Mono-objective Approach Research Articles

Supporting tactical harvest planning decisions of major fruits through a multi-objective modeling approach by using exact methods

An important stage of the agri-food supply chain is production (agricultural practices and harvesting), where relevant decisions are made in a short period of intensive activity. Regarding the major fruit supply chain, the complexity of these decisions increases because most fruit export companies manage several orchards with different fruit species and varieties. Moreover, the amount of labor and resources must be estimated at least two months in advance of the harvest so that they can be relied upon during the harvest season. In this study, a multi-objective modeling approach for supporting tactical harvest planning decisions of major fruits is developed, aiming to evidence its adequacy and to identify different harvest plans according to the prioritized objectives. Therefore, a multi-objective linear programming (MOLP) model, which considers three conflicting objectives, is proposed. The three objectives correspond to minimizing harvest costs, fruit loss and harvest days. For solving the MOLP model, five a posteriori multi-objective exact methods are compared using a synthetic instance based on a real case. The Pareto frontiers obtained by these methods are analyzed using three metrics related to accuracy, cardinality, and diversity criteria, so that the most suitable method can be selected. Thus, the selected method is applied to an actual case study of a Chilean fruit export company. In addition, the ε-constraint method is used in this case study to compare the obtention of non-dominated solutions in the convex and non-convex regions of the Pareto frontier. The results show that harvest plans vary significantly according to the prioritization of the objective functions. Thus, the percentage difference between each objective function’s worst and best values is around 497% when the harvest costs are minimized, around 1,448% when the fruit loss is minimized, and around 18% when the number of harvest days is minimized. In addition, the adequacy of the multi-objective approach rather than the mono-objective approach to support tactical harvest planning decisions is demonstrated. Furthermore, a scenario analysis for considering changes in the harvest periods is carried out by considering three possible scenarios. This analysis is required because these periods can change every year due to different factors, such as weather conditions and agricultural practices, among others. The results show that the harvest costs and fruit loss increase, on average, 87% and 100%, respectively, when the harvest period is short, that is, one day. Finally, managerial insights about the information obtained by the MOLP model are discussed.

Optimization of BESS placement, technology selection, and operation in microgrids for minimizing energy losses and CO2 emissions: A hybrid approach

This research paper addresses the issue of placement, technology selection and operation of BESS energy storage systems (BESS) in microgrids under a variable distributed generation (DG) and energy demand scenario for an average year of operation. To address this issue, a mixed-integer nonlinear programming (MINLP) model was formulated. The model considers two different objective functions, which are optimized using a mono-objective approach: (i) total energy losses and (ii) total CO2 emissions. The MINLP model was solved using a master–slave methodology based on metaheuristic algorithms. In the master stage, the Chu & Beasley parallel genetic algorithm (PCBGA) was used to solve the BESS placement and technology selection problem, while in the slave stage, the vortex search algorithm (VSA) was used to solve the BESS operation problem. A matrix-based successive approximation power flow was also used in this stage to determine the objective functions and evaluate the technical and operational constraints defined in the mathematical model. To verify the results, the 33-node test system was adjusted to the generation and demand characteristics of the city of Medellín. In addition, the proposed methodology was compared with two optimization techniques used in the master stage, namely the Parallel Monte Carlo (PMC) method and the Parallel Particle Swarm Optimizer (PPSO). Simulation results obtained using MATLAB software for the test scenario showed that the CBGA-VSA methodology outperformed the other techniques in terms of solution quality, repeatability and processing times to minimize total annual operating costs.

Mono- and Multi-Objective CFD Optimization of Graded Foam-Filled Channels.

Graded foam-filled channels are a very promising solution for improving the thermal performance of heat sinks because of their customized structures that leave large amounts of room for heat transfer enhancement. Accordingly, this paper proposes a comprehensive optimization framework to address the design of such components, which are subjected to a uniform heat flux boundary condition. The graded foam is achieved by parameterizing the spatial distributions of porosity and/or Pores Per Inch (PPI). Mono- and multi-objective optimizations are implemented to find the best combination of the foam’s fluid-dynamic, geometrical and morphological design variables. The mono-objective approach addresses the Performance Evaluation Criterion (PEC) as an objective function to maximize the thermal efficiency of graded foams. The multi-objective approach addresses different objective functions by means of Pareto optimization to identify the optimal tradeoff solutions between heat transfer enhancement and pressure drop reduction. Optimizations are performed by assuming a local thermal non-equilibrium in the foam. They allowed us to achieve a 1.51 PEC value with H* = 0.50, ReH = 15000, iε = iPPI = 0.50, ε(0) = 0.85, ε(1) = 0.97, PPI(0) = 5, PPI(1) = 40, and ks→f = 104 as the design variables. For the three multi-objective functions investigated, one can extrapolate the optimum from the Pareto front via the utopia criterion, obtaining = 502 W/m2 K and Δp = 80 Pa, = 2790 and f = 42, = 0.011, and Δp* = 91. The optimal solutions provide original insights and guidelines for the thermal design of graded foam-filled channels.

A cost-benefit multiobjective approach for placement of meters in electrical distribution systems

The present work proposes a multiobjective approach for optimal allocation of metering devices in Electrical Distribution Systems. The proposed approach seeks to obtain solutions that represent attractive investment options for utilities, while providing good performance for State Estimation (SE) when processing measurements from the allocated meters. The requirements of low investment costs and good SE performance are conflicting ones. In this paper, the Pareto multiobjective method, associated with the Modified Monkey Search (MMS) metaheuristic, is employed to obtain concurrent candidate solutions for optimal allocation of smart meters and phasor measurement units. The proposed method, called MMS-Pareto, is employed along with an efficient tool for SE in distribution networks, known as Enhanced Optimal Power Flow. The proposed method is assessed using benchmark systems of the literature, as well as a real Brazilian utility network, being adopted practical and updated costs for the meter devices. Comparisons with a weighted mono-objective approach are presented and the obtained results show that the MMS-Pareto can lead to good trade-off solutions, useful for system planners. Moreover, comparisons with the results obtained when employing other metaheuristics are presented.

Using a many-objective approach to investigate automated refactoring

Abstract Context Software maintenance is expensive and so anything that can be done to reduce its cost is potentially of huge benefit. However, it is recognised that some maintenance, especially refactoring, can be automated. Given the number of possible refactorings and combinations of refactorings, a search-based approach may provide the means to optimise refactorings. Objective This paper describes the investigation of a many-objective genetic algorithm used to automate software refactoring, implemented as a Java tool, MultiRefactor. Method The approach and tool is evaluated using a set of open source Java programs. The tool contains four separate measures of software looking at the software quality as well as measures of code priority, refactoring coverage and element recentness. The many-objective algorithm combines the four objectives to improve the software in a holistic manner. An experiment has been constructed to compare the many-objective approach against a mono-objective approach that only uses a single objective to measure software quality. Different permutations of the objectives are also tested and compared to see how well the different objectives can work together in a multi-objective refactoring approach. The eight approaches are tested on six different open source Java programs. Results The many-objective approach is found to give better objective scores on average than the mono-objective approach and in less time. However, the priority and element recentness objectives are both found to be less successful in multi/many-objective setups when they are used together. Conclusion A many-objective approach is suitable and effective for optimising automated refactoring to improve quality. Including other objectives does not unduly degrade the quality improvements, but is less effective for those objectives than if they were used in a mono-objective approach.

Maximum Diversity Problem. A Multi-Objective Approach

The Maximum Diversity (MD) problem is the process of selecting a subset of elements where the diversity among selected elements is maximized. Several diversity measures were already studied in the literature, optimizing the problem considered in a pure mono-objective approach. This work presents for the first time multi-objective approaches for the MD problem, considering the simultaneous optimization of the following five diversity measures: (i) Max-Sum, (ii) Max-Min, (iii) Max-MinSum, (iv) Min-Diff and (v) Min-P-center. Two different optimization models are proposed: (i) Multi-Objective Maximum Diversity (MMD) model, where the number of elements to be selected is defined a-priori, and (ii) Multi-Objective Maximum Average Diversity (MMAD) model, where the number of elements to be selected is also a decision variable. To solve the formulated problems, a Multi-Objective Evolutionary Algorithm (MOEA) is presented. Experimental results demonstrate that the proposed MOEA found good quality solutions, i.e. between 98.85% and 100% of the optimal Pareto front when considering the hypervolume for comparison purposes.

Role of global buckling in the optimization process of grid shells: Design strategies

The aim of the paper is to propose a new approach for the optimization of grid shell structures based on a mixed sizing/topologic process, which specifically accounts for the global buckling behavior. The approach consists of merging a sequence of sizing and topological optimization processes where local and global buckling phenomena are opportunely introduced. Indeed, in addition to the removal of some members and the increase/decrease of the cross section size of the remaining ones, the proposed approach accounts for the possibility to convert some of the elements composing the grid shell structure, generally trusses, into beam elements with the aim of having an optimized solution also with respect to the global buckling behavior. The selection of the truss members to convert into beams is made by considering two different strategies presented in detail in the paper. Numerical analyses referring to some case studies derived from the current literature are developed by considering the proposed approach and a traditional sizing/topologic mono objective approach. The results obtained from the numerical analyses clearly underline the reliability of the proposed approach and its efficiency in comparison to traditional approaches of structural optimization.

Multiobjective learning in the random neural network

This paper presents a multiobjective learning algorithm for the random neural network RNN. A learning process consists in updating the parameters of the RNN. In a monoobjective approach, this update is made by means of the minimisation of one objective function, which is usually quadratic error of input-output. Here, we state a general procedure to update the parameters that take into account more than one objective. The parameter estimation is described as a multiobjective optimisation problem MOP and it is solved using weighting problem and gradient descent. The solution of MOP is a set called Pareto-set. A case study is presented, where quadratic error of dynamic input-output and the quadratic error of static curve were used to estimate the parameters of the RNN. We show that multiobjective learning improves the quality of models built from limited-range dynamic data.

A Hybrid Multiobjective Evolutionary Approach for Improving the Performance of Wireless Sensor Networks

The increasing in the demand for Wireless Sensor Networks (WSNs) has intensified studies which are dedicated to obtain more energy-efficient solutions, since the energy storage limitation is critical in those systems. Additionally, there are other aspects which usually must be ensured in order to get an acceptable performance of WSNs, such as area coverage and network connectivity. This paper proposes a procedure for enhancing the performance of WSNs: a multiobjective hybrid optimization algorithm is employed for solving the Dynamic Coverage and Connectivity Problem (DCCP) in flat WSNs subjected to node failures. This method combines a multiobjective global on-demand algorithm (MGoDA), which improves the current DCCP solution using a Genetic Algorithm, with a local on line algorithm (LoA), which is intended to restore the network coverage soon after any failure. The proposed approach is compared with an Integer Linear Programming (ILP)-based approach and a similar mono-objective approach with regard to coverage, network lifetime and required running time for achieving the optimal solution provided by each method. Results achieved for a test instance show that the hybrid approach presented can improve the performance of the WSN obtaining good solutions with a considerably smaller computational time than ILP. The multiobjective approach still provides a feasible method for extending WSNs lifetime with slight decreasing in the network mean coverage.

Heuristics for an industrial car sequencing problem considering paint and assembly shop objectives

The aim of this paper is to study the problem of sequencing a set of vehicles within an industrial environment considering the assembly shop objectives, but also the objectives of the paint shop. The first approach is to solve the problem with a mono-objective function. One heuristic (a progressive, construction-sequence algorithm) and three meta-heuristics (simulated annealing, variable neighbourhood search and an evolutionary algorithm) are described and compared. As the mono-objective approach has limited possibilities, a multi-objective heuristic is finally presented and tested. Because of the industrial context of this research, the computation time is a decisive factor to select the appropriate heuristic.

Mono- and multiobjective formulations for the indoor wireless LAN planning problem

Wireless LANs (WLANS) experienced great success in the past five years. This technology has been quickly adopted in private and public areas to provide a convenient networking access. The fast pace of development has often induced an uncoordinated deployment strategy where WLAN planning tools have been barely used. This article highlights the difficulty of planning such wireless networks for indoor environments. The first issue the WLAN planning problem has to face is to accurately describe the quality of a network, based on realistic propagation predictions. The second issue is to implement a search strategy that provides efficient deployment strategies. This article is introduced by a description of previously proposed planning strategies. Their study opens out onto a problem formulation that accounts for coverage, interference level and quality of service (in terms of data throughput per user). This formulation is then introduced as either a mono- or a multiobjective (MO) optimization problem. In the first case, we propose to solve the mono-objective problem with a Tabu search metaheuristic minimizing a weighted sum of the planning criteria. Then, we compare the outcome of this strategy to the results of our previously proposed MO Tabu search strategy. We highlight the fact that efficient solutions are obtained quickly with the mono-objective approach if an appropriate set of weighting coefficients of the evaluation function is chosen. The main issue of mono-objective search is to determine these coefficients. It is a delicate task that often needs several runs of the algorithm. MO search is an interesting alternative heuristic as it directly provides a set of planning solutions that represent several trade-offs between the objectives. Our MO heuristic looks for a set of non-dominated solutions expected to converge to the Pareto front of the problem and selects the most significant ones for the end user. Both QoS-oriented planning methods are illustrated on a realistic environment representing a building floor of about 12 600 m 2 . Results show the assets of both approaches but mainly emphasize the benefit of the MO search strategy that offers several alternative solutions to the radio engineer.