Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

Applied Pareto multi-objective optimization by stochastic solvers

[EN] It is well known that many engineering design problems with different objectives, some of which can be opposed to one another, can be formulated as multi-objective functions and resolved with the construction of a Pareto front that helps to select the desired solution. Obtaining a correct Pareto front is not a trivial question, because it depends on the complexity of the objective functions to be optimized, the constraints to keep within and, in particular, the optimizer type selected to carry out the calculations. This paper presents new methods for Pareto front construction based on stochastic search algorithms (genetic algorithms, GAs and multi-objective genetic algorithms, MOGAs) that enable a very good determination of the Pareto front and fulfill some interesting specifications. The advantages of these applied methods will be proven by the optimization of well-known benchmarks for metallic supported I-beam and gearbox design. (C) 2008 Elsevier Ltd. All rights reserved.This research has been partially financed by GV06-026 Generalitat Valenciana and DPI2005-07835, MEC (Spain)-FEDER.Martínez Iranzo, MA.; Herrero Durá, JM.; Sanchís Saez, J.; Blasco, X.; García-Nieto, S. (2009). Applied Pareto multi-objective optimization by stochastic solvers. Engineering Applications of Artificial Intelligence. 22(3):455-465. https://doi.org/10.1016/j.engappai.2008.10.018S45546522

Multi-Objective Stochastic Optimization Programs for a non-Life Insurance Company under Solvency Constraints

In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order to generate a dotted approximation of the Pareto frontier for bi- and tri-objective programming problems. Numerical experiments are carried out on a set of portfolios to be optimized for an EU-based non-life insurance company. Both performance indicators and risk measures are managed as objectives. Results show that this procedure is effective and readily applicable to achieve suitable risk-reward tradeoff analysis

A simulation-based multi-criteria management system for optimal water supply under uncertainty

For cost and reliability efficiency, optimal design and operation of pressurized water distribution networks is highly important. However, optimizing such networks is still a challenge since it requires an appropriate determination of: (1) dimension of pipe / pump / tank - decision variables (2) cost / network reliability - objective functions and (3) limits or restrictions within which the network must operate - a given set of constraints. The costs mentioned here consist in general of capital, construction, and operation costs. The reliability of a network mainly refers to the intrinsic capability of providing water with adequate volume and a certain pressure to consumers under normal and extreme conditions. These contradicting objective functions are functions of network configuration regarding component sizes and network layout. Because considerable uncertainties finally render the overall task to a highly complex problem, most recent approaches mainly focus only on finding a trade-off between minimizing cost and maximizing network reliability. To overcome these limitations, a novel model system that simultaneously considers network configuration, its operation and the relevant uncertainties is proposed in this study. For solving this multi-objective design problem, a simulation-based optimization approach has been developed and applied. The approach couples a hydraulic model (Epanet) with the covariance matrix adaptation evolution strategy (CMA-ES) and can be operated in two different modes. These modes are (1) simulation–based Single-objective optimization and (2) simulation-based multi-objective optimization. Single-objective optimization yields the single best solution with respect to cost or network reliability, whereas multi-objective optimization produces a set of non-dominated solutions called Pareto optimal solutions which are trade-offs between cost and reliability. In addition, to prevent a seriously under-designed network, demand uncertainties was also taken into account through a so called “robustness probability” of the network. This consideration may become useful for a more reliable water distribution network. In order to verify the performance of the proposed approach, it was systematically tested on a number of different benchmark water distribution networks ranging from simple to complex. These benchmark networks are either gravity-fed or pumped networks which need to be optimally designed to supply urban or irrigation water demand under specific constraints. The results show that the new approach is able: • to solve optimization problems of pressurized water distribution network design and operation regarding cost and network reliability; • to directly determine the pumping discharge and head, thus allowing to select pumps more adequately; • to simulate time series of tank water level; • to eliminate redundant pipes and pumps to generate an optimal network layout; • to respond well to complex networks other than only to simple networks; • to perform with multiple demand loading; • to produce reliable Pareto optimal solutions regarding multi-objective optimization. In conclusion, the new technique can be successfully applied for optimization problems in pressurized water distribution network design and operation. The new approach has been demonstrated to be a powerful tool for optimal network design not only for irrigation but also for an urban water supply

A Novel Hybrid Dimensionality Reduction Method using Support Vector Machines and Independent Component Analysis

Due to the increasing demand for high dimensional data analysis from various applications such as electrocardiogram signal analysis and gene expression analysis for cancer detection, dimensionality reduction becomes a viable process to extracts essential information from data such that the high-dimensional data can be represented in a more condensed form with much lower dimensionality to both improve classification accuracy and reduce computational complexity. Conventional dimensionality reduction methods can be categorized into stand-alone and hybrid approaches. The stand-alone method utilizes a single criterion from either supervised or unsupervised perspective. On the other hand, the hybrid method integrates both criteria. Compared with a variety of stand-alone dimensionality reduction methods, the hybrid approach is promising as it takes advantage of both the supervised criterion for better classification accuracy and the unsupervised criterion for better data representation, simultaneously. However, several issues always exist that challenge the efficiency of the hybrid approach, including (1) the difficulty in finding a subspace that seamlessly integrates both criteria in a single hybrid framework, (2) the robustness of the performance regarding noisy data, and (3) nonlinear data representation capability. This dissertation presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) from Support Vector Machine (SVM) and data independence (unsupervised criterion) from Independent Component Analysis (ICA). The projection from SVM directly contributes to classification performance improvement in a supervised perspective whereas maximum independence among features by ICA construct projection indirectly achieving classification accuracy improvement due to better intrinsic data representation in an unsupervised perspective. For linear dimensionality reduction model, I introduce orthogonality to interrelate both projections from SVM and ICA while redundancy removal process eliminates a part of the projection vectors from SVM, leading to more effective dimensionality reduction. The orthogonality-based linear hybrid dimensionality reduction method is extended to uncorrelatedness-based algorithm with nonlinear data representation capability. In the proposed approach, SVM and ICA are integrated into a single framework by the uncorrelated subspace based on kernel implementation. Experimental results show that the proposed approaches give higher classification performance with better robustness in relatively lower dimensions than conventional methods for high-dimensional datasets

Network Design Model with Evacuation Constraints Under Uncertainty

Abstract: Nepal earthquake, have shown the need for quick response evacuation and assistance routes. Evacuation routes are, mostly, based on the capacities of the roads network. However, in extreme cases, such as earthquakes, roads network infrastructure may adversely affected, and may not supply their required capacities. If for various situations, the potential damage for critical roads can be identify in advance, it is possible to develop an evacuation model, that can be used in various situations to plan the network structure in order to provide fast and safe evacuation. This paper focuses on the development of a model for the design of an optimal evacuation network which simultaneously minimizes construction costs and evacuation time. The model takes into consideration infrastructures vulnerability (as a stochastic function which is dependent on the event location and magnitude), road network, transportation demand and evacuation areas. The paper presents a mathematic model for the presented problem. However, since an optimal solution cannot be found within a reasonable timeframe, a heuristic model is presented as well. The heuristic model is based on evolutionary algorithms, which also provides a mechanism for solving the problem as a stochastic and multi-objective problem