Multi-Objective Trade-Offs between Cost and Reliability in the Replacement of Water Mains

2006 ◽  
Vol 132 (2) ◽  
pp. 79-88 ◽  
Author(s):  
G. C. Dandy ◽  
M. O. Engelhardt
Keyword(s):  
Multi Objective ◽  
Trade Offs ◽  
Water Mains

scholarly journals A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lilla Beke ◽  
Michal Weiszer ◽  
Jun Chen
Keyword(s):  
Shortest Path ◽  
Time Budget ◽  
Exact Algorithm ◽  
Simple Graph ◽  
Shortest Path Problem ◽  
Future Application ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

scholarly journals Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4466
Author(s):  
Maël Riou ◽  
Florian Dupriez-Robin ◽  
Dominique Grondin ◽  
Christophe Le Loup ◽  
Michel Benne ◽  
...  
Keyword(s):  
Power Systems ◽  
Rural Areas ◽  
Energy System ◽  
Design Stage ◽  
Multi Objective Optimization ◽  
Renewable Energy Resources ◽  
Nsga Ii ◽  
Renewable Integration ◽  
Multi Objective ◽  
Trade Offs

Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

A Spectral Optimization Algorithm for Multi-Objective Prototype Selection

1995 ◽  
Author(s):  
Andy Dong ◽  
Alice M. Agogino
Keyword(s):  
Optimization Algorithm ◽  
Parametric Design ◽  
Selection Process ◽  
Objective Evaluation ◽  
Design Synthesis ◽  
Multi Objective ◽  
Trade Offs ◽  
Spectral Optimization ◽  
The Right ◽  
Selection Of

Abstract In design synthesis, engineering prototypes make an ideal representation medium for preliminary designs. Unlike parametric design wherein a pre-specified design is parametrically varied, design synthesis demands artistic creativity and engineering experience to transform the previously known components, relationships and designs into a new form. The process compels the designer to ascertain which prototypes will, in some sense, best satisfy the design task. The challenge in this assignment lies in selecting the “right” design prototype. This selection process typically entails an objective evaluation of different designs that perform the same functions or have similar intended behavior and comparing trade-offs between alternate designs. This paper introduces a multi-objective spectral optimization algorithm for the selection of design prototypes based upon their functional representations. The optimization algorithm returns an index of rank, scoring the functional similarity of the proposed design to the goal design. Two illustrative examples apply the algorithm to the selection of a heat fin and beam.

Multi-objective optimization for safety and reliability trade-off: Optimization and results processing

2018 ◽  
Vol 232 (6) ◽  
pp. 661-676
Author(s):  
Cristina Johansson ◽  
Johan Ölvander ◽  
Micael Derelöv
Keyword(s):  
Decision Making ◽  
Solid Base ◽  
Multi Objective Optimization ◽  
Specific System ◽  
Multi Objective ◽  
Trade Offs ◽  
Design Alternatives ◽  
Safety And Reliability ◽  
Potential Issue ◽  
Selection Of

In early design phases, it is vital to be able to screen the design space for a set of promising design alternatives for further study. This article presents a method able to balance several objectives of different mathematical natures, with high impact on the design choices. The method (MOSART) handles multi-objective optimization for safety and reliability trade-offs. The article focuses on optimization problem approach and processing of results as a base for decision-making. The output of the optimization step is the selection of specific system elements obtaining the best balance between the targets. However, what is a good base for decision can easily transform into too much information and overloading of the decision-maker. To solve this potential issue, from a set of Pareto optimal solutions, a smaller sub-set of selected solutions are visualized and filtered out using preference levels of the objectives, yielding a solid base for decision-making and valuable information on potential solutions. Trends were observed regarding each system element and discussed while processing the results of the analysis, supporting the decision of one final best solution.

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

Multi-objective forestry increases the production of ecosystem services

2020 ◽  
Author(s):  
Olalla Díaz-Yáñez ◽  
Timo Pukkala ◽  
Petteri Packalen ◽  
Manfred J Lexer ◽  
Heli Peltola
Keyword(s):  
Ecosystem Services ◽  
Boreal Forest ◽  
Boreal Forests ◽  
Carbon Balance ◽  
Net Present Value ◽  
Simulation And Optimization ◽  
Multi Objective ◽  
Clear Cut ◽  
Trade Offs ◽  
Continuous Cover Forestry

Abstract Boreal forests produce multiple ecosystem services for the society. Their trade-offs determine whether they should be produced simultaneously or whether it is preferable to assign separate areas to different ecosystem services. We use simulation and optimization to analyse the correlations, trade-offs and production levels of several ecosystem services in single- and multi-objective forestry over 100 years in a boreal forest landscape. The case study area covers 3600 ha of boreal forest, consisting of 3365 stands. The ecosystem services and their indicators (in parentheses) considered are carbon sequestration (forestry carbon balance), biodiversity (amount of deadwood and broadleaf volume), economic profitability of forestry (net present value of timber production) and timber supply to forest industry (volume of harvested timber). The treatment alternatives simulated for each of the stands include both even-aged rotation forestry (thinning from above with clear cut) and continuous cover forestry regimes (thinning from above with no clear cut). First, we develop 200 Pareto optimal plans by maximizing multi-attribute utility functions using random weights for the ecosystem service indicators. Second, we compare the average level of ecosystem services in single- and multi-objective forestry. Based on our findings, forestry carbon balance and the amount of deadwood correlate positively with each other, and both of them correlate negatively with harvested timber volume and economic profitability of forestry. Despite this, the simultaneous maximization of multiple objectives increased the overall production levels of several ecosystem services, which suggests that the management of boreal forests should be multi-objective to sustain the simultaneous provision of timber and other ecosystem services.

Optimal Tolerance Allocation of Automotive Pneumatic Control Valves Based on Product and Process Simulations

2006 ◽  
Author(s):  
Naesung Lyu ◽  
Amane Shimura ◽  
Kazuhiro Saitou
Keyword(s):  
Product Quality ◽  
Production Cost ◽  
Radial Basis Function Network ◽  
Discrete Event ◽  
Computational Method ◽  
Tolerance Allocation ◽  
Production Volume ◽  
Pneumatic Control ◽  
Multi Objective ◽  
Trade Offs

This paper discusses a computational method for optimally allocating dimensional tolerances for an automotive pneumatic control valve. Due to the large production volume, costly tight tolerances should be allocated only to the dimensions that have high influence to the quality. Given a parametric geometry of a valve, the problem is posed as a multi-objective optimization with respect to product quality and production cost. The product quality is defined as 1) the deviation from the nominal valve design in the linearity of valve stroke and fluidic force, and 2) the difference in fluidic force with and without cavitation. These quality measures are estimated by using Monte Carlo simulation on a Radial-Basis Function Network (RBFN) trained with computational fluid dynamics (CFD) simulation of the valve operation. The production cost is estimated by the tolerance-cost relationship obtained from the discrete event simulations of valve production process. A multi-objective genetic algorithm is utilized to generate Pareto optimal tolerance allocations with respect to these objectives, and alternative tolerance allocations are proposed considering the trade-offs among multiple objectives.

scholarly journals A Case for Security-Aware Design-Space Exploration of Embedded Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 22
Author(s):  
Andy D. Pimentel
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Systems Design ◽  
Design System ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Trade Offs

As modern embedded systems are becoming more and more ubiquitous and interconnected, they attract a world-wide attention of attackers and the security aspect is more important than ever during the design of those systems. Moreover, given the ever-increasing complexity of the applications that run on these systems, it becomes increasingly difficult to meet all security criteria. While extra-functional design objectives such as performance and power/energy consumption are typically taken into account already during the very early stages of embedded systems design, system security is still mostly considered as an afterthought. That is, security is usually not regarded in the process of (early) design-space exploration of embedded systems, which is the critical process of multi-objective optimization that aims at optimizing the extra-functional behavior of a design. This position paper argues for the development of techniques for quantifying the ’degree of secureness’ of embedded system design instances such that these can be incorporated in a multi-objective optimization process. Such technology would allow for the optimization of security aspects of embedded systems during the earliest design phases as well as for studying the trade-offs between security and the other design objectives such as performance, power consumption and cost.

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