Multi-objective Performance Evaluation Research Articles

Developing Water Cycle Algorithm for Optimal Operation in Multi-reservoirs Hydrologic System

Optimal operation of multi-objective reservoirs is one of the complex and, sometimes nonlinear, issues in multi-objective hydrologic system optimization. Meta-heuristic algorithms are good optimization tools which look for decision space via simulating the behavior of animals and providing the possibility for presenting a set of points as a set of problem solutions. Therefore, in this study, developing multi-objective water cycle algorithm (MOWCA) was investigated for the optimal operation issue of Halilrood basin reservoir system (Baft, Safarood, and Jiroft Dams) in order to hydropower energy generation of Jiroft Dam, downstream demand supply (drinking, agricultural, and environmental requirements), and flood control for a period of 223 months (from October 2000 to April 2019). Also, the results of the algorithm were compared with those of the well-known non-dominated sorting genetic algorithm II (NSGA-II). To evaluate the efficiency of the used multi-objective algorithms, four performance evaluation criteria including generational distance (GD), metric of spacing (S), metric of spread (Δ), and maximum spread (MS) were used. The results of applying multi-objective performance evaluation criteria showed the superiority of the developed MOWCA method in three criteria of distance, spread, and maximum spread criteria, while the NSGA-II algorithm was superior to the MOWCA only in metric of spacing (S) criterion. Moreover, the MOWCA algorithm with total 236.07 objectives performed better than the NSGA-II algorithm with total 268.01 objectives. In Jiroft Dam’s hydropower energy generation, the MOWCA algorithm with 4278.69 MW power generation was considerably superior to the NSGA-II algorithm with 3138.55 MW MW generation during the studied period. Finally, the obtained results showed higher performance of the MOWCA algorithm than the NSGA-II algorithm in the optimal operation of Halilrood basin multi-objective reservoirs systems with different objectives.

A graphical multi-objective performance evaluation method with architect-friendly mode

In response to the inadaptation and difficulties for architects in the use of engineering simulation tools and optimization methods, a method is proposed for graphical performance evaluation achieved with a developed plugin for Grasshopper as an architect-friendly tool to support design exploration in early stage. The proposed method follows forward workflow for interactive feedback of performance, focusing on thermal and visual comfort upon a variety of design options. A case study of shading design is demonstrated. The demonstration illustrated an intuitive and graphical process for qualitative performance evaluation, which is assisted by an overall ratio ranking the integrated performance of design options for a quantitative comparison. Compared with engineering optimization methods that focus on optimal performance-based solutions, the proposed method presented graphical feedbacks on design performance that are interactive with the designer for performance-informed decision making. In this way, the proposed method stimulates the effective and positive application of engineering tools and judgment at the early stage of iterative design.

Thermo-chromic glazing in buildings: a novel methodological framework for a multi-objective performance evaluation

Transparent adaptive façade components can improve the energy performance and the indoor environmental quality of buildings. Nevertheless, their utilization is not widespread, due also to the lack of a robust methodology to comprehensively evaluate their potentialities and find out their most suitable applications. The present paper introduces a novel methodology to characterize the behavior of a transparent adaptive façade component, a thermo-chromic glazing, and predict its effects, through numerical simulations, on energy performance and visual comfort aspects. An experimental characterization on the thermo-chromic glazing was performed to determine its optical properties at the variation of its surface temperature. The component was found to be able to switch its visible transmittance between 0.71 and 0.13, and its solar transmittance between 0.65 and 0.28. The experimental results were used to feed the numerical model created on purpose to describe the adaptive behavior of the component. Finally, a numerical simulation campaign was performed to assess the effects of the thermo-chromic glazing on energy and visual comfort aspects of an enclosed office located in Turin. It was found that the thermo-chromic glazing reduced the overall energy performance compared to a static selective glazing, but it allows improving the visual comfort conditions within the space considered.

Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning

In this paper, an uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation mixed-integer mathematical programing model is proposed for the response phase of the earthquake. The proposed model includes five echelons as affected areas, distribution centers, hospitals, temporary accommodation centers and temporary care centers. Two objective functions as minimizing the total cost of the location-allocation of facilities and minimizing the amount of the shortage of relief supplies, are considered. The uncertainty is modeled using a scenario-based probability approach. The main decisions made by the proposed model are locating of the temporary care and accommodation centers, allocation of the affected areas to the located centers and hospitals, as well as the allocation of the distribution centers to temporary accommodation centers. Several decisions associated with flow of injured people and commodities between facilities, and decisions associated with number of vehicles between facilities and shortage and inventory level at centers are also made by the proposed model. Several sets of constraints including demand and flow of relief commodities, capacity of centers, transportation of injured people, capacity of transportation vehicles for commodities and injured people, and back up centers are considered in multiple periods of planning in the proposed model. The proposed model is applied in a real case study in Tehran. The model is solved using modified multiple-objective particle swarm optimization (MMOPSO), Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and epsilon constraint method. The performance of the solution procedures is analyzed using multi-objective performance evaluation metrics. The results reveal the superiority of the MMOPSO over the other solution approaches. A preferred solution, from the set of non-dominated solutions generated by MMOPSO, has been selected, analyzed and described. Sensitivity analysis on main parameters of proposed model and the probabilities of the earthquake and failures of the facilities has also been accomplished.

Multi-Objective Performance Evaluation of the Detection of Catenary Support Components Using DCNNs

The goal of this paper is to evaluate from a multi-objective perspective the performance on the detection of catenary support components when using state-of-the-art deep convolutional neural networks (DCNNs). The detection of components is the first step towards a complete automatized monitoring system that will provide actual information about defects in the catenary support devices. A series of experiments in an unified test environment for detection of components are performed using Faster-CNN, R-FCN, SSD, and YOLOv2. Through the comparison of different assessment indicators, such as precision, recall, average precision and mean average precision, the detection performance of the different DCNNs methods for the components of the catenary support devices is analyzed, discussed and evaluated. The experiment results show that among all considered methods, R-FCN is the more suitable for the detection of catenary support components.

An evolutionary approach for multi-objective vehicle routing problems with backhauls

The vehicle routing problem (VRP) is an important aspect of transportation logistics with many variants. This paper studies the VRP with backhauls (VRPB) in which the set of customers is partitioned into two subsets: linehaul customers requiring a quantity of product to be delivered, and backhaul customers with a quantity to be picked up. The basic VRPB involves finding a collection of routes with minimum cost, such that all linehaul and backhaul customers are serviced. A common variant is the VRP with selective backhauls (VRPSB), where the collection from backhaul customers is optional. For most real world applications, the number of vehicles, the total travel cost, and the uncollected backhauls are all important objectives to be minimized, so the VRPB needs to be tackled as a multi-objective problem. In this paper, a similarity-based selection evolutionary algorithm approach is proposed for finding improved multi-objective solutions for VRPB, VRPSB, and two further generalizations of them, with fully multi-objective performance evaluation.

A cross-layer framework for multiobjective performance evaluation of wireless ad hoc networks

In this paper we address the problem of finding the optimal performance region of a wireless ad hoc network when multiple performance metrics are considered. Our contribution is to propose a novel cross-layer framework for deriving the Pareto optimal performance bounds for the network. These Pareto bounds provide key information for understanding the network behavior and the performance trade-offs when multiple criteria are relevant. Our approach is to take a holistic view of the network that captures the cross-interactions among interference management techniques implemented at various layers of the protocol stack (e.g. routing and resource allocation) and determines the objective functions for the multiple criteria to be optimized. The resulting complex multiobjective optimization problem is then solved by multiobjective search techniques. The Pareto optimal sets for an example sensor network are presented and analyzed when delay, reliability and energy objectives are considered.

Multi-objective decision-making methodology to create an optimal design chain partner combination

In today’s highly competitive business environment, many companies adopt the time-to-market strategy to obtain a competitive advantage. To reduce the time and cost of product development and to employ global product development resources, design chain partner evaluation and selection has become a crucial issue. Thus, establishing an optimal design chain partner combination has received significant attention because it has a far-reaching effect on the results of product development. With this perspective, this paper develops an integrated decision-making methodology to assist enterprises as they create an optimal design chain partner combination. First, this study establishes the framework and evaluation models of the criteria for the different roles of design chain partners, including system integration, functional module development and software and component development. Then, this paper applies a weight-restricted DEA (data envelopment analysis) approach to create the models for performance analysis of design chain partners to acquire the performance value of each candidate and select the efficient design chain partners. Moreover, this paper employs the multi-objective performance evaluation model proposed in this paper to analyze the synthesized performance of design chain combinations. Moreover, this research uses a multi-objective genetic algorithm (GA) to search efficiently for the optimal design chain partner combination to minimize product development cost and time and maximize product reliability. Finally, this study employs a derivative new product development project for a digital TV box as a case study to illustrate the efficacy of the proposed methodology.

Applications of multi-objective structure optimization

We present applications of multi-objective evolutionary optimization of feed-forward neural networks (NN) to two real world problems, car and face classification. The possibly conflicting requirements on the NNs are speed and classification accuracy, both of which can enhance the embedding systems as a whole. We compare the results to the outcome of a greedy optimization heuristic (magnitude-based pruning) coupled with a multi-objective performance evaluation. For the car classification problem, magnitude-based pruning yields competitive results, whereas for the more difficult face classification, we find that the evolutionary approach to NN design is clearly preferable.

A multiple-criteria decision methodology for the make-or-buy problem

This paper develops a multiple-criteria decision analysis methodology for the evaluation of make-or-buy strategies. The methodology represents a radical departure from existing frameworks by executing a dynamic, multi-attribute, and multi-objective performance evaluation of make-or-buy alternatives. The methodology contains a model that comprises four objectives: maximization of strategic competitive performance; maximization of managerial performance; minimization of sourcing risks; and maximization of financial performance. The financial objective is measured using the net present value. The attributes for the other objectives are measured using the related concept of net present qualitative/operational flow analysis. These objectives and their attributes are traded-off using composite programming, resulting in a rank ordering of the make-or-buy alternatives. In order to generate the measures of criteria importance, the model applies the analytic hierarchy process. However, since the measures are assumed to be time-dependent, the proposed methodology utilizes the limiting power of stochastic matrices to generate the set of criteria priorities over the planning horizon. The proposed approach has been designed to be applicable in manufacturing as well as in any other function of the value chain. A case study is used to demonstrate the application of the model.