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Balancing conflicting goals among different stakeholders is a challenging problem in various application domains. In this paper, we analyse it in the context of home healthcare. Fairness objective functions for nurses and patients are combined with system-level governance goals of the territorial centers in charge of the assistance service. From the solution of several multi-objective problems including one objective for each actor hierarchically ordered, the best goal for each stakeholder is identified and interesting managerial conclusions are drawn. To efficiently solve large-size instances, we introduce a parallel Adaptive Large Neighborhood Search algorithm with destroy and repair operators customised to solve multi-objective multi-actor problems. The algorithm proves to be highly efficient and effective when compared to a commercial Mixed Integer Programming solver, either in its plain form or enforced by a mild start and a primal heuristic. Additionally, we devise a metaheuristic method to generate the Pareto frontier of an instance. [ABSTRACT FROM AUTHOR]

The present study simultaneously optimizes the three objective functions of pre-tensioned concrete (PT) beams, such as construction and material cost, beam weights, and beam depth, where multiple objectives optimizations (MOO) conflicting with each other are performed. MOO is performed based on 21 input parameters including tendon parameters and the 21 output parameters. Preassigned input parameters are defined by 16 equalities and design requirements are also implemented during the optimization through 19 code-based inequalities. Finally, the five-step ANN-based algorithms are presented to find optimized design parameters subject to external loads within ranges prescribed by inequalities. A Pareto frontier is presented based on the combinations of weight fractions representing contributions made by the three objective functions. ANN-based optimizations are capable of quantifying tendon ratios and rebar areas when concrete sections crack under service loads while optimizing multi-design targets and objective functions at the same time. The ANN-based optimized designs are verified with a structural mechanics-based software, AutoPT. Reductions of 9.1%, 30.1%, and 10.4% in beam depths, costs, and weights, respectively, obtained based on a Pareto frontier which simultaneously optimizes multi-objective functions are compared with probable designs by human engineers, demonstrating a design efficiency for a pre-tensioned concrete (PT) beam.

In light of Switzerland's 2050 energy goals, the nation aims to boost its domestic hydroelectric output, notably focusing on small-scale hydroelectric power plants. Concurrently, there is an effort to renovate hydroelectric plants to make them more environmentally friendly, emphasizing ecological flow regulation to improve river conditions. This study explores the application of a non-proportional flow allocation method to better assess both ecological and economic outcomes. Unlike traditional fixed or proportional flow methods, this approach allows for a more dynamic balance between hydropower generation and riverine ecosystem health. This study focuses on two key species, brown trout and grayling. In particular, this work highlighted that trout are better suited for low-flow conditions (Weighted Usable Area, WUA, peaks below 1 m3/s), while grayling require significantly higher flows (WUA peaks over 4.5 m3/s). This disparity in habitat preferences raises concerns about the current reliance on single-species models, emphasizing the need for multi-species ecological assessment in future studies. When applied to a small hydropower plant in the Swiss Jura, the non-proportional flow method resulted in an improvement of ecological conditions of at least 37.7%, which consequently led to a reduction of the hydroelectric production of at least 10%. Through strategic upgrades to the facility (e.g., by minimizing hydraulic losses, implementing more efficient turbines, or incorporating photovoltaic panels over water channels), it is possible to simultaneously enhance both energy output and environmental sustainability. These findings suggest that non-proportional flow allocation holds significant potential for broader use in sustainable hydropower management, providing a pathway toward meeting both energy production and ecological conservation goals. [ABSTRACT FROM AUTHOR]

Plant breeding is a complex endeavor that is almost always multi-objective in nature. In recent years, stochastic breeding simulations have been used by breeders to assess the merits of alternative breeding strategies and assist in decision-making. In addition to simulations, visualization of a Pareto frontier for multiple competing breeding objectives can assist breeders in decision-making. This paper introduces Python Breeding Optimizer and Simulator (PyBrOpS), a Python package capable of performing multi-objective optimization of breeding objectives and stochastic simulations of breeding pipelines. PyBrOpS is unique among other simulation platforms in that it can perform multi-objective optimizations and incorporate these results into breeding simulations. PyBrOpS is built to be highly modular and has a script-based philosophy, making it highly extensible and customizable. In this paper, we describe some of the main features of PyBrOpS and demonstrate its ability to map Pareto frontiers for breeding possibilities and perform multi-objective selection in a simulated breeding pipeline. [ABSTRACT FROM AUTHOR]

There are still significant technical challenges associated with thermal management of electronic devices such as microprocessors. To improve heat dissipation performance of integrated circuits, a new Fibonacci phyllotaxis design of circular micropin fin heat sinks has been developed. To minimize both chip temperature and pumping power, a multi‐objective optimization technique was employed. The effect of design parameters such as phyllotaxis coefficient, pin fin diameter, and pin fin height on response parameters was numerically investigated using the full factorial design of the experiment. Artificial neural network was coupled with MO‐Jaya, to arrive at a Pareto frontier of optimal compromise solutions. The optimal set of design variables were found to be a height of 300 μm, a diameter of 122.6 μm, and a phyllotaxis coefficient of 130 μm with an inlet velocity of coolant 2.263 m/s. The selected optimum design was then investigated numerically, and the outcomes were compared to those predicted by the MO‐Jaya algorithm. The final confirmed response variables were a maximum temperature of 51.6°C and a pumping power of 0.191 W. The results show that the Fibonacci phyllotaxis structure of the micro pin fin heat sink has better heat‐dissipating performance. [ABSTRACT FROM AUTHOR]

The global population rise has increased vehicles on roads, complicating traffic management. Inefficient traffic control systems cause significant economic losses owing to commuter time wastage, high energy consumption, and greenhouse gas emissions. Traffic signal control systems (TSCSs) are vital in traffic management, impacting traffic flow significantly; therefore, studies are exploring new optimization approaches that adapt to changing traffic conditions. However, they concentrate on either new technology infusion or on control algorithm optimization, and do not holistically address the architectural configuration of the system. In this study, we presented a unique case study by applying an existing systematic framework to the TSCS system architecture design and selection process. This application demonstrates that TSCS enhancement is a multifaceted process that requires a comprehensive assessment of not only technical aspects, such as the control algorithm, but also factors including system architecture, security, and data integrity. Because of the increasing reliance of TSCSs on data exchange between their various subsystems, this case study also adopted a cybersecurity perspective of the system and introduced cyber resiliency as a crucial metric for evaluating TSCS architecture performance. Furthermore, through the applied framework, an optimal TSCS architectural configuration with executable options was identified by generating multiple TSCS architectural configurations using decision option patterns and identifying those on the Pareto frontier to understand the architectural decision‐making process. Traffic engineers and transportation planners can use this case study application as a guide to optimize TSCSs employed in existing transportation networks and design more efficient transportation networks for future urban development. [ABSTRACT FROM AUTHOR]

The Pareto frontier, a concept rooted in economics and multi-objective optimization, represents the interplay between two objectives. In the context of power systems, it is often the case that different objectives have to be considered at the same time, such as the minimization of the operational cost and the minimization of greenhouse gas emissions. However, whether both objectives are achievable or not largely depends on the specific technoeconomic characteristics of the generation units involved. In this context, the current paper presents the Pareto frontier for different combinations of technoeconomic characteristics of generation units, and different types of functions for the operational cost and CO2 emissions, as well as various technologies, including Combined Heat and Power, heat-only and thermal power stations. The analysis reveals a range of shapes for the resulting Pareto frontier and underlines the critical patterns and dependencies within the energy system's operational framework, highlighting the complex interplay between environmental impact and economic feasibility. [ABSTRACT FROM AUTHOR]

Injection molding is the most common method to mass‐produce plastic parts. The high cost and long lead time of traditional injection molding steel molds make 3D‐printed plastic molds a potential substitute for limited production runs and prototype parts. However, results suggest that for some materials, utilizing plastic molds (compared to metal molds) could decrease the mechanical properties of parts, especially the ductility. In order to better understand the issues with utilizing plastic molds, in this work, we use a design of experiments to investigate how the processing parameter changes affect mechanical properties by comparing a semi‐crystalline polypropylene and an amorphous polystyrene material in a metal mold for better control of the controllable variables. Results show that ductility is the most sensitive property to processing conditions, especially for polypropylene. Our analysis shows that the controllable variable that affects ductility the most is mold temperature, then packing pressure, and injection time. Highlights: 3D‐printed plastic molds is a potential substitute for limited production runs.Effects of processing parameters are analyzed through the design of experiments.Ductility is the most sensitive property to processing conditions for the material studied.Mold temperature is the most significant variable for ductility. [ABSTRACT FROM AUTHOR]

To address the critical issue of polarization during lithium-ion battery charging and its adverse impact on battery capacity and lifespan, this research employs a comprehensive strategy that considers the charging duration, efficiency, and temperature increase. Central to this approach is the proposal of a novel negative pulsed charging technique optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This study initiates the creation of an intricate electrothermal coupling model, which simulates variations in internal battery parameters throughout the charging cycle. Subsequently, NSGA-II is implemented in MATLAB to fine-tune pulsed charging and discharging profiles, generating a Pareto front showcasing an array of optimal solutions tailored to a spectrum of goals. Leveraging the capabilities of the COMSOL Multiphysics software 6.2 platform, a high-fidelity simulation environment for lithium-ion battery charging is established that incorporates three charging strategies: constant-current (CC) charging, a multi-stage constant-current (MS-CC) charging protocol, and a pulsed-current (PC) charging strategy. This setup works as a powerful instrument for assessing the individual effects of these strategies on battery characteristics. The simulation results strongly support the superiority of the proposed pulsed-current charging strategy, which excels in increasing the battery temperature and amplifying battery charge capacity. This dual achievement not only bolsters charging efficiency significantly but also underscores the strategy's potential to augment both the practical utility and long-term viability of lithium-ion batteries, thereby contributing to the advancement of sustainable energy storage solutions. [ABSTRACT FROM AUTHOR]

The decathlon consists of ten events with scores which are then aggregated to determine the final ranking. We develop a decathlon scoring method which is far simpler than the existing standard (IAAF1984) tables, as there are only 9 parameters instead of 30 which have an impact on the overall rank. We first identify athletes who are on the Pareto-efficient frontier i.e. those who are not dominated by anyone else. We then remove these frontier athletes and again pick all non-dominated athletes to obtain a second dominating group/Pareto frontier and iterate this procedure for the decathlon data from 1986 to 2020. Each of these groups are then characterized by their set of ten median performances. Improving from the last to the top group can then be seen as a path of progress, leading from the lowest to the highest set of median performances. Every event should have the same importance, so we normalize the data such that the path of progress follows as much as possible a space diagonal of a ten dimensional hypercube. Furthermore, any adjustment of a benchmark does not change any actual decathlon performance, hence there cannot be any unwanted rank reversals. This allows a smooth adjustment of these tables in the future, if for instance a new type of javelin needs to be introduced to reduce the range. We normalize such that current performances between 7000 and 9000 points still fall into the same range with our point tables. [ABSTRACT FROM AUTHOR]

Identifying the most important factors affecting population growth in animal life cycles is an important tool of species conservation. Delta Smelt (Hypomesus transpacificus), an annual fish endemic to the San Francisco Estuary in California (USA), has been provided legal protection since 1993 but 30 years later exists in a conservation‐reliant state on the brink of extinction. Despite considerable controversies about what factors are most responsible for the species' decline, no population growth rate sensitivity comparisons between the most important factors regulating growth have been done. Nor has anyone attempted to quantitatively identify habitat conditions needed to support positive population growth. We developed a set of stage‐structured population models to link habitat indices regulating recruitment of new generations of fish as they metamorphosed into juveniles and the subsequent survival of those fish over several seasons until they reached adulthood. These models are used to quantify drivers of growth rate variation over 30 years. Several complimentary sensitivity analyses indicated freshwater outflow to the estuary during summer had the largest potential to change population growth. Multiple habitat metrics (e.g., food availability, temperature) influencing recruitment and life stage specific survival rates across different seasons interacted in nonlinear ways to determine habitat conditions and water management targets associated with positive population growth. We discuss the implications for freshwater management, Delta Smelt conservation, and the challenges climate change poses for co‐implementation of these two societal priorities. [ABSTRACT FROM AUTHOR]

An electrocoagulation treatment is implemented to eliminate the potential waste hazards derived from excessive amounts of total dissolved solids (TDS) and total suspended solids (TSS) prior to its disposal. The overall objective is to concurrently maximize the removal of TDS and TSS of the wastewater and minimize the total energy cost in the electrochemical process. A fuzzy optimization method is utilized to address this problem. This enables the integration of the two objectives to properly identify the satisfying solution and to determine the optimum variable conditions in terms of the reaction time (10–40 min) and applied current (0.5–2.0 A). This research work aims to develop an optimization model for the electrochemical treatment of oily wastewater. The model incorporates empirical equations to quantify the variables of the removal efficiencies. Additionally, the model considers the total costs associated with energy consumption and addresses the cumulative uncertainty of experimental runs. The ε-constraint method is employed to identify the Pareto front. A fuzzy optimization approach is employed to enable the simultaneous optimization of multiple objectives. By integrating these variables, the research seeks to improve the efficiency and cost of TDS and TSS removal in the electrochemical treatment process. The optimum solution achieved the reaction time and applied current of 40 min and 1.97 A, respectively. This attained an optimum removal (19.34 ± 0.99% for TDS) and (82.15 ± 0.88% for TSS), a total cost of 8.28 USD/m3, and a 95.22% overall degree of satisfaction. [ABSTRACT FROM AUTHOR]

Water resources play an essential role in achieving a multifaceted development society, and their superiority allocation affects the development rate of cities. The model of this research for allocating optimal water resources is constructed with objectives including social, economic, and ecological objectives, and the constraints including water supply, water demand, water transmission, and non-negativity, based on which the objectives are integrated using the Pareto front, and the dimensionless processing and entropy weighting method. Next, the improved marine predator algorithm (IMPA), which uses chaos initialization in the initial population, incorporates the golden sine algorithm in the process seeking and enhances the search capability using the quadratic interpolation method in the result comparison, is contrasted with several algorithms based on different functions for optimal values, standard deviation, and mean values. Then, using Huaying City as the research area, the water distribution scheme for the region in 2021 is obtained. The allocation schemes of local confirm the superiority of IMPA in terms of accuracy and stability, which provides a new idea for water allocation in Huaying City. The results of the experiment show that IMPA is an effective and available choice for solving water resources optimization researches. [ABSTRACT FROM AUTHOR]

The need to make evidence-based decisions in conservation planning for threatened species in the face of limited resources and knowledge is widely recognised as a growing challenge. Increasingly sophisticated decision-support tools and approaches are available to conservation programs. The ability of conservation planners to effectively implement these tools will be key to incorporating complex information into threatened species management. The development of effective decision science approaches does not end when they are made available to planners. Planner and practitioner input into their use and outputs is an important part of incorporating these tools into on-ground conservation. The New South Wales Saving our Species program is a large-scale conservation program with jurisdiction over more than 1100 threatened species, ecological communities and populations. We discuss why co-design is key to successful implementation of decision science in program-level planning; this approach has supported the Saving our Species program to account for forms of knowledge that may otherwise be ignored by data driven optimisation. This paper focuses on the role of conservation planners in developing and applying decision tools. We present three case studies that deployed tools co-developed for the Saving our Species program. Through these case studies, we suggest that effective conservation planning can be best achieved through (1) narrowing down the number of options under consideration, by eliminating sub-optimal choices (2) supporting decision-makers to understand the relative advantages and disadvantages of the choices under consideration and (3) enhancing the effectiveness of decision-support tools by integrating practitioner expertise into their application. [ABSTRACT FROM AUTHOR]

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The existing algorithms for solving multi-objective optimization problems fall into three main categories: Decomposition-based, dominance-based, and indicator-based. Traditional multi-objective optimization problems mainly focus on objectives, treating decision variables as a total variable to solve the problem without considering the critical role of decision variables in objective optimization. As seen, a variety of decision variable grouping algorithms have been proposed. However, these algorithms are relatively broad for the changes of most decision variables in the evolution process and are time-consuming in the process of finding the Pareto frontier. To solve these problems, a multi-objective optimization algorithm for grouping decision variables based on extreme point Pareto frontier (MOEA-DV/EPF) is proposed. This algorithm adopts a preprocessing rule to solve the Pareto optimal solution set of extreme points generated by simultaneous evolution in various target directions, obtains the basic Pareto front surface to determine the convergence effect, and analyzes the convergence and distribution effects of decision variables. In the later stages of algorithm optimization, different mutation strategies are adopted according to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals, thus enhancing the performance of the algorithm. Evaluation validation of the test functions shows that this algorithm can solve the multi-objective optimization problem more efficiently. [ABSTRACT FROM AUTHOR]

This paper addresses a multiobjective version of the Team Orienteering Problem (TOP). The TOP focuses on selecting a subset of customers for maximum rewards while considering time and fleet size constraints. This study extends the TOP by considering two objectives: maximizing total rewards from customer visits and maximizing visits to prioritized nodes. The MultiObjective TOP (MO-TOP) is formulated mathematically to concurrently tackle these objectives. A multistart biased-randomized algorithm is proposed to solve MO-TOP, integrating exploration and exploitation techniques. The algorithm employs a constructive heuristic defining biefficiency to select edges for routing plans. Through iterative exploration from various starting points, the algorithm converges to high-quality solutions. The Pareto frontier for the MO-TOP is generated using the weighted method, epsilon-constraint method, and Epsilon-Modified Method. Computational experiments validate the proposed approach's effectiveness, illustrating its ability to generate diverse and high-quality solutions on the Pareto frontier. The algorithms demonstrate the ability to optimize rewards and prioritize node visits, offering valuable insights for real-world decision making in team orienteering applications. [ABSTRACT FROM AUTHOR]

A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning‐based parameterizations using output from global storm‐resolving models. While neural networks (NNs) can achieve state‐of‐the‐art performance within their training distribution, they can make unreliable predictions outside of it. Additionally, they often require post‐hoc tools for interpretation. To avoid these limitations, we combine symbolic regression, sequential feature selection, and physical constraints in a hierarchical modeling framework. This framework allows us to discover new equations diagnosing cloud cover from coarse‐grained variables of global storm‐resolving model simulations. These analytical equations are interpretable by construction and easily transferable to other grids or climate models. Our best equation balances performance and complexity, achieving a performance comparable to that of NNs (R2 = 0.94) while remaining simple (with only 11 trainable parameters). It reproduces cloud cover distributions more accurately than the Xu‐Randall scheme across all cloud regimes (Hellinger distances < 0.09), and matches NNs in condensate‐rich regimes. When applied and fine‐tuned to the ERA5 reanalysis, the equation exhibits superior transferability to new data compared to all other optimal cloud cover schemes. Our findings demonstrate the effectiveness of symbolic regression in discovering interpretable, physically‐consistent, and nonlinear equations to parameterize cloud cover. Plain Language Summary: In climate models, cloud cover is usually expressed as a function of coarse, pixelated variables. Traditionally, this functional relationship is derived from physical assumptions. In contrast, machine learning (ML) approaches, such as neural networks, sacrifice interpretability for performance. In our approach, we use high‐resolution climate model output to learn a hierarchy of cloud cover schemes from data. To bridge the gap between simple statistical methods and ML algorithms, we employ a symbolic regression method. Unlike classical regression, which requires providing a set of basis functions from which the equation is composed of, symbolic regression only requires mathematical operators (such as +, ×) that it learns to combine. By using a genetic algorithm, inspired by the process of natural selection, we discover an interpretable, nonlinear equation for cloud cover. This equation is simple, performs well, satisfies physical principles, and outperforms other algorithms when applied to new observationally‐informed data. Key Points: We systematically derive and evaluate cloud cover parameterizations of various complexity from global storm‐resolving simulation outputUsing symbolic regression combined with physical constraints, we find a new interpretable equation balancing performance and simplicityOur data‐driven cloud cover equation can be retuned with few samples, facilitating transfer learning to generalize to other realistic data [ABSTRACT FROM AUTHOR]

Applying the solutions defined in the axiomatic bargaining theory to actual bargaining problems is a challenge when the problem is not described by its Utility Possibility Set (UPS) but as a social choice environment specifying the set of alternatives and utility profile underlying the UPS. It requires computing the UPS, which is an operational challenge, and then identifying at least one alternative that actually achieves the bargained solution's outcome. We introduce the axioms of Independence of Non-Strongly-Efficient Alternatives (resp. Weakly) and Independence of Redundant Alternatives. A solution satisfying these axioms can be applied to a simplified problem based on any reduced set of alternatives generating the strong (resp. weak) Pareto frontier of the initial problem, without changing the outcome, making the application of bargaining solutions to actual problems easier. We compare our axioms to usual independence axioms, and discuss their consistency with usual bargaining solutions. Then, we introduce monotonicity conditions corresponding to the existence of an interest group, i.e., agents ranking the alternatives in the same order. For such monotonic social choice environments, we provide a parameterized family of alternatives that generates the Pareto frontier of the bargaining problem, in line with our previous results. Our analysis illustrates that an axiomatic approach can be useful to foster the application of bargaining solutions, in complement to usual computational methods. [ABSTRACT FROM AUTHOR]

Interconnected systems forming complex networks are ubiquitous in many man-made and natural phenomena. When individual systems are aligned towards a desired trajectory, their synchronization’s stability depends on the network’s controllability, often achieved through pinning control. When optimizing controllability, unweighted Laplacian and uniform feedback gains are conventionally used for the pinned nodes, ignoring the significance of link weights, usually leading to suboptimal results. This study improves local stability of the synchronous state by addressing the controllability problem using weighted Laplacian matrices and nonuniform feedback gains. Using the master-stability function method, direct optimization of the controllability measure is formulated as a multi-objective optimization problem with a Pareto frontier and multiple optimal points. This multi-objective optimization problem is simplified into a spectral radius minimization problem, where reformulating it as a semidefinite programming (SDP) problem has led to a unique optimal point on its Pareto frontier. Many interesting analytical results have been established for different families of networks and subnetworks, including the criteria for optimal zero weights that can divide the network or optimal controllability measure of an arbitrary network and its mirrored networks. Additionally, for deterministic scale-free networks, it is demonstrated how the network can break down into smaller replicas and ultimately form a collection of path networks. Numerical simulations using the Rössler model illustrate the feasible region and, interestingly, show that the Pareto frontier of the deterministic scale-free networks is independent of its size.

The current public transportation systems predominantly rely on rigid schedules and service patterns, leading to suboptimal resource allocation that impacts both passengers and transit operators. This inefficiency results in the wastage of resources and dissatisfaction among users. The unsatisfactory passenger experience significantly contributes to the declining ridership, thereby diminishing revenue for transit operators. To specifically address these challenges encountered by Lhasa's public transportation system, we propose a multi-objective model for bus departure timetables. The model aims to synchronize the costs of passenger waiting time and bus operation costs concurrently, accounting for diverse constraints such as actual travel times, operational bus numbers, bus capacity limits, and arrival time distributions. In this research, we establish a multi-objective optimization model with the primary goal of maximizing passenger satisfaction while concurrently optimizing the revenue of the transit company. Implemented in Lhasa, China, we use the Non-Dominated Sorting Genetic Algorithm-Ⅱ to derive Pareto fronts relevant for analysis. The research findings demonstrate a reduction in the frequency of departures by one bus within a one-hour timeframe. Additionally, a substantial 37% decrease is observed in both the count of buses not arriving at stations and the number of passengers waiting at these stations compared to previous timetables. These results suggest promising potential for significant benefits to both the transit company and passengers within the public transportation system.

The production planning problem in the Pinus radiata sawmill industry revolves around determining how to cut a set of logs of different diameters to obtain pieces with a rectangular base, typically of the same length as the original log. The primary objective is often to maximize the volumetric yield or the ratio between the total volume of the produced pieces and the available volume of the logs. Given the scarcity of timber forests, small and medium-sized (SME) sawmills must optimize their operations to maximize the volumetric yield of logs, usually procured from third parties and may vary in quality and dimensions. In this context, the absence of decision support tools directly contributes to inefficient raw material utilization, consequently impacting the business’s profit. This study introduces a multi-objective mixed-integer linear programming model incorporating log availability and product demand as input parameters. The objective functions aim to minimize the total volumetric loss of utilized logs and the surplus quantity associated with products exceeding demand. The model integrates cutting patterns pre-determined for each log diameter as an additional input. The Cutlog© software was used to identify all the optimal cutting patterns. The $\varepsilon $ -constraint method, implemented in the CPLEX© solver, was employed to solve the model. The model was validated using representative instances designed to emulate the challenges faced by SME sawmills. Real industry data, surveys, and commercial records from SME sawmills in southern Chile were utilized. The results confirm the effectiveness of the proposed model in addressing the multi-objective challenges encountered by these businesses. The model successfully identifies multiple solutions on the Pareto frontier, offering valuable insights for decision-making.

Strategic evaluation is the key link of military strategic system and capacity building. Selecting and evaluating the system construction scheme can optimize strategic decision-making and improve strategic management level to a certain extent. This paper constructs an intelligent selection analysis framework of "prospect cost" system construction scheme based on MATE, and uses NSGA-Ⅱ intelligent optimization algorithm to find the Pareto front of alternative scheme in multi-dimensional tradeoff space, which improves the problem of large data volume. Taking a joint operation system as an example, the effectiveness of the proposed method is verified, providing theoretical support for the optimization and evaluation of system construction scheme.

Forests provide multiple ecosystem services, some of which are competitive, while others are complementary. Pareto frontier approaches are often used to assess the trade-offs among these ecosystem services. However, when dealing with spatial optimization problems, one is faced with problems that are computationally complex. In this paper, we study the sources of this complexity and propose an approach to address adjacency conflicts while analyzing trade-offs among wood production, cork, carbon stock, erosion, fire resistance and biodiversity. This approach starts by sub-dividing a large landscape-level problem into four smaller sub-problems that do not share border stands. Then, it uses a Pareto frontier method to get a solution to each. A fifth sub-problem included all remaining stands. The solution of the latter by the Pareto frontier method is constrained by the solutions of the four sub-problems. This approach is applied to a large forested landscape in Northwestern Portugal. The results obtained show the effectiveness of using Pareto frontier approaches to analyze the trade-offs between ecosystem services in large spatial optimization problems. They highlight the existence of important trade-offs, notably between carbon stock and wood production, alongside erosion, biodiversity and wildfire resistance. These trade-offs were particularly clear at higher levels of these optimized services, while spatial constraints primarily affected the magnitude of the services rather than the underlying trade-off patterns. Moreover, in this paper, we study the impact of the size and complexity of the spatial optimization problem on the accuracy of the Pareto frontiers. Results suggest that the number of stands, and the number of adjacency conflicts do not affect accuracy. They show that accuracy decreases in the case of spatial optimization problems but it is within an acceptable range of discrepancy, thus showing that our approach can effectively support the analysis of trade-offs between ecosystem services.

The Flexible Job Shop Scheduling Problem (FJSP) emerges as a challenging extension of the classic Job Shop, presenting NP-Hard characteristics. In the FJSP, a set of jobs, comprising multiple operations, must be allocated to a predefined set of machines, each with its respective execution times. The distribution of operations across machines significantly impacts scheduling efficiency, which can be evaluated and optimized based on various performance criteria and objectives. To address this complex problem, a multi-objective optimization algorithm is employed, focusing on three main performance criteria: completion time of all operations (Makespan), load assigned to the most heavily utilized machine, and the sum of loads across all machines. An FJSP algorithm based on the Artificial Bee Colony (ABC) metaheuristic (FJSP. ABC) generates a Pareto set comprising non-dominated and dominated solutions. These solutions represent optimal or near-optimal production schedules and offer diverse representations, such as Gantt charts. However, the decision-making process for selecting the best production schedule from the Pareto set demands additional considerations. To this end, we propose adopting a decision-making (DM) algorithm based on Fuzzy TOPSIS (Technique for Order of Preference by Similarity to the Ideal Solution in a Fuzzy environment). The DM Fuzzy TOPSIS algorithm accommodates the inclusion of variables not covered by the FJSP algorithm and aids decision-makers in identifying the most suitable production schedule based on the specific requirements of the production system. These additional variables may include maximizing or minimizing machine idleness, balancing the load of operations on machines, etc. Experimental results demonstrate that the application of the proposed algorithm yields values close to the expected outcomes for the analyzed variables proposed. The DM Fuzzy TOPSIS algorithm proves to be a valuable tool for supporting decision-making in production systems, assisting in the selection of the best production schedule among the optimal or near-optimal solutions obtained from the Pareto set. By integrating multi-objective optimization and decision-making techniques, this research contributes to more efficient and informed production scheduling practices, ultimately enhancing overall system performance.

Organic flash cycle facilitates high heat recovery and seamless integration with refrigeration cycle. Moreover, the use of a two-phase ejector in the organic flash cycle enables operation at temperatures below zero and enhances cooling/power production. A parametric study is conducted to scrutinize the influence of key design variables gas turbine inlet temperature (T 3), flash tank inlet temperature (T 14), and air compressor pressure ratio (P 2 /P 1) on plant outputs. P 2 /P 1 is a crucial design factor affecting overall performance, optimizing efficiencies and reducing levelized cost of product at a ratio of 5. Additionally, T 3 has positive effects on several performance indices, but higher values lead to decreased system-generated cooling from 910.2 kW to 774.1 kW. Meanwhile, T 14 has mixed effects, improving system-generated electricity, sustainability index, levelized cost of product, CO 2 emission rate, and exergy efficiency, but negatively impacting system-generated cooling, energy efficiency, and total investment cost rate. The optimization process has a positive impact on system-generated electricity, emitted CO 2 rate, sustainability index, exergo-environmental index, energy efficiency, and exergy efficiency, leading to their respective values reaching 5945 kW, 0.9648 kg/kWh, 1.511, 0.6158, 10889 kW, 42.37%, and 33.82. Meanwhile, system-generated cooling, total investment cost rate, and levelized cost of product show performance degradation. [ABSTRACT FROM AUTHOR]

Metal processing may benefit from innovative lean-and-green datacentric engineering techniques. Broad process improvement opportunities in the efficient usage of materials and energy are anticipated (United Nations Sustainable Development Goals #9, 12). A CO2 laser cutting method is investigated in this study in terms of product characteristics (surface roughness (SR)) and process characteristics (energy (EC) and gas consumption (GC) as well as cutting time (CT)). The examined laser cutter controlling factors were as follows: (1) the laser power (LP), (2) the cutting speed (CS), (3) the gas pressure (GP) and, (4) the laser focus length (F). The selected 10mm-thick carbon steel (EN10025 St37-2) workpiece was arranged to have various geometric configurations so as to simulate a variety of real industrial milling demands. Non-linear saturated screening/optimization trials were planned using the Taguchi-type L9(34) orthogonal array. The resulting multivariate dataset was treated using a combination of the Gibbs sampler and the Pareto frontier method in order to approximate the strength of the studied effects and to find a solution that comprises the minimization of all the tested process/product characteristics. The Pareto frontier optimal solution was (EC, GC, CT, SR) = (4.67 kWh, 20.35 Nm3, 21 s, 5.992 μm) for the synchronous screening/optimization of the four characteristics. The respective factorial settings were optimally adjusted at the four inputs (LP, CS, GP, F) located at (4 kW, 1.9 mm/min, 0.75 bar, +2.25 mm). The linear regression analysis was aided by the Gibbs sampler and promoted the laser power and the cutting speed on energy consumption to be stronger effects. Similarly, a strong effect was identified of the cutting speed and the gas pressure on gas consumption as well as a reciprocal effect of the cutting speed on the cutting time. Further industrial explorations may involve more intricate workpiece geometries, burr formation phenomena, and process economics. [ABSTRACT FROM AUTHOR]

We study the problem of allocating divisible bads (chores) among multiple agents with additive utilities when monetary transfers are not allowed. The competitive rule is known for its remarkable fairness and efficiency properties in the case of goods. This rule was extended to chores by Bogomolnaia, Moulin, Sandomirskiy, and Yanovskaya. For both goods and chores, the rule produces Pareto optimal and envy-free allocations. In the case of goods, the outcome of the competitive rule can be easily computed. Competitive allocations solve the Eisenberg-Gale convex program; hence the outcome is unique and can be approximately found by standard gradient methods. An exact algorithm that runs in polynomial time in the number of agents and goods was given by Orlin. In the case of chores, the competitive rule does not solve any convex optimization problem; instead, competitive allocations correspond to local minima, local maxima, and saddle points of the Nash social welfare on the Pareto frontier of the set of feasible utilities. The Pareto frontier may contain many such points and, consequently, the outcome of the competitive rule is no longer unique. In this paper, we show that all the outcomes of the competitive rule for chores can be computed in strongly polynomial time if either the number of agents or the number of chores is fixed. The approach is based on a combination of three ideas: all consumption graphs of Pareto optimal allocations can be listed in polynomial time; for a given consumption graph, a candidate for a competitive utility profile can be constructed via an explicit formula; each candidate can be checked for competitiveness and the allocation can be reconstructed using a maximum flow computation. Our algorithm immediately gives an approximately-fair allocation of indivisible chores by the rounding technique of Barman and Krishnamurthy. Funding: This work was supported by National Science Foundation (CNS 1518941); Lady Davis Fellowship Trust, Hebrew University of Jerusalem; H2020 European Research Council (740435); Linde Institute at Caltech. [ABSTRACT FROM AUTHOR]

One of the reasons why renewable energies are so attractive compared to fossil fuels is their low environmental impact. In addition, geothermal power plants contribute tremendously to sustainable energy generation for cities despite their lower energy efficiency than fossil fuel plants. The multi-heat recovery will eliminate the applicability defect mentioned. Therefore, this paper studies a novel tri-generation schema with the maximum use of heat loss through a multi-heat recovery technique in two principal processes, namely waste heat-to-power and power-to-H 2 and -purified water. A double-flash binary cycle, Rankine cycle, electrolyzer unit, and reverse osmosis desalination system all play a part in the creation of this system. The technical feasibility of the system is scrutinized based on energy-exergy, environmental, sustainability, and exergoeconomic metrics and bi-objective optimization. Generally, 1st separator pressure made the strongest effect on the measured variables among decision variables. The increase in this parameter led to an upward-and-downward behavior of the net electricity and exergetic efficiency; while the cost of products experienced a converse trend. Also, the produced H 2 and purified water together with the tri-generation gain output ratio augmented. Changes were not observed in net electricity and purified water with the change in 2nd separator pressure, but the H 2 production rate changed significantly. Through bi-objective optimization, net electricity, purified water production rate, and total investment cost rate also significantly increase. Based on the optimum design mode, the CO 2 emission rate and the sustainability index are higher than under the base case design. • Using multi-heat recovery technique in two principal processes, namely, waste heat-to-power and power-to-H2 and -purified water. • Evaluation of system indices based on energy-exergy and exergoeconomic metrics and bi-objective optimization. • Exergetic efficiency, TGOR, and the cost of products improved by 5.19%, 109.52%, and 8.54%, separately, thanks to bi-objective optimization. [ABSTRACT FROM AUTHOR]

Due to the fact that biomass fuel is capable of powering multi-generation systems, has a high-efficiency performance, and produces fewer harmful gases, biomass fuel can prove to be a valuable heat source. In this regard, this study introduces a new biomass-fueled power and hydrogen generation scheme. There are three subsystems involved in the study: a biomass-based gas turbine cycle, a steam flash cycle, and an electrolyzer unit. To begin, a parametric analysis is performed on the system from the perspectives of thermodynamics, thermoeconomic, and the environment. As a next step, four effective variables are evaluated for single-objective and bi-objective optimizations in order to determine the optimal working conditions. The results of bi-objective optimization indicate 48.78% and 41.40% energy and exergy efficiencies for the presented system, separately, with 8093 kW output power, 86.1 kg/day hydrogen production, 8684 t/MWh CO 2 emission, and 27.9 $/MWh Levelized Cost of Product. Compared to the base condition, hydrogen production grows 29.78%, but output power drops by 1.14%. Furthermore, hydrogen Production Optimum Design accounts for the maximum amount of hydrogen production in optimal conditions, producing 94.73 kg/day. The gasifier destroys the most exergy under base and optimum conditions. • Proposal of a novel power/hydrogen cogeneration system including a gas turbine cycle. • The utilization of a steam flash cycle integrated with an electrolyzer to generate more power. • Evaluating system performance using thermodynamic, thermoeconomic, and environmental metrics. • The highest cost is related to the gas turbine in the optimized condition based on output power. • The proposed system performs better than the existing related ones in the literature. [ABSTRACT FROM AUTHOR]

The current public transportation systems predominantly rely on rigid schedules and service patterns, leading to suboptimal resource allocation that impacts both passengers and transit operators. This inefficiency results in the wastage of resources and dissatisfaction among users. The unsatisfactory passenger experience significantly contributes to the declining ridership, thereby diminishing revenue for transit operators. To specifically address these challenges encountered by Lhasa's public transportation system, we propose a multi-objective model for bus departure timetables. The model aims to synchronize the costs of passenger waiting time and bus operation costs concurrently, accounting for diverse constraints such as actual travel times, operational bus numbers, bus capacity limits, and arrival time distributions. In this research, we establish a multi-objective optimization model with the primary goal of maximizing passenger satisfaction while concurrently optimizing the revenue of the transit company. Implemented in Lhasa, China, we use the Non-Dominated Sorting Genetic Algorithm-Ⅱ to derive Pareto fronts relevant for analysis. The research findings demonstrate a reduction in the frequency of departures by one bus within a one-hour timeframe. Additionally, a substantial 37% decrease is observed in both the count of buses not arriving at stations and the number of passengers waiting at these stations compared to previous timetables. These results suggest promising potential for significant benefits to both the transit company and passengers within the public transportation system. [ABSTRACT FROM AUTHOR]

Multi-objective optimization (MOO) considers several objectives to find a feasible set of solutions. Selecting a solution from Pareto frontier (PF) solutions requires further effort. This work proposes a new classification procedure that fits into the analytic hierarchy Process (AHP) to pick the best solution. The method classifies PF solutions using pairwise comparison matrices for each objective. Sectorization is the problem of splitting a region into smaller sectors based on multiple objectives. The efficacy of the proposed method is tested in such problems using our instances and real data from a Portuguese delivery company. A non-dominated sorting genetic algorithm (NSGA-II) is used to obtain PF solutions based on three objectives. The proposed method rapidly selects an appropriate solution. The method was assessed by comparing it with a method based on a weighted composite single-objective function. [ABSTRACT FROM AUTHOR]

The single-flash geothermal cycle (SFGC) is not without its limitations, featuring drawbacks like diminished efficiency, restricted power generation capacity, and the incapability to yield multiple outputs concurrently. Furthermore, the SFGC requires a substantial water supply, potentially leading to adverse environmental consequences. In a concerted effort to enhance overall performance and facilitate the concurrent production of multiple valuable products, this study introduces a multigeneration system (MGS). By integrating additional subsystems into the SFGC framework, including a branched GAX cycle enabled by a thermoelectric generator (TEG), a domestic water heater (DWH), and a reverse osmosis unit, the objective is to surmount these limitations effectively. A thermodynamic and exergoeconomic analysis of the system is conducted and a bi-objective optimization is employed to minimize system cost and maximize exergy efficiency. The parametric study reveals that when degassing ranges are in the range of 0.2–0.37, the system product cost varies from $27.07/MWh to $28.44/MWh. In the optimized scenario there is a decrease of 67.7% in cooling provided by the system. This leads to an increase of 3.5% in generated electricity and a 3% increase in water purification compared to the base scenario. Through optimization the exergy efficiency of the system improves from 61.84% to 62.90% while the multigeneration gain output ratio (MGOR) decreases from 1.40 to 1.38. [ABSTRACT FROM AUTHOR]

This study presents an Interval-based Multi-objective Robust Design Optimization (IB-MORDO) algorithm applied to a vehicle dynamic problem. The proposed algorithm optimizes a full 15 degrees-of-freedom (15-DOF) vehicle model, subjected to a double-lane change (DLC) maneuver under random road profiles, to attain driver comfort and safety. This study does not make assumptions about uncertain parameter statistics; instead, the uncertainties are quantified using a non-probabilistic α-cut level interval analysis. These uncertainties are applied to the system parameters and design variables to ensure robust results. After the optimization process, the root mean square (RMS) vertical acceleration at the driver's seat resulted in a robust solution of 1.041 m/s2 and a parameter interval radius (IR) equals to 0.631 m/s2, whereas the RMS lateral acceleration at the driver's seat resulted in a solution of 1.908 m/s2 with an interval radius of 0.168 m/s2. Unlike the Robust Optimization, the algorithm proposed herein considers uncertainties at system parameters and design variables without assuming any statistical data. An Interval-based Robust Multi-objective Optimization procedure is proposed and tested on a 15-DOF vehicle model. Αn α-cut level methodology is used to deal with the uncertainty propagation. Resulted optimal suspension parameters minimize center and interval radius of driver's vertical and lateral accelerations. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel modeling perspective to the food-bank donation allocation problem under equity and efficiency performance measures. Using a penalty factor in the objective function, our model explicitly accounts for both efficiency and equity, simultaneously. We give the tightest lower and upper bounds of the penalty factor, which can conveniently characterize closed-form optimal solutions for the perfect efficiency and perfect equity cases. Testing our model on the full spectrum of our penalty factor, using real data from Feeding America, we demonstrate that the solutions from our model dominate those of a benchmark from the literature in terms of both equity and efficiency, being on the Pareto frontier. Our sensitivity analysis demonstrates that assisting the food-banks should go hand-in-hand with helping eliminate poverty in the demand population. This will ensure that adding more capacity to the network will always lead to a decrease in the price of equity for the food-banks. On the other hand, our results reveal that encouraging charitability is always beneficial for the food-banks, albeit with diminishing returns. Finally, we extend our model to the case with stochastic receiving capacities and derive additional insights with regards to the inherent trade-offs between equity, efficiency, and reliability in the network. [ABSTRACT FROM AUTHOR]

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The control of large-scale industrial systems has several criteria, such as ensuring high productivity, low production costs and the lowest possible environmental impact. These criteria must be established for all subsystems of the large-scale system. This study is devoted to the development of a hierarchical control system that meets several of these criteria and allows for the separate optimization of each subsystem. Multicriteria optimization is based on the processing of data characterizing production processes, which makes it possible to organize a multidimensional statistical control process. Using neural networks to model the technological processes of subsystems and the method of dynamic principal component analysis (DPCA) to reduce the dimensionality of control problems allows us to find more efficient solutions. Using the example of a two-level hierarchy, we showed a variant of the connection between two subsystems by parameters. [ABSTRACT FROM AUTHOR]

Moral foundations theory proposes that individuals with conflicting political views base their behavior on different principles chosen from a small group of universal moral foundations. This study proposes using a set of widely accepted moral foundations (fairness, in-group loyalty, authority, and purity) as proxies to determine the degree of radicalization of online communities. A fifth principle, care, is generally surpassed by others that are higher in the radicalized groups’ moral hierarchy. Moreover, the presented data-driven methodological framework proposes an alternative way to measure whether a community complies with a certain moral principle or foundation: not evaluating its speech, but its behavior through the interactions of its individuals, establishing a bridge between the structural features of the interaction network and the intensity of communities’ radicalization regarding the considered moral foundations. Two foundations were assessed using the network’s structural characteristics: in-group loyalty measured by group-level modularity, and authority evaluated using group domination, for detecting potential hierarchical substructures within the network. By analyzing a set of Pareto-optimal groups regarding a multidimensional moral relevance scale, the most radicalized communities were identified among those considered extreme in some of their attitudes or views. An application of the proposed framework is illustrated using real-world datasets. The radicalized communities’ behavior exhibited increasing isolation, and their authorities and leaders showed growing domination over their audience. Differences were also detected between users’ behavior and speech, showing that individuals tended to share more “extreme” in-group content than they publish: extreme views get more likes on social media.

An approach is proposed to comprehensively design the injectable manufacturing process considering various options such as batch and continuous operation modes, multi-use and single-use technology (SUT) equipment, and production sequences. The approach comprises four steps: 1) set design objective, 2) obtain base case design, 3) extract characteristic parameters by sensitivity analysis, and 4) obtain deeper understanding by scenario analysis. Modularized models were developed to analyze and evaluate the design alternatives using multiobjective indicators for economic performance and productivity. The approach was demonstrated in a case study. The result indicated that the alternatives using continuous compounding, batch lyophilization, and SUT equipment were the most favorable. Based on the sensitivity analysis and the future development potential, continuous lyophilization was further investigated. The scenario analysis revealed that continuous lyophilization can become competitive or even superior to batch lyophilization because of future technology development and equipment cost reduction.

This study develops a two-dimensional design sensitivity analysis and topology optimization method for multi-objective problems considering both thermal insulation and structural stiffness. The objective function is defined by introducing a weight factor that combines the thermal total energy and compliance for the thermal and structural problems. The derived design sensitivities are validated using the finite difference sensitivities. The Pareto front for designing thermal insulation is obtained by varying the weight function of the objective function and size of the domain. This meant that the geometric feature of the thermal bottleneck outweighed the load-supporting structures as the relative importance of the thermal insulation increased. Compared with the existing designs, the obtained designs are both structurally reliable and thermally insulative in terms of quantitative performance measures. [ABSTRACT FROM AUTHOR]