1. Introduction
A shipyard is a facility for constructing new vessels and repairing existing structures, regardless of damage extent. In fishing ports, shipyards are among the most essential infrastructural components. The shipbuilding industry differs from other sectors owing to its unique operational characteristics. As observed by Lai et al. (2020) and Lee and Nam (2017), customers identify specific requirements, and shipbuilders modify vessels accordingly. Kim et al. (2003) demonstrated that assessing a shipyard’s production capabilities is crucial, as it directly impacts production costs and the potential for cost reduction. Shipyard productivity, in turn, influences the competitiveness of the entire industry. Considering Indonesia’s geographic makeup, which comprises numerous islands separated by vast stretches of sea, maritime transport plays a vital role in connecting remote regions. Ships serve as critical infrastructure that supports the continuity of various industries, particularly those engaged in interisland logistics (Till, 2015).
The shipping industry is developing rapidly; however, many ship owners face challenges during the shipbuilding process due to shipyards failing to meet quality standards. Consequently, ship owners must make informed and effective decisions based on quality, reliability, and cost (Pershing, 2006; Dolz et al., 2024). In Indonesia, shipyard development is essential to support the country’s unique geography, because most of its territory consists of ocean areas (Vakili et al., 2023). Nevertheless, building a modern shipyard requires significant investment. Therefore, every aspect, from facility planning to material flow, must be carefully designed, with particular importance placed on selecting the right location. Shipyards should be situated in port areas to ease material supply and ensure proximity to a skilled workforce, which greatly influences shipyard performance. According to Pratama and Fadillah (2019) and Daulay et al. (2023), selecting the appropriate type of docking facility should consider factors such as vessel size, construction costs, operational efficiency, ease of maintenance, and affordability.
An appropriate shipyard model can be selected by considering several factors, including uncertainty and ambiguity. Specifically, uncertainty can be effectively modeled using fuzzy analytical hierarchy process (fuzzy AHP), which allows decision values to be represented as fuzzy sets. The traditional AHP method concurrently evaluates multiple criteria in the decision-making process by structuring them into a hierarchical framework. This enables decision makers to easily assign relative weights or priorities to each criterion. Furthermore, technique for order preference by similarity to ideal solution (TOPSIS) synthesizes information, identifies the most suitable alternative, and evaluates the relative closeness of each option to both positive and negative ideal solutions. The combination of fuzzy AHP and TOPSIS makes the decision-making process more comprehensive, accommodating both qualitative and quantitative factors. In addition, it improves decision quality and reduces the risk of errors in selecting a shipyard model. Considering the usefulness of these models, this study adopts fuzzy AHP and TOPSIS to address shipyard model selection issues. The fuzzy AHP–TOPSIS methodology has proven to be an effective decision-making method in complex scenarios (Caymaz, 2024). A previous study conducted by Rizwan et al. (2024), titled “Study on shipyard location and model development feasibility for Kutaraja Fishing Port, Aceh,” also applied both methods to recommend the slipway type, which achieved a score of 44.3%. However, that study had several methodological limitations, including the use of conventional AHP without incorporating uncertainty or expert subjectivity and involvement of a limited number of expert participants from homogeneous backgrounds. To address these shortcomings, this study applies a hybrid approach using fuzzy AHP, which accounts for linguistic uncertainty using triangular fuzzy numbers (TFNs), and TOPSIS, which ranks alternatives based on their closeness to an ideal solution. This study further improves methodological robustness by involving 70 expert respondents from four diverse groups–shipyard practitioners, government officers, academics, and vessel owners–to provide a more comprehensive and reliable decision-making framework. Leveraging this enhanced framework, this study aims to identify the most appropriate shipyard model in Aceh using the fuzzy AHP and TOPSIS methods.
2. Materials and Methods
This study aimed to determine the optimal location and model for developing a shipyard at Kutaraja Fishing Port. Although no previous investigations focused specifically on this combined issue, relevant studies included shipyard location analysis at Kutaraja Fishing Port (Nurani et al., 2017) and shipyard model suitability assessments (Pratama and Fadillah, 2019).
The current study employed a survey methodology, chosen for its effectiveness in facilitating data collection and subsequent analysis. Primary data were collected directly from the target population onsite through structured questionnaires and interviews to gather opinions on land capacity, human resources, facility availability, and shipyard production capacity. Using a purposive sampling method, 70 expert respondents were chosen from four main groups: shipyard practitioners (n = 25), including ship construction engineers and technicians; government officials from fisheries and port sectors (n = 20); academics specializing in shipbuilding, marine, and fisheries engineering (n = 15); and fishermen who owned ships and directly used shipyard facilities (n = 10). Respondents were selected based on a minimum of five years of professional experience and direct participation in the construction or use of shipyard facilities in Aceh. The survey took place at Kutaraja Fishing Port in Banda Aceh, which was selected for being a center for deep-sea fisheries activities with a significant need for adequate shipyard infrastructure. Face-to-face interviews were conducted through structured questionnaires, complemented by focus-group discussions. The survey instrument consisted of two main parts. The first part was a paired comparison questionnaire for the fuzzy AHP method. The second part involved an assessment of alternative performance using a linguistic scale, which was subsequently converted into numerical values for the TOPSIS method. Additionally, a separate questionnaire, based on paired comparisons between factors, was also developed to evaluate each alternative location and calculate final scores for each area, following the method of Nurani et al. (2017). This evaluation utilized Super Decisions software.
The criteria weights were calculated using TFNs, whereas the ranking of alternatives was performed using TOPSIS, which determined the relative closeness of each alternative to the ideal solution. Subsequently, Super Decisions software and Microsoft Excel were used to analyze the data, ensuring systematic integration between fuzzy results and multicriteria evaluations.
2.1 Fuzzy AHP
Fuzzy AHP is an extension of conventional AHP with fuzzy logic, designed to address the inherent vagueness, subjectivity, uncertainty, and variability in human perception and judgment, particularly in multicriteria decision-making contexts (Sharma et al., 2021; Mitta et al., 2019). To capture this, pairwise comparisons were represented using TFNs, which encompassed the lower, middle, and upper bounds of an assessment estimate. TFNs specifically assist in reflecting the uncertainty present in linguistic evaluations of human preferences (Sukisno and Singgih, 2019). Fuzzy AHP utilizes these TFNs as a replacement for the nine-point scale used in conventional AHP, thereby improving the validity of object evaluation by enabling hierarchical problem structuring (Rizwan et al., 2023). Given these capabilities, fuzzy AHP was employed in the current study. Once the weights were finalized, normalized weights were calculated for all attributes/factors. Finally, alternatives were ranked using these weighted attributes or factors, with the alternative possessing the highest value being the preferred option (Lohan et al., 2020).
Table 1 displays the fuzzy AHP pairwise comparison scale, which serves as a linguistic-to-fuzzy number conversion for translating judgments (e.g., very important, equally important) into fuzzy values (weights) using TFNs. This scale is essential for transforming qualitative assessments into fuzzy quantitative data, thus facilitating a more realistic and flexible decision-making process that accommodates the inherent uncertainty and ambiguity of human judgment. The next steps in fuzzy AHP are as follows:
-
(1) Determine the fuzzy synthetic extent (Si) value for each criterion in the primary criteria matrix using Eq. (1):
The inverse sum of the TFN judgments for each primary matrix can be computed using the following formula:Here, M is the TFN, i is the row index, j is the column index, -
(2) Establish priority vectors (also known as eigenvectors) to express the element weights. Their values are determined using Eq. (3):
where M1 is the TFN for each criterion (Ki), V is the vector or comparison, m is the median value (middle possibility), l is the lower value (lowest possibility), and u is the upper value (top possibility).Employing M1 as a benchmark, use the V(M2M1) and V(M1M2) values to compare the relative importance of M1 and M2. If the value of M1 exceeds that of M2, the comparison result is 1. Conversely, if the value of M1 is less than M2’s, the result is calculated using the following formula: -
(3) Determine the normalized weight values (W) for the fuzzy vectors. Normalization, achieved by standardizing the equation step by step, produces the following normalized vector weight value (Eqs. (2–5)):
where W is the final weight vector (normalization result), and d(Ai ) is the degree of possibility value of the alternatives or criteria. (4) After completing all calculations, rank the criteria and options to determine the most effective and necessary choices, respectively.
2.2 TOPSIS
TOPSIS was used to identify the most suitable alternative by evaluating the relative proximity of each option to both the positive and negative ideal solutions. This method enables an objective assessment of multiple technical and functional criteria (Wu et al., 2018; Fan et al., 2024). Its integration with evidence-based reasoning procedures allows the incorporation of incomplete or uncertain information from various sources, leading to more accurate decisions, particularly in contexts such as maritime emergencies (Zhao, 2021). TOPSIS has served as a fundamental method in the multi-attribute decision-making (MADM) domain, gaining popularity in applications and as a foundation for various method developments. Modified TOPSIS, an extension of the original method, has also gained popularity owing to its novel use of an objective weighting process based on Shannon entropy theory.
Some modified TOPSIS approaches traditionally required subjective assignment of weights to each attribute prior to calculation. These weights typically represented the decision-makers preference for each attribute. In contrast, the entropy-based version of the modified TOPSIS objectively determines the attribute weights based on the amount of information each attribute provides. Despite both methods using the same Euclidean distance measure, the models differ in how attribute weights are incorporated into the final solution. Based on these considerations, a modified version of TOPSIS was developed using entropy-based objective weighting, because relying on subjective preferences could often be impractical. This method also applies attribute weights differently from conventional TOPSIS when solving MADM problems (Chakraborty, 2022).
The TOPSIS method was used because of the following advantages (Türk and Özkök, 2020; Hozairi and Krisnafi, 2018):
(1) The concept is simple and easy to understand, making this method accessible to various groups, including those who lack a deep technical background. This simplicity is reflected in its straightforward analysis process, which is not overly complicated and facilitates easier implementation in various contexts.
(2) It uses criteria indicators and alternative variables to assist in the decision-making process. Each criterion and alternative is assessed systematically, providing a strong basis for determining the best choice among the available alternatives. Thus, TOPSIS can accommodate various relevant factors in the evaluation process.
(3) The TOPSIS computing system is highly efficient. This method allows the calculation process to be conducted quickly, even for complex data. This efficiency saves time and ensures that the decision results can be immediately used for operational or strategic needs.
(4) TOPSIS provides a measure of alternative performance while delivering decision recommendations in a simple and easily understandable output. This output presents a ranking of the best alternatives and offers a clear picture of the extent to which each alternative approaches the ideal solution.
(5) TOPSIS is known as a faster and more efficient decision-making method than other approaches. This speed is essential in time-sensitive situations requiring rapid analysis results without compromising decision quality. Overall, TOPSIS offers a combination of simplicity, efficiency, and reliability, making it an ideal choice for various research and practical applications.
2.3 Rationale for Criteria Selection
The criteria selection in this study was not random but was instead based on a combination of practical shipyard needs and the results of consultations with experts. The criteria used in the model and land aspects reflected several important variables, including the following:
(1) Sustainable construction and production capacity, indicated by criteria such as partitioned workshop, availability of electricity, and availability of clean water. These facilities represent the shipyard’s ability to support sustainable production.
(2) Logistics and accessibility conditions, reflected in criteria such as land proximity to water and proximity to residential areas, which are important for the efficient transportation of raw materials and labor.
(3) Resistance to weather conditions, represented by both natural and artificial dock protection, which plays a role in protecting the work area from extreme weather and other environmental disturbances.
These criteria were determined through literature studies, discussions with shipyard practitioners, and verification through expert tests. The criteria selection aimed to comprehensively capture the technical, operational, and environmental dimensions, all of which are essential for robust multicriteria decision making.
3. Result
The evaluation using the fuzzy AHP method focused on two main aspects: shipyard model and land suitability. For the shipyard model evaluation, six technical criteria were assessed: partitioned workshop (M1), natural dock protection (M2), artificial dock protection (M3), sanitation facilities (M4), concrete pool (M5), and stone pool (M6). The pairwise comparisons were converted into TFNs. The normalized weights revealed that M1 and M2 had the highest priority values (0.461 and 0.539, respectively), whereas the influence of the remaining criteria was negligible.
Seven criteria were analyzed for land suitability: concrete pool (L1), stone pool (L2), availability of electricity (L3), availability of clean water (L4), flat terrain (L5), distance from residential areas (L6), and proximity to water (L7). The results indicated that L1 and L2 had the highest normalized weights (0.434 and 0.566, respectively), demonstrating their dominant influence on land evaluation. Following the fuzzy AHP weighting process, TOPSIS was applied to rank three alternative shipyard types: slipway, graving dock, and floating dock. The results of the fuzzy AHP and TOPSIS analyses are summarized in Fig. 1.
Fig. 1 shows that the slipway alternative consistently receives the highest scores across all criteria sets and methods. The graving dock ranks second, while the floating dock has the lowest evaluation score. These quantitative outputs provide the final ranking of shipyard alternatives without interpretation, which is addressed in the following discussion section.
4. Discussion
The abundant fishery resources in the region and increasing number of operating vessels indicate a strong demand for strategic infrastructure development to improve vessel efficiency. Furthermore, the type and scale of fishing vessels play a crucial role in determining the appropriate shipyard facilities. For example, small-scale vessels typically require basic infrastructure, such as a sloped slipway and modest docking space, whereas medium- to large-sized vessels may necessitate specialized facilities, including deeper launching basins, reinforced berths, and advanced repair equipment. Similarly, vessels targeting pelagic species may demand faster turnaround times and cold storage support, whereas those targeting demersal species may require different unloading and servicing setups. Consequently, future shipyard planning should incorporate not only geographic and technical feasibility but also vessel-specific operational needs to ensure both practical relevance and optimal performance.
In support of this, Patil and Kant (2014) demonstrated that combining fuzzy AHP and TOPSIS methods can effectively identify optimal shipyard locations. Additionally, selecting an appropriate shipyard model is essential to support the sustainable management of fishery resources in the region. Table 2 presents the relationship between field-based needs and criteria used in fuzzy AHP.
Table 2 indicates that the selected criteria effectively address important issues such as production sustainability, environmental impact, and logistics efficiency, even though these aspects are not explicitly stated as variables in the initial model structure. Table 2 also outlines how practical needs for traditional shipyard development align with specific feasibility criteria. Production capacity is supported by partitioned space, electricity, and clean water. Weather resistance relies on natural and artificial dock protection. Logistics feasibility depends on flat land, distance from settlements, and proximity to water. Workforce welfare is ensured through sanitation, electricity, and water access. Finally, development efficiency is influenced by the presence of construction pools and site proximity to water. These criteria collectively offer a structured basis for evaluating suitable shipyard locations.
Fuzzy AHP analysis was conducted on two key aspects of this study: shipyard model and land suitability. The following section presents the results, reviewing the list of potential criteria evaluated for both the model and land.
Table 3 lists the parameters used to determine the most suitable shipyard type, which were later analyzed using the fuzzy AHP and TOPSIS methods. Each criterion was compared in pairs to assess its relative importance in the decision-making process. The model comprised several major parameters, specifically, partitioned workshop, natural dock protection, artificial dock protection, sanitary availability, concrete pool, and stone pool.
Table 4 summarizes the land parameters, consisting of natural pool, artificial pool, electricity availability, clean water availability, flat contoured land, proximity to water bodies, and distance from residential areas. Each parameter was assessed to determine the most suitable type of dock for application in fishing ports.
Table 5 presents the pairwise comparison matrix of the model criteria (labeled M1 to M6) used in the assessment process via the fuzzy AHP method. Each entry in the table reflects the degree of preference of one criterion over another, based on a standard AHP comparison scale ranging from 1 to 9. For instance, a value of 3 between M1 and M3 indicates that criterion M1 is moderately preferred over M3. Meanwhile, a value of 5 between M1 and M5 signifies a strong preference for M1 over M5.
Fig. 2 shows that M1 (partitioned workshop) and M2 (natural dock protection) hold the highest weights, indicating that they are the most critical criteria in selecting the shipyard model. In contrast, M5 and M6 have the lowest weights, suggesting a lesser influence on the decision-making process.
Table 6 provides the fuzzy synthesis and normalized weights for each criterion. M2 (natural dock protection) and M1 (partitioned workshop) have the highest normalized weights of 0.539 and 0.461, respectively, indicating that they are the most influential factors in the decision-making process. In contrast, M3–M6 have normalized weights of zero, suggesting that they have minimal impact based on expert evaluations.
The pairwise comparison matrix in Table 7 indicates that L1 (concrete pool) and L2 (stone pool) are consistently rated higher than the other land criteria, suggesting their dominant importance in the evaluation. Criteria such as L6 (distance from settlements) and L7 (proximity to water) have the lowest comparative values, reflecting their relatively minor influence in the decision-making process.
Fig. 3 depicts a bar chart showing the weights of the land criteria (L1–L7) based on the pairwise comparison data. The graph shows that criteria L1 and L2 have the highest weights, both reaching a value of 1.0, indicating their dominance in the decision-making process. Criterion L3 follows with a slightly lower weight, whereas criteria L4 through L7 have relatively lower and more balanced weights, signifying a lesser but still relevant influence on the total evaluation.
The criteria weights served as a reference to assess the extent to which each alternative met the established criteria. This assessment was initially conducted qualitatively, and then converted into quantitative values to facilitate further computational analysis.
Table 8 presents the results of the fuzzy AHP analysis for the seven land-related criteria (L1–L7) used in the shipyard model evaluation at Kutaraja Fishing Port. Each criterion was assessed using TFNs, which reflected expert judgment through lower, middle, and upper values. For instance, L1 (concrete pool) has TFNs of 8, 10, and 12, which indicate its perceived importance. These values were synthesized and converted into vector weights to determine the relative significance of each criterion.
The analysis revealed that L2 (stone pool) had the highest normalized weight (0.566), followed by L1 (concrete pool) at 0.434, making them the most influential land criteria. In contrast, L3–L7 had normalized values of zero, indicating minimal impact on the decision-making process. These findings suggest that pond structure-related criteria should be prioritized over utility and topographical aspects when evaluating suitable shipyard land. The resulting weights were integrated into TOPSIS to identify the most appropriate shipyard model.
Table 9 displays the results of a comparative analysis of the three shipyard types slipway, graving dock, and floating dock using two assessment methods: fuzzy AHP and TOPSIS. In the fuzzy AHP results, two weight categories are shown, representing the priority weights based on the model and land criteria. The slipway type achieves the highest scores in both categories, with 0.86525 for the model and 0.8585 for land, indicating that it is the most feasible and preferred shipyard type from both perspectives. The graving dock ranks second, scoring 0.65425 (model) and 0.6745 (land), whereas the floating dock receives the lowest weights of 0.461 (model) and 0.434 (land).
The results confirm that slipway is the most preferred and feasible option, as it aligns with expert judgments captured through fuzzy AHP and objective rankings produced by TOPSIS. Practically, slipways are crucial in shipyards without dry-dock facilities because they offer reliable platforms for vessel construction and repair (Chou et al., 2019). Graving docks function as dry basins along shorelines and are suitable for large-scale operations, whereas floating docks provide mobile support but require high maintenance and extensive infrastructure (Zhang et al., 2024).
5. Conclusions
This study applied a combined fuzzy AHP –TOPSIS methodology to select the most suitable shipyard model for Kutaraja Fishing Port in Aceh. Fuzzy AHP was used to determine the weights of the evaluation criteria by accommodating the uncertainty and subjectivity of expert judgments, whereas TOPSIS ranked the alternatives based on their closeness to the ideal solution.
The slipway model was identified as the most favorable because of its strong performance in key aspects, including compatibility with natural land contours, ease of construction, and suitability for small- to medium-sized fishing vessels, which are predominant in the coastal region of Aceh. The graving dock, although robust and permanent, is less efficient in terms of land use and construction costs in the local context. The floating dock was considered less suitable, as it requires intensive maintenance and does not optimally support small fishing vessels given the water characteristics and facility conditions in Aceh. The methodological framework that integrates expert-driven fuzzy AHP and the ranking capability of TOPSIS offers a replicable and robust tool for similar multicriteria evaluations in other maritime development contexts. These findings not only support the sustainable development of the shipyard infrastructure in Aceh but also provide a practical reference for other coastal regions with similar characteristics, as the slipway model offers an effective and efficient solution.
PDF Links
PubReader
ePub Link
Full text via DOI
Download Citation
Print
