Sequence Of Operations Research Articles

Hydro-mechanical behaviour of reinforced concrete linings in hydraulic tunnels: transient

The deregulation of the power market and the growing reliance on electricity generation from variable renewable energy sources have significantly altered the operational dynamics of hydropower plants. The shift from hydropower systems designed to function as base load plants to Pumped Storage Schemes (PSPs) characterized by more frequent load changes and start/stop sequences of operations significantly increased the relevance of transient conditions with respect to static ones. While existing research has predominantly focused on the effects of water pressure on tunnel linings under steady-state conditions, little to no attention has been given to transient conditions. This paper addresses this gap in the current literature by presenting a novel model for evaluating the behavior of reinforced concrete linings in hydraulic tunnels during transient conditions. This approach allows for a comprehensive understanding of the intricate interaction between fluid flow, tunnel structure, and rock mass. The insights gained have the potential to significantly advance the assessment and safeguarding of the structural integrity of hydraulic systems operating in PSPs.

A Comparative Analysis of Analytical Validation Approaches for Quality Assurance: Exploring Holistic Strategies in the Validation of Quantitative Methods-A Case Study of Hesperidin.

Analytical validation is a sequence of operations aiming to evaluate the accuracy, reliability, and cost of analytical results for making informed decisions in method selection and meeting the requirements of regulatory institutions. This study aims to perform an analytical validation by comparing three different approaches: the accuracy profile, the uncertainty profile, and the conventional validation to assess the capability of each method in confirming the robustness of the results. The accuracy profile offers a comprehensive assessment of analytical performance and integrates systematic and random errors to determine if future results will satisfy the predefined acceptance limits. Meanwhile, the uncertainty profile, which is complementary and innovative, allows the uncertainty estimation from validation data. These approaches were developed after conventional validation that relies on statistical methodologies based on separate evaluations of method criteria to provide a comparative framework for evaluating new methods. This comparison will give recommendations for best practices related to analytical validation. The uncertainty profile is a graphical decision-making tool for determining full validation by integrating analytical validation and the estimation of measurement uncertainty, evaluating two statistical methods: β-expectation tolerance intervals and β-content, γ-confidence tolerance intervals, using a formula introduced by Saffaj δ Ihssane, predicting that 95% of future results will fall within the acceptance limits of ± 5%, revealing that the tolerance intervals for β-expectation are smaller than β-content, γ-confidence. The total error approaches offer robust recommendations for optimal methods for routine application. This study highlights the critical need for appropriate analytical validation and the challenges arising from the absence of clear guidelines for that purpose. Different approaches emphasize the significant impact of the choice of an adequate method, which remains pivotal for providing accurate results under real-world scenarios. Concrete examples and simulations illustrate the viewpoints associated with different approaches to making decisions.

Optimización de Procesos mediante Value Stream Mapping en Empresas Dedicadas a la Crianza y Producción de Pollos de Engorde

This case study shows how a company in the poultry sector manages to optimize one of its main processes through the application of lean manufacturing methodologies. The guiding research focused specifically on the chicken management process of one of the company's existing progenies, evaluating critical factors that will influence the effectiveness of the application of a key tool among lean methodologies: the identification and implementation of value stream maps. Broadly speaking, the main premise seeks to direct the reader on how, under a focus on the system constraints, the company adequately adjusted its VSM, dissected its process and equitably and efficiently distributed its poultry houses to significantly reduce the emerging times between its different operations or process steps and optimize the flow in turn in these. This document is of a quantitative, qualitative and comparative analytical nature, from a descriptive methodological perspective of a case study type. It describes the technical problem of the oppressive stagnation in the production lines in fattening farms, relates the theoretical component together with the operation sequence discussed during the field work, establishes the object of the research and makes a comparative study of the continuous process of the target company versus its competitors. Among the contributions to the literature, it synthesizes the characteristic way of life of breeding farms in Colombia, emphasizing mainly the management and application of work scheduling to their productions through operational cycles that may well lead them to live two stages of their development with different scenarios.

Simulating Chemistry with Fermionic Optical Superlattices

We show that quantum-number-preserving ansatzes for variational optimization in quantum chemistry find an elegant mapping to ultracold fermions in optical superlattices. Using native Hubbard dynamics, trial ground states of molecular Hamiltonians can be prepared and their molecular energies measured in the lattice. The scheme requires local control over interactions and chemical potentials and global control over tunneling dynamics, but foregoes the need for optical tweezers, shuttling operations, or long-range interactions. We describe a complete compilation pipeline from the molecular Hamiltonian to the sequence of lattice operations, thus providing a concrete link between quantum simulation and chemistry. Our work enables the application of recent quantum algorithmic techniques, such as double factorization and quantum tailored coupled cluster, to present-day fermionic optical lattice systems with significant improvements in the required number of experimental repetitions. We provide detailed quantum resource estimates for small nontrivial hardware experiments. Published by the American Physical Society 2025

Bi‐objective minimization of energy consumption and cycle time for the robotic assembly line balancing problem: pseudo‐polynomial case and reduced search space metaheuristic

AbstractEnergy consumption and cycle time are two contradictory optimization objectives for the robotic assembly line balancing problem (RALBP). Indeed, minimizing the cycle time leads to choosing the fastest robots, while minimizing the energy consumption leads to choosing the robots with the smallest powers. In the context of RALBP, cycle time minimization has been extensively studied while energy minimization has been much less considered. Studies dealing with simultaneous minimization of the two later are even scarcer. A bi‐objective RALBP considering simultaneous minimization of cycle time and energy consumption is studied in this paper. The energy consumption is calculated based on recent papers from the literature. It includes energy consumed during both operation time and idle time. In this paper, a pseudo‐polynomial case is solved thanks to an exact algorithm called split. This latter enumerates all Pareto‐optimal solutions corresponding to a given giant sequence of operations. Split is then used as a decoder in a metaheuristic operating in a reduced search space where giant sequences encode solutions. An experimental study is performed on instances taken from the literature to test the suggested encoding–decoding scheme. It shows that the suggested approach yields competitive results compared to the literature.

Becoming Materially Aware with Mushrooms: A Sociomaterial Analysis of Biomaking

AbstractBiomaking and other bio‐oriented creative approaches are beginning to gain traction in education. Operating at the intersections of arts and sciences, they represent a field of integrative practices that involve creative making with the biological. In educational contexts, however, bio‐oriented creative practices have been studied primarily from hylomorphic perspectives that do not account for the roles of different non‐human organisms or other materials that participate in the processes. Drawing on theories of sociomateriality and material agency, and utilising multispecies microethnography, the paper attends to the specificities of making, makers, materials and artefacts in biomaking. It explores a case of a Finnish upper secondary school workshop implemented as part of the school's art curriculum within a larger educational development initiative. In the workshop, makers engaged with webcap mushrooms to extract, use and experiment with their pigments. The analysis builds on constructed operational sequences of the workshop activities, and a scrutiny of their interconnections, stages and participants. The paper shows how making, emergent artefacts and interplays of makers’ intentions and materialities can instate moments of relationality and learning, and thus build makers’ material awareness of the more‐than‐human organisms around them. The study proposes making with the biological as an attunement to our enmeshment with the environment.

Research on flexible weaving planning based on NSGA-II algorithm

Facing the shift in the weaving industry from mass production to a more diversified, small-batch production model, traditional production scheduling systems are no longer capable of meeting the demand for rapid market response. To address this issue, this paper first analyzes the production process of a weaving workshop, identifying key scheduling challenges such as order allocation, equipment selection, and operation sequencing. Based on this analysis, a flexible job shop multi-objective scheduling model tailored for weaving workshops is developed. To handle the multiple constraints and optimization goals inherent in the model, an improved NSGA-II algorithm is proposed. This algorithm combines artificial bee colony (ABC) algorithm for population initialization with simulated annealing (SA) for population filtering. Simulation examples and case studies from actual workshops demonstrate that the improved NSGA-II algorithm outperforms other algorithms in solving the scheduling problem for weaving workshops. The proposed multi-objective scheduling model and its improved algorithm provide accurate and efficient optimization solutions for workshop scheduling.

LOGIC: Logic Synthesis for Digital In-Memory Computing

In-memory processing offers a promising solution for enhancing the performance of data-intensive applications. While analog in-memory computing demonstrates remarkable efficiency, its limited precision is suitable only for approximate computing tasks. In contrast, digital in-memory computing delivers the deterministic precision necessary to accelerate high-assurance applications. Current digital in-memory computing methods typically involve manually breaking down arithmetic operations into in-memory compute kernels. In contrast, traditional digital circuits are synthesized through intricate and automated design workflows. In this paper, we introduce a logic synthesis framework called LOGIC, which facilitates the translation of high-level applications into digital in-memory compute kernels that can be executed using non-volatile memory. We propose techniques for decomposing element-wise arithmetic operations into in-memory kernels while minimizing the number of in-memory operations. Additionally, we optimize the sequence of in-memory operations to reduce non-volatile memory utilization. To address the NP-hard execution sequencing optimization problem, we have developed two look-ahead algorithms that offer practical solutions. Additionally, we leverage data layout re-organization to efficiently accelerate applications that heavily rely on sparse matrix-vector multiplication operations. Our experimental evaluations demonstrate that our proposed synthesis approach improves the area and latency of fixed-point multiplication by 84% and 20% compared to the state-of-the-art, respectively. Moreover, when applied to scientific computing applications sourced from the SuiteSparse Matrix Collection, our design achieves remarkable improvements in area, latency, and energy efficiency by factors of 4.8 ×, 2.6 ×, and 11 ×, respectively.

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.

UAV Icing: Aerodynamic Degradation Caused by Intercycle and Runback Ice Shapes on an RG-15 Airfoil

Electrothermal de-icing systems are a popular approach to protect unmanned aerial vehicles (UAVs) from the performance degradation caused by in-cloud icing. However, their power and energy requirements must be minimized to make these systems viable for small and medium-sized fixed-wing UAVs. Thermal de-icing systems allow intercycle ice accretions and can result in runback icing. Intercycle and runback ice increase the aircraft’s drag, requiring more engine thrust and energy. This study investigates the aerodynamic influence of intercycle and runback ice on a typical UAV wing. Lift and drag coefficients from a wind tunnel campaign and Ansys FENSAP-ICE simulations are compared. Intercycle ice shapes result in a drag increase of approx. 50% for a realistic cruise angle of attack. While dispersed runback ice increases the drag by 30% compared to the clean wing, a spanwise ice ridge can increase the drag by more than 170%. The results highlight that runback ice can significantly influence the drag coefficient. Therefore, it is important to design the de-icing system and its operation sequence to minimize runback ice. Understanding the need to minimize runback ice helps in designing viable de-icing systems for UAVs.

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.