Evaluating a Sustainable Intelligent Logistic System (ILS) Utilizing O-S Data and Holistic Managerial Models

Liu, Hong; Rao, Kamarajugadda Tulasi Vigneswara; Yogi, Kottala Sri; Dhone, Neelkanth

doi:https://doi.org/10.1155/2023/8873464

Journal of Advanced Transportation

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Abstract Introduction Literature Review Conclusions Data Availability Disclosure Conflicts of Interest References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 8873464 | https://doi.org/10.1155/2023/8873464

Evaluating a Sustainable Intelligent Logistic System (ILS) Utilizing O-S Data and Holistic Managerial Models

Hong Liu,¹Kamarajugadda Tulasi Vigneswara Rao,²Kottala Sri Yogi,³and Neelkanth Dhone⁴

Academic Editor: Luigi Dell’Olio

Received24 Apr 2023

Revised10 Jul 2023

Accepted06 Nov 2023

Published27 Nov 2023

Abstract

The intelligent logistic system (ILS) has benefited Industry 4.0. ILS assures clean, on-time manufacturing. Due to its unique architectures, qualities, and sensory aspects, the robot logistic system (RLS) is sought after as an ILS in Industry 4.0. In case of COVID-19, multiple nations routinely used RLS as ILS to cleanse areas, check patients, and monitor crowds on highways. Research documents (RDs) show that prior researchers attempted to build the static robot logistic system (RLS) performance mapping index; however, most indexes measured only anatomy performance of static RLS. Thus, few RDs are edited previously in the context of MRLS. It is sensed that few RDs examined MRLS-linked performance mapping indexes, including only regular subjective (S) or objective (O) designs, excluding mixed S-O architectures. Most RDs constantly execute the linguistic variables related to fuzzy, grey, rough, ambiguous, and intuitionistic sets/scales to tackle the uncertainty connected with MRLS designs. The authors prioritize those as RGs. The authors proposed (1) an MRLS performance mapping index with respect to technical, cost, and value O-S architectures for recruiting MRLS, (2) linear information to assign ratings in a range of min-max values choosing from 1 to 100% without executing the linguistic scale, and (3) Holistic Managerial Models (HMM-1 and HMM-2) to handle subjective ratings and significance, assigned by Ex against evaluated O-S architectures under linear scale (1–100%). To prove the concept, RLS performance mapping is shown. Only MRLS recruitment and selection are covered. The effort helps CIM, FMS, and WCMS create sustainable, cleaner operations and achieve future goals.

1. Introduction

RLS (robot logistic system) is ascertained as an auxiliary unit of production systems such as flexible manufacturing, world class, and computer-integrated manufacturing (CIM) systems, which favors the customized production with agility. It is found that mobile RLS is executed more than static RLS due to mobility around space. The MRLS is employed to perform the rapid logistics operations and positioning the components at accurate location under controlling of soft and hardwire software. MRLS is defined as a movable engine of IoTs such as cyber physical system, gadgets, and sensors, which lead ILS to perform the various critical and hazardous tasks such as right positioning and changing the operations and tools, navigation control, and performing the various toxic practices by programming. MRLS is deployed in serious circumstances to perform the tasks, where humans fail to perform. Therefore, MRLS is described as ILS by various authors under various features such as degree of automation, flexibility, mobility, mechanism, and transportation. MRLS is found as an electric power actuator operated vehicle system, which is manipulated over the several industrial sectors by programming and nonprogramming path for shaping the various complicated and hazardous tasks. In accordance with [1, 2], MRLS is an automatic mechanical-electronic vehicle system, which is capable to navigate around the unstructured route under danger environment. MRLS is able to perform the locomotion and need not be appended with persons as it can control its functions automatically by PLC (Programming Logic Circuit). The authors of [3, 4] argued that MRLS is an autonomous vehicle, which is capable of performing the movement in any surroundings and it can move automatically by evaluating and selecting the direction by sensing the signal from sensor. The authors of [5, 6] described MRLS as an automatic system, which has capability to escape via wheels, tracks, legs, or a combination of them to perform the logistics operations. Later, the authors of [7–10] addressed a note about MRLS and explicated that MRLS is automated ILS, whose functions include exploration (locomotion only), transport of payloads, or to perform the more complex tasks in/onboard by manipulating arms. MRLS is capable of performing independent movement and certain actions by ILS intellectual skills. Essentially, in addition to mobility concern, MRLS is able to perform the function autonomously, without requiring human intervention. MRLS has ability to provide the service to several locations and perform the wide range of tasks for specialized or defined application. During the COVID-19 attack, MRLS was employed for disinfecting facilities and patients, assisting surveillance, and delivering stuff and goods. During COVID-19 attacks, MRLS proved itself as magnificent and intellectual mechatronics device. Apart from usage of MRLS in COVID-19, the application of MRLS has various coverage, i.e., for surgical uses, personal assistance, security, warehouse and distribution applications, and ocean and space exploration. MRLS is found as a grand fighter in case of terror prevention, disaster control, and military usage. Other application areas of MRLS include the agriculture and public road transport, including self-driving motor vehicles.

Today’s production system is intelligent as well as autonomous due to the integration of IoTs (Internet of Things) with Production Queues (PQs) which is called Industry 4.0. Today, the agile manufacturing is only possible across PQs due to MRLS because it is highly automatic and reacts to compensate the customer’s demand swiftly. The MRLS becomes the bone of PQs in Industry 4.0 by addressing the several challenges, i.e., to fulfill the demand of customized products under lead time, to overcome the fierce competition at market place, simulating the production under least cost, etc. It is investigated that aforesaid challenges might be fulfilled if MRLS is audited and selected in accordance with routine operations over PQs. Therefore, there is necessity to design the decision support systems and tools especially for evaluation, recruitment, and selection of the MRLS for defined routine operations to address challenges of Industry 4.0.

To respect above concerns, currently logistics and transportation scholars increased their curiosity towards the area of evaluating, recruiting-benchmarking, and selecting the economic MRLS among others under various technical, cost, and value architectures. MRLS architectures are of two types, where Subjective (S) MRLS architectures deal with subjective information of expert (Ex), while Objective (O) includes the numeric or experimental data. The recent literature and empirical surveys reflected that the Research Documents (RDs) focused on efficiency, effectiveness, and performance improvement as well as the mechanism optimizations of MRLS. Among those RDs, few RDs are linked with recruitment, evaluation, and selection of the MRLS under advanced technical, cost, and value architectures. However, all identified RDs dealt with application of grey, fuzzy, vague, rough, and intuitionistic sets corresponding to linguistic variables to tackle the Subjective (S) information of experts in same problem (for recruiting MRLSs). Aforesaid grounds emphasized the authors to consider the same as Research Gaps (RGs), which are described below.(i)There is need to develop MRLS performance mapping and recruitment index, including advance O (Objective) mixed with S (Subjective) information corresponding to MRLS alternatives.(ii)There is a need to develop a linear information set, which could assist the experts to assign the S-information in a range of 1–100% rating scale against S-architectures of MRLS without using linguistic variables.(iii)There is necessity to frame holistic mathematical model, which can tackle 1–100% rating of experts in the terms of min-max for robust as well as potential evaluation of MRLS among alternatives.

Research Contributions (RCs) are addressed against above said RGs and summarized that there is need to frame the dynamic MRLS performance mapping and recruitment index (consisted of advance O‐S architectures) integrated with robust mathematical model for recruiting the MRLS by using ratings and significance scale in a range of 1–100. The RCs are supposed to be confirmed by further relevant literature survey. The authors attempted to conduct the relevant literature survey in the context of MRLS evaluation and recruitment concerning MRLS evaluation S and O architectures, rating sets and scales, and optimization techniques.

This paper is organized as follows. Section 2 gives the literature review. Section 3 gives a summary of the literature survey and research objectives. Section 4 is devoted to Holistic Managerial Models (HMM-1 and HMM-2) for recruitment: planning and operations. Section 5 gives case study-demonstrated MRLS transportation-recruitment drives (planning and operations). Section 6 includes dominance theory and results and novelties, applications, limitations, and implications. Section 7 gives the conclusions.

2. Literature Review

The authors conducted the relevant literature review as discussed in Table 1.

3. Summary of the Conducted Literature Survey and Research Objectives (ROs)

3.1. Summary of the Literature Survey

The authors attempted to organize the above literature survey in the context of MRLS by executing the open-access Google research search engine. The authors found 150 RDs from leading academic journals and conferences, where 51 are observed not in the line of proposed RO. Later, out of 99, 66 RDs are considered for literature survey as cited across the research work. After in-depth literature survey, only 47 RDs were exclusively tied up with RO (evaluation and benchmarking of MRLS under O-S architecture index).

As said above, out of 47, only 23 RDs are explored to construct the MRLS performance mapping and recruitment index, wherein none of the RDs enrolled the mixed integration of Objective (O) cum Subjective (S) or both architectures; therefore, the authors extracted only crucial and significant MRLS evaluation O-S-architectures from 23 RDs, which can address the challenges of present Industry 4.0. Furthermore, 10 RDs out of 47 are determined in line of different fuzzy, grey, and vague rough rating and weight sets, wherein all 10 RDs focused on Likert and linguistic variables to assess rating and weight against MRLS S-architectures; therefore, none of the RDs dealt with thoughts to assess the rating and significance (weight) against MRLS architectures using experts’ opinion in the terms of min-max value corresponding to 1–100% scale (without using linguistics scale). At last, 14 RDs out of 47 are traced and all 14 RDs enrolled individual multivariable decision-making techniques; therefore, reliability of decision making becomes the high concern for authors; thus, 12 RDs are executed to prepare the Holistic Managerial Models (HMM-1 and HMM-2) to evaluate the robust MRLS under mapping index.

3.2. Research Objectives (ROs)

The summarized report of literature survey potentially confirmed the RCs of Section 1. Therefore, ROs are shaped and pointed out below, and a flowchart of research contributions is also structured as RC guide of presented research work, which is depicted in Figure 1.(i)To construct the dynamic MRLS performance mapping, recruitment, and selection index incorporating the advanced O-S architectures, meeting the objective of the present Industry 4.0.(ii)To invent the logic as well as idea of linear scale to facilitate the experts for assigning the ratings and significance against MRLS and S-O architectures, respectively, in the form of min-max value choosing from 1 to 100% by experts (Exs).(iii)To build Holistic Managerial Models (HMM-1 and HMM-2); this can measure the performance of MRLS, selecting robust and reliable MRLS under application of dominance theory.

4. Holistic Managerial Models (HMM-1 and HMM-2) for Recruitment: Planning and Operations

The proposed models are composed specifically to address the optimization problems of multiple variables under assigning the S-ratings, and significance by experts in a band of max and min value is called as linear information. The concept of information representation is shown in Figures 2 and 3.

These models such as HMM-1-2 have the aptitude to undertake the individual vague or nonambiguity information (in the form of S-significance and ratings) conjunctively in a range of min-max reflected by equations (1) and (2). S-significance and rating values require the subjective assessment from experts. The experts select one low and upper number in % from linear scale (1–100%). The experts assign the S-significance assessment vs architectures and S-ratings assessment vs architectures corresponding to alternatives. In HMM-1-2, equations (3) and (4) are used to summarize and transform the summarized subjective assessment (in the form of S-significance and ratings) into crisp values (CRs), respectively. After evaluation of CRs, equations (5) and (6) are used to normalize the CRs of significance vs architectures.

Among both models, HMM-1 model is developed to aid in the alternative evaluation decision making under multiple architectures. In HMM-1, architectures, whose rating values are beneficial (B) in nature, are normalized by using equation (7), while values of nonbeneficial (C) architectures are transposed into beneficial values and normalized by using equation (8). This model facilitates the experts to assign the S-significance and rating values vs architectures and architectures corresponding to alternatives, respectively, in a range of any value (1–100% point scale). Eventually, equation (9) is executed to decide the alternative rank (max value is preferred for selection).

Next, HMM-1 model is extended to HMM-2 model. HMM-2 also facilitates the experts to assign S-significance and rating values in the range of min-max by using 1–100% point scale. But beneficial (B) as well as nonbeneficial (C) architectures are normalized collectively by using equation (7). Eventually, equation (10) is executed to decide the alternative rank (max value is preferred for selection).

The mathematical representation is formulated here. Presume that there are possible opportunities (alternatives) from which expert’s panel is requested to choose in accordance with architectures both qualitative (subjective) and quantitative (objective). Suppose the subjective/qualitative information of architectures is proposed against opportunities by decision makers .

Just suppose that the information is proposed as set {^MinValue^% ^MaxValue^%}.

Presume that the subjective scores against each architecture corresponding to the opportunities can be computed as follows.

Submission of assigned sets or ratings in percentage as min-max by experts:

Evaluation of Crisp Rating (CR) on obtaining output is set by equation (1).

CR1, CR2, CR3 correspond to

Similarly, submission of assigned two sets or weights in percentage as min-max by experts:

Evaluation of Crisp Weights (CW) on obtaining output is set by equation (3).

CS1, CS2, CS3 correspond to .

Hence, a multiarchitecture matrix can be expressed as follows:

Evaluation of Normalized Significance Weight (NCwv) in a range of {0-1}:

Here the normalization of evaluated max CRs and transposed min-max CRs is done by exploring

Multiplication of evaluated with and respective opportunities :

5. Case Study Demonstrated MRLS Transportation-Recruitment Drives (Planning and Operations)

The recruitment drive of MRLS is demonstrated to ensure the application and validity of the proposed research work. Case study is conducted for an automobile industry to recruit and select the most economical MRLS among others. The proposed index is constructed by advance O-S MRLS architectures, gathered from literature survey, as shown in Figure 4.

The above-depicted MRLS performance mapping, recruitment, and selection index included the speeds degree of freedom , unit load , power , purchasing cost , maintenance cost , depreciation , overall efficiency , fitness to production , and quickness architectures, where power , purchasing cost , maintenance cost , and depreciation are accounted as nonbeneficial, while others are respected as beneficial architectures. In index, , , , , , , are prioritized as Objective (O) architectures and residue architectures are subjective (S) in nature. The MRLS selling companies are requested to send their MRLS quotations against O architectures of MRLS performance mapping index, while S data are assessed by experts of case study company, as shown in Figure 3. Table 2 depicts the index consisting of Objective (O) and S architectures corresponding to MRLS alternatives. Table 3 reflects the definitions of MRLS architectures.

First of all, a team of four experts is constituted by electing the four executives from production, maintenance, purchasing, and design departments.

Prior to overview and judging the performance, the significance against O-S architectures is assessed by team of Exs by assigning the S ratings in a range of 1–100% against all architectures, as exhibited in Table 3.

The aggregation of all assigned significance against architectures is evaluated by using equations (4) and (6). Next similar team of Exs are invited to visit alternative MRLSs of selling company and assign the S-ratings in a range of linear scale (1–100%) by taking min-max subjective perception in % against only S-architectures, as shown in Table 4.

Then, assigned S-ratings are aggregated by usage of equation (2) and next transformed into crisp value by usage of equation (3), as shown in Table 5.

After computing significance of both (S-O) architectures and ratings of S-architectures, the problem seemed to be structured problem of multivariable matrix, as shown in Table 6.

By using equation (5), a multiarchitecture matrix is formed. Later, all the O-S architectures are normalized (0-1) and mixed with their significant architectures corresponding to MRLSS to form multiarchitecture decision-making matrix by using equations (7) and (8) as shown in Table 7.

The economic value of candidate MRLS under O-S architectures is computed by using equations (9) and (10), which is depicted in Tables 8 and 9.

6. Dominance Theory and Results and Novelties, Applications, Limitations, and Implications

Section 6 includes the dominance theory and results (Section 6.1) and novelties, applications, limitations, and implications (Section 6.3).

6.1. Dominance Theory and Results

The dominance theory is prioritized as analytical tool for confirming the most economic and feasible option among available alternative options after comparative analysis. The dominance theory was utilized to provide the benchmarking solution to one of the machine tool alternative evaluation problems in a case study. The said approach is renowned across the benchmarking scholars for evaluating the single or individual rank after comparison. Therefore, the need of holistic approach is found. In the presented work, HMM-1-2 models are implicated for appraising and evaluating the robust decision by exploring the comparative analysis under dominance theory. The computed results by different HMM-1-2 models are supposed to be demonstrated for comparative analysis according to synergy among preference orders of MRLS alternatives, obtained by HMM-1 and HMM-2, respectively. The simulated results from dominance theory to assure the consistent alternative selection by the benchmarking tool are shown in the following.

6.2. Results

As per HMM-1 model, -0.4307 is ascertained as most economical and optimum in all architectures.

As per HMM-2 model, -0.024071 is determined as most economical and optimum in all architectures.

6.3. Novelties, Applications, Limitations, and Implications

6.3.1. Novelties of Research Work

(i)The proposed HMM-1-2 models can tackle the S-ratings of Ex against MRLS architectures in a range of 1–100% (min-max).(ii)The proposed HMM-1-2 models can tackle the fused information i.e., Subjective (S) mixed with Objective (O) ratings or individual Subjective (S) or Objective (O) ratings provided by Exs against MRLS architectures.(iii)The proposed HMM-1-2 models are able to compute and evaluate the significance against both S-O-architectures.(iv)The authors proposed the linear information-based min-max value extraction from 1 to 100% rating scale in linear series, which is simple in nature to learn, understand, and teach to Exs at the time of recruitment of MRLS under multiple mixed S-O-architectures.(v)The proposed linear information idea overcame the drawback of executing the complex fuzzy, grey, rough, and vague sets to address the uncertainty associated with S-architectures of MRLS performance mapping and selection index.(vi)The authors proposed the RLS recruitment and selection index incorporating the advanced and sizzling architectures in meeting sustainability pillars of Industry 4.0.(vii)The authors introduced the dominance theory and implicated for robustly evaluating the performance of MRLS so that appropriate MRLS can be placed for future smoothing of industrial operations.(viii)The proposed models are able to diagnose other logistics evaluation problems of Industry 4.0.

6.4. Industrial Applications of Research Work

The proposed Holistic Managerial Models (HMM-1 and HMM-2) are appropriate for materializing the economical worth of MRLS on bearing the Ex information in a range of linear scale (1–100% rating). The proposed models are also relevant to short out the other micro and medium transportation system evaluation problems such as cars, scooters, and bikes, under alerting objective cum subjective assessment. In future, in case of any disaster or circumstance, the proposed models can be simulated from the same prospectus, i.e., to recruit MRLS as per operations to be performed by them or as per crowd of area or locality. These models imply the supervisory skills to tackle real-life problems, i.e., appraise the economical worth of buses, aeroplanes, helicopters, commercial equipment, JCB, carriers, wagon autos, and trucks on alerting the architectures of MRLS or substituting MRLS recruitment problem or performance mapping index corresponding to O-S data.

6.5. Limitations of Research Work

The proposed models are limited to undertake only MRLS evaluation problems corresponding to O-S or O or S MRLS architecture corresponding proposed index. The models are not active to resolve the linear transportation problems and multiobjective as well as single parameter optimization problems.

6.6. Implications of Research Work

The proposed models discard the implication of managers and Exs or decision makers, as they can easily understand the scale for assigning the S-ratings (1–100% rating) to assign against O-S-architectures of alternative MRLSs. Furthermore, the research has economic impact at global AMSs, as it does not solicit extremely high skill operators to recruit advance transportation systems. The computations can be carried out by usage of Excel or MATLAB under feasible time. It does not require funding to buy unusual/special software.

7. Conclusions

Conclusion includes the descriptions of discussion, economic value, and future research scope.

7.1. Conclusions

The utility of ILS is broadly seen around Industry 4.0. It is learned that TPM (Transportation Performance Measurement) and decision support tools enable Industry 4.0 for acting on the suitable future planning and adapting operations under feed-forward controlling system. The research attempted to add the worth in CIM system by introducing MRLS performance mapping, recruitment, and selection index embedded with HMM-1-2 models, which can simulate the O-S architectures corresponding to alternative MRLSs. The results of the demonstrated RLS problem are shown here:

The authors found after quenching the dominance theory over the evaluated performance of MRLSs that is the best and optimum, satisfying all architectures. The AMS system is advised to plan to recruit for commencing the nice operations and attaining prospectus goals.

7.2. Discussion

The motive to adjoin the dominance theory with HMM-1-2 is to obtain the reliable and potential results. Therefore, synergy analysis is carried out among the evaluated performance of MRLSs by executing dominance theory. It is found that is the most economical and optimum in all architectures. The AMS of MRLS recruitment company is suggested to recruit only third MRLS candidate for future lovely operations.

The development of advanced RLS performance recruitment and selection index is considered as minor originality of research work. The major innovation is sparking around the development of linear scale for choosing rating value from 1 to 100% corresponding to min-max idea to assign the ratings and significance by Ex (without executing linguistic scale) against S and all O-S architectures, respectively. In continuation of that, to simulate MRLS index with 1–100% ratings as well as significance, HMM-1 and HMM-2 are proposed. The models are economical in nature and can be solved manually and by Excel sheet. The feature of research work is bright as the proposed HMM-1 and HMM-2 models can be simulated to tackle other transportation problems under same recruitment index or substitution of architectures of index. The information is formed by using min-max concept, but it can also be formed as min-medium-max if Exs perceived more degree of hesitation. The models are not applicable for resolution of the linear and single and multiobjective optimization problem, and fuzzy, grey, rough, and vague sets cannot be tackled and models are not oriented to tackle the linguistic scale.

7.3. Economic Value

The presented research forum is economic in nature as HMM-1-2 appended with MRLS index is communal to other global industries by usage of social networking sites, e-mail, etc. Microsoft Excel software can be used to compute the results. Any specific software is not required. The work does not require the high scale operator. Moderate technical skill-based operator is sought to express the essence of benchmarking decision. In terms of time, the computation is effortless and least time consuming as experimental data are not sought by MRLS index.

7.4. Future Research Scope

The research dossier has interdisciplinary value because the S-O architectures of MRLS evaluation and benchmarking index can be changed by incorporating the future challenging MRLS architectures. The depicted index can be extended vertically (expanding number of architectures at same level) and horizontally (adding subarchitectures at 2^nd level) according to available alternative features of architectures. In the terms of HMM-1-2 models, the model is unique in nature and can be enrolled over various MRLS indexes to provide benchmarking solution to MRLS alternative evaluation problems in future case studies. The model can also be explored for the purpose of mapping performance of individual MRLS by taking reference of benchmarking limit (standard performance) for selecting and rejecting MLRS. Therefore, the MRLS investors or industries can use the proposed work to hire the MRLS on rent/lease to transport a broad range of materials such as switch, shaft, gear, and steel socket. The scholars can utilize the research work as future research guide and direction to shape advanced MRLS indexes and models.

Data Availability

The data used to support the findings of this study are available in Tables 2–9.

Disclosure

This study is part of remote employment research.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this manuscript.

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Copyright © 2023 Hong Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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