Sequence Of Operations Research Articles

Estimated Tardiness-Based Reinforcement Learning Solution to Repeatable Job-Shop Scheduling Problems

Intelligent manufacturing promises to revolutionize production processes, and it is expected to enhance efficiency and productivity. In this paper, we study the job-shop scheduling problem, which is a key enabler to the realization of intelligent manufacturing. Compared to previous studies, the proposed solution tackles generalized configurations by allowing the repetition of jobs and by assuming the limited capacity of machines and a sequence of operations constituting a job. The proposed solution is fully controlled by the trained reinforcement learning agent that finds the optimal match between the job and machine to schedule. In addition, by introducing the expected tardiness (ETD) metric, the agent can enhance the scheduling performance while effectively handling dynamic action space changes with the action masking technique. To effectively adapt to the particular manufacturing site, we propose a novel training approach that utilizes the average order arrival distribution learned from the historical logs. Such data-driven optimization can train an agent that effectively captures the general and site-specific characteristics of job arrivals, leading to improved generalization performance and a finely tuned model, respectively. To validate the proposed approach, we implement a custom environment with which extensive performance evaluation and comparison are carried out. The evaluation results show that the proposed approach can outperform the conventional heuristic priority dispatching rules under the desired performance criteria such as total tardiness and the total manufacturing cost. To be specific, in terms of the total cost metric, the proposed approach outperforms the considered approaches by 31.69% on average. In addition, the use of ETD can enhance the performance of the conventional approaches by 27.74% on average.

CROSSOVER AND MUTATION OPERATORS IN STOCHASTIC ALGORITHMS

The relevance of illuminating contemporary stochastic algorithms is highlighted, with the past two decades witnessing a rapid development in stochastic algorithms, attributed to increased research capabilities and growing data volumes. These algorithms prove effective in solving complex optimization problems, garnering attention from the global scientific community and practitioners worldwide. An exploration of the role and an overview of key crossover and mutation operators in stochastic algorithms represent a pertinent scientific and practical endeavor, fostering deeper understanding and popularization of this field. A genome category analysis is conducted, accompanied by a detailed review of primary gene encoding methods for practical application. Through a specific example of a genome corresponding to the optimal design of a two-stage helical gearbox, typical and adapted crossover and mutation operators are examined for efficient solution search. Each operator is provided with a textual description and a graphically illustrated representation, enhancing clarity, quality, and expediency in understanding the essence and operational sequences of the operator. The role and significance of crossover and mutation operators in stochastic algorithms are discussed, emphasizing that crossover operators facilitate the combination of beneficial genetic traits, enhancing offspring adaptability. Balanced utilization of these operators alongside other algorithmic stages, such as mutation and selection, is crucial for achieving an optimal balance between algorithm exploitation and search intensification. The importance and functionality of mutation operators in optimization stochastic algorithms are highlighted, indicating that mutation enables the avoidance of algorithmic stagnation in local extrema, preserving genetic diversity and stimulating the search for new optimal solutions. The particular significance of mutation usage in conditions of complex problem structures or large search spaces is noted. Thus, crossover and mutation operators are deemed key elements for enhancing the effectiveness of optimal solution search.

Research on efficient numerical simulation method for integration fracking with production in shale oil reservoir with multi-source data

AbstractHorizontal well hydraulic fracturing technology has significantly enhanced the productivity of shale reservoirs. However, our understanding of the expansion patterns within the complex fracture network and fluid seepage mechanisms under field conditions remains inadequate. Here, this work develops a dynamic geomechanical (DG) model to simulate the complete sequence of operations in hydraulic fracturing. This study utilizes a construction procedure that closely mirrors field practices to establish the DG model. Furthermore, the numerical simulation results of the DG model are calibrated with field data. This work adopts a numerical simulation method that integrates unsteady seepage model for multi-stage fractured horizontal wells with the dilation-recompaction model to develop the DG model. It systematically constructs the geological model of the shale reservoir by utilizing segmented logging data and by segmenting production data. The time series evolution system is developed through an iterative process involving discrete time steps. Results show that the DG model can perform history matching on a multi-stage basis, enabling comprehensive and detailed analysis of the entire reservoir. This process effectively replicates the distribution relationship between each reconstruction zone and the overall productivity. Furthermore, the DG model is capable of accurately simulating the dynamic process of injected high-pressure fluids into the reservoir to fracture the rock and the dynamic evolution law of reservoir properties. Hydraulic fracturing creates a fracture zone that centers on the well’s border and spreads outward radially. The injection volume and failure pressure are significantly correlated with the scale of shale reservoir reconstruction. Following the injection of 790.5 m³ of fracturing fluid in the first stage, the fracture half-length can reach around 148 m, essentially fulfilling the design specifications. Permeability can reach up to 86 mD at this moment, and it can even be maintained at the level of 46 mD during production. In conclusion, the DG model broadens the focus of study on the development of shale reservoirs and lays the groundwork for improving productivity and optimizing hydraulic fracturing design.

Efficient tensor networks for control-enhanced quantum metrology

Optimized quantum control can enhance the performance and noise resilience of quantum metrology. However, the optimization quickly becomes intractable when multiple control operations are applied sequentially. In this work, we propose efficient tensor network algorithms for optimizing strategies of quantum metrology enhanced by a long sequence of control operations. Our approach covers a general and practical scenario where the experimenter applies N−1 interleaved control operations between N queries of the channel to estimate and uses no or bounded ancilla. Tailored to different experimental capabilities, these control operations can be generic quantum channels or variational unitary gates. Numerical experiments show that our algorithm has a good performance in optimizing the metrological strategy for as many as N=100 queries. In particular, our algorithm identifies a strategy that can outperform the state-of-the-art strategy when N is finite but large.

Amortized Analysis via Coalgebra

Amortized analysis is a cost analysis technique for data structures in which cost is studied in aggregate: rather than considering the maximum cost of a single operation, one bounds the total cost encountered throughout a session. Traditionally, amortized analysis has been phrased inductively, quantifying over finite sequences of operations. Connecting to prior work on coalgebraic semantics for data structures, we develop the alternative perspective that amortized analysis is naturally viewed coalgebraically in a category of cost algebras, where a morphism of coalgebras serves as a first-class generalization of potential function suitable for integrating cost and behavior. Using this simple definition, we consider amortization of other sample effects, non-commutative printing and randomization. To support imprecise amortized upper bounds, we adapt our discussion to the bicategorical setting, where a potential function is a colax morphism of coalgebras. We support algebraic and coalgebraic operations simultaneously by using coalgebras for an endoprofunctor instead of an endofunctor, combining potential using a monoidal structure on the underlying category. Finally, we compose amortization arguments in the indexed category of coalgebras to implement one amortized data structure in terms of others.

Germanic Rampart or Roman Encampment?—New Geoarchaeological Evidence at the Roman Conflict Site at Kalkriese (NW‐Germany)

ABSTRACTKalkriese, near Osnabrueck (NW Germany), is considered the location of the ‘Battle of Varus’, where a coalition of Germanic tribes, under the leadership of Arminius, defeated three Legions under the command of Varus in 9 ad. Roman coinage and remains of military equipment prove that the Oberesch site at Kalkriese saw military operations between Germanic tribes and the Roman legions during Early Imperial times, but the sequence and magnitude of the military operations still remain unclear. In this study, we present for the first time absolute dates from the Oberesch site to decipher the general sequence of the Holocene landscape development at Kalkriese, identify the antique surface, and evaluate the ‘Germanic Rampart Theory’ and the ‘Roman Encampment Theory’. The geoprofile encompasses the entire stratigraphic sequence from the Pleistocene base, indicating intensive agricultural use of the area since the Early Neolithic. A fossil topsoil of late Pre‐Roman Iron Age to Roman Imperial Age was identified, which probably represents the antique surface of the Roman conflict site. Our results do not support either the ‘Germanic Rampart Theory’ or the ‘Roman Encampment Theory’, as both linear structures seem to be of High Middle Age origin.

Mathematical modeling and hybrid evolutionary algorithm to schedule flexible job shop with discrete operation sequence flexibility

Mathematical modeling and hybrid evolutionary algorithm to schedule flexible job shop with discrete operation sequence flexibility

Operational considerations for approximating molecular assembly by Fourier transform mass spectrometry

Some of the most common life detection techniques for planetary exploration focus on organic molecule characterization, but life on other planets may not chemically resemble that found on Earth. Therefore, an agnostic detection system of signs of life (biosignatures) is essential. Assembly Theory (AT) is a conceptual tool for understanding evolution and object formation that has been useful in developing an approach to quantify molecular complexity via the Molecular Assembly index, which when combined with abundance, allows the total assembly number of a sample to be calculated. Because AT makes no assumptions about the chemistry of life, it is an agnostic tool that identifies molecular structures that are probabilistically more likely to have arisen via selection and therefore biological processes. AT uses graph theory to quantify molecular complexity by finding the shortest sequence of joining operations (e.g., chemical bonds) required to build a compound from a set of starting materials allowing recursive reuse of units or fragments. For molecules, this number of steps is the MA value. We explore the use of Fourier transform (i.e., Orbitrap) mass spectrometry for approximating MA by quantifying how a molecule breaks apart into fragments. We analyze amino acid and nucleoside standards individually and as mixtures, as well as amino acids from naturally occurring biological and meteoritic sources. Aside from sample type, we evaluate the effect of analyte concentration and fragmentation energies on the generated MA value. Additionally, an older Orbitrap model similar to flight prototype instrumentation, was tested. The raw mass spectrometry data was compared with two different MA processing algorithms - one that uses the parent molecule spectrum and molecular weight (recursive) and one that does not (non-recursive). Concentration, fragmentation energy, and sample type all influence the raw mass spectra. However, the recursive algorithm reports MA estimates that are more consistent across sample types, concentrations, and fragmentation energies. We discuss instrument requirements for approximating MA that can be applied to future flight and sample return missions.

ExaGRyPE: Numerical general relativity solvers based upon the hyperbolic PDEs solver engine ExaHyPE

ExaGRyPE describes a suite of solvers and solver ingredients for numerical relativity that are based upon ExaHyPE 2, the second generation of our Exascale Hyperbolic PDE Engine. Numerical relativity simulations are crucial in resolving astrophysical phenomena in strong gravitational fields and are fundamental in analyzing and understanding gravitational wave emissions. The presented generation of ExaGRyPE solves the Einstein field equations in the standard CCZ4 formulation under a 3+1 foliation and focuses on black hole space-times. It employs a block-structured Cartesian grid carrying a higher-order Finite Difference scheme with full support of adaptive mesh refinement (AMR), it facilitates massive parallelism combining message passing, domain decomposition and task parallelism, and it supports the injection of particles into the grid as static data probes or as moving tracers. We introduce the ExaGRyPE-specific building blocks within ExaHyPE 2, and discuss its software architecture and compute-n-feel.For this, we formalize the creation of any specific astrophysical simulation with ExaGRyPE as a sequence of lowering operations, where a few abstract logical tasks are successively broken down into finer and finer tasks until we obtain an abstraction level which can directly be mapped onto a C++ executable. The overall program logic is fully specified via a domain-specific Python interface, we automatically map this logic onto a more detailed set of numerical tasks, subsequently lower this representation onto technical tasks that the underlying ExaHyPE engine uses to parallelize the application, before eventually the technical tasks in turn are mapped onto small task graphs including the actual astrophysical PDE term evaluations, initial conditions, boundary conditions, and so forth. These can be injected manually by the user, or users might instruct the solver on the most abstract user interface level to use out-of-the-box ExaGRyPE implementations. We end up with a rigorous separation of concerns which shields ExaGRyPE users from technical details and hence simplifies the development of novel physical models. We present the simulations and data for the gauge wave, static single black holes and rotating binary black hole systems, demonstrating that the code base is mature and usable. However, we also uncover domain-specific numerical challenges that need further study by the community in future work.

The Seasonality of Labour in Ceramic Production and Agriculture in the Ancient Greek Rural World

This article explores the seasonality of labour in ceramic production and its correlation with agricultural tasks, focusing on a Hellenistic kiln site in Sant’Angelo Vecchio, southern Italy, in the hinterland of Metaponto. Through a comprehensive examination of archaeological data and the outcomes of an experimental archaeology project, the study estimates the labour required for various stages of the pottery-making operational sequence. The findings reveal that small-scale rural workshops mainly produced limited quantities of items primarily for household and agrarian use. Only two or three individuals, working part-time, were needed to manage the various tasks involved, such as raw material collection, transportation, clay preparation, vessel shaping and kiln firing. The findings also demonstrate that the seasonal nature of their pottery work could be fitted within the seasonal rhythms of agricultural activities. This research highlights the interconnectedness of all productive activities in the region; sites and tasks are intricately linked, fostering a flexible and seasonal labour system in both ancient and modern Mediterranean societies.

Analysis and optimization of cesium dynamics for the HUST negative ion source

The high-density beam in the NBI negative ion source relies on surface production on the converter with low work function, which is facilitated by cesium injection. Maintaining the appropriate cesium conditions during the source operation sequence is essential for achieving optimal cesium coverage on the converter. To better understand cesium cycle in the source and to enhance beam production performance during operation sequences, analysis of cesium dynamics and optimization of cesium operation have been carried out. The analysis model is established based on the Monte-Carlo method with cesium sources, cesium transport and surface process during the operation sequence considered. Cesium distribution and converter surface coverage can be obtained from the model using input parameters from the HUST negative ion source. For the operation in the HUST negative ion source, the influence of temperature settings and time sequence is discussed, with suggested parameters for cesium injection proposed to achieve appropriate cesium coverage on converter surface through operation sequences.

A Fuzzer for Detecting Use-After-Free Vulnerabilities

Fuzzing is an extensively used automated vulnerability detection technique. Most existing fuzzers are guided by edge coverage, which makes them less effective in detecting specific vulnerabilities, especially use-after-free (UAF) vulnerabilities. This is because the triggering of a UAF vulnerability must not only cover a specific memory operation but also satisfy a specific sequence of operations. In this paper, we propose UAF-Fuzzer for detecting UAFs, which consists of static analysis and fuzzing stages. In the static analysis stage, UAF-Fuzzer first uses target identification to determine the basic blocks that may cause UAFs as the target basic blocks; subsequently, it then instruments these target basic blocks. Subsequently, we propose a memory operation evaluation method to assess the complexity of memory operations. In the fuzzing stage, UAF-Fuzzer assigns energy to seeds using a memory evaluation operation and employs a novel seed selection algorithm to prioritize the execution of test cases that are likely to trigger UAF vulnerabilities. We designed and implemented a UAF-Fuzzer to improve the detection of UAFs and compared it with AFL, AFLFast, FairFuzz, MOPT, EcoFuzz, and TortoiseFuzz in terms of UAF vulnerability detection, crash detection, and path discovery. The results showed that UAF-Fuzzer is more effective in terms of detecting UAF vulnerabilities. We have also discovered three UAF vulnerabilities, submitted them to the software maintainer for fixing, and obtained CVE IDs.

Heterogeneity in electric taxi charging behavior: Association with travel service characteristics

A comprehensive understanding of charging behaviors among electric vehicle users is crucial for advancing green transportation and deploying effective charging infrastructure. This study conducted large-scale empirical research using data from electric taxi fleets in Shenzhen to explore the heterogeneity of charging behaviors among taxi drivers. The study hypothesized that distinct charging patterns exist within the electric taxi fleet, impacting fleet-wide fluctuations and repetitions of charging and service activities. Employing a covariate-enhanced latent profile analysis model, we examined unique charging patterns within the fleet and investigated relationships between taxi service attributes and charging behavior heterogeneity. Fleet-wide diurnal fluctuations, daily repetitions, and subgroup-specific charging patterns were identified. At the micro level, operational activity sequence similarity and between-group diversity were assessed. The findings offer valuable insights for policymakers and stakeholders involved in promoting green transportation and optimizing charging infrastructure.

An Algorithm for Creating a Synaptic Cleft Digital Phantom Suitable for Further Numerical Modeling

One of the most significant applications of mathematical numerical methods in biology is the theoretical description of the convectional reaction–diffusion of chemical compounds. Initial biological objects must be appropriately mimicked by digital domains that are suitable for further use in computational modeling. In the present study, an algorithm for the creation of a digital phantom describing a local part of nervous tissue—namely, a synaptic contact—is established. All essential elements of the synapse are determined using a set of consistent Boolean operations within the COMSOL Multiphysics software 6.1. The formalization of the algorithm involves a sequence of procedures and logical operations applied to a combination of 3D Voronoi diagrams, an experimentally defined inner synapse area, and a simple ellipsoid under different sets of biological parameters. The obtained digital phantom is universal and may be applied to different types of neuronal synapses. The clear separation of the designed domains reveals that the boundary’s conditions and internal flux dysconnectivity functions can be set up explicitly. Digital domains corresponding to the parts of a synapse are appropriate for further application of the derived numeric meshes, with various capacities of the included elements. Thus, the obtained digital phantom can be effectively used for further modeling of the convectional reaction–diffusion of chemical compounds in nervous tissue.

Evaluating Surgical Accuracy in Bimaxillary Surgery: A Systematic Review and Meta-Analysis of Mandible-First versus Maxilla-First Approaches.

Mandible-first surgery (MdFS) has gained attention as an alternative to the traditional maxilla-first surgery (MxFS) in bimaxillary procedures. Given the distinct sequence of operations between these approaches, evaluating the clinical advantages of MdFS compared to MxFS is crucial for optimizing surgical decision-making. This systematic review and meta-analysis examine intraoperative achievability and postoperative stability between these two surgical approaches. A thorough literature search was performed using PubMed, Embase, Web of Science, and MEDLINE, covering articles published from 2013 to 2023. Studies included were retrospective, prospective, and randomized trials that compared the accuracy and/or stability of MdFS with MxFS. The primary endpoint for the meta-analysis was the standardized mean difference in surgical accuracy for translational movements, with a secondary focus on rotational accuracy. A total of 11 studies encompassing 712 patients met the inclusion criteria. The analysis suggested that MdFS might reduce accuracy in the sagittal dimension (CI, 0.05 to 0.74) but offered greater achievability in the vertical direction (CI, - 0.47 to - 0.07). Additionally, MdFS was associated with a relatively posterior (CI, - 1.18 to - 0.60) and inferior (CI, - 0.64 to - 0.07) positioning of the maxillomandibular complex. Despite certain limitations, our findings indicate that MdFS can achieve clinical outcomes similar to MxFS in terms of both accuracy and stability. However, further researches with larger sample sizes and more rigorous study designs are necessary to validate these conclusions. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of contents or the online Instructions to Authors www.springer.com/00266 .

Execution of revised BMIM similarity coefficient for part family formation in reconfigurable manufacturing system

The reconfigurable manufacturing system (RMS) is an advanced manufacturing strategy that enables precise adjustment of functionality and capacity to meet fluctuating demands economically. RMS focuses on part families, allowing configurations to be adapted for new part requirements. Optimizing flow line design to produce various parts involves minimizing reconfigurations and associated costs by enhancing operation sequence similarity. This article proposes a novel sequence optimization using the Longest Common Subsequence (LCS) method to reduce bypassing moves and machine idle times. The study introduces a similarity coefficient derived from LCS and employs average linkage hierarchical clustering to categorize parts in a case study. Unlike traditional methods, this approach considers material movements both before the initial machine and after the final processing station, addressing gaps in bypassing move calculations. The impact of different weighting scenarios for Type-II moves (ω) and machine idleness (β) on clustering was examined. For example, with Type-II move weights (ω) of {1.0, 0.6, 0.3, 0.0} and equal weightings for bypassing moves (α) and machine idleness (β) set at 0.5, a threshold value of 0.3 results in eight clusters, such as Cluster 1 {1, 11, 10, 12} and Cluster 3 {3, 5, 6, 4, 15, 9, 13, 14, 7, 8}. Lower threshold values lead to fewer clusters with larger sizes, indicating a more consolidated part family grouping. Various Type-II move weights and material handling scenarios demonstrate how different weighting configurations affect clustering and part family sizes. This approach enhances RMS efficiency by integrating comprehensive material handling considerations and optimizing clustering based on operational similarities.

METHOD OF RECOGNITION OF FPV-UAV RADIO SIGNALS FORMED ACCORDING TO CROSSFIRE AND EXPRESSLRS STANDARDS

Unmanned aerial vehicles (UAVs) with the First Person View (FPV) system are the most common type of attack UAVs used at the tactical level by the armed forces of the russian federation. Timely detection of facts of their using by the enemy and countering them are important tasks solved by units of the Ukrainian Defense Forces. An effective countermeasure way for FPV-UAVs is to create interference in their radio control channels frequency bands. To increase the radio suppression range, it is necessary to use interference matched in frequency and structure, the necessary condition for the formation of which is the presence of a priori information about the structure of the radio signal. Analysis of the available information on the construction of enemy FPV-UAVs shows that their radio control and telemetry channels are usually combined, have time division multiple access, the signals are formed according to CrossFire and ExpressLRS standards and have fixed sets of parameter values that can be used as features in recognition. The proposed FPV-UAV radio signal recognition technique is based on decision tree and minimum metric methods and uses a priori information on frequency, time and modulation parameters of radio signals. The combination of the two decision-making methods made it possible to reduce the computational complexity by eliminating operations for determining the parameters of radio signals that do not correspond to the decisions taken at the previous stages, and provides the possibility of adding new time parameters without changing the developed recognition technique. The method consists of a sequence of operations for estimation radio signal parameters, comparing them with known values, making decisions about the type of standard and command- telemetry radio line operation mode. Only one fragment with a duration of one frequency element is sufficient to recognize the FPV-UAV radio signal, which reduces the technical requirements for detection means in which the method can be used.

Two-Stage Optimization Model Based on Neo4j-Dueling Deep Q Network

To alleviate the power flow congestion in active distribution networks (ADNs), this paper proposes a two-stage load transfer optimization model based on Neo4j-Dueling DQN. First, the Neo4j graph model was established as the training environment for Dueling DQN. Meanwhile, the power supply paths from the congestion point to the power source point were obtained using the Cypher language built into Neo4j, forming a load transfer space that served as the action space. Secondly, based on various constraints in the load transfer process, a reward and penalty function was formulated to establish the Dueling DQN training model. Finally, according to the ε−greedy action selection strategy, actions were selected from the action space and interacted with the Neo4j environment, resulting in the optimal load transfer operation sequence. In this paper, Python was used as the programming language, TensorFlow open-source software library was used to form a deep reinforcement network, and Py2neo toolkit was used to complete the linkage between the python platform and Neo4j. We conducted experiments on a real 79-node system, using three power flow congestion scenarios for validation. Under the three power flow congestion scenarios, the time required to obtain the results was 2.87 s, 4.37 s and 3.45 s, respectively. For scenario 1 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by about 56.0%, 76.0% and 55.7%, respectively. For scenario 2 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 41.7%, 72.9% and 56.7%, respectively. For scenario 3 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 13.6%, 47.1% and 37.7%, respectively. The experimental results show that the trained model can quickly and accurately derive the optimal load transfer operation sequence under different power flow congestion conditions, thereby validating the effectiveness of the proposed model.

VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart Contracts

Since funds or tokens in smart contracts are maintained through specific state variables, contract audit, an effective means for security assurance, particularly focuses on these variables and their related operations. However, the absence of publicly accessible source code for numerous contracts, with only bytecode exposed, hinders audit efforts. Recovering variables and their types from Solidity bytecode is thus a critical task in smart contract analysis and audit, yet this is a challenging task because the bytecode loses variable and type information, only with low-level data operated by stack manipulations and untyped memory/storage accesses. The state-of-the-art smart contract decompilers miss identifying many variables and incorrectly infer the types for many identified variables. To this end, we propose VarLifter, a lifter dedicated to the precise and efficient recovery of typed variables. VarLifter interprets every read or written field of a data region as at least one potential variable, and after discarding falsely identified variables, it progressively refines the variable types based on the variable behaviors in the form of operation sequences. We evaluate VarLifter on 34,832 real-world Solidity smart contracts. VarLifter attains a precision of 97.48% and a recall of 91.84% for typed variable recovery. Moreover, VarLifter finishes analyzing 77% of smart contracts in around 10 seconds per contract. If VarLifter is used to replace the variable recovery modules of the two state-of-the-art Solidity bytecode decompilers, 52.4%, and 74.6% more typed variables will be correctly recovered, respectively. The applications of VarLifter to contract decompilation, contract audit, and contract bytecode fuzzing illustrate that the recovered variable information improves many contract analysis tasks.

An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators

Automatic assessment of the operation ability of operators based on computers is an essential approach for improving the effectiveness of equipment operation training and enhancing equipment safety. Present methods primarily focus on the operation results but pay less attention to the operation procedure. One reason is that there is a lack of a model that has the ability to describe all probable paths to accomplish the same task. Therefore, an operations chain model is put forward for the first time to describe the standard operation procedure and relationships among operations based on the decomposition of operational tasks and the relationships among the various operations required to fulfill the task. A specific operation task corresponds to an operations chain, which will form one or multiple standard operation sequences that will allow trainees to complete the same task through different paths. The Needleman–Wunsch sequence alignment algorithm is introduced to match the trainees’ operation sequence with all standard sequences. The maximum alignment result is the score of the trainees’ operations. An example shows that the operations chain model can accurately describe the complex structure of the standard operating procedures. The Needleman–Wunsch sequence alignment algorithm can objectively evaluate the trainee’s operation capabilities. The combination of the operations chain model and sequence alignment algorithm can form a complete operation procedure assessment method that is friendlier to trainees and has more objective evaluation results. The method will help to improve the effectiveness of the competency assessment of equipment operators.