Programming Techniques Research Articles

Depolarization and polarization transfer rates for the C_2 (X ^1Σ^+_g, a ^3Π_u) + H(^2S_ ) collisions in the solar photosphere

This paper is a continuation of a series of studies that investigated the collisional depolarization of solar molecular lines such as those of MgH, CN and C_2. It is focused on the solar molecule C_2, which exhibits striking scattering polarization profiles, although its intensity profiles are inconspicuous and barely visible. The current interpretation of the C_2 polarization in terms of magnetic fields is incomplete because collisional data are almost completely lacking. We accurately compute the collisional depolarization and polarization transfer rates for the C_2 $(X ^1Σ^+_g, a ^3Π_u)$ by isotropic collisions with hydrogen atoms H $(^2S_ )$. We also investigate the solar implications of our findings. We used the package MOLPRO to obtain potential energy surfaces for the electronic states X ^1Σ^+_g and a ^3Π_u of C_2, and the code MOLSCAT to study the quantum dynamics of the C_2 $(X ^1Σ^+_g, a ^3Π_u)$ + H$(^2S_ )$ systems. We used the tensorial irreducible basis to express the resulting collisional cross sections and rates. Furthermore, sophisticated genetic programming techniques were employed to determine analytical expressions for the temperature and total molecular angular momentum dependence of these collisional rates. We obtained quantum depolarization and polarization transfer rates for the C_2 $(X ^1Σ^+_g, a ^3Π_u)$ + H$(^2S_ )$ collisions in the temperature range T !=! 2,000 -- 15,000 K. We also determined analytical expressions that write these rates as functions of the temperature and total molecular angular momentum. In addition, we show that isotropic collisions with neutral hydrogen can only partially depolarize the lower state of the C_2 lines. This highlights that the approximation of neglecting lower-level polarization is limited in modeling the polarization of C_2 lines. Isotropic collisions with neutral hydrogen atoms are a fundamental ingredient for understanding C_2 polarization.

Global Minimization of Electronic Hamiltonian 1-Norm via Linear Programming in the Block Invariant Symmetry Shift (BLISS) Method.

The cost of encoding a system Hamiltonian in a digital quantum computer as a linear combination of unitaries (LCU) grows with the 1-norm of the LCU expansion. The Block Invariant Symmetry Shift (BLISS) technique reduces this 1-norm by modifying the Hamiltonian action on only the undesired electron-number subspaces. Previously, BLISS required a computationally expensive nonlinear optimization that was not guaranteed to find the global minimum. Here, we introduce various reformulations of this optimization as a linear programming problem, which guarantees optimality and significantly reduces the computational cost. We apply BLISS to industrially relevant homogeneous catalysts in active spaces of up to 76 orbitals, finding substantial reductions in both the spectral range of the modified Hamiltonian and the 1-norms of Pauli and fermionic LCUs. Our linear programming techniques for obtaining the BLISS operator enable more efficient Hamiltonian simulation and, by reducing the Hamiltonian's spectral range, offer opportunities for improved LCU groupings to further reduce the 1-norm.

Research Progress of Neuromorphic Chips

The increasing amount of data in the era of artificial intelligence imposes higher demands on the computational power of neural networks, and in order to fulfill this demand, there is a pressing need to overcome the limitations imposed by the von Neumann architecture's memory wall. Memristors, with their characteristics, are considered the optimal electronic devices for implementing neuromorphic computing. Therefore, in order to better utilize memristors for the design and research of neuromorphic chips, this paper summarizes and comparatively analyzes the memristor characteristics, the RRAM basic principles, memristor array research, crossbar array designs based on memristors, and the study of memristor-based neuromorphic computing chips through the review. The paper emphasizes the challenges that memristor-based neuromorphic computing chips still face in the future, such as non-linear resistance variation. In addition, potential future research directions for amnesia-based neuromorphic computing chips, including amnesia architecture, programming techniques, and instruction set development, are discussed and investigated

Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation

This research addresses complexity in manufacturing systems from an entropic perspective for production improvement. The main objective is to develop and validate a methodology that develops an entropic metric of complexity in an integral way in production environments, through simulation and programming techniques. The methodological proposal is composed of six stages: (i) Case study, (ii) Hypothesis formulation, (iii) Discrete event simulation, (iv) Measurement of entropic complexity by applying Shannon’s information theory, (v) Entropy analysis, and (vi) Statistical analysis by ANOVA. The results confirm that factors such as production sequence and product volume significantly influence the structural complexity of the workstations, with station A being less complex (0.4154 to 0.9913 bits) compared to stations B and C, which reached up to 2.2084 bits. This analysis has shown that optimizing production scheduling can reduce bottlenecks and improve system efficiency. Furthermore, the developed methodology, validated in a case study of the metalworking sector, provides a quantitative framework that combines discrete event simulation and robust statistical analysis, offering an effective tool to anticipate and manage complexity in production. In synthesis, this research presents an innovative methodology to measure static and dynamic complexity in manufacturing systems, with practical application to improve efficiency and competitiveness in the industrial sector.

From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems

Solving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using integer programming algorithms. This study explores various integer programming techniques and their effectiveness in optimising complex scheduling requirements in higher education institutions. We analysed 95 integer programming-based models developed for solving university timetabling problems, covering relevant research from 1990 to 2023. The goal is to provide insights into the evolution of these algorithms and their impact on improving university scheduling. We identify that the implementation rate of models using integer programming is 98%, which is much higher than 34% implementation rates using meta-heuristics algorithms from the existing review. The integer programming models are analysed by the problem types, solutions, tools, and datasets. For three types of timetabling problems including course timetabling, class timetabling, and exam timetabling, we dive deeper into the commercial solvers CPLEX (47), Gurobi (11), Lingo (5), Open Solver (4), C++ GLPK (4), AIMMS (2), GAMS (2), XPRESS (2), CELCAT (1), AMPL (1), and Google OR-Tools CP-SAT (1) and identify that CPLEX is the most frequently used integer programming solver. We explored the uses of machine learning algorithms and the hybrid solutions of combining the integer programming and machine learning algorithms in higher education timetabling solutions. We also identify areas for future work, which includes an emphasis on using integer programming algorithms in other industrial areas, and using machine learning models for university timetabling to allow data-driven solutions.

Size-Dependent Effects in the Modified Green–Lindsay Thermoelasticity Model: Analysis of Ultrashort Pulse Interactions in the Presence of Electromagnetic Fields

Abstract This study provides a more detailed model for wave transmission and reflection in a complex thermodynamic framework. The governing equations are developed from multiphase electromagnetic size-dependent thermoelasticity models. The main focus here is on the development of the Modified-Green Lindsay nonlocal generalized thermoelasticity model that improves previous nonlocal approaches by incorporating additional strain and temperature rate terms. This study also examines how materials react to very short pulses and the effect of external electric and magnetic fields. The highly developed nonlinear equations in this study are solved using advanced programming techniques, including an iterative approach that combines finite element methods with a Newmark time-marching scheme. The results of this work established the nonlocal heat conduction theory of generalized thermoelasticity along with nonlocal continuum theory as a new improvement and progress in the field of thermoelasticity. This model shows that without considering thermal nonlocality interactions, predictions may miss essential aspects of wave behavior. The results show that the materials experience changes in mechanical properties, particularly an increase in hardness, when exposed to stronger magnetic fields. In addition, the results show that as the duration of laser pulses decreases, the speed of wave propagation increases. Shortening the pulse duration increases the wave propagation speed, which can create steep thermal gradients. In the case of pure metals heated by ultra-short pulse lasers, the fast electron heat conduction prevents the formation of significant thermal waves, while these pulses induce more intense and localized thermal stresses. Analysis of wave propagation also shows that the return time can exceed the forward time as the waves react to applied electric and magnetic fields.

Multi-Objective Economic Dispatch Problems: An Evolutionary Programming Approach

Optimizing multiple objectives is a common challenge in complex decision-making scenarios, where improving one objective often comes at the expense of another. In power system generation, two critical objectives must be simultaneously minimized: the total generation cost and the total emissions released. This study introduces a multi-objective optimization method developed to address these dual objectives. The proposed approach aims to identify the optimal solution to the economic dispatch problem, balancing the trade-off between minimizing generation costs and reducing environmental impact by lowering total emissions. Heuristic optimization is used to solve this multi-objective combined economic and emission dispatch (MOCEED) problems through the implementation of Multi-Objective Evolutionary Programming (MOEP). In this study, the Weighted Sum Method (WSM) is combined with the Evolutionary Programming (EP) technique to minimize both objectives. The developed approach is validated on the IEEE 30-Bus RTS, which consists of six generators. The effectiveness of the developed technique is evaluated and compared the results against scenarios without optimization. Simulations are performed using MATLAB software, and the results demonstrate that the proposed Multi-Objective Evolutionary Programming (MOEP) successfully identifies the optimal generator outputs, achieving significant reductions in both total generation cost and emissions. These results highlight the method's capability to provide superior solutions for multi-objective economic dispatch problems.

Aerofoil optimization using SLSQP and validation using numerical and analytical methods

Aircraft design optimization is one among the research enriched topic in the aerospace industry, with enhancing aircraft performance, safety, and efficiency numerous being the prime focus areas. The work done demonstrates the application of the Sequential Least Squares Programming (SLSQP) technique over a symmetrical aerofoil “NACA 0012” to improve its aerodynamic performance. The optimized aerofoil is validated using Design and Analysis Tools for Composite Aircraft (DATCOM) and Computational Fluid Dynamics (CFD) methods. The study focuses on optimizing the performance of a symmetric aerofoil, where drag minimization is crucial, subject to list constraints, such as in the design of fuel-efficient aircraft. The results reveal, the optimized aerofoil has a significant reduction in drag coefficient of closer to 11 % between 8° and 10° compared to the initial design. The validation using DATCOM and CFD methods confirms the accuracy and usefulness of the optimization results. Validation error values are found to be negligible when compared to the optimization data, coming in at 5.7% and 6.5% for DATCOM and CFD, respectively. The paper highlights that the SLSQP technique is efficient and reliable optimization method for designing high-performance aerofoils.

Optimizing Nurse Rostering: A Case Study Using Integer Programming to Enhance Operational Efficiency and Care Quality.

Background/Objectives: This study addresses the complex challenge of Nurse Rostering (NR) in oncology departments, a critical component of healthcare management affecting operational efficiency and patient care quality. Given the intricate dynamics of healthcare settings, particularly in oncology clinics, where patient needs are acute and unpredictable, optimizing nurse schedules is paramount for enhancing care delivery and staff satisfaction. Methods: Employing advanced Integer Programming (IP) techniques, this research develops a comprehensive model to optimise NR. The methodology integrates a variety of constraints, including legal work hours, staff qualifications, and personal preferences, to generate equitable and efficient schedules. Through a case study approach, the model's implementation is explored within a clinical setting, demonstrating its practical application and adaptability to real-world challenges. Results: The implementation of the IP model in a clinical setting revealed significant improvements in scheduling efficiency and staff satisfaction. The model successfully balanced workload distribution among nurses, accommodated individual preferences to a high degree, and ensured compliance with work-hour regulations, leading to optimised shift schedules that support both staff well-being and patient care standards. Conclusions: The findings underscore the effectiveness of IP in addressing the complexities of NR in oncology clinics. By facilitating a strategic allocation of nursing resources, the proposed model contributes to operational excellence in healthcare settings, underscoring the potential of Operations Research in enhancing healthcare delivery and management practices.

A Comparison of Several Linear Genetic Programming Techniques

A comparison between four Genetic Programming techniques is presented in this paper. The compared methods are Multi-Expression Programming, Gene Expression Programming, Grammatical Evolution, and Linear Genetic Programming. The comparison includes all aspects of the considered evolutionary algorithms: individual representation, fitness assignment, genetic operators, and evolutionary scheme. Several numerical experiments using five benchmarking problems are carried out. Two test problems are taken from PROBEN1 and contain real-world data. The results reveal that Multi-Expression Programming has the best overall behavior for the considered test problems, closely followed by Linear Genetic Programming.

Approximate dynamic programming methods in Bayesian preference elicitation

Bayesian preference elicitation is a statistical framework which can aid in complex decision making processes such as engineering design by offering preference-informed recommendations to a decision maker (DM). In this framework, a DM is asked to answer a sequence of queries, called a questionnaire, where in each query they must select their preferred option among two alternatives. A common method to construct the questionnaire is through the D-optimality criterion, which seeks to select queries which lead to more precise estimates of utility model parameters. Past works have proposed and investigated adaptive methods for questionnaire construction using D-optimality as the query selection criterion. All of these adaptive methods have been based on greedy strategies to the best of our knowledge. In this work, we propose and compare various non-greedy methods for adaptive questionnaire construction in the preference elicitation setting. Our methods utilize a combination of enumeration and mixed integer programming techniques to select queries to present to the DM. Through our simulation studies, we find only a marginal improvement in D-efficiency through the use of non-greedy methods, suggesting that greedy methods are sufficient for this problem.

Optimizing Stochastic Transportation Networks with Mixed Constraints Using Pareto Distribution

We propose a novel solution approach combining stochastic programming techniques with Pareto distribution characteristics. This approach involves reformulating the problem into a tractable optimization model using probabilistic constraints and employing advanced algorithms to solve the resulting mixed-integer programming problem. Numerical experiments illustrate the effectiveness of the proposed method and highlight its practical implications for transportation network design and management under uncertain conditions. The results demonstrate that incorporating Pareto-distributed uncertainties into the transportation problem provides a more realistic and adaptable framework for decision-making. The proposed solution approach offers valuable insights for managing complex transportation systems where both stochastic and deterministic factors play a crucial role.

APPLICATION OF PARAMETER STATISTICAL TESTS AND DATA ENVELOPMENT ANALYSIS METHODS IN MODERN BUSINESS

In the absence of a sufficient amount of information for quality business decision-making, i.e. successful performance of activities without unnecessary losses in the consumption of inputs, recently the non-parametric DEA method (Data Envelopment Analysis) is most often used through the linear programming technique. In the event that company managers have enough information to make business decisions, parametric statistical tests are used that compare the company's current performance with optimal performance, i.e. those that are on the edge of efficiency. However, this situation is very rare, so before making business decisions, non-parametric and then parametric statistical tests are carried out in detail. The subject of research of this paper is primarily focused on the simultaneous application of parametric and non-parametric statistical tests in the assessment of the economic efficiency of an economic entity. After the conducted research and analysis of the obtained results, it was determined that the null hypothesis, which claims that the relative efficiency of the warehouse obtained by parametric statistical tests and the DEA method is identical and that the trends have the same direction, could not be fully accepted. Namely, it was found out that the results of one and the same economic situation using the mentioned two types of analysis differ to the extent that they are not adequate for economic decision-making, however identical results were obtained in the assessment of the trend. It can be concluded that the simultaneous application of both methods, as well as its implementation in several iterations, can provide enough quality information for effective decision-making. Stochastic processes that occur during the implementation of business decisions using the DEA technique can be minimized through the simultaneous application of statistical parametric methods and tests for evaluating the expected efficiency of DEA. The effectiveness of this method in any case depends on the size of the sample implemented in the aforementioned statistical analysis. The aforementioned statistical tests enable the measurement and detection of those input parameters that will most effectively contribute to the efficiency of business systems.

Modeling a Green and Reliable Intermodal Routing Problem for Food Grain Transportation Under Carbon Tax and Trading Regulations and Multi-Source Uncertainty

This study addresses an intermodal routing problem encountered by an intermodal transportation operator fulfilling the food grain transportation order of an agri-food company. To enhance the environmental sustainability of food logistics, carbon tax and trading regulations have been employed to reduce the carbon emissions associated with transportation. Multi-source uncertainties, including the company’s demand for food grains and various parameters related to the intermodal transportation activities, are modeled via trapezoidal fuzzy numbers to optimize the comprehensive reliability of the solution. This work incorporates wastage reduction by lowering the wastage costs and formulating a wastage threshold constraint in intermodal routing. Accordingly, a fuzzy mixed-integer nonlinear programming model for a green and reliable intermodal routing problem for food grain transportation is proposed. To overcome the model’s insolvability and the difficulty in finding the global optimum solution to a nonlinear optimization model, a two-stage solution method is developed, employing chance-constrained programming and linearization technique to reformulate the initial model. A numerical case study is given to verify the feasibility of the proposed methods. Sensitivity analysis reveals the influence of confidence levels and wastage threshold, providing insights for the agri-food company to balance economics, reliability, and wastage reduction in food grain transportation. The numerical case study also analyzes the feasibility of carbon tax and trading regulations in reducing carbon emissions, concluding that carbon tax regulations consistently achieve greater reductions and are universally feasible. In contrast, the feasibility of carbon trading regulations depends on confidence levels and wastage threshold. The findings of this work could provide strong quantitative support for intermodal transportation operators and agri-food companies seeking to implement sustainable food grain transportation.

Optimizing High-Load Systems with Asynchronous Programming Techniques

This document focuses on a critical issue in modern application - performance. The goal is to find the best way to improve the efficiency of these systems, with a particular focus on smart home management applications. As the popularity of such systems grows rapidly, enhancing their performance is becoming necessary. To address this need to explore existing challenges and evaluates various solutions. The proposed asynchronous programming approach to solve the listed problems. Performance tests showed the efficiency of this method in comparison with other approaches.

Declarative Approaches to Outcome Determination in Judgment Aggregation

Judgment aggregation (JA) offers a generic formal framework for modeling various settings involving information aggregation by social choice mechanisms. For many judgment aggregation rules, computing collective judgments is computationally notoriously hard. The central outcome determination problem, in particular, is often complete for higher levels of the polynomial hierarchy. This complexity barrier makes it challenging to develop practical exact algorithms to outcome determination. Taking on this challenge, in this work we develop practical exact algorithms for outcome determination under a range of the most central JA rules—namely Kemeny, Slater, MaxHamming, Young, Dodgson, Reversal scoring, Condorcet, Ranked agenda, and LexiMax—by harnessing the declarative approach, in particular, Boolean satisfiability (SAT) and integer programming techniques. For the Kemeny, Slater, MaxHamming, Young, and Dodgson rules, we detail direct approaches based on maximum satisfiability (MaxSAT) and integer programming. For the Reversal scoring, Condorcet, Ranked agenda, and LexiMax rules, we develop iterative algorithms, including algorithms based on the counterexample-guided abstraction refinement (CEGAR) paradigm, making use of recent advances in incremental MaxSAT solving and preferential SAT-based reasoning. We provide an open-source implementation of the algo- rithms, and empirically evaluate them using real-world preference data. We compare the performance of our implementation to a recent approach which makes use of declarative solver technology for answer set programming (ASP). The results demonstrate that our approaches scale significantly beyond the reach of the ASP-based algorithms for all of the judgment aggregation rules considered.

Delegated investment in retirement savings: is there value added?

Abstract We study a discrete-time life cycle retirement planning problem for individual workers with four distinct investment options: self-management with dynamic investment (S), self-management with benchmark investment (B), hire-management with flexible allocation ( $\text{H}_{1}$ ), and hire-management with alpha focus ( $\text{H}_{2}$ ). We examine the investment strategies and consumption patterns during the defined contribution fund accumulation period, ending with a life annuity purchase at retirement to finance post-retirement consumption. Based on the calibrated model using US data, we employ numerical dynamic programming technique to optimize worker’s financial decisions. Our analysis reveals that, despite the agency risk, delegated investments can add value to a worker’s lifetime utility, with the $\text{H}_2$ option yielding the best lifetime utility outcome. However, after taking the fund management fee into consideration, we find that both the $\text{H}_1$ and $\text{H}_2$ options may not offer additional value compared to the S option, yet they still surpass the B option in performance.

Model Predictive Control With Guaranteed Feasibility of Inequality Path Constraints.

This article concerns nonlinear model predictive control (MPC) with guaranteed feasibility of inequality path constraints (PCs). For MPC with PCs, the existing methods, such as direct multiple shooting, cannot guarantee feasibility of PCs because the PCs are enforced at finitely many time points only. Therefore, this article presents a novel MPC framework that is capable of not only achieving stability control but also guaranteeing feasibility of PCs during the rolling optimization stages of MPC. Under the above MPC framework, an algorithm is first proposed by applying the semi-infinite programming technique to the rolling optimization of MPC. However, it takes heavy computational time to achieve guaranteed feasibility of PCs. Therefore, to guarantee feasibility of PCs meanwhile effectively reducing the computation burden of the closed-loop system, an event-triggered sampling mechanism is constructed in the above path-constrained MPC algorithm. Moreover, sufficient conditions are given for asymptotic convergence of the closed-loop systems. Finally, the effectiveness of the proposed results is illustrated via a cart-damper-spring system.

Advancements in Machine Learning for Pipeline Integrity Management: A Comprehensive Review of Predictive and Optimization Techniques

Oil and gas pipeline networks are crucial component of energy infrastructure. However, as the integral elements of the energy transportation system are evolving towards Industry 4.0 and Smart Manufacturing, pipelines network are progressively shifting towards intelligence and digitization. The fields of asset integrity management, operation inspection, corrosion prevention, leak and intrusion detection, and flow assurance are just a few of the areas where machine learning and artificial intelligence (AI) algorithms are becoming more and more important. The predictive and optimization capabilities of these Models/ Algorithms have been leveraged by various Pipeline Network Integrity Management systems to avert pipeline failure, reduce environmental damage, and effectively allocate resources. This study reviews and gives a summary of current advancements in intelligent techniques, encompassing heuristic computations, mathematical programming, and machine learning techniques used in a pipeline network integrity management to enhance various operational and maintenance aspects. This work makes two intellectual accomplishments. Firstly, it offers a technical review of literatures, systematically synthesizing different computational intelligence and machine learning approaches previously applied, along with their methodologies, contributions, drawbacks, and comparisons. Secondly, the study recommends some nature-inspired meta-heuristic algorithms, which encompasses Evolutionary and the Swarm Intelligence Algorithms to be applied for future directions and challenges. Finally, the study underscores the potential of computational intelligence and algorithms for learning methods in predicting and optimizing the efficiency of pipeline network operation and management, emphasizing the necessity for further development of models/algorithms and application to enhance more intricate interactions between pipeline infrastructure monitoring, maintenance, and management.

Analysis and actuation design of a novel at-scale 3-DOF biomimetic flapping-wing mechanism inspired by flying insects

Insects' flight is imbued with endless mysteries, offering valuable inspiration to the flapping-wing robots. Particularly, the multi-mode wingbeat motion such as flapping, sweeping and twisting in coordination presents advantages in promoting unsteady aerodynamics and enhancing lift force. To achieve the flapping-twisting-sweeping motion capability, this paper proposes an at-scale three-degree-of-freedom (3-DOF) mechanism driven by three piezoelectric actuators, which consists of three four-bar mechanisms and a parallel spherical mechanism. Compliant hinges are utilized as rotating joints for power transmission. The DOF and the kinematics analysis are performed. The aerodynamic model of the wing and the mechanical model of the compliant hinges are considered to investigate the required driving force response of the mechanism with wing loads. By employing nonlinear programming techniques, the geometric parameters of three piezoelectric actuators are reverse-designed to match the dynamic response of the mechanism in two flapping conditions. The significance of this work lies in proposing a novel concept of an at-scale multi-DOF wingbeat mechanism, demonstrating the feasibility of this mechanism to mimic the flexible and multi-mode wingbeat movement of insects, and providing an initial mechanism-drive solution.