The role of operational research in green freight transportation

Recent years have witnessed an increased awareness of the negative external impacts of freight transportation. The field of Operational Research (OR) has, particularly in the recent years, continued to contribute to alleviating the negative impacts through the use of various optimization models and solution techniques. This paper presents the basic principles behind and an overview of the existing body of recent research on ‘greening’ freight transportation using OR-based planning techniques. The particular focus is on studies that have been described for two heavily used modes for transporting freight across the globe, namely road (including urban and electric vehicles) and maritime transportation, although other modes are also briefly discussed

Optimisation of a distribution system in the retail industry: An Australian retail industry

This paper develops a mathematical model based on inventory routing problem that aims to minimise transportation cost, inventory carrying cost and optimises delivery schedules in a retail Australian industry. A supply chain is considered which comprises of a single distribution centre, having homogenous fleet of vehicles, supplying a single product to multiple retailers having deterministic demand. The mathematical model takes into account varying level of road congestion.N/

New Swarm-Based Metaheuristics for Resource Allocation and Schwduling Problems

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 10-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 10-01-201

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Agent based simulation of the dial-a-flight problem

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Master of Science in Engineering. Johannesburg, May 2018Agent based simulation and modelling (ABSM) has been noted as a novel method in solving complex problems. This dissertation makes use of the ABSM method in conjunction with a Genetic Algorithm to find good solutions to the dial-a-flight problem. The task is to generate a schedule for a heterogeneous fleet of aircraft, with the objective to reduce operational cost but maintain customer satisfaction. By making use of booking list data from an air taxi business, operating in the Okavango Delta, two agent based models were designed, the first makes use of multi-criteria decision analysis (MCDA) and the other a method proposed by Campbell [7], to test their effectiveness against either upper bound or manual solutions. The solution quality varied between tests, with booking list sizes between 10 and 200 requests producing improvements to the upper bound and manual results with a mean improvement from the benchmarks of 1.61\%. The method could also be refined further by adopting improvement mechanisms to final schedules or by making use of retrospective decision making aided by self learning techniques.MT 201

A Genetic Algorithm for UAV Routing Integrated with a Parallel Swarm Simulation

This research investigation addresses the problem of routing and simulating swarms of UAVs. Sorties are modeled as instantiations of the NP-Complete Vehicle Routing Problem, and this work uses genetic algorithms (GAs) to provide a fast and robust algorithm for a priori and dynamic routing applications. Swarms of UAVs are modeled based on extensions of Reynolds\u27 swarm research and are simulated on a Beowulf cluster as a parallel computing application using the Synchronous Environment for Emulation and Discrete Event Simulation (SPEEDES). In a test suite, standard measures such as benchmark problems, best published results, and parallel metrics are used as performance measures. The GA consistently provides efficient and effective results for a variety of VRP benchmarks. Analysis of the solution quality over time verifies that the GA exponentially improves solution quality and is robust to changing search landscapes - making it an ideal tool for employment in UAV routing applications. Parallel computing metrics calculated from the results of a PDES show that consistent speedup (almost linear in many cases) can be obtained using SPEEDES as the communication library for this UAV routing application. Results from the routing application and parallel simulation are synthesized to produce a more advanced model for routing UAVs

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing