Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

Traditionally, supply chain planning problems consider variables with uncertainty associated with uncontrolled factors. These factors have been normally modelled by complex methodologies where the seeking solution process often presents high scale of difficulty. This work presents the fuzzy set theory as a tool to model uncertainty in supply chain planning problems and proposes the particle swarm optimisation (PSO) metaheuristics technique combined with a backward calculation as a solution method. The aim of this combination is to present a simple effective method to model uncertainty, while good quality solutions are obtained with metaheuristics due to its capacity to find them with satisfactory computational performance in complex problems, in a relatively short time period.This research is partly supported by the Spanish Ministry of Economy and Competitiveness projects 'Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity' (PLANGES-FHP) (Ref. DPI2011-23597) and 'Operations design and Management of Global Supply Chains' (GLOBOP) (Ref. DPI2012-38061-C02-01); by the project funded by the Polytechnic University of Valencia entitled 'Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics' (PAID-06-12); and by the Ministry of Science, Technology and Telecommunications, government of Costa Rica (MICITT), through the incentive program of the National Council for Scientific and Technological Research (CONICIT) (contract No FI-132-2011).Grillo Espinoza, H.; Peidro Payá, D.; Alemany Díaz, MDM.; Mula, J. (2015). Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. International Journal of Bio-Inspired Computation. 7(3):157-169. https://doi.org/10.1504/IJBIC.2015.069557S1571697

Dificultades y posibilidades del algoritmo de optimización de enjambre de partículas para la planificación contemporánea espacial del bosque

We describe here an example of applying particle swarm optimization (PSO) — a population-based heuristic technique — to maximize the net present value of a contemporary southern United States forest plan that includes spatial constraints (green-up and adjacency) and wood flow constraints. When initiated with randomly defined feasible initial conditions, and tuned with some appropriate modifications, the PSO algorithm gradually converged upon its final solution and provided reasonable objective function values. However, only 86% of the global optimal value could be achieved using the modified PSO heuristic. The results of this study suggest that under random-start initial population conditions the PSO heuristic may have rather limited application to forest planning problems with economic objectives, wood-flow constraints, and spatial considerations. Pitfalls include the need to modify the structure of PSO to both address spatial constraints and to repair particles, and the need to modify some of the basic assumptions of PSO to better address contemporary forest planning problems. Our results, and hence our contributions, are contrary to earlier work that illustrated the impressive potential of PSO when applied to stand-level forest planning problems or when applied to a high quality initial population.Se describe aquí un ejemplo de la aplicación de la optimización de enjambre de partículas (PSO) — una técnica heurística basada en la población — para maximizar el valor presente neto de un moderno plan de gestión del bosque del sur de los Estados Unidos, que incluye limitaciones espaciales y restricciones del flujo de madera. Cuando se inicia con condiciones iniciales factibles definidas aleatoriamente, y en sintonía con algunas modificaciones adecuadas, el algoritmo PSO converge gradualmente sobre su solución final y suministra los valores de la función objetivo. Sin embargo, sólo el 86% del valor global óptimo podría lograrse usando la heurística PSO modificada. Los resultados de este estudio sugieren que bajo condiciones de arranque aleatorio de la población inicial, la heurística PSO puede tener una aplicación más bien limitada a los problemas de planificación forestal con objetivos económicos, restricciones de flujo de madera y consideraciones espaciales. Las dificultadas incluyen la necesidad de modificar la estructura de PSO para abordar tanto las limitaciones espaciales como para reparar las partículas, y la necesidad de modificar algunos de los supuestos básicos de PSO para abordar mejor los problemas contemporáneos de la planificación forestal. Nuestros resultados, y por lo tanto nuestra aportación, son contrarios a trabajos anteriores que ilustran el impresionante potencial de PSO cuando se aplica a problemas de planificación forestal a nivel de rodal o cuando se aplica a una población de calidad inicial alta

Spatial and Content-based Audio Processing using Stochastic Optimization Methods

Stochastic optimization (SO) represents a category of numerical optimization approaches, in which the search for the optimal solution involves randomness in a constructive manner. As shown also in this thesis, the stochastic optimization techniques and models have become an important and notable paradigm in a wide range of application areas, including transportation models, financial instruments, and network design. Stochastic optimization is especially developed for solving the problems that are either too difficult or impossible to solve analytically by deterministic optimization approaches. In this thesis, the focus is put on applying several stochastic optimization algorithms to two audio-specific application areas, namely sniper positioning and content-based audio classification and retrieval. In short, the first application belongs to an area of spatial audio, whereas the latter is a topic of machine learning and, more specifically, multimedia information retrieval. The SO algorithms considered in the thesis are particle filtering (PF), particle swarm optimization (PSO), and simulated annealing (SA), which are extended, combined and applied to the specified problems in a novel manner. Based on their iterative and evolving nature, especially the PSO algorithms are often included to the category of evolutionary algorithms. Considering the sniper positioning application, in this thesis the PF and SA algorithms are employed to optimize the parameters of a mathematical shock wave model based on observed firing event wavefronts. Such an inverse problem is suitable for Bayesian approach, which is the main motivation for including the PF approach among the considered optimization methods. It is shown – also with SA – that by applying the stated shock wave model, the proposed stochastic parameter estimation approach provides statistically reliable and qualified results. The content-based audio classification part of the thesis is based on a dedicated framework consisting of several individual binary classifiers. In this work, artificial neural networks (ANNs) are used within the framework, for which the parameters and network structures are optimized based the desired item outputs, i.e. the ground truth class labels. The optimization process is carried out using a multi-dimensional extension of the regular PSO algorithm (MD PSO). The audio retrieval experiments are performed in the context of feature generation (synthesis), which is an approach for generating new audio features/attributes based on some conventional features originally extracted from a particular audio database. Here the MD PSO algorithm is applied to optimize the parameters of the feature generation process, wherein the dimensionality of the generated feature vector is also optimized. Both from practical perspective and the viewpoint of complexity theory, stochastic optimization techniques are often computationally demanding. Because of this, the practical implementations discussed in this thesis are designed as directly applicable to parallel computing. This is an important and topical issue considering the continuous increase of computing grids and cloud services. Indeed, many of the results achieved in this thesis are computed using a grid of several computers. Furthermore, since also personal computers and mobile handsets include an increasing number of processor cores, such parallel implementations are not limited to grid servers only

Utilising restricted for-loops in genetic programming

Genetic programming is an approach that utilises the power of evolution to allow computers to evolve programs. While loops are natural components of most programming languages and appear in every reasonably-sized application, they are rarely used in genetic programming. The work is to investigate a number of restricted looping constructs to determine whether any significant benefits can be obtained in genetic programming. Possible benefits include: Solving problems which cannot be solved without loops, evolving smaller sized solutions which can be more easily understood by human programmers and solving existing problems quicker by using fewer evaluations. In this thesis, a number of explicit restricted loop formats were formulated and tested on the Santa Fe ant problem, a modified ant problem, a sorting problem, a visit-every-square problem and a difficult object classificat ion problem. The experimental results showed that these explicit loops can be successfully used in genetic programming. The evolutionary process can decide when, where and how to use them. Runs with these loops tended to generate smaller sized solutions in fewer evaluations. Solutions with loops were found to some problems that could not be solved without loops. The results and analysis of this thesis have established that there are significant benefits in using loops in genetic programming. Restricted loops can avoid the difficulties of evolving consistent programs and the infinite iterations problem. Researchers and other users of genetic programming should not be afraid of loops

An Adaptive Framework for Improving the Effectiveness of Virtual Enterprises in the Supply Chain

This thesis describes a research project that develops an adaptive framework for improving the effectiveness of virtual enterprises in the supply chains in Mongolia. The research takes empirical and quantitative approach to study the phenomenon of virtual enterprises. Based on a literature review, the factors that influence organisations to join in virtual enterprises are studied by a higher-order factor analysis. As a result, agility is identified as one of the main benefits organisations can gain by joining a virtual enterprise temporarily and changes in business performance are conceived as the measures of effectiveness. Next, a taxonomy of enterprises is developed with five distinguishing clusters that achieve differing levels of agility and business performance. This study suggests that enterprises that are monitoring changes in their business environment take most advantage of agility and achieve the best levels of performance. These findings then allow an adaptive framework based on common reference architectures to be developed as a main contribution of this study. The framework includes a breeding environment as a ‘pool’ of prepared enterprises with the ability to form temporary collaborations to react responsively, rapidly and effectively to the fast-changing opportunities. A structural equation model was used to examine the model fit with the supporting hypotheses, based on the observed data. Then, a powerful clustered expectation maximisation algorithm was applied to the analysis of the grouped enterprises. Finally, a simulation-based case study was conducted to validate the developed framework. The results provide rich empirical evidence of the beneficial impact of virtual enterprises on agile supply chains. The research provides rich empirical evidence of the beneficial impact of virtual enterprises on agile supply chains. It also provides theoretical and managerial insights that can be used to strengthen the drivers, enablers and capabilities that enhance the effectiveness of virtual enterprises collaboration in agile supply chains that can be translated to a global context. These are major contributions the ‘body of knowledge’ in themselves, but the research also adds usefully to the study of applied research methodologies in the area