Set Of Compromise Solutions Research Articles

Operational Scheduling of Household Appliances by Using Triple-Objective Optimization Algorithm Integrated with Multi-Criteria Decision Making

Load scheduling is a key factor in demand side management (DSM), which manages available generation capacity with regard to the required demand. In this paper, a triple-objective load scheduling optimization problem (LSOP) is formulated for achieving optimal cost and peak demand as well as minimum customer inconvenience. A Henry gas solubility optimization (HGSO) algorithm that is based on multi-objective is used for solving LSOP. The proposed HGSO offers a set of compromise solutions that represent the tradeoff between the three objectives of the formulated problem. A set of all compromise solutions from the dominant Pareto front is achieved first, and then ranked by using MCDM so as to optimally sort these solutions. An entropy weighting method (EWM) is then used for computing the weights of various criteria that dominate the LSOP and is provided as a technique for ordering preferences by similarity to achieve the ideal solution (TOPSIS) so as to rank the sorted solutions. Two types of end-users are considered so as to show the effectiveness of the proposed LSOP: non-cooperative and cooperative users. The results of the proposed load scheduling method show the significance of the proposed method for both the cooperative and non-cooperative end-users. The proposed method achieves a cost of energy of R50.62 as a total cost of energy consumed by four non-cooperative end-users. The cost of energy for the cooperative end-users is found to be R47.39. Thus, saving in the energy cost unit is found to be around 5.5% by using the proposed method; moreover, the peak demand value is reduced by 9.7% when non-cooperative end-users becomes cooperative.

Multi-objective numerical optimization of 3D-printed polylactic acid bio-metamaterial based on topology, filling pattern, and infill density via fatigue lifetime and mass.

Infill parameters are significant with regard to the overall cost and saving material while printing a 3D model. When it comes to printing time, we can decrease the printing time by altering the infill, which also reduces the total process extent. Choosing the right filling parameters affects the strength of the printed model. In this research, the effect of filling density and infill pattern on the fatigue lifetime of cylindrical polylactic acid (PLA) samples was investigated with finite element modeling and analysis. This causes the lattice structure to be considered macro-scale porosity in the additive manufacturing process. Due to the need for multi-objective optimization of several functions at the same time and the inevitable sacrifice of other objectives, the decision was to obtain a set of compromise solutions according to the Pareto-optimal solution technique or the Pareto non-inferior solution approach. As a result, a horizontally printed rectangular pattern with 60% filling was preferred over the four patterns including honeycomb, triangular, regular octagon, and irregular octagon by considering the sum of mass changes and fatigue lifetime changes, and distance from the optimal point, which is the lightest structure with the maximum fatigue lifetime as an objective function with an emphasis on mass as an important parameter in designing scaffolds and biomedical structures. A new structure was also proposed by performing a structural optimization process using computer-aided design tools and also, computer-aided engineering software by Dassault systems. Finally, the selected samples were printed and their 3D printing quality was investigated using field emission scanning electron microscopy inspection.

Technological Advancements and Economics in Plant Production Systems: How to Retrofit?

Plant production systems such as plant factories and greenhouses can help promote resilience in food production. These systems could be used for plant protection and aid in controlling the micro- and macro- environments needed for optimal plant growth irrespective of natural disasters and changing climate conditions. However, to ensure optimal environmental controls and efficient production, several technologies such as sensors and robots have been developed and are at different stages of implementation. New and improved systems are continuously being investigated and developed with technological advances such as robotics, sensing, and artificial intelligence to mitigate hazards to humans working in these systems from poor ventilation and harsh weather while improving productivity. These technological advances necessitate frequent retrofits considering local contexts such as present and projected labor costs. The type of agricultural products also affects measures to be implemented to maximize returns on investment. Consequently, we formulated the retrofitting problem for plant production systems considering two objectives; minimizing the total cost for retrofitting and maximizing the yearly net profit. Additionally, we considered the following: (a) cost of new technologies; (b) present and projected cost for human labor and robotics; (c) size and service life of the plant production system; (d) productivity before and after retrofit, (e) interest on loans for retrofitting, (f) energy consumption before and after retrofit and, (g) replacement and maintenance cost of systems. We solved this problem using a multi-objective evolutionary algorithm that results in a set of compromised solutions and performed several simulations to demonstrate the applicability and robustness of the method. Results showed up to a 250% increase in annual net profits in an investigated case, indicating that the availability of all the possible retrofitting combinations would improve decision making. A user-friendly system was developed to provide all the feasible retrofitting combinations and total costs with the yearly return on investment in agricultural production systems in a single run.

Multi-objective optimization of the 3D container stowage planning problem in a barge convoy system

Mainly motivated by the massification of flows in inland shipping, where several barges can be assembled in convoys to transport a large number of containers to and from the hinterland, we introduce an original extension of the Container Stowage Planning Problem (CSPP) (which we denote the 3D – barge Convoy Container Stowage Planning Problem (3D –CCSPP)). The requirements for stowing all containers in a barge convoy entail additional constraints related to both the containers and the convoy. In this paper, the 3D –CCSPP is dealt with, on the basis of its relation to the 3D Bin-Packing Problem (3D-BPP), in which the filled objects represent the containers, and the bins indicate the convoy barges. We consider the multi-objective aspect of the problem with three realistic functions to be optimized: shifting movements, the convoy stability and the number of real-used barges. This is a new and effective aspect considering the state of the art of CSPP in inland shipping. We develop a multi-objective mathematical formulation of the 3D-CCSPP and propose a novel adaptation of the multi-objective evolutionary algorithm NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) based on a set of heuristics introduced by the BPP resolution methods. Specific encoding method, generation of the initial population and genetic operators are designed based on the mathematical description of the problem. Extensive experiments are then carried out on a varied set of instances. The computational results demonstrate the effectiveness of the proposed approaches, they are evaluated using CPLEX solver and their performance is verified by a set of measurements which are adapted to the problem in real-world applications in relation to each objective function. Additional studies are carried out in sensitivity analysis to provide decision makers with a better knowledge of the problem. They can then choose, from the set of compromise solutions of the Pareto front, the most promising one for practical implementation based on their experience and preferences.

How can ports act to reduce underwater noise from shipping? Identifying effective management frameworks

This paper aims to find mechanisms to align commercial interests with underwater noise reductions from commercial shipping. Through a survey and a series of interviews with representative stakeholders, we find that while acknowledging the wide variations in ports' specificities, port actions could support the reduction in underwater noise emissions from commercial shipping through changes in hull, propeller and engine design, and through operational measures associated with reduced speed, change of route and travel in convoy. Though the impact of underwater noise emissions on marine fauna is increasingly shown to be serious and wide-spread, there is uncertainty in the mechanisms, the contexts, and the levels which should lead to action, requiring precautionary management. Vessels owners are already dealing with significant investment and operating costs to comply with fuel, ballast water, NOx and CO2 requirements. To be successful, underwater noise programs should align with these factors.Based on a multiple criteria decision making (MCDM) approach, we find a set of compromise solutions for a wide range of stakeholders. Ports could propose actions such as discounted port fees and reduced ship waiting times at ports, both depending on underwater noise performance. Cooperation between ports to scale up actions through environmental indexes and classification societies' notations, and integration with other ports' actions could help support this. However, few vessels know their underwater noise baseline as there are very few hydrophone stations, and measurement methodologies are not standardized. Costs increase and availability decreases dramatically if the vessel buyer wants to improve the noise profile. Local demands regarding airborne noise close to airports boosted global pressure on the aviation industry to adopt existing quieting technology. This experience of the aviation noise control could inform the underwater noise process.

Typology Selection of Retaining Walls Based on Multicriteria Decision-Making Methods

In civil engineering and construction, in the selection of the most adequate and sustainable alternative, all of the alternatives and selection criteria, such as the requirements of the construction process (which are often overlooked) and the preferences of designers, clients, or contractors, are not always taken into account. The purpose of this article is to suggest a methodology that may allow studying all of the possible alternatives to find the most ideal solution among all of the existing possibilities for the selection of retaining walls to be built in infrastructures in different environments. For this purpose, all typologies of retaining walls and selection criteria (external requirements, construction requirements, characteristics of the natural land and economic criteria) are first identified. Subsequently, a simple methodological method is proposed, allowing the relative importance of each criterion to be established and allowing us to select the most suitable solution for each situation by successively applying different multicriteria decision-making methods. Finally, the methodology developed is applied to two projects in different locations with different constraints. The results obtained provide a set of compromise solutions that remain as best-rank alternatives when the weights of the criteria change. Therefore, the methodology developed can be applied to the selection of typologies of other structures in future projects.

Analiza protivoklopnih raketnih sistema primenom hibridnog modela AHP-VIKOR metoda višekriterijumskog odlučivanja

The paper presents the selection of the best anti-armor missile system, as a significant weapon whose basic task is to neutralize tanks, armored combat and non -combat vehicles at different firing distances. The complexity of the problem is conditioned by the different tactical and technical characteristics of anti-tank missile systems and the specific conditions of their application have conditioned the application of the model of multi-criteria decision-making. The selection of the most favorable anti-armor missile system was realized using the hybrid model AHP -VIKOR. Using the AHP method, the values of the criterion coefficients were calculated. The VIKOR method was used to select the most favorable alternative (anti-armor missile system). Based on the obtained results, a set of compromise solutions was defined, on the basis of which the decision maker can decide on the selection of the best anti-armor missile system.

Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

The potential costs and benefits of a combination of asset management actions on the water distribution network are predicted. Two types of actions are considered: maintenance actions and renewal actions. Leak detection and reparation of failures on connections and pipes define the set of potential maintenance actions to be carried out. Renewal actions concern connections, pipes, and meters. All these actions represent the model’s decision variables in order to determine a trade-off between two objectives: (i) the maximization of the water efficiency rate and (ii) the minimization of the total cost of actions to be carried out on the water system. The assessment of objective functions is ensured by an artificial neural network (ANN) trained on a French mandatory database «SISPEA». A non-dominated sorting genetic algorithm (NSGA-II) is coupled to the ANN to reach the set of compromised solutions representing potential actions to achieve. Applied to a real water distribution system in the southeast of France, the proposed decision model indicates that the improvement of water efficiency rate (WER) in the short term requires increasing operation expenditures (OPEX), which represent 99% of the total cost. Results show the existence of a threshold effect that implies to use the budget in a certain way to improve performance. A potential solution can be chosen by the decision maker among the generated Pareto front with regard to the constraint on the budget and the targeted WER.

Multiobjective Calibration of a Global Biogeochemical Ocean Model Against Nutrients, Oxygen, and Oxygen Minimum Zones

AbstractGlobal biogeochemical ocean models rely on many parameters, which govern the interaction between individual components, and their response to the physical environment. They are often assessed/calibrated against quasi‐synoptic data sets of dissolved inorganic tracers. However, a good fit to one observation might not necessarily imply a good match to another. We investigate whether two different metrics—the root‐mean‐square error to nutrients and oxygen and a metric measuring the overlap between simulated and observed oxygen minimum zones (OMZs)—help to constrain a global biogeochemical model in different aspects of performance. Three global model optimizations are carried out. Two single‐objective optimizations target the root‐mean‐square metric and a sum of both metrics, respectively. We then present and explore multiobjective optimization, which results in a set of compromise solutions. Our results suggest that optimal parameters for denitrification and nitrogen fixation differ when applying different metrics. Optimization against observed OMZs leads to parameters that enhance fixed nitrogen cycling; this causes too low nitrate concentrations and a too high global pelagic denitrification rate. Optimization against nutrient and oxygen concentrations leads to different parameters and a lower global fixed nitrogen turnover; this results in a worse fit to OMZs. Multiobjective optimization resolves this antagonistic effect and provides an ensemble of parameter sets, which help to address different research questions. We finally discuss how systematic model calibration can help to improve models used for projecting climate change and its effect on fisheries and climate gas emissions.

Development of a model for the integrated management of the international delivery chains formation

We propose a model for the formation of international logistics chains based on an integrated multicriterial analysis of potential cargo transportation routes and delivery cost. The application of the systemic approach when planning a delivery helps to ensure rational allocation and efficient servicing of traffic flows among the objects along the transportation-logistic network. At the first stage of modeling, the analysis of information about the objects of the transport and logistic system is carried out. We determine the region of efficient use of different modes of transport and construct a set of alternative chains of good delivery. Based on the analysis of the impact factors, the criteria are determined and the constraints are accepted to a given problem for a series of techno-economic, logistical, technological, environmental and other indicators. At the second stage, the multicriterial problem of synthesis (engineering) of the transport and logistics system of delivery is solved. We built an algorithm for the substantiation of effective international multi-modal transport and technological schemes of cargo delivery. The proposed algorithm provides a comprehensive assessment of the cost depending on delivery conditions in accordance with the Incoterms rules and the customs regime. To substantiate the route, the region of effective solutions for a DM is determined, based on solving a problem on the vector optimization for multiple criteria. Formation of effective schemes of cargo delivery from France to Ukraine along the route from Haut-Mauco to the warehouse Agro-Soyuz-Terminal in the city of Dnipro was explored. Based on modeling applying the package of symbolic computations in the programming environment Maple-7, we obtained a full set of compromise solutions to the vector optimization problem for the criteria of cost, delivery time, and ecological impact. We present a comprehensive assessment of the cost of goods delivery in containers using mixed transportation under the Incoterms EXW, CPT delivery conditions (delivery to Dnipro), FOB and CIF, and the customs regime of import and import (warehouse). The proposed model could be applied by transportation, logistic, customs brokering companies when substantiating alternative routes of goods delivery based on a multicriterial analysis of information.

Restoring recloser-fuse coordination by optimal fault current limiters planning in DG-integrated distribution systems

Abstract In this paper, the Fault Current Limiter (FCL) is used to restore the coordination between the protection devices in distribution systems with high-level of DG penetration. The FCL allocation may be described as an optimization problem involving multiple objective functions which are contradictory and of different dimensions. So, it is formulated as a multi-objective constrained nonlinear programming problem. The interaction among different objectives gives rise to a set of compromised solutions, largely known as the Pareto-optimal solutions. The objectives are to simultaneously minimize: the increase in fault current levels due to the penetration of DG, voltage sag, and the total cost (size) of required limiters. The optimization problem is solved using Particle Swarm Optimization (PSO). The method is applied to two distribution test systems. Effects of different operating factors are assessed. Comparative analysis of results is provided.

A new algorithm for combined dynamic economic emission dispatch with security constraints

The primary objective of CDEED (combined dynamic economic emission dispatch) problem is to determine the optimal power generation schedule for the online generating units over a time horizon considered and simultaneously minimizing the emission level and satisfying the generators and system constraints. The CDEED problem is bi-objective optimization problem, where generation cost and emission are considered as two competing objective functions. This bi-objective CDEED problem is represented as a single objective optimization problem by assigning different weights for each objective functions. The weights are varied in steps and for each variation one compromise solution are generated and finally fuzzy based selection method is used to select the best compromise solution from the set of compromise solutions obtained. In order to reflect the test systems considered as real power system model, the security constraints are also taken into account. Three new versions of DHS (differential harmony search) algorithms have been proposed to solve the CDEED problems. The feasibility of the proposed algorithms is demonstrated on IEEE-26 and IEEE-39 bus systems. The result obtained by the proposed CSADHS (chaotic self-adaptive differential harmony search) algorithm is found to be better than EP (evolutionary programming), DHS, and the other proposed algorithms in terms of solution quality, convergence speed and computation time.

Multi-agent Patrolling: Multi-Objective Approach of the Event Detection by a Mobile Wireless Sensors Network

Patrolling an environment involves a team of agents whose goal usually consists in continuously visiting its most relevant areas as frequently as possible. Hence, patrolling consists in continuously visiting relevant areas of an environment, in order to efficiently surpervise or control it. The Ant Colony Optimization (ACO) is adopted as the solution approach that efficiently solves the multiagent patrolling problem. Two stratagies are employed: all agents are located on the same node at the initial time, and the agents are dispersed over the graph, they start to patrol from their new locations. This paper mainly describes the formulation problem of event detection by a multi-objective approach, an ACO and multi-agent approach are used to model and simulate the detection of events. Different parameters are evaluated in order to minimize them. The minimization of Worst Idleness, Energy consumption and Communicational Idleness are not compatible. It is therefore necessary to seek compromise solutions . The set of compromise solutions is called Pareto Front. The set of solutions and Pareto Front are presented respectively for the cases of non-dispersion and dispersion of agents.

The Exact Solution of the Environmental/Economic Dispatch Problem

In this paper, the exact analytical solution for the environmental/economic dispatch (EED) optimization problem is presented for the first time. The EED, which simultaneously satisfies multiple contradictory criteria, is stated as a multiobjective optimization problem (MOP). Our paper has improved several aspects of a previous analytical approach. First, we take into account the unit capacity constraints in the exact formulae. Second, we obtain the set of compromise solutions known as Pareto optimal solutions and, third, our treatment of transmission losses satisfies the power balance constraint. In contrast with the known heuristic methods used in the literature, which only provide a reasonable solution (suboptimal, nearly global optimal), our method provides the global solution. Moreover, our method can obtain the Pareto optimal set under different loading conditions. The performance of the proposed technique is validated using a standard test system.

A strength Pareto evolutionary algorithm-based conflict assessment framework of electricity market participants' objectives in generation maintenance scheduling

SUMMARY Conflicts between independent system operators (ISOs) and power producers, each preferring the annual maintenance plans based on their own objectives, have resulted from power system restructuring. Therefore, the ISO needs to specify how much the producers are free to adjust their maintenance programs on their own, without violating reliability criteria. In this article, a conflict assessment framework is described to characterize the degree of conflict among the electricity market participants' (ISO and producers) objectives. Four steps, namely, information gathering, mathematical modeling of the objectives, conflict front discovery, and finally the sensitivity analysis, were proposed as the basic steps of the framework. In this way, a multiobjective optimization problem was introduced by which a set of compromise solutions, denoted as a Pareto-optimal front, can be found. To discover the front, the strength Pareto evolutionary algorithm was used as a robust evolutionary optimization technique. For the sensitivity analysis, two parameters, namely, the front curve relative change and its degree of linearity, were used. The proposed framework was tested on IEEE-RTS, and its capabilities were assessed and appreciated. Copyright © 2011 John Wiley & Sons, Ltd.

Solving a dial-a-ride problem with a hybrid evolutionary multi-objective approach: Application to demand responsive transport

Demand responsive transport allows customers to be carried to their destination as with a taxi service, provided that the customers are grouped in the same vehicles in order to reduce operational costs. This kind of service is related to the dial-a-ride problem. However, in order to improve the quality of service, demand responsive transport needs more flexibility. This paper tries to address this issue by proposing an original evolutionary approach. In order to propose a set of compromise solutions to the decision-maker, this approach optimizes three objectives concurrently. Moreover, in order to intensify the search process, this multi-objective evolutionary approach is hybridized with a local search. Results obtained on random and realistic problems are detailed to compare three state-of-the-art algorithms and discussed from an operational point of view.

A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm

Nowadays, time and cost are familiar criteria for every logistic provider, and both have been long treated to be minimized simultaneously. However, the criteria are naturally conflicted even with flexibilities and/or constraints appeared in the logistic networks. This paper is concerned with three-level logistic networks with potential suppliers, distributed centers (DCs), and deterministic demands from available consumers. The networks also benefit from potential direct shipments from suppliers to consumers as long as suppliers and DCs facilities might face limited capacity in their own seasonal supplying and warehousing processes. The goal is (re)configure the networks in order to minimize response time to consumers, transportation cost and facility cost. Therefore, the networks are formulated as multiple criteria decision making problems, which have more than one configuration through the time and cost optimizing at the same time. Due to the flexibility and the constraints, the decision maker(s) needs a set of compromise solutions for the networks that represent optimal configurations based on the objectives without considering prior knowledge. To this end, the problems are formulated into four individual logistic network models varying with the flexibility option and/or the capacitated facilities. To find the compromise solutions, Pareto-based multi-objective evolutionary algorithm, NSGA-II is customized and then utilized to deal with an illustrative case study. The results are analyzed through the two performance measures, hypervolume and the number of optimal solutions obtained so far.

Redundancy allocation problems considering systems with imperfect repairs using multi-objective genetic algorithms and discrete event simulation

This paper considers a multi-objective genetic algorithm (GA) coupled with discrete event simulation to solve redundancy allocation problems in systems subject to imperfect repairs. In the multi-objective formulation, system availability and cost may be maximized and minimized, respectively; the failure-repair processes of system components are modeled by Generalized Renewal Processes. The presented methodology provides a set of compromise solutions that incorporate not only system configurations, but also the number of maintenance teams. The multi-objective GA is validated via examples with analytical solutions and shows its superior performance when compared to a multi-objective Ant Colony algorithm. Moreover, an application example is presented and a return of investment analysis is suggested to aid the decision maker in choosing a solution of the obtained set.

Lift maximization with uncertainties for the optimization of high‐lift devices

AbstractIn this paper, the aerodynamic shape optimization problems with uncertain operating conditions have been addressed. After a review of robust control theory and the possible approaches to take into account uncertainties, the use of Taguchi robust design methods in order to overcome single point design problems in aerodynamics is proposed. Under the Taguchi concept, a design with uncertainties is converted into an optimization problem with two objectives which are the mean performance and its variance, so that the solutions are as less sensitive to the uncertainty of the input parameters as possible. Furthermore, the modified non‐dominated sorting genetic algorithms are used to capture a set of compromised solutions (Pareto front) between these two objectives. The flow field is analyzed by Navier–Stokes computation using an unstructured mesh. In order to reduce the number of expensive evaluations of the fitness function a response surface modeling is employed to estimate the fitness value using the polynomial approximation model. During the solution of the optimization problem, a semi‐torsional spring analogy is used for the adaption of the computational mesh to all the obtained geometrical configurations. The proposed approach is applied to the robust optimization of the 2D high‐lift devices of a business aircraft by maximizing the mean and minimizing the variance of the lift coefficients with uncertain free‐stream angle of attack at landing flight condition. Copyright © 2009 John Wiley & Sons, Ltd.

Multi-objective optimization of the sandwich panels with prismatic cores using genetic algorithms

In the present study, a new type of sandwich panels with prismatic cores, which are capable of load bearing as well as cooling, is optimized to have minimum weight and maximum heat transfer performance. In order to simultaneously minimize the total weight and maximize the heat transfer performance, a multi-objective optimization approach has been developed using genetic algorithms. A set of compromised solutions, known as the tradeoff surface, is obtained. The tradeoff information between the two objectives is exploited in terms of multi-functionality of the sandwich panels, and the relation between the two objectives is quantified in the present study. The detailed configurations and dimensions of the sandwich panels at the optima are provided. Some basic characteristics of the sandwich panels with prismatic cores have been observed in terms of their multi-functionality.