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Vibronic coupling is a critical mechanism in chemical reactions. However, its quantitative evaluation is challenging due to mathematical complexity and programming difficulty, and its experimental proof is often elusive due to overlap among neighboring states. Here, after exciting a vibrational level (ν = 0, 1, 2) of the intermediate N 1 s → π g * core-excited state in N2 molecules, we separate the resonant Auger decay channels that lead to the lowest dissociation limit in the two-dimensional energy correlation maps. From three kinetic energy release spectra of these channels at different vibrational quantum numbers, we give the first experimental proof of the vibronic coupling between two resonant Auger final states 12Πg and 22Πg. [ABSTRACT FROM AUTHOR]

The target-oriented robust optimization (TORO) approach converts the original objectives to system targets and instead maximizes an uncertainty budget or robustness index. Machine learning techniques are used to tighten uncertainty sets which also reduce the pessimism of robust solutions. However, existing approaches operate only on a one-way sequential flow, where the data estimation and optimization modules implement their tasks independently. This overlooks the potential of capturing feedback within the solution framework to update forecasts and decisions based on the recent realizations of the uncertainty and system outcomes. This research proposes a novel closed-loop data-driven TORO framework, leveraging on the power of machine learning and feedback systems for optimization under uncertainty. The framework provides an array of solutions for various risk appetites and supports efficient decision-making as computational tractability is retained. A hypothetical case study is solved on a combined inventory and routing problem to demonstrate its applicability and features. [ABSTRACT FROM AUTHOR]

The selection of process parameters is crucial in 3D printing for product manufacturing. These parameters govern the operation of production machinery and influence the mechanical properties, production time, and other aspects of the final product. The optimal process parameter settings vary depending on the product and printing application. This study identifies the most suitable cluster of process parameters for producing rotating components, specifically impellers, using carbon-reinforced Polyether Ether Ketone (CF-PEEK) thermoplastic filament. A mathematical programming technique using a rating method was employed to select the appropriate process parameters. The research concludes that an infill density of 70%, a layer height of 0.15 mm, a printing speed of 60 mm/s, a platform temperature of 195 °C, an extruder temperature of 445 °C, and an extruder travel speed of 95 mm/s are optimal process parameters for manufacturing rotating components using carbon-reinforced PEEK material. [ABSTRACT FROM AUTHOR]

We consider the problem of designing lightweight, load-bearing planar frame structures for additive manufacturing, which can be formulated as a nonlinear, non-convex mathematical programming problems. Even using state-of-the-art commercial solvers, exact methods are only capable of solving small unrealistic instances (with very few variables). In this paper, we develop a heuristic method which is fast and capable of solving the design problem for larger-scale, weight-optimized, planar frame structures for additive manufacturing. The approach explicitly considers manufacturability constraints stemming from the use of additive manufacturing technology and leverages the problem structure imposed by these constraints. The proposed heuristic method is based on iteratively resolving a relaxed master problem on a reduced ground structure. The approach differs from the existing methods in two important aspects. First, we consider planar frame optimization master problem directly (instead of simpler but less relevant truss optimization). Secondly, we employ both element and node addition, which allows us to enforce additive manufacturability constraints without using binary variables (and hence, avoiding the need for computationally expensive integer programming). [ABSTRACT FROM AUTHOR]

This paper introduces a simultaneous optimization approach to synthesizing work and heat exchange networks (WHENs). The proposed work and heat integration (WHI) superstructure enables different thermodynamic paths of pressure and temperature-changing streams. The superstructure is connected to a heat exchanger network (HEN) superstructure, enabling the heat integration of hot and cold streams identified within the WHI superstructure. A two-step solution strategy is proposed, consisting of initialization and design steps. In the first step, a thermodynamic path model based on the WHI superstructure is combined with a model for simultaneous optimization and heat integration. This nonlinear programming (NLP) model aims to minimize operating expenditures and provide an initial solution for the second optimization step. In addition, hot and cold streams are identified, enabling additional model reduction. In the second step of the proposed solution approach, a thermodynamic path model is combined with the modified HEN model to minimize the network's total annualized cost (TAC). The proposed mixed integer nonlinear programming (MINLP) model is validated by several examples, exploring the impact of the equipment costing and annualization factor on the optimal network design. The results from these case studies clearly indicate that the new synthesis approach proposed in this paper produces solutions that are consistently similar to or better than the designs presented in the literature using other methodologies. [ABSTRACT FROM AUTHOR]

A resource-effective society considers a circular economy, and this attitude enters the construction industry as well. Various circular building approaches and strategies are introduced to promote a circular economy. However, the design process is complex because it evolves from available components. To address such a gap, in this article, we address design adjustments with the aim of maximizing the reusing of components from an architectural viewpoint, utilizing an applied research method. To achieve the goal, mathematical programming is suggested to make the best use of available components bearing a system approach in circular design in mind. The objective function is the allocation of reclaimed building materials and the decision variables are the needed components. The issue was categorized as integer nonlinear programming (INLP) solved for global optimality utilizing a genetic algorithm (GA). Design variants define different component allocations; thus, the optimum allocation defines the evolved design. Mathematical programming permits defining component allocation variants and the constraints associated with the project. The result of optimization defines the optimum allocation; consequently, it defines the evolved design. The presented procedure is an innovative method that aims to simplify the complexity associated with adjusting the design based on available components. It promotes procurement efficiency because it makes the best use of available material. Moreover, the minimum energy consumption for transportation due to local material usage leads to reuse efficiency. [ABSTRACT FROM AUTHOR]

An essential process in earthworks is projecting effective routes for material allocation. In highway construction, haul road planning is a challenge for practitioners once there are few consolidated methodologies to rationally select existing haul routes or construct new ones. Some authors indicated geographic information system (GIS)-based methods for routing through least-cost algorithms. However, most proposed methodologies overlook the logistics related to allocations, not considering supply–demand relations in their criteria. Consequently, through a case study in Brazil, we present an earthwork optimization system that integrated established mathematical programming techniques for allocation and a GIS-based algorithm for haul road construction. In summary, we formulated a model capable of selecting different routes based on their impact on earthworks logistics cost. Thus, we built several paths between source and demand points utilizing two methods. First, based on recent literature, we used a GIS-based least-cost algorithm using the measuring attractiveness by a categorical-based evaluation technique (MACBETH) as a multicriteria support system incorporating multiple economic and environmental parameters. Second, we built routes based on Euclidian distance between the same source and demand points, analyzing if the savings in operational costs would payoff additional expenses of choosing a shorter route. After applying our methodology to the case study, we observed that the proposed procedure presented a lower budget in a reasonable processing time compared with the original project. Therefore, we inferred that the strategy of proposing path alternatives can be significantly efficient when applied to highways. Based on the results, we also found opportunities for improvement of our GIS step once it generated long paths when the distance between source and demand was short. [ABSTRACT FROM AUTHOR]

The oil and gas field surface gathering pipeline network is an important hub for transporting oil and gas products outward, and its optimal design is crucial to reducing the construction cost of the pipeline system. Star-star topology is often applied to the layout of surface gathering pipeline networks in oil and gas fields. However, the optimization of star-star topology is often carried out under the constraint of the fixed station type, which may easily lead to a waste of station capacity and increase investment or make the optimization results meaningless. In this article, a mixed integer linear programming mathematical model for the overall optimization of star-star pipeline network layout considering the station location-allocation problem is constructed in discrete space with coupled station type constraints, and the branch-and-bound method is used to obtain the global optimal solution. Three cases verify the feasibility and effectiveness of the model and algorithm. The results show that the maximum avoided capacity waste is 20 × 104 m3/d and the maximum investment can be reduced by 8961.6 × 104 Yuan, which shows that the proposed model can effectively improve the economy and rationality of the pipeline system, and has important guiding significance for the construction of oil and gas fields. [ABSTRACT FROM AUTHOR]

The kinematic plastic limit, unified shakedown criteria, and reduced kinematic formulation have been utilized to study the plastic collapse modes of truss structures subjected to complex loading cycles. The development from a series of local alternating plastic failures toward the global instantaneous or incremental collapse of the structures with increasing load ranges has been identified. The finite element method and optimization techniques are implemented to effectively solve the resulting mathematical programming problems, with illustrative examples and explanations. [ABSTRACT FROM AUTHOR]

In the face of the increasingly dire threat of global climate change, reducing carbon emissions has become an urgent priority for governments and corporations worldwide. The aluminum alloy wheel manufacturing industry bears an even heavier burden for emission mitigation due to its high production volume, complex processes, and proportionally higher carbon footprint. With impending carbon taxes and trading policies looming, the industry urgently needs to strike a balance between maximizing profits and minimizing carbon emissions. Leveraging real-world industry data, this research develops four green Activity-Based Costing (ABC) models and utilizes optimization software to compare the following scenarios: non-continuous carbon tax, carbon tax with trading, tiered tax with exemptions, and exemptions combined with trading. Results demonstrate that integrating carbon trading and targeted tax reductions can improve corporate financial positions without severely compromising environmental goals. Although identifying optimal balance points remains a highly complex process, this study equips enterprises and policymakers with quantitative tools to navigate fluctuating carbon regulatory environments. As national policies progress, more multifaceted dynamic carbon tax models will likely provide more profound insights for sustainable development. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study. [ABSTRACT FROM AUTHOR]

In the logistics center (warehouse or distribution center), customer orders need to be picked up by the pickers. In this research, we examine the order-picking problem with sequencedependent constraints with two decision variables (container start time and product quantity) in a distribution center with a one-directional conveyor. The decision-making is based on the developed two variations of two-step matheuristics. At first, the main order-picking problem is divided into two subproblems. Next, each step of each variant of the subproblem is solved using a mathematical programming-based technique. Both matheuristics were better in 85 of 120 test instances compared to the initial model solved by mathematical programming. Pickers matheuristics were better on average at 46.56%, while Buffers matheuristics were better on average at 46.87%. The proposed matheuristics approach allows distributors to schedule orders in the logistics center fast enough and with fewer resources. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing need to understand the complex interdependencies between critical infrastructure systems, for example, electric power, natural gas, and potable water. Whereas open-source and commercial tools for the independent simulation of these systems are well established, frameworks for cosimulation with other systems are nascent and tools for co-optimization are scarce—the major challenge being the hidden combinatorics that arise when connecting multiple-infrastructure system models. Building toward a comprehensive solution for modeling interdependent infrastructure systems, this work presents InfrastructureModels, an extensible, open-source mathematical programming framework for co-optimizing multiple interdependent infrastructures. This work provides new insights into methods and programming abstractions that make state-of-the-art independent infrastructure models composable with minimal additional effort. To that end, this paper presents the design of the InfrastructureModels framework, documents key components of the software's implementation, and demonstrates its effectiveness with three case studies on canonical co-optimization tasks arising in interdependent infrastructure systems. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: The work was funded by Los Alamos National Laboratory's Directed Research and Development project "The Optimization of Machine Learning: Imposing Requirements on Artificial Intelligence" and the U.S. Department of Energy's Office of Electricity Advanced Grid Modeling projects "Joint Power System and Natural Gas Pipeline Optimal Expansion Planning" and "Coordinated Planning and Operation of Water and Power Infrastructures for Increased Resilience and Reliability." This work was carried out under the U.S. DOE contract no. [DE-AC52-06NA25396]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0118) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0118). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel data-driven optimization approach for trajectory-based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users' trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, whereas optimization is applied to resolve demand-capacity imbalances by means of a mathematical programming model that judiciously assigns a feasible four-dimensional trajectory and a possible ground delay to each flight. The methodology has been tested on instances extracted from the Eurocontrol data repository. With more than 32,000 flights considered, we solve the largest instances of the ATFM problem available in the literature in short computational times that are reasonable from the practical point of view. As a by-product, we highlight the trade-off between preferences and delays as well as the potential benefits. Indeed, computing efficient solutions to the problem facilitates a consensus between the network manager and airspace users. In view of the level of accuracy of the solutions and the excellent computational performance, we are optimistic that the proposed approach can make a significant contribution to the development of the next generation of air traffic flow management tools. [ABSTRACT FROM AUTHOR]

This study investigates a green multimodal routing problem with soft time window. The objective of routing is to minimize the total costs of accomplishing the multimodal transportation of a batch of goods. To improve the feasibility of optimization, this study formulates the routing problem in an uncertain environment where the capacities and carbon emission factors of the travel process and the transfer process in the multimodal network are considered fuzzy. Taking triangular fuzzy numbers to describe the uncertainty, this study proposes a fuzzy nonlinear programming model to deal with the specific routing problem. To make the problem solvable, this study adopts the fuzzy chance-constrained programming approach based on the possibility measure to remove the fuzziness of the proposed model. Furthermore, we use linear inequality constraints to reformulate the nonlinear equality constraints represented by the continuous piecewise linear functions and realize the linearization of the nonlinear programming model to improve the computational efficiency of problem solving. After model processing, we can utilize mathematical programming software to run exact solution algorithms to solve the specific routing problem. A numerical experiment is given to show the feasibility of the proposed model. The sensitivity analysis of the numerical experiment further clarifies how improving the confidence level of the chance constraints to enhance the possibility that the multimodal route planned in advance satisfies the real-time capacity constraint in the actual transportation, i.e., the reliability of the routing, increases both the total costs and carbon emissions of the route. The numerical experiment also finds that charging carbon emissions is not absolutely effective in emission reduction. In this condition, bi-objective analysis indicates the conflicting relationship between lowering transportation activity costs and reducing carbon emissions in routing optimization. The sensitivity of the Pareto solutions concerning the confidence level reveals that reliability, economy, and environmental sustainability are in conflict with each other. Based on the findings of this study, the customer and the multimodal transport operator can organize efficient multimodal transportation, balancing the above objectives using the proposed model. [ABSTRACT FROM AUTHOR]

We analyzed the existence of solutions for a sixth-order boundary value problem. Initially, we established the operator that redefines the problem as a fixed-point problem and elucidated its main properties. Subsequently, we investigated the existence of solutions within the function space C*[0,1], employing the robust framework of Krasnoselskii's fixed-point theorem. Additionally, we introduced an innovative numerical methodology to explore solutions to this problem. This approach involves discretizing the problem, thereby formulating a nonlinear system. We proposed a solution strategy through the application of mathematical programming techniques for the determination of numerical solutions. [ABSTRACT FROM AUTHOR]

The berth allocation problem determining the berthing time and position for incoming vessels in port operations has garnered increased attention within the global transportation network. This study focuses on the berth allocation problem with a continuous quay and dynamic vessel arrivals. With the overarching goal of enhancing service quality and optimizing berth utilization rates, this article proposes a mathematical programming model that minimizes the total waiting time of vessels and the overall completion time of vessel service. The formulated model is a mixed-integer linear programming problem that deterministic optimization techniques can globally solve. For large-scale problems, this study develops a genetic algorithm optimization approach to improve computational efficiency in reaching a near-optimal solution. Several numerical experiments are conducted to demonstrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is playing a pivotal role in transforming various industries, and Wireless Sensor Networks (WSNs) are emerging as the key drivers of this innovation. This research explores the utilization of a heterogeneous network model to optimize the deployment of sensors in agricultural settings. The primary objective is to strategically position sensor nodes for efficient energy consumption, prolonged network lifetime, and dependable data transmission. The proposed strategy incorporates an offline model for placing sensor nodes within the target region, taking into account the coverage requirements and network connectivity. We propose a two-stage centralized control model that ensures cohesive decision making, grouping sensor nodes into protective boxes. This grouping facilitates shared resource utilization, including batteries and bandwidth, while minimizing box number for cost-effectiveness. Noteworthy contributions of this research encompass addressing connectivity and coverage challenges through an offline deployment model in the first stage, and resolving real-time adaptability concerns using an online energy optimization model in the second stage. Emphasis is placed on the energy efficiency, achieved through the sensor consolidation within boxes, minimizing data transmission hops, and considering energy expenditures in sensing, transmitting, and active/sleep modes. Our simulations on an agricultural farmland highlights its practicality, particularly focusing on the sensor placement for measuring soil temperature and humidity. Hardware tests validate the proposed model, incorporating parameters from the real-world implementation to enhance calculation accuracy. This study provides not only theoretical insights but also extends its relevance to smart farming practices, illustrating the potential of WSNs in revolutionizing sustainable agriculture. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel methodology called Particle Dynamics Method (PDM) for identifying and quantifying influential nodes in complex networks. Inspired by Newton's three laws of motion and the universal gravitation law, PDM is based on a mathematical programming method that leverages node degrees and shortest path lengths. Unlike traditional centrality measures, PDM is easily adaptable to different network sizes and models, making it a versatile tool for network analysis. Our updated version of PDM also considers the direction of each force, resulting in more reliable results. To evaluate PDM's performance, we tested it on a set of benchmark networks with distinct characteristics and models. Our results demonstrate that PDM outperforms other methodologies in the literature, as removing the identified influential nodes results in a significant decrease in network efficiency and robustness. The key feature of PDM is its flexibility in defining distance, which can be adapted to various network types. For instance, in a transportation network, distance can be defined by the flow between nodes, while in an academic publication system, the quartile of the journal could be used. Our research not only demonstrates the effectiveness of PDM but also highlights the influence of universities in the higher education and global university ranking networks, shedding light on the dynamics of these networks. Our interdisciplinary work has significant potential for collaborations between optimization, physics, and network science. This study opens up avenues for future research, including the extension of PDM to multilayer networks and the generalization of the metrics of monolayer networks for this purpose. [ABSTRACT FROM AUTHOR]

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk. [ABSTRACT FROM AUTHOR]

We consider the constrained optimization problem for a smooth function defined on a Banach space with smooth constraints of equality and inequality type. We show that for this problem, under the known sufficient second-order optimality conditions, the set of Lagrange multipliers can be replaced by a smaller set. [ABSTRACT FROM AUTHOR]

The effective operation of any manufacturing enterprise cannot be achieved without optimal allocation of available resources. Efficient distribution of input materials, entering production, and procurement costs are significant parts of production costs. One of the primary requirements to ensure a given company's production efficiency and competitive capability is its optimization. An area within this context is the distribution of, for instance, dense material required for constructing concrete structures such as bridges, roads, buildings, and more. When cutting dense material, it is necessary to create a plan for optimal cutting to minimize waste. This paper aims to create a methodology for optimizing the cutting of metal rods with the goal of waste reduction. The software MS Excel and Python solutions have been developed and used based on the methods employed. The computed solutions can serve as a foundation for creating a data repository, which holds information about purchased materials and realized production and material remnants that can be utilized in subsequent cutting. Elements of the data repository will encompass cutting plans and remnants resulting from the implementation of calculated cutting plans. The data repository enables a multidimensional view for managing the cutting process. [ABSTRACT FROM AUTHOR]

Sustainability is becoming increasingly important as a topic in general supply chains and cold chains. This study addresses the critical need for an integrated and comprehensive approach to supply chain design that simultaneously maximizes cost efficiency and incorporates environmental sustainability, focusing specifically on cold chains. Prior research has identified a gap in existing studies, highlighting the absence of an overarching framework from the logistics providers' perspective. Our novel mathematical programming model introduces a unique strategy that carefully balances environmental concerns and cost optimization, addressing the shortcomings found in previous works. By integrating variables related to distribution, transportation, and inventory management, the model aims to minimize overall expenses and environmental impact, specifically regarding energy consumption and carbon emissions. We formulate and solve the optimization problem through rigorous mathematical programming, offering a flexible solution applicable to various logistics scenarios. The results demonstrate a significant reduction in overall expenses and environmental effects, providing logistics providers with a powerful tool for informed decision-making and sustainable supply chain management. This research contributes to the evolving field of supply chain sustainability by presenting a practical and effective model for maximizing cost savings while promoting environmental responsibility. The study concludes with compelling statistics, revealing a noteworthy decrease in overall expenses by €25 128 146.73 (100.25%) and environmental impact by 17 359.39 (100.04%). The outcomes show a notable decrease in general expenses and environmental effects, giving logistics providers a strong instrument for wise choices and sustainable supply chain management. [ABSTRACT FROM AUTHOR]

The method of Almon reduces multicollinearity in some degree in distributed lag model, however multicollinearity may not be recovered since Almon estimator depends on the use of ordinary least squares technique. In this context, Almon ridge estimator including one biasing parameter is commonly preferred in this model. Based on recent advances, biased estimators that have more than one biasing parameter are stated as advantageous to one biasing parameter estimators. One of two-parameter estimators is Almon two-parameter ridge estimator of Özbay (Iran J Sci Tech Trans Sci 43: 1819–1828, 2019) which regulates the multicollinearity with its first biasing parameter and improves the quality of fit of regression with its second biasing parameter. As for another method to eliminate multicollinearity, exact linear restrictions are employed for the Almon two-parameter ridge estimator and restricted Almon two-parameter ridge estimator was introduced by Özbay and Toker (Considering linear constraints for Almon two parameter ridge estimation. 11th International Statistics Congress (ISC 2019), Muğla, Turkey, 2019). In this article, the issue of selecting the biasing parameters of the restricted and unrestricted Almon two-parameter ridge estimators is handled with the approach of mathematical programming instead of traditional selection methods. Different scenarios in which mean square error is minimized or coefficient of multiple determination is maximized are constituted by this mathematical programming approach. In real-life data analysis, we focus on global warming as a trend topic to demonstrate the effect of mathematical programming approach on the mentioned estimators. The dataset in question comprises carbon dioxide emission that has adverse effects on global warming via increasing average global temperature. [ABSTRACT FROM AUTHOR]

In this research, the problem of optimal conjunctive operation of surface and groundwater systems is investigated by proposing a cyclic storage approach. This problem is solved here using mathematical programming and some efficient Meta heuristic algorithms. For this purpose, the mathematical model of this system is defined and first solved by the nonlinear programming (NLP) method. In addition, the performance of the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gravitational search algorithm (GSA) and, particle swarm optimization (PSO) algorithms are also studied to solve this problem. Here, two case studies, meaning a hypothetical benchmark system and conjunctive use of Buchan dam reservoir and Miandoab aquifer located in the catchment area of Urmia Lake (ZarrinehRoud catchment area) as a real problem, are considered to study the performance of proposed methods. In other words, for the hypothetical benchmark system, the results show that the optimal operating cost and related computational time are equal to 5.2428 Billion Rials and 5400 s, respectively, obtained by using the NLP method. In addition, in comparison with the result of the NLP method, the operation costs increased by 26.36%, 26.1%, 44.91% and, 21.28% using ABC, GA, GSA and, PSO algorithms, respectively. However, the computational time is extremely decreased in comparison with the related value of the NLP method using these algorithms for a particular case. In other words, for the real benchmark system, the results show that the optimal operating cost and related computational time are equal to 139.0145 Billion Rials and 259,200 s, respectively, obtained by using the NLP method. In addition, in comparison with the result of the NLP method, the operation costs increased by 43.74%, 32.32%, and 50.57% using ABC, GA and, PSO algorithms, respectively. However, by using this algorithm, the computational time is extremely decreased in comparison with the related value of the NLP method for a particular case. Furthermore, in both case studies, the water demands are fully stratified using the proposed methods. Therefore, the obtained results show the efficiency and effectivity of the proposed methods to solve this complex optimization problem considering a cyclic storage approach. [ABSTRACT FROM AUTHOR]

This article deals with a class of constrained nonsmooth multiobjective programming problems (NMOPP) in the setting of Hadamard manifolds. The generalized Guignard constraint qualification (GGCQ), Abadie constraint qualification (ACQ), and the generalized ACQ (GACQ) are introduced in the framework of Hadamard manifolds for NMOPP using the notion of Clarke subdifferentials. Subsequently, by employing GGCQ and geodesic quasiconvexity assumptions, we establish Karush–Kuhn–Tucker (abbreviated as, KKT)-type necessary criteria of Pareto efficiency for NMOPP. Moreover, we establish that ACQ and GACQ are sufficient criteria for satisfaction of GGCQ. Several nontrivial numerical examples are furnished in manifold settings to demonstrate the validity of the derived results. To the best of our knowledge, this is the first time that ACQ, GACQ, GGCQ, and KKT-type necessary criteria of Pareto efficiency for NMOPP have been studied in manifold setting using Clarke subdifferentials. [ABSTRACT FROM AUTHOR]

Despite various supervised methods and algorithms, prediction methods do not necessarily provide the optimal values of the outputs. In this paper, an approach is proposed based on the integration of the clustering algorithm and a new mathematical programming model for predicting the efficient and optimal level of hospital outputs. In the first stage, units are evaluated using data envelopment analysis (DEA) and then, high-efficiency units are selected. In the second stage, the selected units are clustered based on the inputs of the units using the fuzzy C-means algorithm. For each unit to be estimated, the corresponding cluster is found and the closest unit to the target unit is determined. Finally, using the proposed new mathematical programming model, the optimal values are estimated based on the more efficient unit. A case study on Iranian hospitals illustrates the implementation of algorithms and methods and shows the abilities of the proposed approach. [ABSTRACT FROM AUTHOR]

As an important part of the energy transportation system, oil and gas pipelines are developing toward intelligence and digitalization. Vigorously developing and constructing pipeline networks based on intelligent methods such as big data and neural networks will help improve the efficiency of operation and management. This work provides an overview of recent developments of intelligent methods, including machine learning approaches, heuristic algorithms, and mathematical programming, used in the pipeline industry that can provide beneficial support for various applications enhancing operation and maintenance. Several aspects such as operating condition recognition, safety monitoring, pipeline remaining life prediction, and fault detection are reviewed and discussed. The study shows the need to focus on improving management level and ensuring pipeline safety. [ABSTRACT FROM AUTHOR]

This study considers equilibrium problems, focusing on identifying finite solutions for feasible solution sequences. We introduce an innovative extension of the weak sharp minimum concept from convex programming to equilibrium problems, coining this as weak sharpness for solution sets. Recognizing situations where the solution set may not exhibit weak sharpness, we propose an augmented mapping approach to mitigate this limitation. The core of our research is the formulation of augmented weak sharpness for the solution set. This comprehensive concept encapsulates both weak sharpness and strong non-degeneracy within feasible solution sequences. Crucially, we identify a necessary and sufficient condition for the finite termination of these sequences under the premise of augmented weak sharpness for the solution set in equilibrium problems. This condition significantly broadens the scope of the existing literature, which often assumes the solution set to be weakly sharp or strongly non-degenerate, especially in mathematical programming and variational inequality problems. Our findings not only shed light on the termination conditions in equilibrium problems but also introduce a less stringent sufficient condition for the finite termination of various optimization algorithms. This research, therefore, makes a substantial contribution to the field by enhancing our understanding of termination conditions in equilibrium problems and expanding the applicability of established theories to a wider range of optimization scenarios. [ABSTRACT FROM AUTHOR]

In this research, we develop an extension of the stochastic routing model with a fixed capacity for the distribution of perishable products with a time window. We use theoretical probability distributions to model the life of transported products and travel times in the network. Our main objective is to maximize the probability of delivering products within the established deadline with a certain level of customer service. Our project is justified from the perspective of reducing the pollution caused by greenhouse gases generated in the process. To optimize the proposed model, we use a Generic Random Search Algorithm. Finally, we apply the idea to a real problem of designing strategies for the optimal management of perishable food distribution routes that involve a time window, the objective being to maximize the probability of meeting the time limit assigned to the route problem by reducing, in this way, the pollution generated by refrigerated transport. [ABSTRACT FROM AUTHOR]

In order to reduce complexity when designing multi-media communication networks, researchers often consider spanning tree problems defined on edge-labeled graphs. The earliest setting addressed in the literature aims to minimize the number of different media types, i.e., distinct labels, used in the network. Despite being extensively addressed, such a setting completely ignores edge costs. This led to the definition of more realistic versions, where budgets for the total cost, or the number of distinct labels allowed, were introduced. In this paper, we introduce and prove the NP-hardness of the Budgeted Labeled Minimum Spanning Tree problem, consisting in minimizing the cost of a spanning tree while satisfying specified budget constraints for each label type. This problem combines the challenges of cost efficiency and label diversity within a fixed budgetary framework, providing a more realistic and practical approach to network design. We provide three distinct mathematical programming formulations of the problem and design a Lagrangian approach to derive tighter lower bounds for the optimal solution of the problem. The performances of the proposed methods are assessed by conducting a series of computational experiments on a variety of randomly generated instances, which showed how the complexity of the problem increases as the size of the network, as well as the number of labels, increase and the budget restrictions are tightened. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a multi-objective mathematical programming model for the trade-off of time, cost, and quality in the project-scheduling problem (PSP) by taking priorities and resource constraints as well as activity preemption into account. First, a small-sized problem instance that was a sub-project of an oil and gas construction project was used for te validation of the proposed model and algorithm. Subsequently, considering the sensitivity, complexity, and importance of oil and gas projects, the proposed model was implemented in a large-sized oil and gas construction project. Considering the NP-hardness of this problem, the NSGA-II metaheuristic algorithm was used to deal with the time, cost, and quality trade-off problem. Finally, a sensitivity analysis was implemented on the three main parameters of time, cost, and quality to investigate the effects of changes on the results. The findings show that the proposed model is more sensitive to cost changes, so an increase in project costs leads to a drastic change in the values of other objective functions. [ABSTRACT FROM AUTHOR]

The significance of regional dynamics in the process of economic development and regional development has increased as a result of significant factors like competitiveness, human resource development, and observation of the global market. In this study, mathematical programming-based cluster analysis has been conducted to group the regions in Türkiye according to sectoral employment rates. A mixed integer mathematical model is presented that maximizes the smallest of the out-of-cluster distances while minimizing the largest within-cluster distance. Level 2-26 sub-regions in Türkiye are clustered according to sectoral employment data for 2021 and 2022. As a result, two clusters were obtained for both years in our country according to employment status by gender on a sectoral basis. One of these clusters is where the employment rate of the agricultural sector is higher than other sectors, and the other is where the employment rate of the industrial and service sectors is higher. When the 2021 and 2022 clusters are compared, in total, TR22, TR32, TR33, TRC3; in men, TR21, TR22, TR32, TR52, TR81; In women, it was observed that TRC1 regions were assigned to different clusters. By implementing a successful employment policy as human resource development across the national government, it will be possible to ensure the balanced growth of provinces located in Türkiye's various geographical areas. [ABSTRACT FROM AUTHOR]

One of my last assignments at the university of Gothenburg was to teach a sequence of three seminars in programming for prospective teachers (n=37). The three seminars are given in the introduction of this manuscript. Since this was a course for prospective upper secondary teacher of mathematics, it was decided that it should be a course in programming for learning mathematics. This manuscript is a research article but also a manuscript about programming in GeoGebra, Python, and Wolfram Alpha. The examples I shared with my students led me to write a book about programming. See the reference list. [ABSTRACT FROM AUTHOR]

In this paper, we consider a Markov decision process (MDP) with a Borel state space X⊔ {Δ}, where Δ is an absorbing state (cemetery), and a Borel action space A. We consider the space of finite occupation measures restricted on X\times A and the extreme points in it. It is possible that some strategies have infinite occupation measures. Nevertheless, we prove that every finite extreme occupation measure is generated by a deterministic stationary strategy. Then, for this MDP, we consider a constrained problem with total undiscounted criteria and J constraints, where the cost functions are nonnegative. By assumption, the strategies inducing infinite occupation measures are not optimal. Then our second main result is that, under mild conditions, the solution to this constrained MDP is given by a mixture of no more than J +1 occupation measures generated by deterministic stationary strategies. [ABSTRACT FROM AUTHOR]

The primary focus of the warehouse relocation problem is to systematically and efficiently move items already stored in the warehouse to newly planned storage locations, which is a critical issue for the successful operation of Automated Storage and Retrieval Systems (AS/RS). There is limited research on the warehouse relocation problem for AS/RS with multiple loading and unloading stations. To address this issue, this research proposed an approach to rearrange the materials stored in a unit-load AS/RS with multiple loading stations to a target assignment. A mathematical model based on integer programming was built to determine the optimal sequence for relocating storage locations and arrange the sequence of movement. Since this is at least NP-hard to solve, a heuristic algorithm was designed to solve this problem in several segments, enabling its application in practical scenarios with larger data scales to enhance the operational efficiency of automated storage systems. Computational experiments were conducted using various problem sizes to assess the performance of the proposed algorithm. Additionally, this study applied the proposed method to plan storage relocation for a large-scale automated AS/RS, which is operational at a computer hardware manufacturer in Taiwan, as a means to verify the feasibility and effectiveness of the method proposed in this research. [ABSTRACT FROM AUTHOR]

In most project portfolio selection (PPS) situations, the presence of multiple attributes and decision-maker preference is inevitable. As Multi-criteria Decision Analysis (MCDA) methods provide a framework well-suited to deal with these challenges in PPS problems, the use of MCDA methods in real-life PPS problems has increased in recent years. This paper provides a comprehensive literature review of the use of different MCDA methods and their individual or combined utilization with other modeling techniques to support PPS problems. First, we summarize how MCDA methods are used in different modeling approaches. Second, we examine the mathematical models that are generally used to combine MCDA with mathematical programming techniques to solve PPS problems with resource constraints. Third, we present the drawbacks of combined utilization and discuss recent advances. Finally, we visualize the summary of the reviewed papers as a decision tree to assist researchers and practitioners in the use of MCDA methods in a specific PPS context and propose some future research directions. [ABSTRACT FROM AUTHOR]

LNG liquefaction process is energy and cost intensive. Minimisation of energy consumption is the main target of LNG liquefaction process design and operational optimisation. A new mathematical programming methodology for optimisation of LNG liquefaction process is presented. The proposed methodology links mathematical programming and thermodynamic analysis to construct a MINLP (Mixed Integer Nonlinear Programming) model. Rigorous thermodynamic analysis was carried out to build Excel-based thermodynamic property models. Excel models were linked to GAMS through GDXXRW (GAMS Data Exchange) facility. Based on the developed model, LINDO global solver in GAMS was used to address the MINLP problem, and the optimisation results were eventually verified by detailed simulation. In parallel, optimal design and operational conditions can be achieved. To depict the efficacy of the new methodology, a C3MR LNG liquefaction process case study was investigated where the total energy consumption of the compressor was reduced by 15.77%. Deep thermodynamic analysis was performed to provide complete understanding of the process. [ABSTRACT FROM AUTHOR]

The authors propose considering the trend of a non-stationary time series as a linear regression with unknown switching points. The method of evaluating the switching points based on intelligent data analysis using statistical criteria is described and substantiated. [ABSTRACT FROM AUTHOR]

This document provides a summary of research papers presented at a conference on mathematical optimization for fair social decisions. The conference honored Michel Balinski, a mathematician and economist known for his work in transportation problems, matching algorithms, voting theory, and fair representation. The papers covered a range of topics including optimal partisan gerrymandering, approval-based apportionment, and strategy-proof voting rules. Additionally, the document includes summaries of papers in the fields of network formation problems, game theory, and social choice theory, covering topics such as the median problem, minimum cut problems, network stability, splitting games, price of anarchy, online learning algorithms, cooperative games, and fair division. These summaries provide a quick overview of each paper's main contributions, helping library patrons determine their relevance to their research interests. [Extracted from the article]

Decision making problems are typically concerned with maximizing efficiency. In contrast, we address problems where there are multiple stakeholders and a centralized decision maker who is obliged to decide in a fair manner. Different decisions give different utility to each stakeholder. In cases where these decisions are made repeatedly, we provide efficient mathematical programming formulations to identify both the maximum fairness possible and the decisions that improve fairness over time, for reasonable metrics of fairness. We apply this framework to the problem of ambulance allocation, where decisions in consecutive rounds are constrained. With this additional complexity, we prove structural results on identifying fair feasible allocation policies and provide a hybrid algorithm with column generation and constraint programming-based solution techniques for this class of problems. Computational experiments show that our method can solve these problems orders of magnitude faster than a naive approach. [ABSTRACT FROM AUTHOR]

LU and Cholesky matrix factorization algorithms are core subroutines used to solve systems of linear equations (SLEs) encountered when solving an optimization problem. Standard floating-point algorithms are highly efficient but remain susceptible to the accumulation of round-off errors, which can lead solvers to return feasibility and optimality claims that are actually invalid. This paper introduces a novel direct solution approach for solving sequences of closely related SLEs encountered in nonlinear programming efficiently and without round-off errors. Specifically, it introduces rank-one update algorithms for the round-off error–free factorization framework, a tool set built on integer-preserving arithmetic that has led to the development and implementation of extremely reliable subroutines for solving SLEs occurring in linear programming. The formal guarantees of the presented algorithms are established through the derivation of theoretical insights. Their advantages are supported with computational experiments, which demonstrate upward of 75× improvements over exact factorization runtimes on fully dense matrices with more than one million entries. A significant advantage of the featured integer-preserving framework is that the length of any matrix coefficient produced by its algorithms is bounded polynomially in the size of the inputs without having to resort to greatest common divisor operations, which are required by and thereby hinder an efficient implementation of exact rational arithmetic approaches. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0331) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0331). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

This paper is the fruit of a partnership with Renault. Their reverse logistic requires solving a continent-scale multiattribute inventory routing problem (IRP). With an average of 30 commodities, 16 depots, and 600 customers spread across a continent, our instances are orders of magnitude larger than those in the literature. Existing algorithms do not scale, so we propose a large neighborhood search (LNS). To make it work, (1) we generalize existing split delivery vehicle routing problems and IRP neighborhoods to this context, (2) we turn a state-of-the-art matheuristic for medium-scale IRP into a large neighborhood, and (3) we introduce two novel perturbations: the reinsertion of a customer and that of a commodity into the IRP solution. We also derive a new lower bound based on a flow relaxation. In order to stimulate the research on large-scale IRP, we introduce a library of industrial instances. We benchmark our algorithms on these instances and make our code open source. Extensive numerical experiments highlight the relevance of each component of our LNS. Funding: This work was supported by Renault Group. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0342. [ABSTRACT FROM AUTHOR]

Capacitated lot sizing is a frequently encountered production-planning problem in manufacturing companies. However, many real life factors are usually not taken into account during the model development phase for these problems. In this study, an integrated lot-sizing and scheduling problem of a paper bag production firm is considered. A mixed integer mathematical programming model is proposed for modeling and solving the problem. Setup cost/time between product and period changes are considered in the proposed model. It is aimed to minimize the total cost of production, holding inventory, backorder, procurement and setup for product type change and product period change. It is observed that productive utilization of manufacturing resources and the generation of more effective production plans are possible by the application of the proposed model. [ABSTRACT FROM AUTHOR]