Facility Location Selection Using Fuzzy TOPSIS: Evidence from Cheng Hua Engineering

Facility Location Selection Using Fuzzy TOPSIS: Evidence from Cheng Hua Engineering

Arvin Raj Gunabalan¹, Ahmad Taufik Nursal^2*, Khairunnisa Abdul Aziz³, Adam Shariff Adli Aminuddin⁴ & Tisya Farida Abdul Halim⁵

^1,2,3 Faculty of Industrial Management, University Malaysia Pahang Al-Sultan Abdullah, Lebuhraya Tun Razak, 26300, Gambang, Pahang, Malaysia.

⁴Centre for Mathematical Sciences, University Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300 Kuantan, Pahang, Malaysia

⁵Fakulti Perniagaan & Komunikasi (FPK), University Malaysia Perlis (UNIMAP), Kampus Uniciti Alam, Sungai Chuchuh, 02100, Padang Besar, Perlis.

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90900094

Received: 22 August 2025; Accepted: 30 August 2025; Published: 30 September 2025

ABSTRACT

Choosing a manufacturing site is a classic multi-criteria problem: numbers and narratives matter, and both are often uncertain. Methods that rely only on crisp inputs struggle to reflect how managers actually judge trade-offs across cost, infrastructure, regulation, and workforce. To address this, we develop a fuzzy TOPSIS decision model that accepts linguistic assessments, aggregates them across criteria, and ranks candidate locations by their closeness to an ideal solution. This study ground the model in a Malaysian case (Cheng Hua Engineering), organizing the assessment around four dimensions operational efficiency, financial considerations, regulatory/environmental compliance, and workforce/social factors. The analysis identifies Rawang as the preferred site, narrowly ahead of Tanjung Malim, and a sensitivity check confirms the ranking’s stability under different weight settings. By integrating sustainability-adjacent concerns and handling ambiguity in expert judgment, the study offers a transparent, adaptable framework that decision-makers can reuse for strategic facility location choices in similar contexts.

Keywords— Location selection, Fuzzy TOPSIS, Manufacturing, MCDM.

INTRODUCTION

Choosing an optimal site for a manufacturing facility is more than a logistics exercise; it is a strategic move with long-run consequences for competitiveness, operating performance, and sustainability ambitions. In practice, location decisions require managers to reconcile many sometimes competing considerations, including operating costs, compliance obligations, supply-chain connectivity, and the depth and stability of the local talent pool (Huang et al., 2020). Traditional treatments that center primarily on cost and transport efficiency, while useful, can downplay broader priorities such as environmental performance, readiness for technological change, and resilience to shocks and market volatility (Watson et al., 2021). In Malaysia, these choices carry added weight. Manufacturing contributes more than 22% to national GDP, and recent policy directions—Industry4WRD and the New Industrial Master Plan 2030 push firms toward digital transformation, greener processes, and continuous upskilling (DOSM, 2021). Yet, despite the sector’s importance, empirical studies of facility location in the Malaysian setting remain relatively sparse, especially those that integrate classical financial drivers with newer socio-environmental dimensions.

To close this gap, our study employs the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS), a robust MCDM approach designed to model the ambiguity inherent in expert judgment. By pairing linguistic variables with fuzzy logic, the method represents how decision-makers actually reason when evidence is incomplete, rather than forcing precise point inputs (Hariri et al., 2023). We demonstrate the approach through a Cheng Hua Engineering case, evaluating candidate sites across operational, financial, regulatory environmental, and social criteria. The result is both a theoretically grounded contribution showing how fuzzy TOPSIS can clarify trade-offs under uncertainty and a practical guide for managers making facility location decisions in Malaysia’s evolving industrial landscape.

PROBLEM STATEMENT

The manufacturing sector remains a pillar of economic growth worldwide, driving GDP, jobs, and technological progress. In 2021 it contributed over 16% of global GDP and supported more than 350 million jobs (International Labor Organization, 2020). In Malaysia, manufacturing accounted for 22.3% of GDP, with strong footprints in automotive, E&E, and machinery (Department of Statistics Malaysia, 2021). Malaysia’s position as a regional node for advanced manufacturing makes facility location a strategically consequential choice, shaping long-run competitiveness, operating performance, and sustainability outcomes. Yet site selection is intrinsically multi-faceted: firms must navigate costs, infrastructure, regulation, labor supply, supply-chain robustness, and environmental impacts simultaneously. Conventional approaches, which tend to privilege cost minimization and logistics alone, have been criticized for overlooking the broader interplay of social, environmental, and strategic considerations (Watson et al., 2021). The result can be avoidable logistics costs, weakened competitive posture, and exposure to shocks. To move beyond these limits, multi-criteria decision-making (MCDM) techniques such as AHP, MOORA, and TOPSIS have been widely applied to supplier selection, renewable energy siting, and infrastructure planning (Abdel-Basset et al., 2021; Sequeira et al., 2021; Devadas et al., 2020). However, their use in manufacturing facility location is still relatively thin, especially when sustainability and resilience must be considered together. A further drawback in much of the literature is the reliance on crisp inputs that cannot reflect the ambiguity in expert judgments an issue that FUZZY TOPSIS is designed to handle more naturally (Hariri et al., 2023). These gaps are particularly evident in Malaysia. Few studies explicitly align location-selection frameworks with national strategies like Industry4WRD and NIMP 2030, despite their emphasis on sustainability, digital transformation, and high-skilled talent. Empirical validations grounded in real industrial cases are also scarce, which limits practical uptake by decision-makers. Accordingly, there is a need for a decision-support approach that integrates operational, financial, environmental, and social criteria under uncertainty, while remaining consistent with Malaysia’s policy direction and industrial realities. This study meets that need by developing and applying a FUZZY TOPSIS based framework, demonstrated through a case analysis of Cheng Hua Engineering.

LITERATURE REVIEW

The manufacturing sector remains one of the world’s primary engines of growth and industrial progress. In 2021 it accounted for about 16% of global GDP roughly USD 12.8 trillion (World Bank). A handful of economies China, the United States, Japan, Germany, and South Korea produced more than 60% of that output (UNIDO). Despite its scale, the sector continues to grapple with supply chain disruptions, labor shortages, and mounting expectations for greener, more responsible operations (Devadas et al., 2020). In Malaysia, manufacturing is likewise central to the economy, contributing 23.6% of GDP in 2021 (Department of Statistics Malaysia). The structure of the industry is led by electrical and electronics (39.3% of output), followed by machinery and equipment (15.4%), petroleum products (8.1%), and automotive (4.7%) (MIDA).

To stay competitive, Malaysia has rolled out policies such as the National Industrial Revolution 4.0 Policy and the Second Industrial Master Plan, prioritizing digitalization, automation, and the attraction of foreign investment (MEIF, 2022). Even so, significant hurdles persist: a shortage of skilled workers, dependence on foreign labor, and slow movement into higher-tech, knowledge-intensive segments (Kieu et al., 2021). In this setting, choosing where to locate a manufacturing facility becomes a strategic decision with long-term consequences, shaping operational efficiency, regulatory compliance, access to talent, connectivity, and ultimately the firm’s competitive edge.

Location Selection Criteria

Location choice is a strategic decision with durable consequences for firm performance. It cannot be reduced to a single objective or a narrow cost calculus; rather, it requires an integrated appraisal of interdependent criteria aligned with a firm’s industry position and strategic intent. Prior research converges on a common set of determinants market conditions, depth and cost of talent, infrastructure endowments, financing conditions and incentives, regulatory predictability, and socio-environmental context that jointly shape competitiveness and sustainability outcomes at the plant level (Abdel-Basset et al., 2021; Sequeira et al., 2021; Nenzhelele et al., 2023). Among these, proximity to demand centers frequently proves decisive because it compresses lead times, lowers transport costs, and heightens responsiveness. Labor-market considerations extend beyond headcount and wages to include place-based quality-of-life attributes—housing affordability, healthcare and education access, and recreational amenities that underpin satisfaction, retention, and productivity (Kumaresan et al., 2024). Operational logistics form the backbone of day-to-day performance. Robust transport networks, supplier accessibility, and reliable raw-material flows reduce variability, dampen disruption risk, and moderate operating costs (Devadas et al., 2020). Financial conditions interact with these realities. Land prices, labor and utility costs, local tax regimes, and targeted incentives (e.g., grants, rebates, accelerated allowances) can be outcome-determinative when candidate locations are otherwise comparable (Kakooza et al., 2023). None of this operates outside institutional constraints: labor regulation, environmental standards, zoning, and permitting practices together with their enforcement cultures shape the predictability and defensibility of operations over time (Arslankaya & Çelik, 2021). Forward-looking factors are increasingly salient. Firms screen for growth headroom, expected market expansion, and linkages to universities, research institutes, and innovation hubs that enable technology transfer, knowledge spillovers, and human-capital upgrading (Aballay et al., 2023). Resilience and security concerns disaster-risk profiles, crime rates, and socio-cultural compatibility are especially consequential for cross-border projects, where alignment with local norms and workforce stability can influence long-run viability (Huang et al., 2020). In practice, these strategic elements interact with operational and financial determinants, raising the premium on frameworks that can accommodate heterogeneous evidence and competing priorities.

A comparative synthesis of eight recent studies reveals a stable pattern that can be organized into three analytically distinct groupings. First, a set of core criteria cost, taxation and financial incentives, and cultural social conditions exhibits near-universal relevance across industries and geographies (Abdel-Basset et al., 2021; Kumaresan et al., 2024; Devadas et al., 2020). These criteria represent the fiscal, economic, and social foundations upon which most facility-location frameworks are built. Second, a cluster of operational and risk-related criteria energy reliability, supplier proximity, transportation infrastructure, environmental compliance, disaster exposure, and security functions as a suite of performance enablers and risk buffers. While broadly applicable, their thresholds and measurement choices vary with technology intensity and supply-chain design. Third, a set of context-dependent criteria, customer proximity, zoning stringency, and access to innovation ecosystems exhibits importance that is contingent on the business model, industry characteristics, and market conditions. Notably, several studies implicitly subsume zoning and legal issues under the broader “regulatory environment,” which can mask heterogeneity that becomes material in permit-intensive or land-constrained projects. This three-tier classification is useful for both theory building and applied evaluation. It provides a structured baseline that separates universal drivers from contingent ones, reducing model-misspecification risk and mitigating the tendency to overweight what is easiest to quantify. In practice, the core criteria form the non-negotiable foundation of most analyses. Operational and risk criteria then act as tunable levers for efficiency and continuity, amenable to benchmarking through comparable indicators (e.g., outage frequency, supplier density).

Table 1.  Summary of location criteria

MCDM For Location Selection Problems in Malaysia

Multi-Criteria Decision-Making (MCDM) is a well-established approach for addressing decision problems that involve the simultaneous evaluation of multiple, and often conflicting, criteria. As a subfield of Operations Research (OR), MCDM is particularly suited to contexts where decision-makers must balance competing priorities, manage varying measurement units, and identify optimal or near-optimal solutions from several alternatives (Quaiser & Srivastava, 2023). MCDM methodologies employ structured models incorporating techniques such as normalization, parameter configuration, and systematic procedures for detecting and ranking optimal solutions (Stević et al., 2020). Owing to these strengths, MCDM has been applied across a wide range of fields. For instance, Quaiser and Srivastava (2023) applied MCDM to evaluate student eligibility for educational programs, while Nenzhelele et al. (2023) employed a hybrid MCDM framework combining DEMATEL and EDAS techniques to select internship candidates for a startup. In the renewable energy sector, Abdel-Basset et al. (2021) developed a neuromorphic MCDM framework to assess and prioritize sustainability dimensions of bioenergy production technologies, integrating environmental, technical, and economic factors. Although this application was not directly related to location selection, it demonstrates the versatility of MCDM in complex decision environments.

Within the manufacturing sector, MCDM techniques have increasingly been used in studies that acknowledge the relevance of facility location. For example, Kumaresan et al. (2024) applied the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to optimize maintenance parameters for manufacturing machines using a particle swarm optimization (PSO) algorithm. While the focus was on maintenance optimization, the study highlighted the importance of MCDM methods in considering both quantitative and qualitative factors—an essential capability for facility location problems. Similarly, Arslankaya and Çelik (2021) applied Fuzzy AHP (Analytic Hierarchy Process) and Fuzzy MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) in green supplier selection for the steel door industry. Although not focused on location selection, their study demonstrated the ability of MCDM approaches to integrate environmental, operational, and economic dimensions, reinforcing the adaptability of these techniques to a variety of industrial decision-making scenarios, including facility location.

Table 2. Application of MCDM Location Selection

Fuzzy TOPSIS, which combines fuzzy set theory with the TOPSIS method, is widely adopted for resolving uncertainty in decision-making (Huang et al., 2020). It is particularly effective when criteria or input data are vague, as fuzzy logic can better capture real-world ambiguity (Chen, 2000). Unlike traditional crisp approaches, Fuzzy TOPSIS allows decision-makers to use linguistic terms mapped to fuzzy numbers, enabling more flexible and realistic modeling of preferences and judgments. This makes the process more intuitive and consistent with human reasoning (Hariri et al., 2023).

METHODOLOGY

This study lays out a complete strategy that specifies research design, data collection methods, and analytical procedures. To ground the analysis in practice, a case study with Cheng Hua Engineering Works Sdn. Bhd. is incorporated alongside semi-structured interviews with experienced decision makers to identify and prioritize location-selection criteria and to inform a reusable decision support orientation that is later used for model validation.

Phase 1 Literature study and Data Collection

This research began by identifying and prioritizing criteria for manufacturing facility location decisions through an in-depth literature review and feedback from decision-makers. Four core criterion families were identified: operational, financial, regulatory/environmental, and workforce availability. Key sub-criteria included raw material accessibility, infrastructure adequacy, and energy costs. Survey responses from mid- and senior-level managers, supplemented by semi-structured interviews, revealed varied awareness of sustainability-related factors such as green-energy programs. However, cost and energy availability consistently emerged as the most influential concerns. Items that lacked consensus, such as demographic experience, were excluded from further analysis. The outcome of this phase was a weighted assessment matrix that integrates both quantitative indicators and qualitative judgments, enabling evidence-based comparison of alternative sites. To construct this model, a Multi-Criteria Decision-Making (MCDM) approach was employed, specifically FUZZY TOPSIS, which evaluates multiple interrelated criteria while accounting for uncertainty in expert judgment. Consistent with prior studies, decisive considerations included cost, infrastructure, labour, and market proximity (Placa et al., 2025). The FUZZY TOPSIS model thus provided a systematic, transparent, and reliable procedure for ranking candidate sites based on overall suitability.

A comprehensive literature synthesis preceded the expert interviews. Drawing on 20–34 scholarly sources, the review initially identified 40 potential criteria, of which 34 were directly relevant to location selection. These were grouped into four families: workforce and social environment (5 criteria), financial (8 criteria), regulatory/environmental (6 criteria), and operational (15 criteria). During the assessment process, overlapping and redundant criteria were consolidated into broader categories. The synthesis ultimately yielded a refined taxonomy of 17 criteria, comprising two regulatory/environmental factors, four workforce and social factors, seven operational factors, and four cost-related aspects. This final set preserved both quantitative and qualitative dimensions while eliminating noise from less relevant items, ensuring alignment with Malaysia’s industrial context (Ma et al., 2024).

Figure 1 Decision Hierarchical 

For subsequent modelling clarity, the finalized sub-criteria are mapped as follows: A1–A6 belong to operational efficiency, A7–A10 to financial considerations, A11–A12 to regulatory/environmental factors, and A13–A15 to workforce and social environment. This mapping links expert-elicited weights to a consolidated evidence base and feeds directly into the FUZZY TOPSIS procedure for ranking candidate locations.

Phase 2 Development of Decision Model

Develops a conceptual decision model for manufacturing site selection that combines a Multiple-Criteria Decision-Making framework with the FUZZY TOPSIS technique to address uncertainty and subjectivity in expert judgment. Four criterion families structure the evaluation operational efficiency, financial considerations, regulatory and environmental factors, and workforce/social environment with representative sub-criteria such as raw-material access, transportation infrastructure and supplier proximity (operational), energy costs, tax incentives and growth potential (financial).

Table 3. Decision Makers Background

Expert assessments were elicited using linguistic variables, which were subsequently mapped to triangular fuzzy numbers. These values were then aggregated to construct a fuzzy decision matrix, normalized according to benefit and cost criteria, and processed through the FUZZY TOPSIS method to compute each site’s closeness coefficient relative to the ideal solution. This process produced an interpretable ranking of candidate locations.The model architecture was iteratively refined through simulation exercises and feedback from industry experts to ensure both realism and managerial applicability. A standardized seven-level linguistic scale was adopted to improve consistency across multiple decision-makers, while the aggregation of fuzzy judgments accommodated the heterogeneity of stakeholder priorities. Overall, this approach integrates quantitative metrics (e.g., costs, infrastructure, and logistics) with qualitative assessments (e.g., workforce quality, regulatory compliance, and social environment) in a transparent and robust procedure. The FUZZY TOPSIS model therefore enables decision-makers to systematically compare alternative sites and establish an evidence-based ranking from most to least suitable.

Table 4 Linguistic variable for the importance weight

Table 5 Linguistic variable for rating of the candidate

The development of the FUZZY TOPSIS decision model followed a structured sequence of steps to ensure systematic evaluation of location alternatives. First, the criteria were defined and grouped into four main families: operational efficiency, financial considerations, regulatory and environmental factors, and workforce and social environment. From the initial pool, decision-makers agreed on a refined set of fifteen attributes mapped across these families to capture both quantitative and qualitative dimensions relevant to Malaysia’s industrial context. Second, weights were assigned to each criterion using linguistic variables such as Very Low, Low, Medium, High, and Very High, which were then converted into triangular fuzzy numbers to accommodate uncertainty in expert judgment. Third, the location alternatives were evaluated against all criteria using the same linguistic scale, producing a fuzzy decision matrix that aggregated expert assessments. Fourth, the decision matrix was normalized to account for both benefit criteria (where higher values are preferable) and cost criteria (where lower values are preferable), after which the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) were determined to represent the best and worst possible performance across all criteria. Finally, the distance of each location alternative from FPIS and FNIS was calculated, and the closeness coefficient (CCi) was computed to establish a ranking. Locations with higher CCi values were judged more suitable, yielding a transparent, evidence-based ordering of alternatives for facility location decisions.

Table 6 Combine decision matrix

Next, is to find normalize fuzzy decision matrix. There are two formulas to normalize fuzzy decision matrix for benefit criteria and cost criteria or called non-beneficial criteria. The formula is shown as below as 𝑐∗𝑗 for benefit criteria and 𝑎⁻𝑗 for cost benefits.

Table 7 Normalize decision matrix

Step 5 is to compute the weighted normalized fuzzy decision matrix using below formula:

, where i = 1,2, …, m, where j = 1,2, …, m, and  is the weighted for the criteria that has been decided

Table 8 Weighted normalizes fuzzy decision matrix.

To find the distance between each weighted normalize fuzzy decision matric to the FPIS and FNIS by using formula as shown above.

Table 9 Distance from FPIS

Table 10 Distance from FNIS

Table 11 Ranking of each location

The study concludes the ranking of location alternatives by assigning the highest closeness coefficient (CCi) a rank of 1, followed by subsequent alternatives in descending order. The results indicate that Rawang achieved the highest CCi value and was therefore identified by senior management as the most suitable location for establishing the new manufacturing facility.

Phase 3: Evaluation of Decision Model

The location alternatives were ranked based on their closeness coefficient (CCi), with the highest CCi assigned Rank 1. Rawang obtained a CCi value of 0.5386 (Rank 1), narrowly surpassing Tanjung Malim with a CCi of 0.5372 (Rank 2). As a result, Rawang was identified as the preferred location for the establishment of the new manufacturing facility. To evaluate the robustness of the model, a sensitivity analysis was conducted by setting all criterion weights to zero except for C2 (financial considerations), which was assigned a mid-level linguistic weight (“Medium/Fair”). This adjustment allowed the study to isolate and assess the influence of financial factors on the overall ranking. The resulting outcomes are presented in Table 12.

Table 12 Sensitivity Analysis

Table 13 Results of changes

Sensitivity analysis produced clear shifts in site ratings, showing the model is highly responsive to minor changes in criterion weights or linguistic terms. This responsiveness is desirable: it captures nuanced preference changes, adapts to stakeholder priorities, and confirms the FUZZY TOPSIS model’s robustness and reliability as a comprehensive, decision-ready framework

CONCLUSIONS AND DISCUSSION

This study employs FUZZY TOPSIS within a multi-criteria decision-making (MCDM) architecture to appraise manufacturing facility location alternatives. The evaluation integrates financial, operational, regulatory, and workforce/social criteria so that both quantitative indicators and expert qualitative judgments are reflected, while uncertainty is treated explicitly through fuzzy sets. In the empirical application to Cheng Hua Engineering, Rawang attains the highest suitability (CCi = 0.5386), narrowly surpassing Tanjung Malim (CCi = 0.5372). The slim margin underscores that location outcomes emerge from interacting trade-offs rather than a single dominant driver. Sensitivity tests in which financial criteria are given priority show that Rawang consistently remains top-ranked, reinforcing model robustness and aligning with prior evidence on the enduring salience of cost and incentives in siting decisions (Devadas et al., 2020; Abdel-Basset et al., 2021).

At the same time, the near-equivalence between the two sites indicates that non-financial elements—logistics readiness, availability of skilled labour, and regulatory compliance—carry comparable weight for sustained competitiveness. The work advances the literature in three ways. First, it addresses the limitations of crisp MCDM formulations by adopting fuzzy logic to capture the vagueness inherent in managerial judgment, improving descriptive fidelity and decision realism (Hariri et al., 2023). Second, it proposes a holistic assessment structure that integrates financial, operational, regulatory, and social considerations, extending beyond the cost- or logistics-centric treatments common in earlier studies (Huang et al., 2020; Kieu et al., 2021). Third, it offers context-specific validation in Malaysia, where empirical studies that align facility-location choices with Industry4WRD and the New Industrial Master Plan 2030 remain relatively scarce (Kumaresan et al., 2024). From a managerial standpoint, the FUZZY TOPSIS pipeline provides a transparent and defensible ranking mechanism, supports what-if analyses through weight adjustments, and allows firms to recalibrate decisions as priorities evolve—for example, to privilege sustainability metrics or workforce development objectives. For Malaysian manufacturers, the approach facilitates coherence between firm-level competitiveness and national policy aims related to digitalization, skills upgrading, and environmental stewardship. It is also recommended that the discussion section expand on the managerial implications of the findings. In particular, the paper should address implementation feasibility (data requirements, expert panel formation, toolchain integration, and governance for periodic re-weighting); strategic alignment (how the criteria set and weights map to corporate strategy, risk appetite, and sustainability targets); and potential barriers (organisational resistance, capability gaps in fuzzy MCDM, regulatory uncertainty, and hidden costs of compliance). Detailing these issues will help practitioners translate model outputs into operational roadmaps and investment decisions.

In conclusion, FUZZY TOPSIS is demonstrated to be a practical and robust method for strategic facility-location selection, enabling decision-makers to reconcile multiple objectives under uncertainty and to generate replicable, audit-ready outcomes that span short-term efficiency and long-term sustainability. Future research could augment the framework with GIS-based spatial analytics, incorporate environmental and carbon-intensity indicators, and develop hybrids that couple FUZZY TOPSIS with techniques such as DEMATEL or VIKOR to model interdependencies among criteria and stress-test rankings under alternate strategic scenarios.

REFERENCES

1. Aballay, C., Quezada, L., & Sepúlveda, C. (2023). Model for Technology Selection in the Context of Industry 4.0 Manufacturing. Processes, 11(10). https://doi.org/10.3390/pr11102905
2. Abdel-Basset, M., Gamal, A., Chakrabortty, R. K., & Ryan, M. (2021). Development of a hybrid multi-criteria decision-making approach for sustainability evaluation of bioenergy production technologies: A case study. Journal of Cleaner Production, 290. https://doi.org/10.1016/j.jclepro.2021.125805
3. Arslankaya, S., & Çelik, M. T. (2021). Green supplier selection in steel door industry using fuzzy AHP and fuzzy Moora methods. Emerging Materials Research, 10(4). https://doi.org/10.1680/jemmr.21.00011
4. Chen, X., Van Hillegersberg, J., Topan, E., Smith, S., & Roberts, M. (2021). Application of data-driven models to predictive maintenance: Bearing wear prediction at TATA steel. Expert Systems with Applications, 186. https://doi.org/10.1016/j.eswa.2021.115699
5. Devadas, S., Guzman, J., Kim, Y. E., Loayza, N., & Pennings, S. (2020). Malaysia’s Economic Growth and Transition to High Income An Application of the World Bank Long Term Growth Model (LTGM). http://www.worldbank.org/prwp.
6. Hariri, A., Domingues, P., & Sampaio, P. (2023). Integration of multi-criteria decision-making approaches adapted for quality function deployment: an analytical literature review and future research agenda. In International Journal of Quality and Reliability Management (Vol. 40, Issue 10, pp. 2326–2350). Emerald Publishing. https://doi.org/10.1108/IJQRM-02-2022-0058
7. Huang, S. W., Liou, J. J. H., Tang, W., & Tzeng, G. H. (2020). Location selection of a manufacturing facility from the perspective of supply chain sustainability. Symmetry, 12(9). https://doi.org/10.3390/SYM12091418
8. Kakooza, J., Tusiime, I., Namiyingo, S., Nabwami, R., & Basemera, M. (2023). Business choice, location decision and success of small and medium enterprises in Uganda. Journal of Money and Business, 3(1), 108–121. https://doi.org/10.1108/jmb-08-2022-0041
9. Kieu, P. T., Nguyen, V. T., Nguyen, V. T., & Ho, T. P. (2021). A spherical fuzzy analytic hierarchy process (Sf-ahp) and combined compromise solution (cocoso) algorithm in distribution center location selection: A case study in agricultural supply chain. Axioms, 10(2). https://doi.org/10.3390/axioms10020053
10. Kumaresan, V., Saravanasankar, S., & Di Bona, G. (2024). Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs. Journal of Quality in Maintenance Engineering, 30(1), 133–152. https://doi.org/10.1108/JQME-10-2022-0070
11. Ma, Y., Zhang, J., & Shi, J. (2024). Influence of Rail Fastening System on the Aerodynamic Performance of Trains under Crosswind Conditions. Fluid Dynamics and Materials Processing, 20(12), 2843–2865. https://doi.org/10.32604/fdmp.2024.055205
12. MEIF2022. (n.d.).
13. Nenzhelele, T., Trimble, J. A., Swanepoel, J. A., & Kanakana-Katumba, M. G. (2023). MCDM Model for Evaluating and Selecting the Optimal Facility Layout Design: A Case Study on Railcar Manufacturing. Processes, 11(3). https://doi.org/10.3390/pr11030869
14. Placa, A. La, Autelitano, F., Neduzha, L., Tiutkin, O., & Giuliani, F. (2025). Roles and functions of asphalt sub-ballast in the modern maintenance of the European railways. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2024.12.003
15. Quaiser, R. M., & Srivastava, P. R. (2023). Outbound open innovation effectiveness measurement between big organizations and startups using Fuzzy MCDM. Management Decision. https://doi.org/10.1108/MD-07-2022-0990
16. Sequeira, M., Hilletofth, P., & Adlemo, A. (2021). AHP-based support tools for initial screening of manufacturing reshoring decisions. Journal of Global Operations and Strategic Sourcing, 14(3), 502–527. https://doi.org/10.1108/JGOSS-07-2020-0037
17. Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers and Industrial Engineering, 140. https://doi.org/10.1016/j.cie.2019.106231
18. Watson, I., Ali, A., & Bayyati, A. (2021). The station location and sustainability of high-speed railway systems. Infrastructure Asset Management, 9(2), 60–72. https://doi.org/10.1680/jinam.21.00008
19. Yousefi, H., Motlagh, S. G., & Montazeri, M. (2022). Multi-Criteria Decision-Making System for Wind Farm Site-Selection Using Geographic Information System (GIS): Case Study of Semnan Province, Iran. Sustainability (Switzerland), 14(13). https://doi.org/10.3390/su14137640