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In multiobjective optimization problems, Pareto optimal solutions representing different tradeoffs cannot be ordered without incorporating preference information of a decision maker (DM). In interactive methods, the DM takes an active part in the solution process and provides preference information iteratively. Between iterations, the DM can learn how achievable the preferences are, learn about the tradeoffs, and adjust the preferences. Different interactive methods have been proposed in the literature, but the question of how to select the best-suited method for a problem to be solved remains partly open. We propose an experimental design for evaluating interactive methods according to several desirable properties related to the cognitive load experienced by the DM, the method's ability to capture preferences and its responsiveness to changes in the preferences, the DM's satisfaction in the overall solution process, and their confidence in the final solution. In the questionnaire designed, we connect each questionnaire item to be asked with a relevant research question characterizing these desirable properties of interactive methods. We also conduct a between-subjects experiment to compare three interactive methods and report interesting findings. In particular, we find out that trade-off-free methods may be more suitable for exploring the whole set of Pareto optimal solutions, while classification-based methods seem to work better for fine-tuning the preferences to find the final solution. [ABSTRACT FROM AUTHOR]

Background: This publication presents a review, multiple criteria optimization models, and a practical example pertaining to the integration of automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. This publication addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. Methods: A review of the existing literature synthesizes key concepts, such as facility location problems, vehicle routing problems and the mathematical programming approach, to optimize supply chain operations. Conceptual optimization models are formulated to solve the complex decision-making process involved in last-mile delivery, considering multiple objectives, including cost minimization, delivery time optimization, service level minimization, capacity optimization, vehicle minimization and resource utilization. Results: The multiple criteria approaches combine the vehicle routing problem and facility location problem, demonstrating the practical applicability of the proposed methodology in a real-world case study within a logistics company. Conclusions: The execution of multi-criteria models optimizes automated smart locker deployment, capillary distribution design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics sector. [ABSTRACT FROM AUTHOR]

We propose a decision support approach, called DESMILS, to solve multi-item lot sizing problems with a large number of items by using single-item multiobjective lot sizing models. This approach for making lot sizing decisions considers multiple conflicting objective functions and incorporates a decision maker's preferences to find the most preferred Pareto optimal solutions. DESMILS applies clustering, and items in one cluster are treated utilizing preferences that the decision maker has provided for a representative item of the cluster. Thus, the decision maker provides preferences to solve the single-item lot sizing problem for few items only and not for every item. The lot sizes are obtained by solving a multiobjective optimization problem with an interactive method, which iteratively incorporates preference information and supports the decision maker in learning about the trade-offs involved. As a proof of concept to demonstrate the behavior of DESMILS, we solve a multi-item lot sizing problem of a manufacturing company utilizing their real data. We describe how the supply chain manager as the decision maker found Pareto optimal lot sizes for 94 items by solving the single-item multiobjective lot sizing problem for only ten representative items. He found the solutions acceptable and the solution process convenient saving a significant amount of his time. [ABSTRACT FROM AUTHOR]

Background: This publication presents a review, multiple criteria optimization models, and a practical example pertaining to the integration of automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. This publication addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. Methods: A review of the existing literature synthesizes key concepts, such as facility location problems, vehicle routing problems and the mathematical programming approach, to optimize supply chain operations. Conceptual optimization models are formulated to solve the complex decision-making process involved in last-mile delivery, considering multiple objectives, including cost minimization, delivery time optimization, service level minimization, capacity optimization, vehicle minimization and resource utilization. Results: The multiple criteria approaches combine the vehicle routing problem and facility location problem, demonstrating the practical applicability of the proposed methodology in a real-world case study within a logistics company. Conclusions: The execution of multi-criteria models optimizes automated smart locker deployment, capillary distribution design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics sector.

There many decision problems where numerous partial achievement functions are considered impartially which makes the distribution of achievements more important than the assignment of several achievements to the specific criteria. Such models are generally related to the evaluation and optimization of various systems which serve many users where quality of service for every individual user defines the criteria. This applies to various technical systems, like to telecommunication ones among others, as well as to social systems. An example arises in location theory, where the clients of a system are entitled to equal treatment according to some community regulations. This paper presents an implementation of decision support framework for such problems. This platform is designed for multiple criteria problems analyzed with the reference distribution approach. Reference distribution approach is an extension of the reference point method.

Performance of a software is an important feature to determine the quality of the software developed. Performance testing of modular software is a time consuming and costly task. Several performance testing tools (PTTs) are available in the market which help software developers to test their software performance. In this paper, we propose an integrated multiobjective optimization model for evaluation and selection of best-fit PTT for modular software system. The total performance tool cost is minimized and the fitness evaluation score of the PTTs is maximized. The fitness evaluation of PTT is done based on various attributes by making use of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The model allows the software developers to select the number of PTTs as per their requirement. The individual performance of the modules is considered based on some performance properties. The reusability constraints are considered, as a PTT can be used in the same module to test different properties and/or it can be used in different modules to test same or different performance properties. A real-world case study from the domain of enterprise resource planning (ERP) is used to show the working of the suggested optimization model. [ABSTRACT FROM AUTHOR]

We study how visual interaction techniques considered in visual analytics can be utilized when implementing interactive multiobjective optimization methods, where a decision maker iteratively participates in the solution process. We want to benefit from previous research and avoid re-inventing ideas. Our aim is to widen awareness and increase the applicability of interactive methods for solving real-world problems. As a concrete approach, we introduce seven high-level tasks that are relevant for interactive methods. These high-level tasks are based on low-level tasks proposed in the visual analytics literature. In addition, we give an example on how the high-level tasks can be implemented and demonstrate this in the context of a real-world multiobjective optimization problem related to wastewater treatment plant operation. Finally, we make recommendations for implementations of interactive methods. We conclude that task-based visual analytics can help in implementing interaction between human decision makers and interactive multiobjective optimization methods. [ABSTRACT FROM AUTHOR]

Deterministic global optimization algorithms like Piyavskii–Shubert, direct, ego and many more, have a recognized standing, for problems with many local optima. Although many single objective optimization algorithms have been extended to multiple objectives, completely deterministic algorithms for nonlinear problems with guarantees of convergence to global Pareto optimality are still missing. For instance, deterministic algorithms usually make use of some form of scalarization, which may lead to incomplete representations of the Pareto optimal set. Thus, all global Pareto optima may not be obtained, especially in nonconvex cases. On the other hand, algorithms attempting to produce representations of the globally Pareto optimal set are usually based on heuristics. We analyze the concept of global convergence for multiobjective optimization algorithms and propose a convergence criterion based on the Hausdorff distance in the decision space. Under this light, we consider the well-known global optimization algorithm direct, analyze the available algorithms in the literature that extend direct to multiple objectives and discuss possible alternatives. In particular, we propose a novel definition for the notion of potential Pareto optimality extending the notion of potential optimality defined in direct. We also discuss its advantages and disadvantages when compared with algorithms existing in the literature. [ABSTRACT FROM AUTHOR]

In many decision making problems, a decision maker needs computer support in finding a good compromise between multiple conflicting objectives that need to be optimized simultaneously. Interactive multiobjective optimization methods have a lot of potential for solving such problems. However, the growth of complexity in problem formulations and the abundance of data bring new challenges to be addressed by decision makers and method developers. On the other hand, advances in the field of artificial intelligence provide opportunities in this respect. We identify challenges and propose directions of addressing them in interactive multiobjective optimization methods with the help of multiple intelligent agents. We describe a generic architecture of enhancing interactive methods with specialized agents to enable more efficient and reliable solution processes and better support for decision makers. [ABSTRACT FROM AUTHOR]

Bakery products are one of the main sources of dietary sodium intake of the world's population. During the last decade, sodium intake has increased worldwide and nowadays the World Health Organization recommends reducing sodium intake by up to 2 g Na/day. KCl is the leading substitute for reducing sodium in bakery products. Therefore, the main purpose of our study was to investigate the impact of sodium reduction on dough's rheological properties by reformulating the dough recipe using two types of salts, namely NaCl and KCl, with different amounts added to wheat flour. In order to establish their combination for obtaining the optimum rheological properties of dough, the response surface methodology (RSM) by the Design Expert software was used. The effect of combined NaCl and KCl salts were made on mixing, viscometric and fermentation process by using Farinograph, Extensograph, Amylograph and Rheofermentometer devices. On dough's rheological properties, KCl and NaCl presented a significant effect (p < 0.01) on water absorption, stability, energy, dough resistance to extension, falling number and all Rheofermentometer-analyzed values. Mathematical models were achieved between independent variables, the KCl and NaCl amounts, and the dependent ones, dough rheological values. The optimal values obtained through RSM for the KCl and NaCl salts were of 0.37 g KCl/100 g and 1.31 g NaCl/100 g wheat flour, which leads to a 22% replacement of NaCl in the dough recipe. [ABSTRACT FROM AUTHOR]

Purpose: To assess three advanced radiation therapy treatment planning tools on the intensity‐modulated radiation therapy (IMRT) quality and consistency when compared to the clinically approved plans, referred as manual plans, which were planned without using any of these advanced planning tools. Materials and Methods: Three advanced radiation therapy treatment planning tools, including auto‐planning, knowledge‐based planning, and multiple criteria optimization, were assessed on 20 previously treated clinical cases. Three institutions participated in this study, each with expertise in one of these tools. The twenty cases were retrospectively selected from Cleveland Clinic, including five head‐and‐neck (HN) cases, five brain cases, five prostate with pelvic lymph nodes cases, and five spine cases. A set of general planning objectives and organs‐at‐risk (OAR) dose constraints for each disease site from Cleveland Clinic was shared with other two institutions. A total of 60 IMRT research plans (20 from each institution) were designed with the same beam configuration as in the respective manual plans. For each disease site, detailed isodoseline distributions and dose volume histograms for a randomly selected representative case were compared among the three research plans and manual plan. In addition, dosimetric endpoints of five cases for each site were compared. Results: Compared to the manual plans, the research plans using advanced tools showed substantial improvement for the HN patient cases, including the maximum dose to the spinal cord and brainstem and mean dose to the parotid glands. For the brain, prostate, and spine cases, the four types of plans were comparable based on dosimetric endpoint comparisons. Conclusion: With minimal planner interventions, advanced treatment planning tools are clinically useful, producing a plan quality similarly to or better than manual plans, improving plan consistency. For difficult cases such as HN cancer, advanced planning tools can further reduce radiation doses to numerous OARs while delivering adequate dose to the tumor targets. [ABSTRACT FROM AUTHOR]

Real-world optimization problems may involve a number of computationally ex-pensive functions with a large number of input variables. Metamodel-based optimization methods can reduce the computational costs of evaluating expensive functions, but this does not reduce the dimension of the search domain nor mitigate the curse of dimensionality effects. The dimension of the search domain can be reduced by functional anova decomposition involving Sobol' sensitiv-ity indices. This approach allows one to rank decision variables according to their impact on the objective function values. On the basis of the sparsity of effects principle, typically only a small number of decision variables signifficantly affects an objective function. Therefore, neglecting the variables with the smallest impact should lead to an acceptably accurate and simpler metamodel for the original problem. This appealing strategy has been applied only to single-objective optimization problems so far. Given a high-dimensional optimization problem with multiple objectives, a method called anova-mop is proposed for defining a number of independent low-dimensional subproblems with a smaller number of objectives. The method allows one to define approximated solutions for the original problem by suitably combining the solutions of the subproblems. The quality of the approximated solutions and both practical and theoretical aspects related to decision making are discussed. [ABSTRACT FROM AUTHOR]

The algorithm of method VIKOR intended for ranging of alternatives, and results of its analysis are given. It is shown that some shortcomings are inherent in known method VIKOR. It is caused by necessity of use of additional balancing factor, and also by indeterminate form of interpretation of gained results. It is noted that elimination of these shortcomings is connected with necessity of essential alteration of VIKOR method. However its basic idea about comparison of alternatives by two criteria may be used to create a new outranking method – the VIKOR-kernel method. This new method will be intended not for ranging of alternatives, but for formation of a core which will contain or one best alternative, or some incomparable alternatives which are the most perspective for the further analysis. For this new method settlement dependences, and also rules of comparison of alternatives which provide core formation are given. Possibility of practical use of the offered VIKOR-kernel method is demonstrated by two settlement instance. The results for these two settlement instance with using wellknown multiple criteria optimization methods are given in literature. Comparison of results of the calculations gained with use known and new methods, testifies that use of the VIKOR-kernel method in a combination with other methods allows to raise validity of developed recommendations or to find perspective alternatives which have not been revealed by other methods. These results allow to draw a conclusion that using of the offered VIKOR-kernel method can be perspective enough for multiple criteria optimization of complex objects.

In this paper, first we show that the variance used in the Markowitz’s mean-variance model for the portfolio selection with its numerous modifications often does not properly present the risk of portfolio. Therefore, we propose another treating of portfolio risk as the measure of possibility to earn unacceptable low profits of portfolio and a simple mathematical formalization of this measure. In a similar way, we treat the criterion of portfolio’s return maximization as the measure of possibility to get a maximal profit. As the result, we formulate the portfolio selection problem as a bicriteria optimization task. Then, we study the properties of the developed approach using critical examples of portfolios with interval and fuzzy valued returns. The α-cuts representation of fuzzy returns was used. To validate the proposed method, we compare the results we got using it with those obtained with the use of fuzzy versions of seven widely reputed methods for portfolio selection. As in our approach we deal with the bicriteria task, the three most popular methods for local criteria aggregation are compared using the known example of fuzzy portfolio consist of five assets. It is shown that the results we got using our approach to the interval and fuzzy portfolio selection reflect better the essence of this task than those obtained by widely reputed traditional methods for portfolio selection in the fuzzy setting.

Bakery products are one of the main sources of dietary sodium intake of the world’s population. During the last decade, sodium intake has increased worldwide and nowadays the World Health Organization recommends reducing sodium intake by up to 2 g Na/day. KCl is the leading substitute for reducing sodium in bakery products. Therefore, the main purpose of our study was to investigate the impact of sodium reduction on dough’s rheological properties by reformulating the dough recipe using two types of salts, namely NaCl and KCl, with different amounts added to wheat flour. In order to establish their combination for obtaining the optimum rheological properties of dough, the response surface methodology (RSM) by the Design Expert software was used. The effect of combined NaCl and KCl salts were made on mixing, viscometric and fermentation process by using Farinograph, Extensograph, Amylograph and Rheofermentometer devices. On dough’s rheological properties, KCl and NaCl presented a significant effect (p < 0.01) on water absorption, stability, energy, dough resistance to extension, falling number and all Rheofermentometer-analyzed values. Mathematical models were achieved between independent variables, the KCl and NaCl amounts, and the dependent ones, dough rheological values. The optimal values obtained through RSM for the KCl and NaCl salts were of 0.37 g KCl/100 g and 1.31 g NaCl/100 g wheat flour, which leads to a 22% replacement of NaCl in the dough recipe.

This paper is a presentation of a web based decision support system “WebOptim” for solving single and multiple criteria optimization problems. It targets a wide range of user typeseducators, researchers, managers and business people. It also provides two types of communication interfaces user friendly graphical interface for human interaction and programming interface for machine communication with other third party software systems. The interfaces facilitate the problem solving process of different types of optimization problems, mainly single and multi-objective programming optimization problems with continuous or integer variables.

This work addresses the multiple criteria simulation-optimization problem. This problem entails using an optimization strategy to manipulate the parameters of a simulation model to arrive at the best possible configurations in the presence of several performance measures in conflict. Pareto Efficiency conditions are used in an iterative framework based on experimental design and pairwise comparison. In particular, this work improves upon and replaces the use of Data Envelopment Analysis to determine the efficient frontier, and replaces the use of a single-pass algorithm previously proposed by our research group. The results show a rapid convergence to a more precise characterization of the Pareto-efficient frontier. In addition, the capability of the method to deal with fifty decision variables simultaneously is demonstrated through a study regarding the fine-tuning of a manufacturing line. [ABSTRACT FROM AUTHOR]

Solving real-life data-driven multiobjective optimization problems involves many complicated challenges. These challenges include preprocessing the data, modelling the objective functions, getting a meaningful formulation of the problem, and supporting decision makers to find preferred solutions in the existence of conflicting objective functions. In this paper, we tackle the problem of optimizing the composition of microalloyed steels to get good mechanical properties such as yield strength, percentage elongation, and Charpy energy. We formulate a problem with six objective functions based on data available and support two decision makers in finding a solution that satisfies them both. To enable two decision makers to make meaningful decisions for a problem with many objectives, we create the so-called MultiDM/IOPIS algorithm, which combines multiobjective evolutionary algorithms and scalarization functions from interactive multiobjective optimization methods in novel ways. We use the software framework called DESDEO, an open-source Python framework for interactively solving multiobjective optimization problems, to create the MultiDM/IOPIS algorithm. We provide a detailed account of all the challenges faced while formulating and solving the problem. We discuss and use many strategies to overcome those challenges. Overall, we propose a methodology to solve real-life data-driven problems with multiple objective functions and decision makers. With this methodology, we successfully obtained microalloyed steel compositions with mechanical properties that satisfied both decision makers. [ABSTRACT FROM AUTHOR]

Present work intends the mathematical modeling and solution algorithm for the optimal materials and it thickness selection of the refractory linning of steel casting ladles task. In it solution the Exploration of Variable Codes Algorithm of the Integration of Variables Method is used. The proposed model constitutes an improvement of the previously published one. The obtained numeric results and their comparison with the published previous algorithm, applied to the proposed model are offered. The numeric comparison of both algorithms sample that the one proposed in this article offers better results that the one used for the comparison. [ABSTRACT FROM PUBLISHER]

In this paper, multiple criteria optimization has been investigated. A new decision support system (DSS) has been developed for interactive solving of multiple criteria optimization problems (MOPs). The weighted-sum (WS) approach is implemented to solve the MOPs. The MOPs are solved by selecting different weight coefficient values for the criteria and solving single-criterion optimization problems (SOPs). However, the WS approach does not guarantee either finding evenly distributed solutions of a Pareto front, or finding Pareto optimal solutions in non-convex regions. In order to solve the problems, some methods have been proposed. In this paper, an adaptive weighted-sum (AWS) approach has been used. We suggest presenting the solutions, obtained not only by the weighted-sum method, but also by the adaptive weighted-sum approach, to a decision maker (DM). Thus the decision making will be facilitated.

This paper shows that the concepts and methodology contained in the system theory and operations research are suitable for application in the planning and control of the sustainable development. The sustainable development problems can be represented using the state space concepts, such as the transition of system, from the given initial state to the final state. It is shown that sustainable development represents a specific control problem. The peculiarity of the sustainable development is that the target is to keep the system in the prescribed feasible region of the state space. The analysis of planning and control problems of sustainable development has also shown that methods developed in the operations research area, such as multicriteria optimization, dynamic processes simulation, non-conventional treatment of uncertainty etc. are adequate, exact base, suitable for resolution of these problems.

El presente trabajo versa sobre la generación, planificación y optimización de secuencias de ensamble mecánico a partir de su modelo geométrico tridimensional. Se soporta sobre un enfoque que integra tanto información geométrica como restricciones tecnológicas del proceso de ensamble. En el desarrollo de la misma quedó demostrado, que una vez conocido el modelo geométrico tridimensional de un ensamble, la aplicación de criterios tecnológicos y geométricos al proceso inverso de desensamble y su posterior tratamiento con el método de algoritmos evolutivos, genera una planificación optimizada del su proceso de ensamble mecánico. La integración de la información permite disminuir el número de secuencias a evaluar y de elementos a procesar, con lo que se evita la generación y evaluación de todas las secuencias posibles con la consecuente disminución del tiempo de procesamiento. Como resultado de la aplicación del modelo integrado propuesto, se obtiene la planificación del proceso de ensamble mecánico con una reducción del tiempo de ensamble debido a que en las secuencias de ensamble obtenidas se reduce el número de cambios de dirección de ensamble, los cambios de herramientas y de puestos de trabajo, así como se minimiza la distancia a recorrer debido al cambio de puestos de trabajo. Esto se logra mediante un modelo de optimización multiobjetivo basado en algoritmos evolutivos.This work deals with the combinatorial problem of generating and optimizing feasible assembly sequences and doing the process planning involving tools and work places. The assembly sequences are obtained from a 3D model of the assembled parts based on mating conditions along with a set of technological criteria, which allows automatically analyzing and generating the sequences. The generated assembly sequences are preprocessed and optimized for the assembly Process Planning using Genetic Algorithms. This approach integrates the geometric and technological information of the assembly process, which allows reducing the number of elements and sequences to be processed with the consequent processing time and cost reduction.

El presente trabajo está dedicado a la optimización bajo criterios múltiples de la planificación de procesos de ensamble mecánico a partir de su modelo geométrico tridimensional. Se soporta sobre un enfoque que integra tanto información geométrica como restricciones tecnológicas del proceso de ensamble. En el desarrollo de la misma quedó demostrado, que una vez conocido el modelo geométrico tridimensional de un ensamble, la aplicación de criterios tecnológicos y geométricos al proceso inverso de desensamble y su posterior tratamiento con métodos evolutivos, genera planes deensamble mecánico próximos a los óptimos de acuerdo al sistema de preferencias del decisor. La integración de la información permite disminuir el número de secuencias a evaluar y de elementos a procesar, con lo que se evita la generación y evaluación de todas las secuencias posibles con la consecuente disminución del tiempo de procesamiento. Como resultado de la aplicación del modelo integrado propuesto, se obtiene la planificación del proceso de ensamblemecánico con una reducción del tiempo de ensamble debido a que en las secuencias obtenidas se reduce el número de cambios de dirección de ensamble, los cambios de herramientas y de puestos de trabajo, así como se minimiza la distancia a recorrer debido al cambio de puestos de trabajo. Esto se logra mediante un modelo de optimización multiobjetivo basado en algoritmos genéticos.This work deals with the combinatorial problem of generating and optimizing technologically feasible assembly sequences and process planning involving tools and work places. The assembly sequences and related technological decisions are obtained from a 3D model of the assembled parts based on mating conditions along with a set of technological criteria, which allows automatically analyzing and generating the sequences. The generated assembly sequences are pre-processed and optimized for the assembly Process Planning using Genetic Algorithms. This approach integrates the geometric and technological information of the assembly process, which allows reducing the number of elements and sequences to be processed with the consequent processing time and cost reduction.

Because of size and covariance matrix problems, computing much of anything along the nondominated frontier of a large-scale (1000-3000 securities) portfolio selection problem with semi-continuous variables is a task that has not previously been achieved. But given (a) the speed at which the nondominated frontier of a classical portfolio problem can now be computed and (b) the possibility that there might be overlaps between the nondominated frontier of the classical problem and that of the same problem but with semi-continuous variables, the paper shows how considerable amounts of the nondominated frontier of a large-scale mean-variance portfolio selection problem with semi-continuous variables can be computed in very little time. [ABSTRACT FROM AUTHOR]

Numerical simulation and optimization of cryosurgeries are considered. The Stefan problem that describes thermal processes running in the course of surgery is solved numerically. The finite volume method and enthalpy approach are used. The problem of optimization of the probe positions is solved to improve the surgery efficiency. The optimization problem is solved using nonparametric multiple criteria optimization techniques. [ABSTRACT FROM AUTHOR]

Planning is one of the most important aspects of project management. A project plan defines objectives, activities and timeframe for project realization. To be able to define the required timeframe for project realization it is important to prepare its schedule. The purpose of this paper is to present the project scheduling problem as a multiple criteria decision making problem and to solve it using two evolutionary algorithms: SPEA2 and an evolutionary algorithm driven by the fuzzification of Pareto dominance. A comparison of these two approaches is conducted to investigate if it is reasonable to use the fuzzification of the Pareto dominance relation in evolutionary algorithms for the multiple criteria project scheduling problem. [ABSTRACT FROM AUTHOR]

There has been an increasing interest in multicriteria optimization (MCO) of nonlinear process network problems in recent years. Several mathematical models have been developed and solved using MCO methodologies including ϵ -constraint, weighted sum, and minimum distance. In this paper, we investigate the bi-objective nonlinear network synthesis problem and propose an effective algorithm, ϵ -OA, based on augmented ϵ -constraint and logic-based outer approximation (OA). We provide theoretical characterization of the proposed algorithm and show that the solutions generated are efficient. We illustrate the effectiveness of our novel algorithm on two benchmark problems. The ϵ -OA is compared to the straightforward use of OA with augmented ϵ -constraint algorithm ( ϵ -con + OA), the augmented ϵ -constraint without OA ( ϵ -MINLP), and the traditional ϵ -constraint (T- ϵ -con). Based on the results, our novel algorithm is very effective in solving the bi-objective generalized disjunctive programming problems in the synthesis of process networks. [ABSTRACT FROM AUTHOR]

Power distribution utilities must transport quality and reliable electric energy to all of the customers in a given network within specified targets. Failure of one of the network components is the main factor that compromises a system's reliability. Maintenance actions, such as repairs and component replacements, are employed to avoid power interruptions or to resume the healthy network operation. This paper reports the relationship between maintenance activities and reliability as an optimization problem with two criteria: cost of maintenance activities and the maximum value for a system average interruption frequency index planning period. Solving these problems for each network provides local efficient solutions and associated trade-off curves. These solutions are optimally combined to solve a global-level multiple criteria optimization problem, revealing the local efficient solutions and associated trade-off curves for a group of networks or for a whole company. The procedure solves the hierarchical multiple criteria maintenance resources allocation problem and provides information to assess the decisions on maintenance activities at all decision levels in the network management. This procedure is applied to one illustrative example and real-life case studies to demonstrate its benefits. [ABSTRACT FROM AUTHOR]

One of the primary aims of performance-oriented design is to develop spaces that achieve acceptable levels of human comfort. Wind-induced airflow plays a significant role in improving occupants’ comfort in a building. This paper explores the extent to which simulation and evaluation of natural airflow can potentially be a contributing parameter in the conception of performance-aware designs. The authors employed Fast Fluid Dynamics simulation to study the natural ventilation performance of a pavilion and a genetic algorithm to optimize the shape of the pavilion’s facades’ openings. A performance analysis was conducted whereby an array of automated feedback loops carried out by a genetic algorithm produce a number of acceptable solutions. Hence the facade openings of these solutions are considered to be adequate to provide natural ventilation in the indoor environment. The study demonstrates the ability of the optimization model to yield design instances that fulfill a number of environmental criteria related to airflow and human comfort. In this light, the authors suggest that the aforementioned method can be used as an experimentation platform to influence and guide the designer during the initial stages of the design process towards a more performance-driven solution. [ABSTRACT FROM AUTHOR]