Collaborative location routing problem for sustainable supply chain design with profit sharing

To identify the sustainability synergies in a collaborative Moroccan dry food wholesale chain, we investigate the financial and ecological effects of implementing horizontal cooperation between three competitor shippers. For business-to-business networks, the key objective is to ensure last-mile delivery to their clients in metropolitan areas. The implementation of this alliance requires studying different aspects, among them the design of the transportation network, fair profit sharing, and collaborative delivery planning. Limited studies have considered the effects of integrating facility location and vehicle routing by addressing multiple goals in the design of a sustainable collaborative supply chain. We model the problem as a periodic two-echelon echelon-periodic location routing problem to integrate different decision levels. In order to investigate the trade-offs between the two opposing goals, a multi-objective approach is adopted. The Epsilon constraint method is used to generate a compromise between economic and ecological impacts. Cost and carbon emission sharing are assessed through the Shapley value mechanism. Furthermore, to determine the effect of parameter changes on the attained savings, a scenario analysis is performed. Results show the positive effect of collaboration among shippers and the importance of using integrated network design models. Environmental considerations in the pursuit of economic goals affect the gains produced and result in various transportation network structures. The performance of the coalition varies under different scenarios. Managerial implications are presented.

Collaborative optimal allocation of water resources and sewage discharge rights in watershed cities: considering equity among water sectors

Water supply systems in watershed cities face challenges due to increasing water demand and arbitrary sewage discharge allocations. Previous studies have primarily focused on water resource allocation and sewage discharge rights, neglecting the intricate interactions between the two. This study introduces a novel approach by integrating sewage discharge rights into the watershed's water resource allocation mechanism. A multi-objective optimization model was developed, employing the Gini coefficient to balance the equitable and economic aspects across various water sectors. This model takes into account the distinct water demands and sewage discharge requirements of different sectors. The findings of this study are as follows: (a) the Gini coefficients for water demand allocation and sewage discharge rights allocation exhibit simultaneous optimization and display consistent trends; (b) when the importance of sewage discharge relative to other water users increases, the return on investment for domestic and industrial water use decreases, but the fairness of water distribution improves; (c) proper allocation of sewage discharge rights can effectively enhance the economic value of agricultural water use. Overall, this strategy has the potential to enhance both the equality and economic benefits of the water supply system while ensuring the sustainable utilization of water and sewage rights in the basin cities.

A comparison among optimization software to solve bi-objective sectorization problem

In this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective ones, whose results are regarded as ideal points for the objective functions in the bi-objective model. Anti-ideal points are also searched similarly. Then, using the ideal and anti-ideal points, the bi-objective model is redefined as a single-objective one and solved. The difficulties of solving the models, which are basically non-linear, are discussed. Furthermore, the models are linearized, in which case how the number of variables and constraints changes is discussed. Mathematical models are implemented in Python's Pulp library, Lingo, IBM ILOG CPLEX Optimization Studio, and GAMS software, and the obtained results are presented. Furthermore, metaheuristics available in Python's Pymoo library are utilized to solve the models' single- and bi-objective versions. In the experimental results section, benchmarks of different sizes are derived for the problem, and the results are presented. It is observed that the solvers do not perform satisfactorily in solving models; of all of them, GAMS achieves the best results. The utilized metaheuristics from the Pymoo library gain feasible results in reasonable times. In the conclusion section, suggestions are given for solving similar problems. Furthermore, this article summarizes the managerial applications of the sectorization problems.

Adaptive forward collision warning system for hazmat truck drivers: Considering differential driving behavior and risk levels

The risky driving behavior of hazmat truck drivers is a crucial factor in many severe traffic accidents. In-vehicle Advanced Driving Assistance Systems (ADAS), integrating vehicle active safety and driver assistance technology, has been installed into hazmat trucks aiming to reduce driving risks during emergencies. This paper presents an enhanced dynamic Forward Collision Warning (FCW) model tailored for hazmat truck drivers with different driving characteristics and risk levels. Our objective is to determine the optimal moment to alert drivers during risky situations. The novelty of our approach lies in analyzing the driver's response mechanism to the warning by considering their characteristics and real-time driving risk levels. We employ a multi-objective optimization method that integrates real-time driving risk, driver acceptance, and driving comfort to calculate the optimal warning time. Our findings indicate that the appropriate warning time is similar for all drivers under high-level risks, while significant differentiation exists for different driver categories under mid-level and low-level risks. Additionally, aggressive drivers tend to follow leading vehicles closely and exhibit lower deceleration intentions when faced with dangers compared to normal and cautious drivers. Our research outcomes enable the development of user profiles for hazmat truck drivers based on extensive historical driving records, facilitating the analysis of driver response differences to FCWs. This enhances driving safety and improves driver trust in ADAS systems.

Simulation-based multi-objective optimization framework for sustainable management of coastal aquifers in semi-arid regions

Groundwater is a strategic source of water supply, especially in arid and semi-arid coastal regions. Growing demand, along with scarce water sources, may impose intense pressure on this precious resource. This pressure will degrade water quality for future use and cause social inequality, despite supplying current needs. A novel sustainable management model for water allocation is developed to address these interconnected concerns in coastal aquifers. Three aspects of sustainable development are considered: groundwater quality with total dissolved solids (TDS) indicator for the environmental part, gross value added from water for the economic efficiency, and the Gini coefficient for social inclusion and equity. The problem is solved with a simulation-based multi-objective optimization framework using a numerical variable-density simulation code and three approved evolutionary algorithms, NSGA-II, NRGA, and MOPSO. The obtained solutions are integrated to enhance the solutions' quality by using each algorithm's strengths and dominated members' elimination. In addition, the optimization algorithms are compared. The results showed that NSGA-II is the best in terms of solutions quality, with the least number of total dominated members (20.43%) and a 95% success rate of obtained Pareto front. NRGA was supreme in finding extreme solutions, the least computational time, and diversity, with an 11.6% higher diversity value than the second competitive NSGA-II. MOPSO was the best in spacing quality indicator, followed by NSGA-II, showing their great arrangement and evenness in obtained solution space. MOPSO has the propensity for premature convergence and needs more stringent stopping criteria. The method is applied to a hypothetical aquifer. Still, the obtained Pareto fronts are determined to assist decision-makers in real-world coastal sustainable management problems by illustrating existing patterns among different objectives.

Time and energy optimal trajectory generation for coverage motion in industrial machines

The increase in computer numerical control machine efficiency highly contributes to environmental emission reduction and energy-savings. Path and trajectory optimizations are used to improve machine efficiency in a coverage motion such as pocket milling, polishing, inspection, gluing, and additive manufacturing. Several studies have proposed coverage motion optimization in improving machine efficiency for time and energy consumption. Ensuring the smoothness and satisfaction of the machine constraints in coverage motion is necessary. This paper proposes a multi-objective path and trajectory optimization to obtain a trade-off between time and energy consumption for coverage motion. Jerk limited acceleration profiles describe the trajectory where velocity profiles generated for each linear segment attain desirable velocities. The energy model of an industrial two-axis feed drive system is used in finding solutions to the optimization problem. The non-dominated sorting genetic algorithm II generates a Pareto front for trade-off time and energy consumption solutions. Simulation results of the proposed method are validated through experiments using the industrial two-axis feed drive system. Experimental results show the effectiveness of the proposed approach where time reduction and energy savings are 10.05% and 2.10%, respectively. In addition, the optimized path has a lower maximum error of 76.6% compared to the constantly commanded velocity optimized path.

Multi-objective evolutionary multitasking algorithm based on cross-task transfer solution matching strategy

The superior performance of evolutionary multitasking (EMT) algorithms is largely owing to the potential synergy between tasks. Current EMT algorithms only involve a unidirectional process of transferring individuals from the source task to the target task. This method does not consider the search preference of the target task in the process of finding transferred individuals; therefore, the potential synergy between tasks is not fully utilized. Herein, we propose a bidirectional knowledge transfer method, which refers to the search preference of the target task in the process of finding transferred individuals. These transferred individuals fit the search process well for the target task. In addition, an adaptive strategy for adjusting the intensity of the knowledge transfer is proposed. This method enables the algorithm to adjust the intensity of knowledge transfer independently according to the living conditions of the individuals to be transferred to balance the convergence of the population with the computational intensity of the algorithm. The proposed algorithm is compared with comparison algorithms on 38 multi-objective multitasking optimization benchmarks. Experimental results show that the proposed algorithm is not only outperforming other comparison algorithms in more than 30 benchmarks, but also has considerable convergence efficiency.

Multi-objective optimization design of the Hinge Sleeve of Cubic based on Kriging

In this study, the multi-objective optimal design of the Hinge Sleeve of Cubic (HSC) was achieved by combining the central composite design (CCD), Kriging and multi-objective genetic algorithm (MOGA) approaches. Firstly, the model of the HSC was established and the appropriate design variables were selected. The mass, the maximum deformation and the maximum equivalent stress of the HSC were taken as the optimization objectives. After comparative analysis of the parameters, the parameter with the greatest influence on the optimization objectives was selected as the geometric constraint. Subsequently, according to the results of the experimental design, the Kriging model was used to establish the response surface optimization model of the objective function. And finally the best optimization results were obtained by using MOGA. The experimental results show that the optimization strategy is reliable and the mass of the optimized model is reduced by 24.84%, which achieves the lightweight design of the HSC while meeting the actual production requirements, saves the design cost and improves the material utilization.

Workforce scheduling efficiency assessment in construction projects through a multi-objective optimization model in the COVID-19 context

The COVID-19 disease has caused a drastic stoppage in the construction industry as a result of quarantines. For this reason, this study focuses on the workforce scheduling problem when working under COVID labor distancing constraints, and additional costs derived from deviation hours or hiring new employees that managers must assume on a project due to circumstances. A multi-objective mixed integer linear programming model was developed and solved using weighting and epsilon constraint methods to evaluate workforce scheduling and the mentioned COVID costs. The first objective function corresponds to the sum of the total extra hours; the second objective function represents the total non-worked but paid hours. Two sets of experiments are presented, the first based on a design of experiments that seeks to determine the relationship between the proposed objective functions and a methodology to determine the cost of considering COVID constraints. The second set of experiments was applied in a real company, where the situation without COVID vs with COVID, and without allowing extra hours vs with COVID allowing extra hours were compared. Obtained results showed that hiring additional employees to the man-crew leads the company to increase the extra hours cost up to 104.25%, being more convenient to keep a workforce baseline and to pay extra hours costs. Therefore, the mathematical model could represent a potential tool for decision-making in the construction sector, regarding the effects of COVID-19 costs on workforce scheduling construction projects. Consequently, this work contributes to the construction industry by quantifying the impact of COVID-19 constraints and the associated costs, offering a proactive approach to address the challenges posed by the COVID-19 pandemic for the construction sector.

Modeling and multi-objective optimal control of reaction-diffusion COVID-19 system due to vaccination and patient isolation

In this paper, a reaction-diffusion COVID-19 model is proposed to explore how vaccination-isolation strategies affect the development of the epidemic. First, the basic dynamical properties of the system are explored. Then, the system's asymptotic distributions of endemic equilibrium under different conditions are studied. Further, the global sensitivity analysis of R 0 is implemented with the aim of determining the sensitivity for these parameters. In addition, the optimal vaccination-isolation strategy based on the optimal path is proposed. Meantime, social cost C ( m , σ ) , social benefit B ( m , σ ) , threshold R 0 ( m , σ ) three objective optimization problem based on vaccination-isolation strategy is explored, and the maximum social cost ( M S C ) and maximum social benefit ( M S B ) are obtained. Finally, the instance prediction of the Lhasa epidemic in China on August 7, 2022, is made by using the piecewise infection rates β 1 ( t ) , β 2 ( t ) , and some key indicators are obtained as follows: (1) The basic reproduction numbers of each stage in Lhasa, China are R 0 ( 1 : 8 ) = 0.4678 , R 0 ( 9 : 20 ) = 2.7655 , R 0 ( 21 : 30 ) = 0.3810 and R 0 ( 31 : 100 ) = 0.7819 ; (2) The daily new cases of this epidemic will peak at 43 on the 20th day (August 26, 2022); (3) The cumulative cases in Lhasa, China will reach about 640 and be cleared about the 80th day (October 28, 2022). Our research will contribute to winning the war on epidemic prevention and control.

An improved multi-objective marine predator algorithm for gene selection in classification of cancer microarray data

Gene selection (GS) is an important branch of interest within the field of feature selection, which is widely used in cancer classification. It provides essential insights into the pathogenesis of cancer and enables a deeper understanding of cancer data. In cancer classification, GS is essentially a multi-objective optimization problem, which aims to simultaneously optimize the two objectives of classification accuracy and the size of the gene subset. The marine predator algorithm (MPA) has been successfully employed in practical applications, however, its random initialization can lead to blindness, which may adversely affect the convergence of the algorithm. Furthermore, the elite individuals in guiding evolution are randomly chosen from the Pareto solutions, which may degrade the good exploration performance of the population. To overcome these limitations, a multi-objective improved MPA with continuous mapping initialization and leader selection strategies is proposed. In this work, a new continuous mapping initialization with ReliefF overwhelms the defects with less information in late evolution. Moreover, an improved elite selection mechanism with Gaussian distribution guides the population to evolve towards a better Pareto front. Finally, an efficient mutation method is adopted to prevent evolutionary stagnation. To evaluate its effectiveness, the proposed algorithm was compared with 9 famous algorithms. The experimental results on 16 datasets demonstrate that the proposed algorithm can significantly reduce the data dimension and obtain the highest classification accuracy on most of high-dimension cancer microarray datasets.

An approach for joint optimization of probabilistic group test based on cost and time value: taking nucleic acid detection of COVID-19 as an example

In recent years, the world has encountered many epidemic impacts caused by various viruses, COVID-19 has spread and mutated globally since its outbreak in 2019, causing global impact. Nucleic acid detection is an important means for the prevention and control of infectious diseases. Aiming at people who are susceptible to sudden and infectious diseases, considering the control of viral nucleic acid detection cost and completion time, a probabilistic group test optimization method based on the cost and time value is proposed. Firstly, different cost functions to express the pooling and testing costs are used, a probability group test optimization model that considers the pooling and testing costs is established, the optimal combination number of samples for nucleic acid testing is obtained, and the positive probability and the cost functions of the group testing on the optimization result are explored. Secondly, considering the impact of the detection completion time on epidemic control, the sampling ability and detection ability were incorporated into the optimization objective function, then a probability group testing optimization model based on time value is established. Finally, taking COVID-19 nucleic acid detection as an example, the applicability of the model is verified, and the Pareto optimal curve under the minimum cost and shortest detection completion time is obtained. The results show that under normal circumstances, the optimal combination number of samples for nucleic acid detection is about 10. Generally, 10 is used to calculate for the convenience of organization, arrangement and statistics, except for cases where there are special requirements for testing cost and detection completion time.

Wastewater reuse and energy saving require a more decentralized urban wastewater system? Evidence from multi-objective optimal design at the city scale

Decentralization is recognized as an emerging solution for a more sustainable urban wastewater system (UWS) for the future. However, the debate of centralization vs. decentralization at the system's planning stage remains unresolved, mainly due to the complexity of the system's spatial structure and the multiple design objectives, such as water reuse and energy conservation. This paper presents the Sustainable Urban Wastewater System Generator (SUWStor) as a tool to address this issue. Integrating a graph representation of the system structure and the ant colony algorithm, SUWStor can produce Pareto optimal solutions for system design under three objectives: minimizing the capital cost, minimizing the operational energy consumption, and maximizing the water reuse capacity. The model is used for system design in a 100-square-km new city, the Xiong'an New District in China. Compared to the solution based on human experience, the model can reduce the system's capital cost by 7% and the operational energy in the pipe network by 26%, while maintaining the water reuse capacity at 100%. With this model, the relation between the optimal system layout and the choice over different design objectives can be discussed for any given area. In our case study, the optimal capacity of WWTPs for the lowest-cost solution is 48,000 m³ per day, leading to a total number of WWTPs of 5. As the water reuse level increases to maximum, the optimal capacity reduces to 15,000 m³ per day, where the number of WWTPs is 16. The model is also able to perform significantly better than the locally optimized results, in which only the WWTP locations are fixed at their optimal values. This demonstrates the importance of a global optimization model in designing the integrated UWS.

Two-step approach based multi-objective groundwater remediation using enhanced random vector functional link integrated with evolutionary marine predator algorithm

We here propose a two-step approach-based simulation-optimization model for multi-objective groundwater remediation using enhanced random vector functional link (ERVFL) and evolutionary marine predator algorithm (EMPA). In this study, groundwater flow and solute transport models are developed using MODFLOW and MT3DMS. The ERVFL network is used to approximate the flow and transport models, enhancing the computational performance. This study also improves the robustness of the ERVFL network using a kernel density estimator (KDE) based weighted least square approach. We further develop the EMPA by modifying the marine predator algorithm (MPA) using elite opposition-based learning, biological evolution operators, and elimination mechanisms. In the multi-objective version of EMPA, the non-dominated/Pareto-optimal solutions are stored in an external repository using an archive controller and adaptive grid mechanism to promote better convergence and diversity of the Pareto front. The proposed methodologies are applied for multi-objective groundwater remediation of a hypothetical unconfined aquifer based on the two-step method. The first step directly integrates flow and transport models with EMPA and finds the optimal locations of pumping wells by minimizing the percent of contaminant mass remaining in the aquifer. In the second step, the ERVL-based proxy model is integrated with EMPA and used for multi-objective optimization while explicitly using the pumping well locations obtained in the first step. The multi-objective optimization generates a Pareto-optimal solution representing the relationship between the rate of pumping and the amount of contaminant mass in the aquifer. Further analyses show a significant advantage of the two-step approach over a traditional method for multi-objective groundwater remediation.

Bi-objective optimization of biomass solid waste energy system with a solid oxide fuel cell

Thesolid oxide fuel cell (SOFC), as an economically friendly power generation system, shows a promising prospect for the future while hydrogen supply as its fuel is one of the main challenges. In this paper, an integrated system is described and evaluated by energy, exergy, and exergoeconomic, aspects. To find an optimum design state three models were analyzed to reach higher energy and exergy efficiency while system cost is at its lower value. After the first and main models, a Stirling engine reuses the first model's waste heat to generate power and enhance efficiency. In the last model, a proton exchange membrane electrolyzer (PEME) is considered for hydrogen production purposes by using the surplus power of the Stirling engine. The components validation is performed in comparison with the data presented by related studies. Optimization is applied by exergy efficiency, total cost, and hydrogen production rate considerations. The results show that the total cost of the model (a), (b), and (c) is 30.36 ($/GJ), 27.48 ($/GJ), and 33.82 ($/GJ), and the energy efficiency is 31.6%, 51.51%, 46.61% and the exergy efficiency is 24.07%, 33.0.9%, 29.28% respectively with the cost of at the optimum condition achieved by 2708 A/m² current density, 0.84 utilization factor, 0.38 recycling anode ratio, 1.14 air blower and 1.58 fuel blower pressure ratio. The optimum rate of hydrogen production will be 138.2 kg/day and the overall product cost will be 57.58 $/GJ. In general, the proposed integrated systems show a good performance in both thermodynamics and environmental and economic aspects.

Integrating sustainability into production scheduling in hybrid flow-shop environments

Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop floors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factors in production processes. This study proposes a new approach for optimizing the hybrid flow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers' flexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversification, and quality metrics, providing more versatile and better solutions for production and energy efficiency.

Maximizing projects' profitability, environmental score, and quality: a multi-project scheduling and material ordering problem

The proper trade-off between various project costs is often disregarded when planning projects. This leads to several detrimental effects, such as inaccurate planning and higher total cost, far more significant in a multi-project environment. To overcome this limitation, this study proposes a combined approach for the multi-project scheduling and material ordering problem (MPSMOP), which maintains the proper trade-off among various costs. Moreover, the environmental impact and project quality objectives are optimized alongside the economic criterion. The proposed methodology involves three stages: (a) quantifying the environmental performance of suppliers; (b) measuring the activities' quality through the Construction Quality Assessment System approach; and (c) building and solving the mathematical model of the MPSMOP. The MPSMOP is modeled as a tri-objective optimization approach aiming to determine project scheduling and material ordering decisions so that the net present value, environmental score, and total quality of implemented projects are maximized simultaneously. As the proposed model comes into the nondeterministic polynomial optimization problem category, two powerful metaheuristics are customized and used to solve the problem. The efficiency of both algorithms was assessed on several datasets. The proposed framework is applied to railway construction projects in Iran as a case study, which presents the validity of the model and the decision-making options provided to managers.

A multi-objective multi-tasking evolutionary algorithm based inverse mapping and adaptive transformation strategy: IM-MFEA

Multi-tasking optimization algorithm attracts much attention because the knowledge transfer between tasks enables the algorithm to process multiple related tasks simultaneously. However, negative knowledge transfer occasionally occurs, which may weaken the performance of the algorithm. To reduce the impact of negative knowledge transfer, a multi-objective multi-tasking optimization algorithm (IM-MFEA) based on inverse model mapping and an objective transformation strategy is proposed. First, correlation analysis is applied in an inverse mapping strategy to improve the accuracy of the inverse mapping model. Then, following the pattern of using the source domain solutions to assist the optimization of the target domain, the adaptive transformation strategy is used to improve the quality of the source domain solution in the objective space. These transformed solutions are reconstructed through the inverse mapping strategy. Finally, these reconstructed source domain solutions are mated with the target domain solutions to generate competitive offspring individuals for the target domain. To verify the effectiveness of the IM-MFEA, comprehensive experiments were conducted on nine multi-objective multi-factorial optimization (MFO) benchmark problems. Empirical results demonstrate that IM-MFEA is superior to other algorithms in 90% of test instances by inverted generational distance (IGD) and hypervolume (HV) value indicators.

Generative machine learning-based multi-objective process parameter optimization towards energy and quality of injection molding

The high energy intensity and rigorous quality demand of injection molding have received significant interest under the background of the soaring production of global plastic industry. As multiple parts can be produced in a multi-cavity mold during one operation cycle, the weight differences of these parts have been demonstrated to reflect their quality performance. In this regard, this study incorporated this fact and developed a generative machine learning-based multi-objective optimization model. Such model can predict the qualification of parts produced under different processing variables and further optimize processing variables of injection molding for minimal energy consumption and weight difference amongst parts in one cycle. Statistical assessment via F₁-score and R² was performed to evaluate the performance of the algorithm. In addition, to validate the effectiveness of our model, we conducted physical experiments to measure the energy profile and weight difference under varying parameter settings. Permutation-based mean square error reduction was adopted to specify the importance of parameters affecting energy consumption and quality of injection molded parts. Optimization results indicated that the processing parameters optimization could reduce ~ 8% energy consumption and ~ 2% weight difference compared with the average operation practices. Maximum speed and first-stage speed were identified as the dominating factors affecting quality performance and energy consumption, respectively. This study could contribute to the quality assurance of injection molded parts and facilitate energy efficient and sustainable plastic manufacturing.

Technical assessment of novel organic Rankine cycle driven cascade refrigeration system using environmental friendly refrigerants: 4E and optimization approaches

Hydrocarbons are a promising working fluid for organic Rankine cycles and refrigeration systems that have gained increasing attention in recent decades as a replacement for the currently used higher global warming potential fluids. A novel standalone cascade refrigeration system (CRS) has been developed in this study for simultaneous ultra-low temperature cooling and heating production to medical applications most suitable for rural and power outages. Using the energy utilization factor (EUF) metrics, neopentane was chosen as the working fluid for the organic Rankine cycle (ORC) system among the selected hydrocarbon (HC) refrigerants. The standalone ORC-CRS system performance was investigated in terms of cooling and heating capacity, EUF, exergy, and environmental metrics by varying both ORC and CRS key temperature parameters. The exergy destruction of individual components was evaluated, and it was found that the ORC evaporator has the highest exergy destruction, followed by the expander. The highest EUF and exergy efficiency for a combined ORC-CRS system was about 0.64 and 38%. The maximum cooling and heating capacity of novel standalone CRS system was achieved by 70 kW and 225 kW respectively at a typical operating condition. At a higher ORC evaporation temperature, the exergy efficiency of the ORC-CRS system is decreased, leading to reduction in SI. The standalone ORC-CRS system at ORC evaporator temperature of 150 °C achieved the maximum CO₂ emission tax savings of $ 6.4 × 10⁷ over a 15-year lifetime period. The total annualized cost index of the combined cooling heating system is in the range between 35 and 60 $/h for the variable operating condition. Non-dominated sorting genetic algorithm II (NSGA-II) method was used to conduct the multi-objective optimization analysis, and the technique for order of preference by similarity to ideal solution (TOPSIS) method used to found the optimal point, which had the following values of TAC 42.22 $/h and exergy efficiency was around 46.10%.

Research on high efficiency and high dynamic optimal matching of the electro-hydraulic servo pump control system based on NSGA-II

An electro-hydraulic servo pump control system (hereinafter referred to as EHSPCS) is a volume servo control unit that is highly integrated with servo motors, fixed-displacement pumps, hydraulic cylinders and functional valve groups. Because of its unique volume direct-drive control mode, the dynamic performance of the system is limited, and the thermal power loss is large, which seriously restricts the improvement of the working quality of the system. To improve the dynamic performance of the system and reduce the thermal power loss to the maximum extent, a multi-objective optimization design method for the EHSPCS is proposed by comprehensively considering the dynamic and efficient energy-saving characteristics of the system. The evaluation model of the dynamic period of the hydraulic cylinder and the thermal power loss of the servo motor are given. Parameters such as the electromagnetic torque of the servo motor, displacement of the hydraulic pump, and working area of the hydraulic cylinder are intelligently optimized by a non-dominated sorting genetic algorithm with elite strategy (NSGA-II). The Pareto front of multi-objective optimization and the corresponding Pareto solution set are obtained; thus, the optimal matching of the system characteristics is realized. Finally, the relevant theory of the multi-objective optimization algorithm is applied to optimize the performance parameters of the hydraulic servo motor, and the prototype is tested in engineering. The experimental results show that the dynamic period of the hydraulic servo motor is accelerated after optimization, and the thermal power loss is significantly reduced. The dynamic and efficient energy-saving characteristics of the system are improved, which further verifies the feasibility of the proposed theory.

A novel rumor detection with multi-objective loss functions in online social networks

COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor detection or set a fixed threshold to attain early detection that unfortunately cannot adapt to various rumors. In this paper, we focus on textual rumors in online social networks and propose a novel rumor detection method. We treat the detection time, accuracy and stability as the three training objectives, and continuously adjust and optimize this objective instead of using a fixed value during the entire training process, thereby enhancing its adaptability and universality. To improve the efficiency, we design a sliding interval to intercept the required data rather than using the entire sequence data. To solve the problem of hyperparameter selection brought by integration of multiple optimization objectives, a convex optimization method is utilized to avoid the huge computational cost of enumerations. Extensive experimental results demonstrate the effectiveness of the proposed method. Compared with state-of-art counterparts in three different datasets, the recognition accuracy is increased by an average of 7%, and the stability is improved by an average of 50%.

A two-echelon location routing problem considering sustainability and hybrid open and closed routes under uncertainty

Location-routing is an extremely important problem in supply chain management. In the location-routing problem, decisions are made about the location of facilities such as distribution centers as well as the set of vehicle routes. Today, organizations seek to reduce the transportation cost by outsourcing leading to a particular kind of transportation problems known as open routing. However, the increasing attention to environment have led to paying more attention to environmental issues and reducing the environmental impacts of logistics activities. To this end, in this paper, both open and closed routes were simultaneously addressed by developing a multi-objective mixed integer linear programming model that included three economic, environmental, and social responsibility aspects. The three objective functions of the proposed model encompass the minimization of total costs and greenhouse gas emissions, and the maximization of employment rate and economic development. Also, in this study, a different type of routing was considered in each echelon. A small-sized problem instance was solved using the Augmented Epsilon Constraint (AEC) method with the CPLEX Optimizer Solver for the validation of the proposed model. Moreover, the sensitivity analysis was performed to investigate the effect of changing main parameters on the values of the objective function. Due to the NP-Hardness of the problem, two efficient metaheuristic algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Stochastic Fractal Search (MOSFS) were exploited to solve the medium and large size problems. The performance of the algorithms was compared on the basis of six different well-known indexes of Time, MID, RAS, Diversity, Spacing, and SNS. According to the obtained results, the performance of the MOSFS algorithm was %20, %9, %11.22, %10.03, and %19.06 higher than the performance of the NSGA-II on the basis of SNS, RAS, MID, Diversity, and Time indexes, respectively. On the other hand, the NSGA-II performance was %6.3 higher than the MOSFS performance in terms of Spacing index.

Optimization and trade-off framework for coupled green-grey infrastructure considering environmental performance

Implementing runoff control infrastructure has been regarded as an efficacious measure in stormwater management. The issue of its cost-effectiveness is a primary concern for decision makers since it is an exorbitant investment. However, most of existed studies only concentrated on the cost-effectiveness optimization of runoff control infrastructure, especially green infrastructure, between hydrological and economic aspects, and therefore, the potential layout scenarios with high extra environmental benefits could be neglected in the traditional two-dimensional frameworks. In this study, a novel carbon dioxide equivalent-based index was quantified to represent the extra environmental benefits of runoff control infrastructure besides stormwater management and was further integrated into the assessment framework. The effectiveness of green and grey infrastructure was comprehensively evaluated and traded off between hydrological, environmental and economic aspects. The results demonstrated that grey infrastructure is a better measure than green infrastructure when only hydrological (HF index) and economic (CI index) performances were considered. Nevertheless, the environmental performance (EROI index) of green infrastructure prevails over grey infrastructure, and when optimizing green and grey infrastructure simultaneously in the three-dimensional framework considering environmental effectiveness, green infrastructure is comparable with grey infrastructure. Furthermore, an appropriate composition of coupled green-grey infrastructure is requisite, which could achieve an optimal trade-off between hydrological and environmental effectiveness. The sources of environmental benefits were also identified and analyzed from three representative preference scenarios. The findings of the study could serve as a trade-off basis between green and grey infrastructure, as well as between EROI and HF.

Developing explicit customer preference models using fuzzy regression with nonlinear structure

In online sales platforms, product design attributes influence consumer preferences, and consumer preferences also have a significant impact on future product design optimization and iteration. Online review data are the most intuitive feedback from consumers on products. Using the value of online review information to explore consumer preferences is the key to optimize the products, improve consumer satisfaction and meet consumer requirements. Therefore, the study of consumer preferences based on online reviews is of great importance. However, in previous research on consumer preferences based on online reviews, few studies have modeled consumer preferences. The models often suffer from the nonlinear structure and the fuzzy coefficients, making it challenging to build explicit models. Therefore, this study adopts a fuzzy regression approach with a nonlinear structure to model consumer preferences based on online reviews to provide reference and insight for subsequent studies. First, smartwatches were selected as the research object, and the sentiment scores of product reviews under different topics were obtained by text mining on the product online data. Second, a polynomial structure between product attributes and consumer preferences was generated to investigate the association between them further. Afterward, based on the existing polynomial structure, the fuzzy coefficients of each item in the structure were determined by the fuzzy regression approach. Finally, the mean relative error and mean systematic confidence of the fuzzy regression with nonlinear structure method were numerically calculated and compared with fuzzy least squares regression, fuzzy regression, adaptive neuro fuzzy inference system (ANFIS) and K-means-based ANFIS, and it was found that the proposed method was relatively more effective in modeling consumer preferences.