Repair Tasks Research Articles

Hydrogen-bonded Supramolecular Network Enabled Gentle Reprogramming of Liquid Crystal Elastomer toward Evolutionary Robot.

In nature, many organisms augment chances of survival by reprogramming their structures to evolving environment, among which sea squirts being a prime example. Such reprogramming has been demonstrated in liquid crystal elastomer (LCE) actuator assembled with heat assistance. However, the required temperature being higher than the actuation temperature limits its application. Here, we reported a hydrogen-bonded supramolecular network LCE to construct soft modular and reprogrammable actuator by assembling with a gentle heat treatment. Leveraging the Michael addition reaction, we introduced hydrogen bonding to the LCE matrix with functionalized pyridine monomers. Experimental and molecular dynamics modeling proved the efficient dynamic hydrogen bond exchange at 60°C, significantly lower than the actuating temperature of the LCE. This gave rise to the reversible and robust adhesion of the same collection of LCE modules capable of being built into different bilayers and performing various morphing upon a short thermal stimulation. Therefore, we demonstrated that these comparatively weak cross-links enabled reconfiguration of the LCE actuator. With the developed hydrogen-bonding LCEs, we built proof-of-concept modular reprogrammable robot, performing crawling, sailing, and microcircuit repair tasks. This bioinspired and efficient method for evolutionary LCE robot offers a viable path for further development of intelligent actuators sustainable in complex environments.

Hydrogen‐bonded Supramolecular Network Enabled Gentle Reprogramming of Liquid Crystal Elastomer toward Evolutionary Robot

In nature, many organisms augment chances of survival by reprogramming their structures to evolving environment, among which sea squirts being a prime example. Such reprogramming has been demonstrated in liquid crystal elastomer (LCE) actuator assembled with heat assistance. However, the required temperature being higher than the actuation temperature limits its application. Here, we reported a hydrogen‐bonded supramolecular network LCE to construct soft modular and reprogrammable actuator by assembling with a gentle heat treatment. Leveraging the Michael addition reaction, we introduced hydrogen bonding to the LCE matrix with functionalized pyridine monomers. Experimental and molecular dynamics modeling proved the efficient dynamic hydrogen bond exchange at 60°C, significantly lower than the actuating temperature of the LCE. This gave rise to the reversible and robust adhesion of the same collection of LCE modules capable of being built into different bilayers and performing various morphing upon a short thermal stimulation. Therefore, we demonstrated that these comparatively weak cross‐links enabled reconfiguration of the LCE actuator. With the developed hydrogen‐bonding LCEs, we built proof‐of‐concept modular reprogrammable robot, performing crawling, sailing, and microcircuit repair tasks. This bioinspired and efficient method for evolutionary LCE robot offers a viable path for further development of intelligent actuators sustainable in complex environments.

Exploring Automated Assertion Generation via Large Language Models

Unit testing aims to validate the correctness of software system units and has become an essential practice in software development and maintenance. However, it is incredibly time-consuming and labor-intensive for testing experts to write unit test cases manually, including test inputs ( i.e., prefixes) and test oracles ( i.e., assertions). Very recently, some techniques have been proposed to apply Large Language Models (LLMs) to generate unit assertions and have proven the potential in reducing manual testing efforts. However, there has been no systematic comparison of the effectiveness of these LLMs, and their pros and cons remain unexplored. To bridge this gap, we perform the first extensive study on applying various LLMs to automated assertion generation. The experimental results on two independent datasets show that studied LLMs outperform six state-of-the-art techniques with a prediction accuracy of 51.82% \(\sim\) 58.71% and 38.72% \(\sim\) 48.19%. The improvements achieve 29.60% and 12.47% on average. Besides, as a representative LLM, CodeT5 consistently outperforms all studied LLMs and all baselines on both datasets, with an average improvement of 13.85% and 26.64%, respectively. We also explore the performance of generated assertions in detecting real-world bugs, and find LLMs are able to detect 32 bugs from Defects4J on average, with an improvement of 52.38% against the most recent approach EditAS . Inspired by the findings, we construct a simplistic retrieval-and-repair-enhanced LLM-based approach by transforming the assertion generation problem into a program repair task for retrieved similar assertions. Surprisingly, such a simplistic approach can further improve the prediction accuracy of LLMs by 9.40% on average, leading to new records on both datasets. Besides, we provide additional discussions from different aspects ( e.g., the impact of assertion types and test lengths) to illustrate the capacity and limitations of LLM-based approaches. Finally, we further pinpoint various practical guidelines ( e.g., the improvement of multiple candidate assertions) for advanced LLM-based assertion generation in the near future. Overall, our work underscores the promising future of adopting off-the-shelf LLMs to generate accurate and meaningful assertions in real-world test cases and reduce the manual efforts of unit testing experts in practical scenarios.

Towards robust neural networks: Exploring counterfactual causality-based repair

Despite the widespread applications of neural networks, increasing concerns arise regarding their reliability and security. This paper introduces a novel repair approach integrating counterfactual causality with a multi-objective algorithm to mitigate critical security property issues, including backdoor attacks, safety violations, and unfairness. Firstly, we abstract the neural network into a counterfactual structure causal model, utilizing a causal trace to allocate contributions to each hidden layer neuron for output prediction. Secondly, ”faulty” neurons are identified through fault location, utilizing counterfactual causality between positive and negative example-guided attributes and neuronal contribution. Finally, an ablation study determines the number of neurons for repair. The multi-objective algorithm NSGA-III is then applied to optimize the faulty neurons, adjusting weight parameters to rectify erroneous behavior and eliminate vulnerabilities while maximizing the model’s accuracy. This method achieves notable Pareto optimization and effectively eliminates vulnerabilities in neural networks. In comparative experiments against CARE (CAusality-based REpair) techniques, our Counterfactual Causality-Based Repair (CCBR) method significantly reduces backdoor attack success rates and security violations to less than 1%, while maintaining model accuracy and ensuring security for normal inputs. In fairness repair tasks, CCBR improves fairness by 92.56%, a substantial 19.56% increase over CARE. These results affirm the robustness and practicality of CCBR, establishing it as a valuable tool for enhancing neural network safety and fairness.

Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems

To fully harness the potential of wind turbine systems and meet high power demands while maintaining top-notch power quality, wind farm managers run their systems 24 h a day/7 days a week. However, due to the system’s large size and the complex interactions of its many components operating at high power, frequent critical failures occur. As a result, it has become increasingly important to implement predictive maintenance to ensure the continued performance of these systems. This paper introduces an innovative approach to developing a head-mounted fault display system that integrates predictive capabilities, including deep learning long short-term memory neural networks model integration, with anomaly explanations for efficient predictive maintenance tasks. Then, a 3D virtual model, created from sampled and recorded data coupled with the deep neural diagnoser model, is designed. To generate a transparent and understandable explanation of the anomaly, we propose a novel methodology to identify a possible subset of characteristic variables for accurately describing the behavior of a group of components. Depending on the presence and risk level of an anomaly, the parameter concerned is displayed in a piece of specific information. The system then provides human operators with quick, accurate insights into anomalies and their potential causes, enabling them to take appropriate action. By applying this methodology to a wind farm dataset provided by Energias De Portugal, we aim to support maintenance managers in making informed decisions about inspection, replacement, and repair tasks.

Intersection-union dual-stream cross-attention Lova-SwinUnet for skin cancer hair segmentation and image repair

Skin cancer images have hair occlusion problems, which greatly affects the accuracy of diagnosis and classification. Current dermoscopic hair removal methods use segmentation networks to locate hairs, and then uses repair networks to perform image repair. However, it is difficult to segment hair and capture the overall structure between hairs because of the hair being thin, unclear, and similar in color to the entire image. When conducting image restoration tasks, the only available images are those obstructed by hair, and there is no corresponding ground truth (supervised data) of the same scene without hair obstruction. In addition, the texture information and structural information used in existing repair methods are often insufficient, which leads to poor results in skin cancer image repair. To address these challenges, we propose the intersection-union dual-stream cross-attention Lova-SwinUnet (IUDC-LS). Firstly, we propose the Lova-SwinUnet module, which embeds Lovasz loss function into Swin-Unet, enabling the network to better capture features of various scales, thus obtaining better hair mask segmentation results. Secondly, we design the intersection-union (IU) module, which takes the mask results obtained in the previous step for pairwise intersection or union, and then overlays the results on the skin cancer image without hair to generate the labeled training data. This turns the unsupervised image repair task into the supervised one. Finally, we propose the dual-stream cross-attention (DC) module, which makes texture information and structure information interact with each other, and then uses cross-attention to make the network pay attention to the more important texture information and structure information in the fusion process of texture information and structure information, so as to improve the effect of image repair. The experimental results show that the PSNR index and SSIM index of the proposed method are increased by 5.4875 and 0.0401 compared with the other common methods. Experimental results unequivocally demonstrate the effectiveness of our approach, which serves as a potent tool for skin cancer detection, significantly surpassing the performance of comparable methods.

Integrated scheduling of multi-site repair tasks considering customer preferences under a novel two-dimensional combination warranty strategy

Current warranty strategies typically focus on the interests of either the manufacturer or the customer, and fail to meet the diverse needs of the customers, as they only consider a single reimbursement or repair method. Furthermore, these strategies neglect out-of-warranty customers, resulting in a loss of profit for manufacturers. To serve both in-warranty and out-of-warranty customers, a novel two-dimensional combination warranty strategy is proposed in this paper which combines two reimbursement methods and two repair methods. Under the two-dimensional combination warranty strategy, we establish an integrated scheduling model for determining repair strategies and allocating service resources, referred to as 2DCW-MSRASM. In 2DCW-MSRASM, the manufacturer arranges appropriate warranty strategies for in-warranty orders according to the corresponding product conditions (age and usage), while for out-of-warranty customers, the manufacturer makes decisions based on its own resource situation and customer preferences. The objective of 2DCW-MSRASM is to achieve an optimal scheduling that maximizes the manufacturer’s profit and the satisfaction of both in-warranty and out-of-warranty customers. In this model, TODIM (an acronym in Portuguese of Interactive and Multi-Criteria Decision Making) is utilized to evaluate the repair services from the customer’s perspective, which considers the limited rationality of decision-makers. An improved non-dominated neighbor immune algorithm (INNIA) is developed to solve the model, incorporating an initialization method and three problem-oriented local search operators to enhance search capability. Following that, extensive experiments are conducted to demonstrate the superiority of INNIA. Eventually, the proposed strategy is demonstrated to possess noteworthy advantages when the order size is large and the proportion of in-warranty orders is high.

Variational sparse diffusion and its application in mesh processing

PurposeThe primary objective of this study is to tackle the enduring challenge of preserving feature integrity during the manipulation of geometric data in computer graphics. Our work aims to introduce and validate a variational sparse diffusion model that enhances the capability to maintain the definition of sharp features within meshes throughout complex processing tasks such as segmentation and repair.Design/methodology/approachWe developed a variational sparse diffusion model that integrates a high-order L1 regularization framework with Dirichlet boundary constraints, specifically designed to preserve edge definition. This model employs an innovative vertex updating strategy that optimizes the quality of mesh repairs. We leverage the augmented Lagrangian method to address the computational challenges inherent in this approach, enabling effective management of the trade-off between diffusion strength and feature preservation. Our methodology involves a detailed analysis of segmentation and repair processes, focusing on maintaining the acuity of features on triangulated surfaces.FindingsOur findings indicate that the proposed variational sparse diffusion model significantly outperforms traditional smooth diffusion methods in preserving sharp features during mesh processing. The model ensures the delineation of clear boundaries in mesh segmentation and achieves high-fidelity restoration of deteriorated meshes in repair tasks. The innovative vertex updating strategy within the model contributes to enhanced mesh quality post-repair. Empirical evaluations demonstrate that our approach maintains the integrity of original, sharp features more effectively, especially in complex geometries with intricate detail.Originality/valueThe originality of this research lies in the novel application of a high-order L1 regularization framework to the field of mesh processing, a method not conventionally applied in this context. The value of our work is in providing a robust solution to the problem of feature degradation during the mesh manipulation process. Our model’s unique vertex updating strategy and the use of the augmented Lagrangian method for optimization are distinctive contributions that enhance the state-of-the-art in geometry processing. The empirical success of our model in preserving features during mesh segmentation and repair presents an advancement in computer graphics, offering practical benefits to both academic research and industry applications.

A Pilot Study Testing the Novel Use of a Snake Bite Compression Device for Field Hemostasis and Wound Repair in an Animal Model.

A commercially available snake bite device was pilot tested for novel use as a method of hemostasis and wound repair at a noncompressible site in a live swine model. The device is light, is plastic, uses a hook-and-loop strap attachment, and is easily deployed. The device could offer a method for the field repair of an actively bleeding laceration at a noncompressible site in an austere environment. This was an interventional, prospective, controlled study in a large animal model. The study was approved by the Rhode Island Hospital Institutional Review Board (IRB) and the Animal Welfare Committee/Institutional Animal Care and Use Committee and the Lifespan Research Conflict of Interest Committee. Each animal acted as its own control. Blood loss was measured and compared between repairs of standardized incisions with and without the device's application. The lacerations were sutured closed. Two proceduralists alternated tasks of wound repair versus blood collection. Blood loss was measured by using gauze sponges to capture the blood during a 30-second free-bleeding period and during the repair itself. Using a one sample t-test (the expected difference in blood loss between the two incision repair methods = 0 if the null hypothesis were true), we calculated the mean difference in the deltas between the repair methods. The mean delta difference was 3.1 g (SE ± 0.97). The t-test demonstrated that there was a significantly greater blood loss during the standard repair method, t(9) = 3.11, P < 0.01 than during the repair with the device in place (see Fig.2). A statistical power analysis conducted showed that with a sample size of 10 animals, there was sufficient statistical power to detect this significant effect (β = 0.82, α < 0.05, one-tailed). There was statistically significantly less blood loss during the repairs with the device's application. This feasibility experiment demonstrates that a commercially available snakebite device may be useful for hemostasis during laceration repair at anatomic sites not amenable to application of tourniquets or compressive dressings. Strengths of the study include the prospective controlled design, including the use of each animal as its own control; alternating proceduralists to account for any variability in suturing efficiency; and the statistical significance of the results despite the small number of subjects. One weakness is that the time required for each repair was not measured. The device's portability and reusability suggest applicability in austere medical environments. Future studies could include timing the repairs, using a skin stapler or wound adhesive instead of sutures, applying a hemostatic agent before the repair, and sequentially applying the device to wounds longer than the device.

ANALYSIS OF MAN-HOUR REQUIREMENTS AND SCHEDULING FOR FLOATING DOCK REPAIRS

A floating dock (FD) is a platform for ship construction and repair. Structural damages to a FD can significantly reduce ship docking productivity, leading to substantial losses for shipyard companies. In order to restore its operational capabilities, repairing the damaged FD becomes essential. This research aims to plan and analyze the schedule to ensure the efficiency and effectiveness of FD repair activities. The process begins with the establishment of a Work Breakdown Structure (WBS), followed by determining the required man hours (MH) and scheduling using the Precedence Diagram Method (PDM). Finally, the S-curve is made for monitoring the progress of the FD repairment. The findings of this study, based on a case study involving a 6700 TLC FD, reveal a WBS comprising 66 repair tasks including two tasks for FD testing and finishing in addition to four areas of hull, walls, deck and tanks, piping systems, and machinery. The MH requirement for these tasks is 4048 MH. From the scheduling results, it was found that the duration was 12 days, with a total of 53 critical activities and 25 critical paths in the floating dock repair work using the precedence diagram method.

EVALUATION OF THE EFFICIENCY OF THE AUTOMATED ALGORITHM FOR DRAWING SHIP HULL SURFACES

The efficiency of application of the algorithm for automated construction of ship hull surfaces using NURBS approximation is shown. In the majority of modern shipbuilding CAD-systems a considerable share of labor intensity is occupied by preparation of the ship surface model in the first approximation on the basis of electronic theoretical drawing. For carrying out such works in shipbuilding CAD-systems there is a corresponding computer toolkit, which allows to display geometrically clear smooth forms of such surfaces as a ship hull. The works of domestic and foreign researchers in this field have been analyzed. The proposed solutions have significant disadvantages: they require large computing power for training and use. The paper shows a modern approach to solving the issue of reducing the labor intensity of graphical constructions and, consequently, time costs by automating the procedures of computer modeling using web technology. Computer modeling of bulbous bow ends and tunnel-type stern parts is a labor-intensive process of manual editing of hull geometry. Experimental studies of the developed automated algorithm and software product for this type of work have been carried out. The comparative analysis of labor intensity of construction of the most complex part of the ship surface – the bow tip of ships of three projects using the developed software product and two professional CAD-systems – KOMPAS-3D and Rhinoceros – is given. The comparison showed that the developed algorithm and program are much more efficient to use compared to the toolkit of the compared CAD systems. The automated algorithms developed in the process of research can be used in shipbuilding practice, ship repair tasks, as well as for hydrostatic calculation programs.

Stress in the tank case at mounting displacements and loads on nozzles

In the practice of operating capacitive equipment, production moments often arise due to technical re-equipment and changes in the operating parameters of tanks to meet specific requirements for equipment design in the technological process. Common reconstruction activities involve modifying process nozzles in technical device housings to align with process piping project specifications. Consequently, the stressed-deformed state in vessel bodies before and after reconstruction can vary significantly. During manufacturing or repair tasks, permissible installation displacements, such as the shifting of edges in joined cylindrical shell housings, may occur within regulatory limits. This study focuses on analyzing stress variations and stress distribution zones, considering the combined effects of these factors.

Integrated scheduling method for fleet wave sorties and maintenance of naval distributed platforms

Managing operational command and maintenance support activities for carrier-based aircraft in a multi-platform system is challenging. This research presents an integrated mathematical model for scheduling deployment, sortie, and maintenance of carrier-based aircraft. The model helps decision-makers formulate fleet allocation plans, create sortie flight schedules, and coordinate repairs for future maritime distributed operations. We have introduced a novel bi-level iteration optimization method that improves traditional manual empirical scheduling methods. An advanced version, a two-stage optimization method, can handle a large number of states and solution spaces while ensuring accuracy. Our statistical validation shows that the two-stage optimization method has an insignificant deviation of 1–2% (with an average of −0.91%) from accurate results. Importantly, this method significantly reduces computational time (with an average of 98.25%), which enhances decision-making accuracy and real-time feasibility. Finally, the research discusses a cross-platform linkage mechanism for distributing aircraft repair tasks. This mechanism aims to enhance mission readiness and operational metrics by avoiding backlogs and reducing inefficiencies caused by concentrating maintenance on a single platform. The results of this study provide a foundation for decision-making regarding future naval distributed systems and their operations.

Wind Field Digital Twins Sandbox System for Transmission Towers.

Given the digitalization trends within the field of engineering, we propose a practical approach to engineering digitization. This method is established based on a physical sandbox model, camera equipment and simulation technology. We propose an image processing modeling method to establish high-precision continuous mathematical models of transmission towers. The calculation of the wind field is realized by using wind speed calculations, a load-wind-direction-time algorithm and the Continuum-Discontinuum Element Method (CDEM). The sensitivity analysis of displacement- and acceleration-controlled transmission tower loads under two different wind direction conditions is conducted. The results show that the digital model exhibits a proportional relationship with the physical dimensions of the transmission tower model. The error between the numerical simulation results and the experimental results falls within a reasonable range. Nodes at higher positions of the transmission tower experience significantly higher forces compared to those at lower positions, and the structural forms with larger windward projected areas yield similar simulation results. The proposed digital twin system can help monitor the performance of structural bodies and assess the disaster degree in extreme conditions. It can guide specific maintenance and repair tasks.

Exploring the repair process of a 3D printer using augmented reality-based guidance

In recent years, additive manufacturing (AM) techniques have transcended their typical rapid prototyping role and become viable methods to directly manufacture end products in a highly versatile manner. Due to its low cost and relative ease of use, fused deposition modeling (FDM) has become the most universally applied AM technology. Nonetheless, skilled operators are often still required to perform maintenance, diagnostic, and repair tasks. Such operators need to be adequately trained. Here, Augmented reality (AR) technology could be used to automate this training and help to promptly provide new operators with the necessary skills to perform specific tasks as required. However, the most effective approach to designing such AR-based assistance systems has not yet been fully explored. Consequently, we address this need by reporting on how to design such guiding systems using well-known design engineering methodologies. We then further assess the applicability of our approach through a user study with domain experts. In addition, we complete our assessment with heuristical verification of system expressiveness to reason about the influence of cognitively important components of the AR interface on the operators.

Evaluating digital work instructions with augmented reality versus paper-based documents for manual, object-specific repair tasks in a case study with experienced workers

Manual repair tasks in the industry of maintenance, repair, and overhaul require experience and object-specific information. Today, many of these repair tasks are still performed and documented with inefficient paper documents. Cognitive assistance systems have the potential to reduce costs, errors, and mental workload by providing all required information digitally. In this case study, we present an assistance system for object-specific repair tasks for turbine blades. The assistance system provides digital work instructions and uses augmented reality to display spatial information. In a user study with ten experienced metalworkers performing a familiar repair task, we compare time to task completion, subjective workload, and system usability of the new assistance system to their established paper-based workflow. All participants stated that they preferred the assistance system over the paper documents. The results of the study show that the manual repair task can be completed 21% faster and with a 26% lower perceived workload using the assistance system.

Katana : Dual Slicing Based Context for Learning Bug Fixes

Contextual information plays a vital role for software developers when understanding and fixing a bug. Consequently, deep learning based program repair techniques leverage context for bug fixes. However, existing techniques treat context in an arbitrary manner, by extracting code in close proximity of the buggy statement within the enclosing file, class, or method, without any analysis to find actual relations with the bug. To reduce noise, they use a predefined maximum limit on the number of tokens to be used as context. We present a program slicing based approach, in which instead of arbitrarily including code as context, we analyze statements that have a control or data dependency on the buggy statement. We propose a novel concept called dual slicing , which leverages the context of both buggy and fixed versions of the code to capture relevant repair ingredients. We present our technique and tool called Katana , the first to apply slicing-based context for a program repair task. The results show that Katana effectively preserves sufficient information for a model to choose contextual information while reducing noise. We compare against four recent state-of-the-art context-aware program repair techniques. Our results show that Katana fixes between 1.5 and 3.7 times more bugs than existing techniques.

A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track

Inspection and repair interventions play vital roles in the asset management of railways. Autonomous mobile manipulators possess considerable potential to replace humans in many hazardous railway track maintenance tasks with high efficiency. This paper investigates the prospects of the use of mobile manipulators in track maintenance tasks. The current state of railway track inspection and repair technologies is initially reviewed, revealing that very few mobile manipulators are in the railways. Of note, the technologies are analytically scrutinized to ascertain advantages, unique capabilities, and potential use in the deployment of mobile manipulators for inspection and repair tasks across various industries. Most mobile manipulators in maintenance use ground robots, while other applications use aerial, underwater, or space robots. Power transmission lines, the nuclear industry, and space are the most extensive application areas. Clearly, the railways infrastructure managers can benefit from the adaptation of best practices from these diversified designs and their broad deployment, leading to enhanced human safety and optimized asset digitalization. A case study is presented to show the potential use of mobile manipulators in railway track maintenance tasks. Moreover, the benefits of the mobile manipulator are discussed based on previous research. Finally, challenges and requirements are reviewed to provide insights into future research.

ПРИМЕНЕНИЕ ИНФОРМАЦИОННОГО МОДЕЛИРОВАНИЯ ДЛЯ РЕШЕНИЯ ЗАДАЧ ТЕХНИЧЕСКОГО ОБСЛУЖИВАНИЯ И РЕМОНТА ЗДАНИЙ И СООРУЖЕНИЙ

Currently, there is an active development of digital technologies in the construction sector. One of the main directions of digitalization of construction is the use of building information modeling technologies (BIM) at all stages of the life cycle of buildings and structures. The principle of using these technologies is the complex processing of geometric and attribute information about the object, its elements and its application to solve design, construction and operational tasks. The pace of BIM implementation at the operational stage is lagging behind, but the interest of asset owners is growing in the possibilities of reducing the complexity and improving the efficiency of building maintenance by organizing a centralized repository of up-to-date and reliable information about the object based on an information model. In this article, the operational formation of defect certificates and statements of necessary material and technical means intended for routine repairs is considered, as one of the directions of application of the information model for solving maintenance and repair tasks. The traditional approach to working with these documents is associated with the repeated entry of repetitive information into data accounting systems, and sometimes with handwritten forms and the lack of the possibility of simultaneous work of all participants on documentation. The proposed approach to electronic reporting will make it possible to reduce the amount of routine work of the maintenance service specialists and eliminate errors related to the human factor in the preparation of reports.

Pricing game between repair-and-support ships and shipyards under multiple repair tasks on a remote ocean

Novel repair-and-support ships can conduct spot systemic maintenance and repair (MR) working for damaged offshore platforms on a remote ocean. This causes competition against shipyards when repair-and-support ships enter the MR market of offshore platforms. Therefore, to wisely decide the optimal strategy for repair-and-support ships, competition analysis is conducted between the repair-and-support ship and shipyard when providing MR services for damaged offshore platforms on a remote ocean from the three aspects of space, time and cost. A pricing game model of the repair-and-support ship and shipyard under multiple repair tasks on a remote ocean is formulated, considering the choice preference of repair decision-makers and the characteristics of the repair-and-support ship as a compound player. Aiming at the particularities of this model, a solution method based on an iterative algorithm is designed to address the pricing game model. It is proven that the designed solution method has advantages in global optimality and CPU running time by comparing it with an enumeration method. In the numerical part, we obtain the optimal pricing strategy and management insights into the technical gaps and damage degree of offshore platforms. The results can provide theoretical and decision support for the pricing strategy of repair-and-support ships entering the MR market of offshore platforms.