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In modern manufacturing, dynamic allocation of resources is key to enhancing production efficiency and competitiveness. With the advancement of intelligent manufacturing and Industry 4.0, the manufacturing environment has become increasingly complex and variable, making traditional static resource allocation methods insufficient for practical needs. This paper aims to explore multi-objective optimization methods for dynamic manufacturing resource allocation by constructing a multi-objective optimization model and proposing an improved NSGA-II algorithm to address this issue. The study demonstrates that the improved algorithm significantly enhances population diversity and global search capability, effectively coping with dynamic manufacturing environments to provide efficient and reliable resource allocation solutions. This research not only offers new insights into dynamic resource allocation but also serves as a valuable reference for related fields. [ABSTRACT FROM AUTHOR]

Flux Balance Analysis (FBA) is a constraint-based method that is commonly used to guide metabolites through restricting pathways that often involve conditions such as anaplerotic cycles like Calvin, reversible or irreversible reactions, and nodes where metabolic pathways branch. The method can identify the best conditions for one course but fails when dealing with the pathways of multiple metabolites of interest. Recent studies on metabolism consider it more natural to optimize several metabolites simultaneously rather than just one; moreover, they point out the use of metaheuristics as an attractive alternative that extends FBA to tackle multiple objectives. However, the literature also warns that the use of such techniques must not be wild. Instead, it must be subject to careful fine-tuning and selection processes to achieve the desired results. This work analyses the impact on the quality of the pathways built using the NSGAII and MOEA/D algorithms and several novel optimization models; it conducts a study on two case studies, the pigment biosynthesis and the node in glutamate metabolism of the microalgae Chlorella vulgaris, under three culture conditions (autotrophic, heterotrophic, and mixotrophic) while optimizing for three metabolic intermediaries as independent objective functions simultaneously. The results show varying performances between NSGAII and MOEA/D, demonstrating that the selection of an optimization model can greatly affect predicted phenotypes. [ABSTRACT FROM AUTHOR]

Nonwoven fabrics are used in many industries, and surface treatment by plasma can significantly change their physical and mechanical properties by changing the surface chemistry and morphology. In this paper, oxygen/argon plasma has been applied to the spunbond nonwoven fabrics to predict the obtained properties. Therefore, by using the central composite design and considering 4 independent factors including the fabric weight, fabric direction, plasma treatment duration and oxygen ratio, 51 various samples were prepared and their bending rigidity and formability were measured. SEM images showed that the surface roughness increases due to the plasma treatment. Statistical analysis revealed that all the mentioned independent factors have a significant effect on the measured parameters directly or reciprocally. Also, the use of a neural network model with two hidden layers optimized with a method according to the genetic algorithm can predict the bending rigidity and formability based on the independent factors with errors of less than 7% and 9%, respectively. The errors resulting from the surface response method for the same parameters with the same order are about 19% and 17%. The introduced method can be used as a tool to help researchers in the field of mechanical properties.

Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used to model time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necessary to study parameter optimization for the LSTM model. The sparrow search algorithm (SSA) holds advantages over traditional optimization algorithms in several aspects, such as no need for prior knowledge, fewer parameters, fast convergence, and high scalability. However, the algorithm still faces some challenges, such as the tendency to become trapped in the local optimum and the imbalance between global search ability and local search ability. Therefore, on the basis of SSA, this study examined the Levy flight strategy, sine search strategy, and step size factor adjustment strategy to improve it. This algorithm, improved by three strategies, is called the improved sparrow search algorithm (ISSA). Then, the ISSA-LSTM model was established. Furthermore, considering the limitations of SSA in dealing with multi-objective problems, the fast non-dominated sorting genetic algorithm (NSGAII) was introduced, and the ISSA-NSGAII model was established. Finally, experimental validation was performed using real blast furnace operation data, which demonstrated the proposed algorithm's superiority in parameter optimization for the LSTM model and prediction for real industrial data. [ABSTRACT FROM AUTHOR]

This study focuses on investigating how grid-stiffened composite shells behave under external hydrostatic pressure. The critical buckling load is calculated using the first-order shear deformation theory (FSDT) and the Ritz method. Various factors, including shell thickness, angle of helical stiffeners, rib section area, and the number of stiffeners, are examined to understand their impact on the buckling load. To optimize the design, three multi-objective optimization algorithms are employed: Nondominated Sorting Genetic Algorithm II (NSGAII), Multiobjective Particle Swarm Optimization (MOPSO), and a hybrid method that combines NSGAII and MOPSO. The hybrid method intelligently divides the population into two groups and uses NSGAII and MOPSO to efficiently explore and exploit the solution space. The results yield a Pareto optimal front that showcases diverse solutions across different regions, providing decision-makers with the flexibility to select the solution that best fits their preferences. The solutions obtained through these algorithms are compared based on their diversity and distribution throughout the Pareto front. [ABSTRACT FROM AUTHOR]

In this study, we introduce an innovative approach that utilizes complex networks and the k_core method to address community detection in weighted networks. Our proposed bi-objective model aims to simultaneously discover non-overlapping communities while ensuring that the degree of similarity remains below a critical threshold to prevent network degradation. We leverage the k_core structure to detect tightly interconnected node groups, a concept particularly valuable in edge-weighted networks where different edge weights indicate the strength or importance of node relationships. Beyond maximizing the count of k_core communities, our model seeks a homogeneous weight distribution across edges within these communities, promoting stronger cohesion. To tackle this challenge, we implement two multi-target algorithms: Non-dominated Sorting Genetic Algorithm II (NSGAII) and a Multi-Objective Simulated Annealing (MOSA) algorithm. Both algorithms efficiently identify non-overlapping communities with a specified degree 'k'. The results of our experiments reveal a trade-off between maximizing the number of k_core communities and enhancing the homogeneity of these communities in terms of their minimum weighted interconnections. Notably, the MOSA algorithm outperforms NSGAII in both small and large instances, demonstrating its effectiveness in achieving this balance. This approach sheds light on effective strategies for resolving conflicting goals in community detection within weighted networks.

Flux Balance Analysis (FBA) is a constraint-based method that is commonly used to guide metabolites through restricting pathways that often involve conditions such as anaplerotic cycles like Calvin, reversible or irreversible reactions, and nodes where metabolic pathways branch. The method can identify the best conditions for one course but fails when dealing with the pathways of multiple metabolites of interest. Recent studies on metabolism consider it more natural to optimize several metabolites simultaneously rather than just one; moreover, they point out the use of metaheuristics as an attractive alternative that extends FBA to tackle multiple objectives. However, the literature also warns that the use of such techniques must not be wild. Instead, it must be subject to careful fine-tuning and selection processes to achieve the desired results. This work analyses the impact on the quality of the pathways built using the NSGAII and MOEA/D algorithms and several novel optimization models; it conducts a study on two case studies, the pigment biosynthesis and the node in glutamate metabolism of the microalgae Chlorella vulgaris, under three culture conditions (autotrophic, heterotrophic, and mixotrophic) while optimizing for three metabolic intermediaries as independent objective functions simultaneously. The results show varying performances between NSGAII and MOEA/D, demonstrating that the selection of an optimization model can greatly affect predicted phenotypes.

In recent decades, the use of supply chain management is essential for developing new technologies and setting the ground in expanding major global markets to integrate suppliers, manufacturers, warehouses, and stores effectively. In addition, the growing competition in the modern business environment has created an increasing trend of new products and improved product quality for attracting more consumers. However, an increase in the related costs and uncertainty in these innovations requires the development of algorithms to solve optimization problems. Therefore, this study is aimed to implement a multi-product supply chain including raw material suppliers, factories, distributors, and customers to maximize the quality and minimize costs. To this aim, supplier quality, distribution centers, and manufacturing products were considered for the quality model, while warehousing costs, product production, transportation, defective raw materials, and the like were regarded for the cost model. Then, the NSGAII algorithm was used for solving the created optimization problem, and accordingly, the optimal Pareto points were calculated. Based on the results, the proposed model can give the manufacturer the ability to decide on a multi-product and multi-time supply chain by involving cost and quality variables. Thus, the owner can manage the supply chain of the factory effectively. [ABSTRACT FROM AUTHOR]

Distribution of non-medical products is a very interesting problem in healthcare supply chain logistics. It has a considerable impact on profit, especially when products have to be transported firstly from the manufacturing centres to intermediate healthcare facilities then to the final healthcare establishments. This transportation is ensured at two levels by selecting the best intermediate facilities and the best trips. In this paper, we address the two-echelon location-distribution problem and we develop a multi-objective mathematical formulation for minimising two objective functions. The first is the total distribution cost which is the sum of transportation costs, the opening cost of intermediate healthcare facilities and the usage cost of vehicles. The second is the total horizon time of the distribution of products. For solving this new problem, we propose two solution approaches, the multi-objective particle swarm optimisation algorithm improved by variable neighbourhood search heuristics and the non-dominated sorting genetic algorithm combined also with the same heuristics. They are tested on thirty problems and compared with different related works. Moreover, a real case study of non-medical products distribution is studied. The validation and efficiency of the algorithms are based on several performance metrics which are presented later. [ABSTRACT FROM AUTHOR]

Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used to model time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necessary to study parameter optimization for the LSTM model. The sparrow search algorithm (SSA) holds advantages over traditional optimization algorithms in several aspects, such as no need for prior knowledge, fewer parameters, fast convergence, and high scalability. However, the algorithm still faces some challenges, such as the tendency to become trapped in the local optimum and the imbalance between global search ability and local search ability. Therefore, on the basis of SSA, this study examined the Levy flight strategy, sine search strategy, and step size factor adjustment strategy to improve it. This algorithm, improved by three strategies, is called the improved sparrow search algorithm (ISSA). Then, the ISSA-LSTM model was established. Furthermore, considering the limitations of SSA in dealing with multi-objective problems, the fast non-dominated sorting genetic algorithm (NSGAII) was introduced, and the ISSA-NSGAII model was established. Finally, experimental validation was performed using real blast furnace operation data, which demonstrated the proposed algorithm’s superiority in parameter optimization for the LSTM model and prediction for real industrial data.

This paper proposes a bi-objective capacitated vehicle routing problem with two types of customers based on priority. The priority customers must be served earlier compared to non-priority customers. This paper aims to minimize the total distance traveled by all the vehicles and minimize customers' average latency. This paper considers three scenarios for the average latency calculation based on the customer type. In the first scenario, this paper considers only priority customers' average latency. The second scenario considers the latency of all customers, ignoring the priority. The third scenario considers the average latency of all customers, but priority customers must be served first. A hybrid metaheuristic based on Greedy Randomized Adaptive Search Procedure (GRASP) and Non-dominated Sorting Genetic Algorithm (NSGAII) is developed to solve the proposed model. The proposed model is solved for some of the benchmark data sets from VRP literature, and finally, the results are analyzed with the help of some performance metrics. [ABSTRACT FROM AUTHOR]

There are many problems with hospital departments that decrease patients' satisfaction and hospitals' performance. Although there are many qualitative and quantitative methods to improve related performances, some of them consider a single department and the rest don't consider related priorities. Therefore, this paper proposes an integrated model based on Six Sigma to consider the performances of all departments with priorities based on experts' knowledge. This model utilizes qualitative methods including fishbone diagrams and Healthcare Failure Mode and Effect Analysis to find problems and their priorities based on experts' knowledge and data. Then, quantitative methods including a mathematical model, Discrete Event Simulation, and Non-dominated Sorting Genetic Algorithm II are utilized to find solutions for preferred problems as a simulation-based multi-objective optimization model. Based on a case study in a well-known general hospital, the proposed model finds the hospital's most important problems and represents the impacts of different improvement plans before implementation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Industrial Management Perspective / Chashm/&āz-I Mudīriyyat-I Ṣan̒atī is the property of Shahid Beheshti University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Efficient and timely dispatch of maintenance personnel for fault detection and failure recovery play a key role towards safe operation of power grid and has become a challenging issue. To address this challenge, this paper proposes a new optimal strategy, namely adaptive NSGAII (NSGAII/A), for dispatching maintenance personnel in the event of security risk that can improve manpower efficiency and maintain the security of power grid at a minimum cost. To solve this optimization problem, we firstly model the optimization objectives and constraints in the process of maintenance personnel scheduling. Secondly, we improve the legacy nondominated sorting genetic algorithm II (NSGAII) to combat its shortcomings in the calculation of congestion and the strategy of individual selection. On one hand, NSGAII/A takes the absolute value of the average congestion degree minus the standard deviation as the individual congestion degree, so as to reduce the impact of the congestion degree of individual optimization objectives. Furthermore, it can prevent the algorithm from converging too fast and causing the problem of local optimization. On the other hand, we adopt the elitist control strategy to replace the elitist retention strategy of NSGAII. The extensive experimental results demonstrate that NSGAII/A has advantages in terms of the average value of optimization objectives, the maintenance completion degree, and the distribution of non-dominated solution set in the process of population optimization. [ABSTRACT FROM AUTHOR]

The application of a genetic algorithm inspired multi-objectives optimization algorithms, known as the Non-dominated Sorting Genetic Algorithm (NSGAii) in solving optimization problems related to Water Distribution System (WDS) is critically reviewed. The list of variations of NSGAii used by other researchers in solving WDS are assessed, including an improved version of NSGAii and its combination with other algorithms in solving the problems. The optimization problems include WDS optimal design, pipe rehabilitation strategies, water quality, leakages and pump operation. The existing algorithm has been improved to solve multi-objectives and yet researchers are keen in using the NSGAii algorithm. Improvised and revolutionary algorithms, such as RNSGAii, Ɛ-NSGAii and NSGAiii, still received less attention even though these algorithms have been introduced since 2005 and model capabilities to solve more complex problem is proved. The application of these algorithms should be seriously considered due to increasing aging of pipe in the future.

Abstract Nowadays, the winter is getting harsher and harsher in Northern China. Thus, the centralized heating systems (CHSs) are playing even more irreplaceable, essential and critical roles in ensuring general public’s livelihood than never ever before. CHSs are normally composed of one or several combined heat and power (CHP) plants (units) and an extensive vein like district heating networks (DHNs) connecting with chemical plants, paper mills, food processing factories, hospitals, hotels, universities, prisons and residential complexes. A CHP plant in Northern China usually consumes coal to heat the cold water into steam to drive high-pressure turbines and low pressure turbines to generate electricity. Then the low-temperature steam is used to heat up the cold water in a main pipe into hot water travelling through the DHNs to provide heat to each end nodes. The returned water will be heated again for reuse and the surplus steam will be released into air through cooling towers. In 2020, China promised to the world that carbon dioxide will peak in 2030 and net-zero emission will happen in 2060. On the one hand, CHP plants need to guarantee enough hot water flowing within each household’s heating radiator. On the other hand, they should cut down on the consumption on non-renewable resources. Lowering water temperature, adjusting water volume and reducing water pressure will all contribute to energy-saving purpose. Lowering water temperature and reducing water pressure may cause too much heat losses during long-distance transmission in frigid winter. Therefore, a reasonable water volume adjustment becomes an advisable action comparatively. Here, we transfer the hot water supply volume optimization problem (HWSVOP) into a heat exchange station (HES) valve angle adjustment problem (SHWESVAAP). Then, a multi-objective mathematical model is established considering balancing the satisfactory degree of each household in residential quarters and the hot water volume (HWV) in the main pipe. And a hybrid polar bear optimization algorithm integrated with chemical reaction optimization (HA-PBO-CRO) is designed to optimize the valve angle (VA) in each HES. The comparative results between HA-PBO-CRO and non-dominant sorting genetic algorithm (NSGAII) demonstrate HA-PBO-CRO is superior to NSGAII with better Pareto frontiers on one hand and provide a critical reference supporting the management in a CHP plant to make a right decision on what to do to cut energy consumption while satisfying customers’ needs.

In this paper, the procedure of finding the coefficients of an equation to describe the thermal conductivity of refrigerants low in global warming potential (GWP) is transformed into a multi-objective optimization problem by constructing a multi-objective mathematical model based on the Pareto approach. For the first time, the NSGAII algorithm was used to describe a thermophysical property such as thermal conductivity. The algorithm was applied to improve the performance of existing equations. Two objective functions were optimized by using the NSGAII algorithm. The average absolute relative deviation was minimized, while the coefficient of determination was maximized. After the minimization process, the optimal solution located on the Pareto frontier was chosen through a comparative analysis between ten selection methods available in the literature. The procedure generated a new set of coefficients of the studied equation that decreased its average absolute relative deviation by 0.24%, resulting in better performance over the entire database and for fluids with a high number of points. Finally, the system model was compared with existing literature models to evaluate its suitability for predicting the thermal conductivity of low-GWP refrigerants. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to their small dimension and isolated energy systems, islands face a significant energy supply challenge. In general, they use fossil fuels for electricity generation. Fossil fuels are a major source of CO2 emissions, and they are very costly. The cost of electricity generation on islands is up to 10 times higher than on the mainland. This situation without a doubt represents a financial burden for the islanders. Using renewable sources, especially solar and wind sources, offers great potential for power generation in remote locations, as they are a clean and inexhaustible source of energy. Electrifying these zones with a hybrid system consisting of a photovoltaic (PV) and wind systems associated to a hydraulic and an electrochemical storage system is a promising alternative. The purpose of this study is to optimize the dimension of the components generation of systems, especially for a remote island in Tunisia. The first part of this object outlines the PV-wind-battery-hydraulic generation system architecture and modeling. The optimal sizing of the device additives, satisfying two criteria with the aid of evolutionary algorithms NSGAII, is defined inside the second part of this article. The outcomes are discussed from the point of view of the importance of the system dimension and in terms of compliance with the criteria through the study of the most optimal particular configurations. [ABSTRACT FROM AUTHOR]

This study presents the optimization of a multilayer wire-on-tube condenser exposed to forced convection, using the Optimized Multi-objective Particle Swarm Optimization (OMOPSO) algorithm. The maximization of the heat transfer and the minimization of the heat exchange area were defined as objective functions. In the optimization process, the variations of eight geometric parameters of the condenser were analyzed, and the Multi-objective Evolutionary Algorithm based on Decomposition (MOEAD), Non-dominated Sorting Genetic Algorithm-II (NSGAII), and OMOPSO algorithms were statistically explored. Furthermore, the condenser optimization analysis was extended to the use of alternative refrigerants to R134a such as R600a and R513A. Among the relevant results, it can be commented that the OMOPSO algorithm presented the best option from the statistical point of view compared to the other two algorithms. Thus, optimal designs for the wire-on-tube condenser were defined for three proposed study cases and for each refrigerant, providing an overview of compact designs. Likewise, the reduction of the condenser area was analyzed in more detail, presenting a maximum reduction of 15% for the use of R134a compared to for the current design. Finally, the crossflow condition was studied with respect to the current one, concluding in a greater heat transfer and a smaller heat exchange surface. [ABSTRACT FROM AUTHOR]

Studies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and CO 2 uptake. The results from the conducted experimental design show a favorable difference in the number of solutions achieved compared to a classic tool solving FBA. [ABSTRACT FROM AUTHOR]

Efficient pullulan production has long been a central research focus. This study used maltodextrin as the carbon source for pullulan production by Aureobasidium pullulans fermentation. A hybrid optimization approach, integrating orthogonal experimental design (OED), backpropagation artificial neural network (BP-ANN), and elite strategy non-dominated sequential genetic algorithm-II (NSGA-II), was developed. Range analysis based on OED revealed that MgSO 4 ·7H 2 O significantly affects production but less impacts molecular weight, while pH notably influences molecular weight with a lesser effect on production, underscoring the need for multi-objective optimization. The BP-ANN model showed strong predictive capabilities, with goodness-of-fit values of 0.984 and 0.980 for production and molecular weight, respectively. Using this model as the fitness function for the optimization algorithm enhanced efficiency. Taking cost factors into account, the BP-ANN-NSGA-II algorithm identified the optimal fermentation medium conditions, resulting in a 6.89 % increase in production, a 368.97 % increase in molecular weight, and a 42.49 % reduction in cost. The maximum comprehensive optimization efficiency is 63.73 %, and the multi-objective optimization is better than the single objective optimization. This method significantly guides the improvement of pullulan fermentation optimization efficiency., Competing Interests: Declaration of competing interest The authors declare that they have no conflict interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

آب به­عنوان مهم­ترین و محدودکننده­تـرین نهاده تولیدی در اغلب مناطق کشاورزی ایران خودنمایی می­کند. از طرفی استفاده بهینه از منابع آب یکی از مهم­ترین مسائل مطرح در کشاورزی پایدار به­شمار می­آید. آب به­عنوان یک منبع قابل تجدید همواره به­عنوان یک رکن اصلی کشاورزی مطرح بوده است. در مطالعه حاضر یک مدل بهینه­سازی به منظور حداکثر نمودن سود و حداقل برهم خوردن پایداری منابع آب در کانال­های L1، L2، L3، L4، L4A، L5، L6 ، L7، L8، L9، L10، L20 در شبکه آبیاری و زهکشی دشت قزوین توسعه داده شد. مدل بهینه­سازی با استفاده از الگوریتم ژنتیک چندهدفه با دو تابع هدف مجزا ارایه گردید. برای واسنجی مزرعه­ای مدل، نسبت کشت، هزینه تولید و قیمت فروش گیاهان عمده و مهم به همراه نسبت کشت آن­ها در سال زراعی 1394 جمع­آوری گردید. نتایج، حاکی از افزایش سود خالص به میزان 64 درصد نسبت به الگوی کشت موجود از طریق کاهش سطوح زیرکشت محصولات با سود خالص کم و افزایش سطح زیرکشت محصولات با درآمد بالا می­باشد. افزایش درآمد حاصله از تغییر در الگوی کشت ثابت می­کند اصلاح الگوی کشت موجود نیاز به بازنگری و تغییر دارد. هم­چنین با توجه به تعریف تابع هدف دوم، میزان آب مصرفی در شرایط بهینه 20 درصد کاهش یافت.

The ammonothermal method is considered the most promising method of fabricating bulk gallium nitride (GaN) crystals. This paper improves the ammonothermal growth model by replacing the heater-long fixed temperature boundary with two resistance heaters and considering the real thermal boundary outside the shell. The relationship between power values and temperatures of dissolution and crystallization is expressed by the backpropagation (BP) neural network, and the optimal power values for specific systems are found using the non-dominated sorting genetic algorithm (NSGAII). Simulation results show that there are several discrepancies between updated and simplified models. It is necessary to build an ammonothermal system model with resistance heaters as a heat source. Then large-sized GaN crystal growth is analyzed based on the well-developed numerical model. According to the simulation results, both the increasing rate and maximum stable values of the metastable GaN concentration gradient are reduced for a larger-sized system, which is caused by the inhomogeneity of heat transfer in the autoclave. [ABSTRACT FROM AUTHOR]

Solving Multi-Objective Mixed Integer NonLinear Programming (MO-MINLP) problems is a point of interest for many researchers as they appear in several real-world applications, especially in the mechanical engineering field. Many researchers have proposed using hybrids of metaheuristics with mono-objective branch and bound. Others have suggested using heuristics with Multi-Criteria Branch and Bound (MCBB). A general hybrid approach is proposed based on MCBB and Non-dominated Sorting Genetic Algorithm 2 (NSGAII) to enhance the approximated Pareto front of MO-MINLP problems. A computational experiment based on statistical assessment is presented to compare the performance of the proposed algorithm (BnB-NSGAII) with NSGAII using well-known metrics from the literature. To evaluate the computational efficiency, a new metric, the Investment Ratio (IR), is proposed that relates the quality of solution to the consumed effort. Experimental results on five real-world mechanical engineering problems and two mathematical ones indicate that BnB-NSGAII could be a competitive alternative for solving MO-MINLP problems. [ABSTRACT FROM AUTHOR]

In recent years, despite the rapid growth of cloud computing platforms, the technology confronts significant challenges including virtualization, load balancing, fault tolerance and most important of all, task scheduling. Considering the last challenge, because of high number of users and significant growth of number of tasks, there are some limitations such as MakeSpan, high resource utilization rate and executive costs in task scheduling algorithms. In this study, an effective method called HATMOG based on the smart hybrid of multiple criteria decision making algorithms of AHP-TOPSIS and Non-Dominated Sorting Genetic Algorithm (NSGAII) has been used to improve task scheduling in the cloud. The proposed method was done in two phases. In the first phase, the tasks which entered the cloud environment were placed in separate queues and then, according to task length, number of required processor elements and task expire time were sorted by AHP-TOPSIS algorithm in their priority queues. This phase helped on time assignment of more important tasks to the most appropriate virtual machines significantly that resulted in response time decrease and optimized resource utilization. In the second phase, the sorted tasks with prioritized queues were assigned to the appropriate virtual machines using NSGAII. Task assignment to virtual machine is an NP-Hard issue and NSGAII helped the efficiency improvement of cloud computing environment significantly because of the high convergence speed in finding close to optimal solution. The results of numerous simulations in Cloudism showed that the proposed method improved MakeSpan comparing TOPSIS-PSO, AHP-TOPSIS-PSO, NSGAII and PSO by 17.76, 155.73, 5.05 and 171.35 percent respectively and the average resource utilization by 15.94, 176.59, 4.83 and 176.65 percent respectively. [ABSTRACT FROM AUTHOR]

This study proposes a multi-objective model of the capacitated vehicle routing problem to envisage delivering perishable items. The main objectives of the model are to minimize the quality degradation of the perishable items to be delivered, and delivery costs. Here we consider the m-ring star type of distribution network to match the real-world scenario perfectly. To establish a trade-off between the quality of the items and the delivery cost, we consider both the refrigerated and general vehicles. The customers are categorized into two types based on their position and the vehicle type by which the items are served. The demands of the cities are known in advance. It is also considered that the refrigerated vehicle's capacity is homogeneous and higher than the capacity of the general type of vehicle. The proposed model is solved using a non-dominated sorting genetic algorithm and Strength Pareto evolutionary algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with construction site layout planning (CLSP) problem in the context of a multi-objective optimization model. According to the operational plans in Iran, in execution of the construction projects, special places are considered to accommodate the respective facilities in the jobsite. As an important part of project execution, the site layout planning has always been of concern for clients, contractors and consultants which is majorly intended to layout the facilities by properly arranging the them such as offices, warehouses, residences etc. such that materials, information and staff could be transferred optimally. Noteworthy, an appropriate layout improves level of safety and efficiency, reduces transport costs, prevents formation of bottlenecks and obstructions ahead of materials and equipment transfer particularly in mega projects. The site layout could be arranged with respect to the decision maker’s opinions based on different criteria. The model’s target functions include maximizing the safety level of construction facilities along with minimizing the corresponding costs and finally, minimizing the respective risks in potential areas. The constraints involve unique layout of each facility as well as the allowable distance between various facilities. Given the fact that the CSLP is classified as an NP-hard problem, it would be difficult to derive viable results using mathematical models whose solution process is time-taking as well. In this respect, to solve the numerical examples in a realistic representation scheme, NSGAII and MOGWO metaheuristic algorithms are applied. In order to compare efficiency of the proposed algorithms with the results derived from mathematical model solution, a number of standard measures have been used. The results indicate that the Pareto frontier produced by the proposed algorithms in numerical examples, is significantly in agreement with the optimal Pareto frontier in the case of small sizes approving great efficiency of the algorithms. Moreover, it was observed that in medium and large sizes, performance of the MOGWO surpasses that of NSGAII, making it appropriate for real-size cases. Lastly, results of this study could be considered as a management tool to make optimal strategic decisions aiming to develop the CSLP studies.

Disaster situation could suddenly apply large number of injured people and disarrange the emergency medical service simultaneously (EMS). Using all facilities, ambulance, rescue helicopter and medical drone as integrated part and making decision for assigning to patient’s efficiency are concerns of EMS. Therefore, in this study we introduce a new biobjective model for EMS in order to reduce the rate of mobility and mortality with aim of reducing latest service completion time and total cost of system simultaneously. By recognition level of injury, we consider two types of patients: i. red code patients who have been injured seriously and have to be taken to a hospital. And ii. Green code patients who are injured slightly and are treated at the same place. Since making decision and responding to disaster situation should be with high quality within seconds, for this purpose after validating and solving that by augmented ε-constraint method by GAMS, we applied two NSGA-II and customized Bees algorithm for multi objective solution that called Multi Objective Bees Algorithm (MOBA) in dynamics and uncertain situation for coping high frequency within very short response time and near the optimum solution result. The quality of the result is considered for choosing the metaheuristic solution. At the end, sensitive analysis is implemented on the model and the effect of reducing and increasing some of parameters on the model is investigated. [ABSTRACT FROM AUTHOR]

Reviewing natural disasters that occurred in recent decades indicates that the occurrence of such disasters has imposed heavy costs and casualties on governments and societies and led to growing concerns in this field. In this regard, adopting proper decisions and taking appropriate and real-time measures in each phase of the crisis management cycle can reduce possible damages during disasters and decrease the vulnerability of society. Hence, the present study aims to propose a fuzzy goal programming (FGP) model in two stages of the primary disaster and the secondary disaster. The initial disaster aims at providing relief services and commodities to disaster-affected areas while the purpose of the secondary disaster, which happens after the occurrence of the primary disaster, is to provide aid to disaster centers and transfer injured individuals to relief centers. The proposed mathematical model was first endorsed by using the FGP approach and then, validated by using the NSGAII metaheuristic algorithm and adjusting the parameters of the Taguchi method. The results revealed that the proposed model could improve the programming and flexibility of relief measures in disaster-affected areas in both primary and secondary stages. It was also found that the use of a metaheuristic algorithm facilitated the evaluation and decision-making procedures in big disasters and verified the efficiency of the algorithm in large dimensions. [ABSTRACT FROM AUTHOR]

Supplier selection, order allocation and production planning are important and challenging decisions in supply chain management. There are many studies on mentioned topics separately. In this paper, a multi-objective mathematical model proposed to optimize a sustainable supplier selection problem with order allocation and production planning simultaneously. This study considers a multi-supplier, multi-product, multi-item and multi-period supply chain. The designed mathematical model seeks to maximize total profit and minimize unsatisfied demand and total risk along with enforcing sustainability criteria in selecting suppliers. Supplier selection is a virtual process in every manufacturing company. On the other hand, this research considers all the important aspects of this problem. Therefore, the proposed framework can be implemented in many different companies like electronic, food, chemical industry. The proposed model is solved utilizing two metaheuristic algorithms including NSGA II and MOPSO. Moreover, algorithms are tuned utilizing Taguchi analysis. Furthermore, ten sample problems are generated and results are compared to identify the best algorithm for the proposed model.

In this paper, the procedure of finding the coefficients of an equation to describe the thermal conductivity of refrigerants low in global warming potential (GWP) is transformed into a multi-objective optimization problem by constructing a multi-objective mathematical model based on the Pareto approach. For the first time, the NSGAII algorithm was used to describe a thermophysical property such as thermal conductivity. The algorithm was applied to improve the performance of existing equations. Two objective functions were optimized by using the NSGAII algorithm. The average absolute relative deviation was minimized, while the coefficient of determination was maximized. After the minimization process, the optimal solution located on the Pareto frontier was chosen through a comparative analysis between ten selection methods available in the literature. The procedure generated a new set of coefficients of the studied equation that decreased its average absolute relative deviation by 0.24%, resulting in better performance over the entire database and for fluids with a high number of points. Finally, the system model was compared with existing literature models to evaluate its suitability for predicting the thermal conductivity of low-GWP refrigerants.

Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAIIIR, a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions' quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%. [ABSTRACT FROM AUTHOR]

This study presents the optimization of a multilayer wire-on-tube condenser exposed to forced convection, using the Optimized Multi-objective Particle Swarm Optimization (OMOPSO) algorithm. The maximization of the heat transfer and the minimization of the heat exchange area were defined as objective functions. In the optimization process, the variations of eight geometric parameters of the condenser were analyzed, and the Multi-objective Evolutionary Algorithm based on Decomposition (MOEAD), Non-dominated Sorting Genetic Algorithm-II (NSGAII), and OMOPSO algorithms were statistically explored. Furthermore, the condenser optimization analysis was extended to the use of alternative refrigerants to R134a such as R600a and R513A. Among the relevant results, it can be commented that the OMOPSO algorithm presented the best option from the statistical point of view compared to the other two algorithms. Thus, optimal designs for the wire-on-tube condenser were defined for three proposed study cases and for each refrigerant, providing an overview of compact designs. Likewise, the reduction of the condenser area was analyzed in more detail, presenting a maximum reduction of 15% for the use of R134a compared to for the current design. Finally, the crossflow condition was studied with respect to the current one, concluding in a greater heat transfer and a smaller heat exchange surface.

Software developers commonly use third-party software libraries to reduce implementation efforts and mitigate errors in their source code while building high-quality and reliable software. To support software evolution, newer libraries are released periodically with added features, improvements in performance as well as critical updates such as bug fixes. Hence, older, existing (source) libraries need to be replaced with their newer, updated (target) counterparts, in a process known as library migration. Typically, library migration is a time-consuming, manual, and error-prone process that requires developers to analyze the source and target library code and its documentation. Specifically, developers examine Application Programming Interface (API) implementations and documentation to replace each source API with a target API without modifying the underlying software functionality. While recent works have used various techniques to recommend suitable target library replacements, these approaches do not generalize well, e.g., when a source library method needs to be replaced by one or more target library APIs or methods. To address this limitation, we propose the use of multi-objective evolutionary algorithms to identify suitable method replacements during library migration. In particular, we formulate library migration at the method-level as a multi-objective combinatorial optimization problem and examine the performance of 7 multi-objective evolutionary algorithms: UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD. We use method signature and documentation similarity, and co-occurrence probability to accurately recommend one or more target library methods. We evaluate our approach by conducting an empirical study on 9 popular Java library migrations mined from 57,447 open-source projects on GitHub. Our results demonstrate that UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD achieve 90%, 89%, 94%, 90%, 91%, 94%, and 71% precision on average, and 83%, 23%, 58%, 63%, 58%, 60% and 17% average recall respectively. In the interest of reproducibility, we make all code and results publicly available at: http://bit.ly/MOO-api-migration. [ABSTRACT FROM AUTHOR]

Studies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and CO2 uptake. The results from the conducted experimental design show a favorable difference in the number of solutions achieved compared to a classic tool solving FBA.

The ammonothermal method is considered the most promising method of fabricating bulk gallium nitride (GaN) crystals. This paper improves the ammonothermal growth model by replacing the heater-long fixed temperature boundary with two resistance heaters and considering the real thermal boundary outside the shell. The relationship between power values and temperatures of dissolution and crystallization is expressed by the backpropagation (BP) neural network, and the optimal power values for specific systems are found using the non-dominated sorting genetic algorithm (NSGAII). Simulation results show that there are several discrepancies between updated and simplified models. It is necessary to build an ammonothermal system model with resistance heaters as a heat source. Then large-sized GaN crystal growth is analyzed based on the well-developed numerical model. According to the simulation results, both the increasing rate and maximum stable values of the metastable GaN concentration gradient are reduced for a larger-sized system, which is caused by the inhomogeneity of heat transfer in the autoclave.

Reconstructing power systems has changed the traditional planning of power systems and has raised new challenges in transmission expansion planning (TEP). In this paper, investment cost, cost of density and dependability have been considered three objectives of optimization. Also, multi-objective genetic algorithm NSGAII was used to solve this non-convex and mixed integer problem. A fuzzy decision method has been used to choose the final optimal answer from the Pareto solutions obtained from NSGAII. Moreover, to confirm the efficiency of NSGAII multi-objective genetic algorithm in solving TEP problem, the algorithm was implemented in an IEEE 24 bus system and the gained results were compared with previous works in this field. [ABSTRACT FROM AUTHOR]

Minimum spanning tree problem (MSTP) has allured many researchers and practitioners due to its varied range of applications in real world scenarios. Modelling these applications involves the incorporation of indeterminate phenomena based on their subjective estimations. Such phenomena can be represented rationally using uncertainty theory. Being a more realistic variant of MSTP, in this article, based on the principles of the uncertainty theory, we have studied a multi-objective minimum spanning tree problem (MMSTP) with indeterminate problem parameters. Subsequently, two uncertain programming models of the proposed uncertain multi-objective minimum spanning tree problem (UMMSTP) are developed and their corresponding crisp equivalence models are investigated, and eventually solved using a classical multi-objective solution technique, the epsilon-constraint method. Additionally, two multi-objective evolutionary algorithms (MOEAs), non-dominated sorting genetic algorithm II (NSGAII) and duplicate elimination non-dominated sorting evolutionary algorithm (DENSEA) are also employed as solution methodologies. With the help of the proposed UMMSTP models, the practical problem of optimizing the distribution of petroleum products was solved, consisting in the search for symmetry (balance) between the transportation cost and the transportation time. Thereafter, the performance of the MOEAs is analyzed on five randomly developed instances of the proposed problem.