Sequence Planning Research Articles

Man-in-the-Loop Control and Mission Planning for Unmanned Underwater Vehicles

UUVs (unmanned underwater vehicles) perform tasks in the marine environment under direction from a commander through a mother ship control system. In cases where communication is available, a UUV task re-planning system was designed to ensure task completion despite uncertain events faced by UUVs. First, the XML language standardizes the expression of UUV task elements. Second, considering the time sequence and spatial path planning requirements of human-supervised UUV control tasks, time sequence planning based on a genetic algorithm and spatial path planning based on an improved genetic algorithm were designed to plan near-optimal approximate spatial paths for control tasks. Third, uncertainties encountered during UUV task execution were classified so that the commander could adjust according to the situation or invoke the control task re-planning algorithm to re-plan. Finally, a simulation platform was built using the QT development environment to simulate human-supervised UUV control task planning and re-planning, verifying the algorithm’s design effectiveness.

An Assembly Sequence Planning Method Based on Multiple Optimal Solutions Genetic Algorithm

Assembly sequence planning (ASP) is an indispensable and important step in the intelligent assembly process, and aims to solve the optimal assembly sequence with the shortest assembly time as its optimization goal. This paper focuses on modular cabin construction for large cruise ships, tackling the complexities and challenges of part assembly during the process, based on real engineering problems. It introduces the multiple optimal solutions genetic algorithm (MOSGA). The MOSGA analyzes product constraints and establishes a mathematical model. Firstly, the traditional genetic algorithm (GA) is improved in the case of falling into the local optimum when facing complex problems, so that it can jump out of the local optimum under the condition of satisfying the processing constraints and achieve the global search effect. Secondly, the problem whereby the traditional search algorithm converges to the unique optimal solution is solved, and multiple unique optimal solutions that are more suitable for the actual assembly problem are solved. Thirdly, for a variety of restrictions and emergencies that may occur during the assembly process, the assembly sequence flexible planning (ASFP) method is introduced so that each assembly can be flexibly adjusted. Finally, an example is used to verify the feasibility and effectiveness of the method. This method improves the assembly efficiency and the diversity of assembly sequence selection, and can flexibly adjust the assembly sequence, which has important guiding significance for the ASP problem.

A Graph-based framework for assembly sequence planning of a cable harness

This paper focuses on assembly sequence planning (ASP) for cable harness, which is an essential but not yet well-addressed problem for automated cable assembly. A systematic approach is proposed to obtain all feasible assembly sequences taking account of the effects of the topological structure as well as various types of assembly tasks. The cable representation is first extended to consider the assembled relations of tasks along the entire cable. Then, the effects of a typical task on the assembly feasibility of the entire cable are analyzed. Finally, a framework is proposed to generate all feasible assembly sequences for the cable assembly. The proposed framework is verified using typical cable harnesses with single and multi-branches.

Care delivery and patient self-management for people with primary liver cancer receiving 4R care sequence plans.

465 Background: The 4R Oncology Model (4R=Right information/care/patient/time) supports patient self-management1 and quality improvement.2 Here we report the impact of using 4R Care Sequence plans for patients with primary liver cancer cared for by a multi-disciplinary liver cancer team. Methods: We tested the impact of 4R care sequence plans on patient self-management and care delivery referrals at a liver cancer program via patient surveys. 4R Plans were provided to patients with primary liver cancer from Jan – Aug ’23 (4R cohort). Patient-reported data were analyzed to compare the control cohort of patients who received care pre-4R, Jan - Dec ’22 (N=42) to the 4R cohort (N=17). Statistical analysis used Fisher’s exact test. Results: Patient survey response rates: Control cohort 54% (42/78), 4R cohort 55% (17/31). Comparing the control cohort to the 4R cohort respectively, patients were male (52%, 65%), White (64%, 64%), Black (26%, 18%), average age (63.2, 64.7), and reported income less than $30,000 (45%, 53%). The 4R cohort significantly improved care delivery in three metrics, and directionally improved in the other care delivery metrics as compared to the control cohort (table). All four patient self-management metrics significantly improved in the 4R cohort vs. the control cohort (Table). Conclusions: The use of 4R Care Sequence plans for patients with primary liver cancer facilitated improvements in care delivery and patient self-management metrics. While there were directional improvements in all metrics, there is opportunity and room for additional improvements. The team will continue to work on efforts to improve these metrics and optimize the use of 4R Care Sequence plans for patients with primary liver cancer. 1. Trosman, JCOOP 2021. 2. Liu, JCOOP 2022. [Table: see text]

Unloading sequence planning for autonomous robotic container-unloading system using A-star search algorithm

In autonomous unloading systems, one of the major challenges is planning the unloading sequence in cluttered logistics container environments. Proper unloading sequence planning is crucial to avoid damage to the packages caused by collision and collapse. At the same time, the planned sequence has to minimize the effort during the unloading process, such as energy consumption, time taken, and distance moved, to enhance efficiency. This paper presents a sequence planning method based on the A-star search algorithm applied to unloading randomly stacked unidentical boxes from logistics containers. To represent the general overlap relationships of boxes in a cluttered environment, we utilize an adjacency matrix structure and involve it in the state of the unloading sequence planning. Moreover, by adopting the A-star search algorithm, the method can minimize the cost of system movement for an efficient unloading sequence. As a result, our method can determine the unloading sequence while considering the relationships between packages. Simultaneously, it achieves better performance compared to the existing sequence planning method in terms of moving distance, both in structured and unstructured cases. Finally, we construct a robotic unloading system and present experimental results in the simulated container environment.

Automated workflow generation supporting the value stream design of reconfigurable robot assembly cells

The paper addresses the automated generation of value stream optimized assembly workflows for reconfigurable robot cells. The focus is on the generation of asynchronous concurrent action workflows from assembly task descriptions. The main motivation is to enable automated search for optimized solutions within the enormous design spaces of these systems which results from their structural and procedural configurability. The proposed approach conducts task assignment, task sequencing and logical task planning (AI Planning) for multiple agents. The approach combines existing methods from the distinct research fields of robot task sequencing and discrete task planning. The contribution of the research is twofold. Firstly, the paper proposes a generic domain meta model enabling automated workflow generation for multi-agent assembly tasks. Secondly, a workflow generation approach based on artificial intelligence planning combined with the game-theoretic concept of extensive form games is presented. The approach iteratively generates a cooperative strategy for acting agents which is subsequently optimized in terms of individual strategies. The obtained logical action plans are further transformed into parametric concurrent action schedules to support downstream design activities. Both the meta model and the workflow generation approach are applied in a case study and provide valuable information on achievable assembly speedup.

Generation of human–robot collaboration disassembly sequences for end-of-life lithium–ion batteries based on knowledge graph

Abstract The disassembly of end-of-life lithium–ion batteries (EOL-LIBs) is inherently complex, owing to their multi-state and multi-type characteristics. To mitigate these challenges, a human–robot collaboration disassembly (HRCD) model is developed. This model capitalizes on the cognitive abilities of humans combined with the advanced automation capabilities of robots, thereby substantially improving the disassembly process’s flexibility and efficiency. Consequently, this method has become the benchmark for disassembling EOL-LIBs, given its enhanced ability to manage intricate and adaptable disassembly tasks. Furthermore, effective disassembly sequence planning (DSP) for components is crucial for guiding the entire disassembly process. Therefore, this research proposes an approach for the generation of HRCD sequences for EOL-LIBs based on knowledge graph, providing assistance to individuals lacking relevant knowledge to complete disassembly tasks. Firstly, a well-defined disassembly process knowledge graph integrates structural information from CAD models and disassembly operating procedure. Based on the acquired information, DSP is conducted to generate a disassembly sequence knowledge graph (DSKG), which serves as a repository in graphical form. Subsequently, knowledge graph matching is employed to align nodes in the existing DSKG, thereby reusing node sequence knowledge and completing the sequence information for the target disassembly task. Finally, the proposed method is validated using retired power LIBs as a case study product.

Addressing a Collaborative Maintenance Planning Using Multiple Operators by a Multi-Objective Metaheuristic Algorithm

Selective maintenance has a significant impact on the sustainable management of maintenance operations. The collaboration of multiple maintenance teams/operators is helpful to achieve sustainability for selective maintenance sequence planning. For products with a large number of components, a single maintenance team/operator is inefficient due to a long completion time which is not acceptable for emergency planning. Providing specific and efficient maintenance sequence planning is critical to effectively handle different types of emergencies (e.g., wartime) while avoiding vague task assignments to multiple maintenance teams/operators. For scheduling many maintenance jobs while improving the efficiency and quality of maintenance operations, this study proposes a collaborative maintenance planning based on the concept of imperfect maintenance. In this regard, this study develops a multi-objective optimization model to optimize parallel maintenance sequences considering maintenance profit, maintenance cost, maintenance team, and resource limitations. We show the feasibility of the proposed multi-objective optimization model through a real case of maintenance practice for the components of an assistor device. For analyzing the complexity of the proposed maintenance sequence planning problem, this study introduces a new multi-objective metaheuristic algorithm which is an enhanced multi-objective gravitational search algorithm (EMOGSA) to find high-quality Pareto solutions for the proposed problem. Different multi-objective evaluation metrics are used to study the performance of the proposed algorithm. From the results, the proposed model and developed solution algorithm can help maintenance decision-makers to determine complex maintenance planning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper deals product with a maintenance and proposes gravitational search algorithm based on only maintenance task, which maintenance task. The goal of this paper is to analyze the maintenance problem from the perspective of collaboration of multiple maintenance teams/operators.

Information Entropy Evaluation Method for the Disassemblability of Smartphones

Smartphones are a vast category of consumer electronic products. Design For Disassembly (DFD) is a methodology that can effectively reduce the disassembly, recycling, and maintenance costs of smartphones. However, due to the diverse constraints posed by the connections among smartphone components, the variability of disassembling tools makes it challenging to objectively characterize the disassemblability of the smartphone. Therefore, information entropy is introduced to characterize the complex state of the system. Disassemblability can be expressed by calculating the operating time of the disassembling tool through information entropy. After multiplying by a constrained quantity factor, the Improved Disassembling Tool Entropy (IDTE) responds to changes in disassemblability as the structural level changes. According to the structure and recycling direction, the disassembling level of smartphones can be divided into module level, part level, and hybrid part-module level. Based on the Maynard Operational Sequencing Technique (MOST), the basic unit of operating time of the disassembling tool is calculated. For the hybrid selective Disassembly Sequence Planning (DSP) of the part and module levels, the Improved Double Genetic Algorithm (IDGA) model is established to compute the optimal disassembly sequence corresponding to each program level. This model calculates the IDTE corresponding to the optimal disassembly sequences at each disassembling level. The validity of IDTE was verified by a coupled comparison test between IDTE and theoretical disassembly time. Finally, an analysis of the disassemblability variations was conducted for two different models of smartphones based on their structure.

A novel reinforcement learning framework for disassembly sequence planning using Q-learning technique optimized using an enhanced simulated annealing algorithm

Abstract The increase in Electrical and Electronic Equipment (EEE) usage in various sectors has given rise to repair and maintenance units. Disassembly of parts requires proper planning, which is done by the Disassembly Sequence Planning (DSP) process. Since the manual disassembly process has various time and labor restrictions, it requires proper planning. Effective disassembly planning methods can encourage the reuse and recycling sector, resulting in reduction of raw-materials mining. An efficient DSP can lower the time and cost consumption. To address the challenges in DSP, this research introduces an innovative framework based on Q-Learning (QL) within the domain of Reinforcement Learning (RL). Furthermore, an Enhanced Simulated Annealing (ESA) algorithm is introduced to improve the exploration and exploitation balance in the proposed RL framework. The proposed framework is extensively evaluated against state-of-the-art frameworks and benchmark algorithms using a diverse set of eight products as test cases. The findings reveal that the proposed framework outperforms benchmark algorithms and state-of-the-art frameworks in terms of time consumption, memory consumption, and solution optimality. Specifically, for complex large products, the proposed technique achieves a remarkable minimum reduction of 60% in time consumption and 30% in memory usage compared to other state-of-the-art techniques. Additionally, qualitative analysis demonstrates that the proposed approach generates sequences with high fitness values, indicating more stable and less time-consuming disassembles. The utilization of this framework allows for the realization of various real-world disassembly applications, thereby making a significant contribution to sustainable practices in EEE industries.

VASCO: Volume and Surface Co-Decomposition for Hybrid Manufacturing

Additive and subtractive hybrid manufacturing (ASHM) involves the alternating use of additive and subtractive manufacturing techniques, which provides unique advantages for fabricating complex geometries with otherwise inaccessible surfaces. However, a significant challenge lies in ensuring tool accessibility during both fabrication procedures, as the object shape may change dramatically, and different parts of the shape are interdependent. In this study, we propose a computational framework to optimize the planning of additive and subtractive sequences while ensuring tool accessibility. Our goal is to minimize the switching between additive and subtractive processes to achieve efficient fabrication while maintaining product quality. We approach the problem by formulating it as a Volume-And-Surface-CO-decomposition (VASCO) problem. First, we slice volumes into slabs and build a dynamic-directed graph to encode manufacturing constraints, with each node representing a slab and direction reflecting operation order. We introduce a novel geometry property called hybrid-fabricability for a pair of additive and subtractive procedures. Then, we propose a beam-guided top-down block decomposition algorithm to solve the VASCO problem. We apply our solution to a 5-axis hybrid manufacturing platform and evaluate various 3D shapes. Finally, we assess the performance of our approach through both physical and simulated manufacturing evaluations.

Selective disassembly sequence planning under uncertainty using trapezoidal fuzzy numbers: A novel hybrid metaheuristic algorithm

Nowadays, recycling end-of-life (EoL) products has emerged as a vital approach to address resource scarcity. Within the recycling process, disassembly plays a pivotal role and has garnered substantial attention from researchers. Disassembly sequence planning (DSP) is a crucial method to enhance disassembly efficiency. Among the various DSP models, selective disassembly sequence planning (SDSP) has gained prominence as a means to save time and reduce costs. It empowers operators to locate specific components or materials in real time, thereby boosting efficiency and minimising resource wastage. However, a notable research gap exists in the domain of SDSP, particularly in uncertain environments. To bridge this gap and render SDSP solutions more practical for real-world disassembly operations, this study adopts trapezoidal fuzzy numbers to represent uncertain information within the disassembly process and formulates a comprehensive SDSP model. In response to the intricate challenges posed by this problem, we propose a hybrid approach termed nondominated sorting genetic algorithm-II with simulated large neighborhood search (NSGA–II–SLNS). This innovative algorithm leverages the strengths of the nondominated sorting genetic algorithm-II (NSGA-II), simulated annealing algorithm (SA), and large neighborhood search (LNS). Additionally, we introduce several novel search operators into NSGA–II–SLNS, including a crossover and mutation strategy based on chaotic mapping, as well as a local search operator founded on the SA criterion and LNS. To assess the effectiveness of the proposed algorithm and model, extensive numerical case studies are conducted in this research. The outcomes contribute to the advancement of rapid, nearly optimal SDSP strategies in the face of uncertainty and ambiguity in problem settings.

Semantic knowledge-driven A-GASeq: A dynamic graph learning approach for assembly sequence optimization

In the context of an increasingly automated and personalized manufacturing mode, efficient assembly sequence planning (ASP) has emerged as a critical factor for enhancing production efficiency, ensuring product quality, and satisfying diverse market demands. To address this need, our study first transforms the assembly topology and process into a weighted precedence graph, wherein parts represent nodes, and the assembly interconnections between parts constitute weighted edges. Then, we formulate the quantitative models of semantic knowledge, encompassing three facets: assembly direction changes, assembly stability, and part assembly interference, and thus constructs a heuristic function. We propose a novel dynamic graph learning algorithm, i.e., assembly-oriented graph attention sequence (A-GASeq), utilizing the heuristic information as edge weights of the assembly graph structure to incrementally direct the search towards optimal sequences. The performance of A-GASeq is first evaluated utilizing three key metrics: area under the receiver operation characteristic curve (AUC), precision score, and time consumption. The results reveal the superiority of our model over competing state-of-the-art graph learning models using a real-world dataset. Concurrently, we apply the algorithm to actual industrial products of diverse complexity, thereby demonstrating its broad utility across different complex products and its potential for addressing complex assembly sequence planning problems in the field of smart manufacturing.

Functional connectivity alterations in the frontoparietal network and sensorimotor network are associated with behavioral heterogeneity in blepharospasm.

Primary blepharospasm (BSP) is a clinically heterogeneous disease that manifests not only as spasmodic closure of the eyelids but also sometimes with apraxia of eyelid opening (AEO). This cross-sectional study aimed to investigate differences in the neural mechanisms of isolated BSP and BSP-associated AEO subtypes, which may reveal the pathophysiology underlying different phenotypes. A total of 29 patients manifested as isolated BSP, 17 patients manifested as BSP associated with AEO, and 28 healthy controls underwent resting-state functional near-infrared spectroscopy (fNIRS). We assessed functional connectivity (FC) between regions of interest (ROIs) in the fronto-parietal control network (PFCN) and sensorimotor network (SMN). We also examined the relationship between altered FC and behavioral data. In the FPCN, ROI- analyses showed decreased FC between the left premotor cortex and supramarginal gyrus in the BSP with AEO group compared to the isolated BSP group. In the SMN, both subgroups showed hypoconnectivity of the left premotor cortex with the right primary motor cortex, primary sensory cortex, and somatosensory association cortex. This hypoconnectivity was positively correlated with the total number of botulinum toxin A treatments, which suggests that long-term botulinum toxin A treatment may modulate motor sequence planning and coordination. These findings showed different connectivity alterations in neural networks associated with motor and cognitive control among different behavioral phenotypes of BSP. The identification of specific alterations in various networks that correspond to clinical heterogeneity may inform the identification of potential biomarkers for early diagnosis and personalized neuromodulation targets for treating different BSP subphenotypes.

Intervention to improve patient self-management and satisfaction in breast and lung cancers using the 4R Oncology model: Results by cancer, race and deprivation index.

326 Background: Patient self-management (pSM) is important for optimal cancer care, health equity and mitigation of clinician shortage (Howell 2021, Alfano 2022). 4R Oncology is an intervention fostering pSM and team-based care with a novel Care Sequence plan. 4R (Right Info / Care / Patient / Time) has been shown to improve pSM in breast cancer (Trosman 2021) but has not been studied for impact in other cancers or whether impact is affected by race or neighborhood deprivation index (NDI). 4R effect on patient satisfaction with care – important in care quality and reimbursement – is also unknown. We examined 4R impact on pSM and satisfaction in lung and breast cancers and whether it was influenced by race and NDI. Study took place at 3 centers of a large health system Oct 2020 - Dec 2021. Methods: Surveys of patient: 4R cohort who received 4R plans (N=192) and historical control who received care pre-4R (N=103). Cohorts included breast cancer stages 0-3 and all lung cancer stages. Results: Survey response: 53% (control), 42% (4R). 4R significantly improved 4 of 5 pSM metrics from control to 4R cohort in each cancer (table). Satisfaction with care improved in 3 of 5 metrics in lung and 2 metrics in breast cancer. pSM significantly improved in all metrics from control to 4R cohort for non-White patients, and in 2 of 5 metrics for White non-Hispanics. Satisfaction significantly increased in 3 metrics in both race groups. NDI did not affect 4R impact: both higher and lower NDI groups benefited similarly in all metrics from control to 4R cohorts (data not shown). Conclusions: 4R is effective in improving patient self-management and satisfaction with care across different cancers. Non-White patients derived more self-management benefit than White non-Hispanic, indicating that 4R may help address racial disparities. Deprivation index results suggest that 4R benefits both lower and higher socioeconomic status patients. Further model refinement should address remaining gaps along relevant metrics.[Table: see text]

A novel MILP model and an improved genetic algorithm for disassembly line balancing and sequence planning with partial destructive mode

Collaborative optimization of disassembly line balancing and disassembly sequence planning can effectively reduce ineffective operation time. Considering the uncertainty of the connection relationship between parts of end-of-life products, a partial destructive disassembly mode based on the disassembly feasibility is introduced. In this study, a mixed integer linear programming (MILP) model of disassembly line balancing and sequence planning (DLBSP) is constructed. The aim is to minimize the number of stations, smoothness index, and energy consumption, and maximize the disassembly profit simultaneously. To obtain high-quality disassembly schemes, a multi-objective improved genetic algorithm is proposed. An encoding and decoding strategy based on problem characteristics is designed to improve the quality of the initial solutions. The feasibility of crossover and mutation operations is ensured by introducing precedence constraints, and a single-point insertion neighborhood strategy is designed to improve the local search ability of the algorithm. Then, the effectiveness of the proposed model and algorithm is verified in a small-scale case, and the performance of the proposed algorithm is better than that of nine meta-heuristics through 22 test cases. Next, the proposed model and algorithm are applied to a real CRT TV disassembly case. It is shown that the new disassembly scheme can reduce two stations, improve smoothness by 99.83%, increase profits by 89.75%, and reduce energy consumption by 24.66% compared to the original scheme. Finally, a DLBSP optimization system is developed, which can reduce the application difficulty of the proposed model and algorithm.

Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm

Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has posed an ongoing challenge in contemporary research. This study centers on disassembly sequence planning (DSP) in the context of industrial equipment maintenance, with a primary aim to mitigate the adverse effects of uncertainty. To effectively address this challenge, we introduce a multi-objective DSP problem and utilize triangular fuzzy numbers from fuzzy logic to manage uncertainty throughout the disassembly process. Our objectives encompass minimizing disassembly time, reducing tool changes and directional reversals, and improving responsiveness to emergency maintenance needs. Recognizing the complexities of this problem, we present an innovative multi-objective enhanced water wave optimization (EWWO) algorithm, integrating propagation, refraction, and breaking wave operators alongside novel local search strategies. Through rigorous validation with real-world industrial cases, we not only demonstrate the algorithm’s potential in solving disassembly maintenance challenges but also underscore its exceptional performance in producing high-quality and efficient solutions. In comparison to other algorithms, EWWO provides significant advantages in multi-objective evaluation metrics, including Hypervolume (HV), Spread, and CPU time. Moreover, the application of triangular fuzzy numbers offers a comprehensive evaluation of solutions, empowering decision makers to make informed choices in diverse scenarios. Our findings lead to the conclusion that this research provides substantial support for addressing uncertainty in the field of industrial equipment maintenance, with the potential to significantly enhance the efficiency and quality of disassembly maintenance processes.

Research on dynamic decision-making for product assembly sequence based on Connector-Linked Model and deep reinforcement learning

Assembly sequence planning (ASP) is a critical challenge in the manufacturing industry, involving the arrangement of components to maximize production efficiency and reduce costs. Solving the ASP problem is not only hindered by its NP-hard nature, making it difficult to obtain optimal solutions, but also requires making rapid decisions in the face of dynamic changes in resource availability during the actual assembly process. This paper proposes a Deep reinforcement learning (DRL) approach to tackle the ASP problem. DRL learns optimal assembly strategies through interactions between an intelligent agent and its environment, using a reward function as a long-term objective to guide decision-making, ultimately enabling the acquisition of near-optimal solutions. DRL exhibits high scalability and reusability, leveraging previously acquired knowledge to adapt to dynamic changes in the assembly environment. First, a connector-based graph structure is introduced to decompose assembly products into a series of connector-based assembly elements. These elements are then transformed into a matrix format for use as input to the DRL method. Second, an encoder-decoder architecture tailored to the ASP problem is developed. This architecture utilizes mask mechanism to ensure that the generated assembly sequences adhere to assembly priority constraints while adapting to dynamic changes in resource availability during assembly. Finally, policy gradient methods are employed to train the DRL model, enabling it to generate assembly sequences with lower costs and higher efficiency. Experimental results demonstrate the effectiveness of the proposed approach. Furthermore, compared to heuristic algorithms, DRL exhibits faster decision-making capabilities and better adaptability to dynamic resource changes during the assembly process.

Assembly Sequence and Assembly Path Planning of Robot Automation Products Based on Discrete Particle Swarm Optimization

The product assembly process in industrial production is a time-consuming and labour-intensive link, so the use of robots for intelligent assembly can achieve high-efficiency operations to a large extent. During the intelligent assembly process, robots need to carry out three key processes: product information modelling, assembly route planning and assembly sequence planning. The experiment examines assembly sequence planning and assembly route planning. In light of the deficiencies of the current assembly sequence planning technique, we propose a new approach, which is difficult to calculate, the experiment proposes to introduce the DPSO algorithm into the assembly sequence planning model. Meanwhile, to prevent the model from entering local optimal, the experiment adds disturbance operation on the basis of DPSO. That is, a part is randomly selected to be inserted into another position in the disassembly sequence and at the same time, the rest of the parts are moved to form a new disassembly sequence to update the particle's location. In addition to the assembly sequence planning, the experiment aims to improve the planning path obtained by the existing RRT algorithm, which is not smooth enough, that is, to simplify the installation path by reducing the change of direction. The simulation experiment findings demonstrate that the addition of the disturbance to DPSO algorithm successfully prevents the model from entering a local optimum state. The moving distance of the parts before path optimization is 52.0012mm and the number of moving direction changes is 8 times, while the moving distance of the parts after path optimization is reduced to 42.8094mm and the moving direction is changed only 2 times.

Assembly sequence planning and evaluating for deep oil and gas corer based on graph theory

Fidelity corers play a crucial role in exploring and evaluating deep oil and gas resources. Due to the complexity of the corer parts, an improper assembly sequence can lead to inefficiencies and reduced reliability, impacting the success of obtaining oil and gas samples. This paper presents a novel assembly sequence planning method based on graph theory and multi-objective evaluation. The graph-theoretic cut set algorithm is utilized to generate all possible assembly sequences based on a directed graph which represents corer component assembly information. To address deep oil and gas corer requirements, the interpretative structural modeling method (ISM) is employed to construct a comprehensive stability-reliability-accuracy assurance-easiness-cost economy (SRAEC) indicator system. Through indicators weight assignment and multi-objective evaluation, the optimal solution of assembly sequence is identified from a global perspective. The proposed method is applied to a deep-sea gas hydrate corer, resulting in the identification of the most suitable assembly sequence for both field drilling and laboratory testing. This optimized sequence improves the assembly reliability and efficiency, contributing to the overall success and efficiency of the corer in obtaining oil and gas samples. The study provides valuable insights for designing and practically applying deep oil and gas coring tools.