Anal., 10 (2013), 341–351. https://doi.org/10.1287/deca.2013.0277 doi: 10.1287/deca.2013.0277
![]() |
[23] |
C. M. Lee, A Bayesian approach to determine the value of information in the newsboy problem, Int. J. Prod. Econ., 112 (2008), 391–402. https://doi.org/10.1016/j.ijpe.2007.04.005 doi: 10.1016/j.ijpe.2007.04.005
![]() |
[24] |
S. Santos, A. Gaspar, D. J. Schiozer, Value of information in reservoir development projects: Technical indicators to prioritize uncertainties and information sources, J. Petrol. Sci. Eng., 157(2017), 1179–1191. https://doi.org/10.1016/j.petrol.2017.08.028 doi: 10.1016/j.petrol.2017.08.028
![]() |
[25] |
S. Ben Amor, J. M. Martel, Multiple criteria analysis in the context of information imperfections: Processing of additional information, Oper. Res., 5 (2005), 395–417. https://doi.org/10.1007/BF02941128 doi: 10.1007/BF02941128
![]() |
[26] | J. Bernardo, A. Smith, Bayesian theory, 2 Eds., Wiley & Sons, New York, 2000. |
[27] | S. J. Armstrong, Combining forecasts principles of forecasting: A handbook for researchers and practitioners, Kluwer Academic Publishers, Norwell, MA, 2001,417–439. |
[28] |
R. L. Winkler, Y. Grushka-Cockayne, K. C. Lichtendahl, V. Jose, Probability forecasts and their combination: A research perspective, Decis. Anal., 16 (2019), 239–260. https://doi.org/10.1287/deca.2019.0391 doi: 10.1287/deca.2019.0391
![]() |
[29] |
D. P. Morton, E. Popova, A Bayesian stochastic programming approach to an employee scheduling problem, IIE Trans., 36 (2004), 155–167. https://doi.org/10.1080/07408170490245450 doi: 10.1080/07408170490245450
![]() |
[30] |
O. Dowson, D. P. Morton, B. K. Pagnoncelli, Partially observable multistage stochastic programming, Oper. Res. Lett., 48 (2020), 505–512. https://doi.org/10.1016/j.orl.2020.06.005 doi: 10.1016/j.orl.2020.06.005
![]() |
[31] |
O. Compte, P. Jehiel, Auctions and information acquisition: sealed bid or dynamic formats? Rand. J. Econ., 38 (2007), 355–372. https://doi.org/10.2307/25046310 doi: 10.2307/25046310
![]() |
[32] |
P. Miettinen, Information acquisition during a Dutch auction, J. Econ. Theory, 148 (2013), 1213–1225. https://doi.org/10.1016/j.jet.2012.09.018 doi: 10.1016/j.jet.2012.09.018
![]() |
[33] |
E. M. Azevedo, D. M. Pennock, W. Bo, E. G. Weyl, Channel auctions, Manage Sci., 66 (2020), 2075–2082. https://doi.org/10.1287/mnsc.2019.3487 doi: 10.1287/mnsc.2019.3487
![]() |
[34] |
N. Golrezaei, H. Nazerzadeh, Auctions with dynamic costly information acquisition, Oper. Res., 65 (2017), 130–144. https://doi.org/10.1007/s00199-007-0301-0 doi: 10.1007/s00199-007-0301-0
![]() |
[35] |
Q. Fu, K. Zhu, Endogenous information acquisition in supply chain management, Eur. J. Oper. Res., 201 (2010), 454–462. https://doi.org/10.1016/j.ejor.2009.03.019 doi: 10.1016/j.ejor.2009.03.019
![]() |
[36] |
G. Li, H. Zheng, S. P. Sethi, X. Guan, Inducing downstream information sharing via manufacturer information acquisition and retailer subsidy, Decision Sci., 51 (2020), 691–719. https://doi.org/10.1111/deci.12340 doi: 10.1111/deci.12340
![]() |
[37] |
Q. Fu, Y. Li, K. Zhu, Costly information acquisition under horizontal competition, Oper. Res. Lett., 46 (2018), 418–423. https://doi.org/10.1016/j.orl.2018.05.003 doi: 10.1016/j.orl.2018.05.003
![]() |
[38] |
H. Cao, X. Guan, T. Fan, L. Zhou, The acquisition of quality information in a supply chain with voluntary vs. mandatory disclosure, Prod. Oper. Manag., 29 (2020), 595–616. https://doi.org/10.1111/poms.13130 doi: 10.1111/poms.13130
![]() |
[39] |
Y. Song, T. Fan, Y. Tang, F. Zou, Quality information acquisition and ordering decisions with risk aversion, Int. J. Prod. Res., 59 (2021), 6864–6880. https://doi.org/10.1080/00207543.2020.1828640 doi: 10.1080/00207543.2020.1828640
![]() |
[40] |
A. Madansky, Inequalities for stochastic linear programming problems, Manage. Sci., 6 (1960), 197–204. https://doi.org/10.1287/mnsc.6.2.197 doi: 10.1287/mnsc.6.2.197
![]() |
[41] |
M. A. Stulman, Some aspects of the distributional properties of the expected value of perfect information (EVPI), J. Oper. Res. Soc., 33 (1982), 827–836. https://doi.org/10.1057/jors.1982.178 doi: 10.1057/jors.1982.178
![]() |
[42] |
D. Bertsimas, M. Sim, The price of robustness, Oper. Res., 52 (2004), 35–53. https://doi.org/10.1287/opre.1030.0065 doi: 10.1287/opre.1030.0065
![]() |
[43] |
B. Colson, P. Marcotte, G. Savard, An overview of bilevel optimization, Ann. Oper. Res., 153 (2007), 235–256. https://doi.org/10.1007/s10479-007-0176-2 doi: 10.1007/s10479-007-0176-2
![]() |
[44] | A. Ben-Tal, L. E. Ghaoui, A. Nemirovski, Robust optimization, Princeton, NJ: Princeton University Press, 2009, 28–60. |
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13. | Muhammad Usman, Muhammad Hamid, Dianchen Lu, Zhengdi Zhang, Innovative coupling of s-stage one-step and spectral methods for non-smooth solutions of nonlinear problems, 2024, 204, 01689274, 329, 10.1016/j.apnum.2024.05.026 | |
14. | Alaa Khadim Mohammed, Salam Jasim Majeed, N. Aldahan, A.J. Ramadhan, Fear induce bistability in an ecoepidemiological model involving prey refuge and hunting cooperation, 2024, 97, 2117-4458, 00150, 10.1051/bioconf/20249700150 | |
15. | Ashraf Adnan Thirthar, Zahraa Albatool Mahdi, Prabir Panja, Santanu Biswas, Thabet Abdeljawad, Mutualistic behaviour in an interaction model of small fish, remora and large fish, 2024, 0228-6203, 1, 10.1080/02286203.2024.2392218 | |
16. | Yashra Javaid, Shireen Jawad, Rizwan Ahmed, Ali Hasan Ali, Badr Rashwani, Dynamic complexity of a discretized predator-prey system with Allee effect and herd behaviour, 2024, 32, 2769-0911, 10.1080/27690911.2024.2420953 | |
17. | Ankur Jyoti Kashyap, Hemanta Kumar Sarmah, Complex Dynamics in a Predator–Prey Model with Fear Affected Transmission, 2024, 0971-3514, 10.1007/s12591-024-00698-7 | |
18. | Bapin Mondal, Ashraf Adnan Thirthar, Nazmul Sk, Manar A. Alqudah, Thabet Abdeljawad, Complex dynamics in a two species system with Crowley–Martin response function: Role of cooperation, additional food and seasonal perturbations, 2024, 221, 03784754, 415, 10.1016/j.matcom.2024.03.015 | |
19. | ASHRAF ADNAN THIRTHAR, PRABIR PANJA, AZIZ KHAN, MANAR A. ALQUDAH, THABET ABDELJAWAD, AN ECOSYSTEM MODEL WITH MEMORY EFFECT CONSIDERING GLOBAL WARMING PHENOMENA AND AN EXPONENTIAL FEAR FUNCTION, 2023, 31, 0218-348X, 10.1142/S0218348X2340162X | |
20. | Md Golam Mortuja, Mithilesh Kumar Chaube, Santosh Kumar, Dynamic analysis of a predator-prey model with Michaelis-Menten prey harvesting and hunting cooperation in predators, 2025, 1598-5865, 10.1007/s12190-025-02482-4 | |
21. | Yan Li, Jianing Sun, Spatiotemporal dynamics of a delayed diffusive predator–prey model with hunting cooperation in predator and anti-predator behaviors in prey, 2025, 198, 09600779, 116561, 10.1016/j.chaos.2025.116561 | |
22. | Sangeeta Saha, Swadesh Pal, Roderick Melnik, Nonlocal Cooperative Behavior, Psychological Effects, and Collective Decision‐Making: An Exemplification With Predator–Prey Models, 2025, 0170-4214, 10.1002/mma.11010 |