We propose a novel approach to explore the trade-offs between four features of students' well-being (anxiety, motivation, sense of belonging, and bullying). On the one hand, a multiobjective interval problem is fo...
详细信息
We propose a novel approach to explore the trade-offs between four features of students' well-being (anxiety, motivation, sense of belonging, and bullying). On the one hand, a multiobjective interval problem is formulated by considering these distinct components of well-being as objective functions, being then instantiated with confidence intervals obtained from distinct econometric estimations. Then, the problem is solved through the use of a reference point approach that allows accounting for the decision maker's preferences by considering a set of weight vectors that can be used to express his/her preferences regarding the importance that should be given to each objective function. The results provide information on how the improvement of one objective might affect the remaining objectives. Furthermore, the student's profile corresponding to each scrutinized solution is also made available. Overall, the results claim that bullying is the most affected objective, highlighting the need to foster antibullying education policies in Spanish schools, according to PISA 2015 data. Finally, some educational polices are suggested in order to enhance students' well-being.
This paper presents a new two-phase algorithm for the bi-objective minimum spanning tree (BMST) problem. In the first phase, it computes the extreme supported efficient solutions resorting to both mathematical program...
详细信息
This paper presents a new two-phase algorithm for the bi-objective minimum spanning tree (BMST) problem. In the first phase, it computes the extreme supported efficient solutions resorting to both mathematical programming and algorithmic approaches, while the second phase is devoted to obtaining the remaining efficient solutions (non-extreme supported and non-supported). This latter phase is based on a new recursive procedure capable of generating all the spanning trees of a connected graph through edge interchanges based on increasing evaluation of non-zero reduced costs of associated weighted linear programs. Such a procedure exploits a common property of a wider class of problems to which the minimum spanning tree (MST) problem belongs, that is the spanning tree structure of its basic feasible solutions. Computational experiments are conducted on different families of graphs and with different types of cost. These results show that this new two-phase algorithm is correct, very easy to implement and it allows one to extract conclusions on the difficulty of finding the entire set of Pareto solutions of the BMST problem depending on the graph topology and the possible correlation of the edge costs.
In this paper, we extend the concept of quasidifferential to a new notion called semi-quasidifferential. This generalization is motivated by the convexificator notion. Some important properties of semiquasidifferentia...
详细信息
In this paper, we extend the concept of quasidifferential to a new notion called semi-quasidifferential. This generalization is motivated by the convexificator notion. Some important properties of semiquasidifferentials are established. The relationship between semi-quasidifferentials and the Clarke sub differential is studied, and a mean value theorem in terms of semi-quasidifferentials is proved. It is shown that this notion is helpful to investigate nonsmooth optimization problems even when the objective and/or constraint functions are discontinuous. Considering a multiobjective optimization problem, a characterization of some cones related to the feasible set is provided. They are used for deriving necessary and sufficient optimality conditions. We close the paper by obtaining optimality conditions in multi objective optimization in terms of semi-quasidifferentials. Some outcomes of the current work generalize the related results existing in the literature. (c) 2021 Elsevier B.V. All rights reserved.
This paper develops a bi-level multi-objective model for road pricing optimization considering land use and transportation effects. The upper-level problem determines a cordon-based road pricing scheme, while the lowe...
详细信息
This paper develops a bi-level multi-objective model for road pricing optimization considering land use and transportation effects. The upper-level problem determines a cordon-based road pricing scheme, while the lower-level problem models the interaction between land use and transportation. To facilitate decision-making in a scenario characterized by a hierarchical ordering of objectives, a novel alpha-conditional lexicographic optimization method is established, which uses an alpha value to capture the decision-maker's perceived acceptability of the trade-off between different objectives with respect to the hierarchical objective ordering. The properties associated with this approach are derived, and an algorithm to find the alpha-conditional lexicographic dominance solutions is developed. To solve the model, a revised genetic algorithm is further developed to illustrate how the proposed alpha-conditional lexicographic optimization method can be embedded into existing heuristic or metaheuristic methods. A case study using data from Jiangyin, China, demonstrates the significance of considering land use effects when evaluating road pricing scenarios. The results reveal the trade-off between transportation and various land use objectives and the variation of such a trade-off among different types of traffic analysis zones. It is demonstrated that the proposed alpha-conditional lexicographic approach can improve most of the land use objective values while ensuring that the total travel time is constrained within an acceptable range, enabling a balance between various land use and transportation objectives. (C) 2021 Elsevier B.V. All rights reserved.
In this paper we consider linear bilevel programming problems with multipleobjective functions at the lower level. We propose a general-purpose exact method to compute the optimistic optimal solution, which is based ...
详细信息
In this paper we consider linear bilevel programming problems with multipleobjective functions at the lower level. We propose a general-purpose exact method to compute the optimistic optimal solution, which is based on the search of efficient extreme solutions of an associated multiobjective linear problem with many objective functions. We also explore a heuristic procedure relying on the same principles. Although this procedure cannot ensure the global optimal solution but just a local optimum, it has shown to be quite effective in problems where the global optimum is difficult to obtain within a reasonable timeframe. A computational study is presented to evaluate the performance of the exact method and the heuristic procedure, comparing them with an exact and an approximate method proposed by other authors, using randomly generated instances. Our approach reveals interesting results in problems with few upper-level variables.(c) 2022 Elsevier B.V. All rights reserved.
Budget allocation problems in portfolio management are inherently multi-objective as they entail different types of assets of which performance metrics are not directly comparable. Existing asset management methods th...
详细信息
Budget allocation problems in portfolio management are inherently multi-objective as they entail different types of assets of which performance metrics are not directly comparable. Existing asset management methods that either consolidate multiple goals to form a single objective ( a priori ) or populate a Pareto optimal set ( a posteriori ) may not be sufficient because a decision maker (DM) may not possess comprehensive knowledge of the problem domain. Moreover, current techniques often present a Pareto optimal set with too many options, making it counter-productive. In order to provide the DM with a diverse yet compact solution set, this paper proposes a three-step approach. In the first step, we employ different approximation functions to capture investment-performance relationships at the asset-type level. These simplified relationships are then used as inputs for the multi-objective optimisation model in the second step. In the final step, Pareto optimal solutions generated by a selected evolutionary algorithm are pruned by a clustering method. To measure the spread of representative solutions over the Pareto front, we present two novel indicators based on average Euclidean distance and cosine similarity between original Pareto solutions and representative solutions. Through numerical examples, we demonstrate that this approach can provide a set of representative solutions that maintain high integrity of the original Pareto front. We also put forward suggestions on choosing appropriate approximation functions, pruning methods, and indicators. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )
Development of microwave components is an inherently multi-objective task. This is especially pertinent to the design closure stage, i.e., final adjustment of geometry and/or material parameters carried out to improve...
详细信息
Development of microwave components is an inherently multi-objective task. This is especially pertinent to the design closure stage, i.e., final adjustment of geometry and/or material parameters carried out to improve the electrical performance of the system. The design goals are often conflicting so that the improvement of one normally leads to a degradation of others. Compact microwave passives constitute a representative case: reduction of the circuit footprint area is detrimental to electrical figures of merit (e.g., the operating bandwidth). Identification of the best available trade-off designs requires multiobjective optimization (MO). This is a computationally expensive task, especially when executed at the level of full-wave electromagnetic (EM) simulation. The computational complexity issue can be mitigated through the employment of surrogate modeling methods, yet their application is limited by a typically high nonlinearity of system responses, and the curse of dimensionality. In this paper, a novel technique for fast MO of compact microwave components is proposed, which allows for sequential rendition of the trade-off designs using triangulation of the already available Pareto front as well as rapid refinement algorithms. Our methodology is purely deterministic;in particular, it does not rely on population-based nature-inspired procedures. The three major benefits are low computational cost, possibility of handling explicit design constraints, and a capability of producing a visually uniform representation of the Pareto front. The algorithm is demonstrated using a compact branch-line coupler and a three-section impedance matching transformer. In both cases, considerable savings are obtained over the benchmark, here, the state-of-the-art surrogate-assisted MO technique.
Organizations have to allocate resources, time, and workforce in many projects at the same time. Selection and scheduling of the projects have a significant impact on effective project management. However, most of the...
详细信息
Organizations have to allocate resources, time, and workforce in many projects at the same time. Selection and scheduling of the projects have a significant impact on effective project management. However, most of the studies in the literature do not solve the selection and scheduling problems simultaneously. This study aims to design an interactive process to integrate selection and scheduling processes in the project management. For this purpose, a new multi objectiveprogramming model is proposed. The project scores are presented as belief de-grees (i.e., distributions to linguistic term levels) that are gathered as a result of the weighted cumulative belief degree approach. By the use of the belief degrees, projects could be selected and scheduled based on the satis-faction level of the problem owner. The proposed model considers conditions and restrictions in management of business development projects such as the progress percentage of the projects in a period, the complementary and mutual exclusive relations between projects, etc. An interactive solution procedure is developed in order to solve the proposed model. The proposed model and the solution procedure are applied in an information technology company for their business development projects
A new exact algorithm for bi-objective linear integer problems is presented, based on the classic epsilon-constraint method and algebraic test sets for single-objective linear integer problems. Our method provides the...
详细信息
A new exact algorithm for bi-objective linear integer problems is presented, based on the classic epsilon-constraint method and algebraic test sets for single-objective linear integer problems. Our method provides the complete Pareto frontier N of non-dominated points and, for this purpose, it considers exactly vertical bar N vertical bar single-objective problems by using reduction with test sets instead of solving with an optimizer. Although we use Grobner bases for the computation of test sets, which may provoke a bottleneck in principle, the computational results are shown to be promising, especially for unbounded knapsack problems, for which any usual branch-and-cut strategy could be much more expensive. Nevertheless, this algorithm can be considered as a potentially faster alternative to IP-based methods when test sets are available. (C) 2019 Elsevier B.V. All rights reserved.
Peer-to-peer (P2P) lending has emerged as an alternative method of financing. Keeping pace with this development, many P2P lending studies have provided approaches to select investment portfolios for individual lender...
详细信息
Peer-to-peer (P2P) lending has emerged as an alternative method of financing. Keeping pace with this development, many P2P lending studies have provided approaches to select investment portfolios for individual lenders. However, none of these approaches consider how long it takes for an individual loan to be fully funded so as to reduce the opportunity cost incurred due to delayed investment. In this paper, we propose a goal programming framework to develop an optimal P2P lending portfolio that considers not only the expected returns but also this opportunity cost for individual investors. First, for each loan proposal, a logistic regression model is used to predict the loan default probability while a Weibull regression is used to determine the opportunity cost incurred due to the time taken to obtain the loan. Next, goal programming is applied to construct a portfolio that minimizes the slack from the desired return on investment as well as the surplus from the preset opportunity cost due to a prolonged bidding period. The proposed approach is then applied to Prosper platform data and is expected to help investors' portfolio decisions in the P2P lending market.
暂无评论