Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction

The multiobjective vehicle routing problem considering customer satisfaction (MVRPCS) involves the distribution of orders from several depots to a set of customers over a time window. This paper presents a self-adaptive grid multi-objective quantum evolutionary algorithm (MOQEA) for the MVRPCS, which takes into account customer satisfaction as well as travel costs. The degree of customer satisfaction is represented by proposing an improved fuzzy due-time window, and the optimization problem is modeled as a mixed integer linear program. In the MOQEA, nondominated solution set is constructed by the Challenge Cup rules. Moreover, an adaptive grid is designed to achieve the diversity of solution sets; that is, the number of grids in each generation is not fixed but is automatically adjusted based on the distribution of the current generation of nondominated solution set. In the study, the MOQEA is evaluated by applying it to classical benchmark problems. Results of numerical simulation and comparison show that the established model is valid and the MOQEA is effective for MVRPCS

Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Move Acceptance in Local Search Metaheuristics for Cross-domain Heuristic Search

Many real-world combinatorial optimisation problems (COPs) are computationally hard problems and search methods are frequently preferred as solution techniques. Traditionally, an expert with domain knowledge designs, and tailors the search method for solving a particular COP. This process is usually expensive, requiring a lot of effort and time and often results in problem specific algorithms that can not be applied to another COP. Then, the domain expert either needs to design a new search method, or reconfigure an existing search method to solve that COP. This prompted interest into developing more general, problem-domain-independent high-level search methods that can be re-used, capable of solving not just a single problem but multiple COPs. The cross-domain search problem is a relatively new concept and represents a high-level issue that involves designing a single solution method for solving a multitude of COPs preferably with the least or no expert intervention. Cross-domain search methods are algorithms designed to tackle the cross-domain search problem. Such methods are of interest to researchers and practitioners worldwide as they offer a single off-the-shelf go-to approach to problem solving. Furthermore, if a cross-domain search method has a good performance, then it can be expected to solve `any' given COP well and in a reasonable time frame. When a practitioner is tasked with solving a new or unknown COP, they are tasked with a decision-making dilemma. This entails the decision of what algorithm they should use, what parameters should be used for that algorithm, and whether any other algorithm can outperform it. A well designed cross-domain search method that performs well and does not require re-tuning can fulfil this dilemma allowing practitioners to find good-enough solutions to such problems. Researchers on the other hand strive to find high-quality solutions to these problems; however, such a cross-domain search method provides them with a good benchmark to which they can compare their solution methods to, and should ultimately aim to outperform. In this work, move acceptance methods, which are a component of traditional search methods, such as metaheuristics and hyper-heuristics, are explored under a cross-domain search framework. A survey of the existing move acceptance methods as a part of local search metaheuristics is conducted based on the hyper-heuristic literature as solution methods to the cross-domain search problem. Furthermore, a taxonomy is provided for classifying them based on their design characteristics. The cross-domain performance of existing move acceptance methods, covering the taxonomy, is compared across a total of 45 problem instances spanning 9 problem domains, and the effects of parameter tuning versus choice of the move acceptance method are explored. A novel move acceptance method (HAMSTA) is proposed to overcome the shortcomings of the existing methods to improve the cross-domain performance of a local search metaheuristic. HAMSTA is capable of outperforming the cross-domain performances of existing methods that are re-tuned for each domain, despite itself using only a single cross-domain parameter configuration derived from tuning experiments that considers 2 instances each from 4 domains; hence, HAMSTA requires no expert intervention to re-configure it to perform well for solving multiple COPs with 37 problem instances unseen by HAMSTA, 25 of which are from unseen domains. HAMSTA is therefore shown to have the potential to fulfil the aforementioned decision-making dilemma

Move Acceptance in Local Search Metaheuristics for Cross-domain Heuristic Search

Many real-world combinatorial optimisation problems (COPs) are computationally hard problems and search methods are frequently preferred as solution techniques. Traditionally, an expert with domain knowledge designs, and tailors the search method for solving a particular COP. This process is usually expensive, requiring a lot of effort and time and often results in problem specific algorithms that can not be applied to another COP. Then, the domain expert either needs to design a new search method, or reconfigure an existing search method to solve that COP. This prompted interest into developing more general, problem-domain-independent high-level search methods that can be re-used, capable of solving not just a single problem but multiple COPs. The cross-domain search problem is a relatively new concept and represents a high-level issue that involves designing a single solution method for solving a multitude of COPs preferably with the least or no expert intervention. Cross-domain search methods are algorithms designed to tackle the cross-domain search problem. Such methods are of interest to researchers and practitioners worldwide as they offer a single off-the-shelf go-to approach to problem solving. Furthermore, if a cross-domain search method has a good performance, then it can be expected to solve `any' given COP well and in a reasonable time frame. When a practitioner is tasked with solving a new or unknown COP, they are tasked with a decision-making dilemma. This entails the decision of what algorithm they should use, what parameters should be used for that algorithm, and whether any other algorithm can outperform it. A well designed cross-domain search method that performs well and does not require re-tuning can fulfil this dilemma allowing practitioners to find good-enough solutions to such problems. Researchers on the other hand strive to find high-quality solutions to these problems; however, such a cross-domain search method provides them with a good benchmark to which they can compare their solution methods to, and should ultimately aim to outperform. In this work, move acceptance methods, which are a component of traditional search methods, such as metaheuristics and hyper-heuristics, are explored under a cross-domain search framework. A survey of the existing move acceptance methods as a part of local search metaheuristics is conducted based on the hyper-heuristic literature as solution methods to the cross-domain search problem. Furthermore, a taxonomy is provided for classifying them based on their design characteristics. The cross-domain performance of existing move acceptance methods, covering the taxonomy, is compared across a total of 45 problem instances spanning 9 problem domains, and the effects of parameter tuning versus choice of the move acceptance method are explored. A novel move acceptance method (HAMSTA) is proposed to overcome the shortcomings of the existing methods to improve the cross-domain performance of a local search metaheuristic. HAMSTA is capable of outperforming the cross-domain performances of existing methods that are re-tuned for each domain, despite itself using only a single cross-domain parameter configuration derived from tuning experiments that considers 2 instances each from 4 domains; hence, HAMSTA requires no expert intervention to re-configure it to perform well for solving multiple COPs with 37 problem instances unseen by HAMSTA, 25 of which are from unseen domains. HAMSTA is therefore shown to have the potential to fulfil the aforementioned decision-making dilemma

Diseño y aplicación de una herramienta para la optimización de rutas de vehículos con aspectos medioambientales

En la actualidad, en torno al 90% de la población de la Unión Europea se encuentra expuesta a altas concentraciones de algunos de los contaminantes atmosféricos más nocivos para la salud, reduciendo la esperanza de vida de la población y ocasionando un fuerte impacto económico en el producto interior bruto de los países. Dentro de los sectores económicos, el transporte se presenta como una de las principales fuentes de contaminación ya que genera niveles nocivos de emisiones contaminantes y es el responsable de hasta el 24% de las emisiones de gases de efecto invernadero (GEI) en la Unión Europea. Estas emisiones dependen en gran medida de los combustibles utilizados, de la carga y tecnología del motor de los vehículos y principalmente de las distancias recorridas. El problema de la distribución de productos desde los almacenes a los usuarios finales juega un papel central en la gestión de algunos sistemas logísticos, donde la determinación de rutas de reparto eficientes es fundamental en la reducción de costes. Este problema en la vida real, se caracteriza por disponer las empresas de distribución de una flota heterogénea, en la que vehículos con diferentes características son incorporados a lo largo del tiempo para una mejor adaptación a las demandas de los clientes. Entre las características más destacadas se encuentran vehículos con diferentes capacidades y antigüedad, usos de combustibles alternativos y tecnología del motor. Por todo ello, en el contexto actual la componente medioambiental tiene que ser añadida en el proceso de toma de decisiones a las estrategias logísticas tradicionales, basadas en costes y tiempos. Esta Tesis Doctoral se ha centrado en su mayor parte al desarrollo de nuevos modelos y algoritmos para la resolución del Problema de Enrutamiento de Vehículos con Flota Fija Heterogénea y Ventanas de Tiempo (HVRPTW), con la consideración adicional de reducir las emisiones de GEI y de partículas contaminantes. La formulación del problema se realiza desde dos perspectivas muy diferenciadas. La primera de ellas incorpora una metodología basada en la estimación de los costes asociados a las externalidades presentes en las actividades del transporte. La segunda perspectiva comprende técnicas de optimización multiobjetivo con asignación de preferencias a priori, en el que el decisor puede establecer sus preferencias por adelantado. La elaboración de las rutas eco-eficientes se plantea mediante modelos lineales de programación matemática y se resuelve usando técnicas cuantitativas. Estas técnicas comprenden algoritmos heurísticos y metaheurísticos que combinan diversos procedimientos avanzados para tratar la complejidad del problema. En particular, esta Tesis describe una heurística de inserción secuencial semi-paralela y una metaheurística híbrida de búsqueda de entorno variable descendente con búsqueda tabú y lista de espera, que introduce una mayor flexibilidad para la resolución de cualquier variante del problema HVRPTW. Los algoritmos han sido aplicados a problemas típicos de recogida y reparto de mercancías de la literatura científica y a un caso real, que comprende la planificación de rutas y personal en una empresa de servicios con características y restricciones muy peculiares. Los resultados demuestran que el algoritmo resuelve de manera eficiente la variante del problema abordado y es extensible para la resolución de otras variantes. El resultado de la Tesis es el desarrollo de una herramienta para la ayuda a la toma de decisiones en el diseño y control de rutas eco-eficientes. Dicha herramienta podrá integrarse con el sistema de información geográfica (GIS) particular de cada empresa y permitirá la visualización de las rutas eco-eficientes, evaluando el impacto producido en los ámbitos económico, energético, operativo y medioambiental. Por ello, la herramienta tendrá un impacto económico directo sobre los usuarios finales y permitirá la comparación de rutas y resultados obtenidos a partir de diferentes alternativas, logrando una mayor competitividad y el cumplimiento de los compromisos de sostenibilidad en la empresa. Por otro lado, a nivel global, la herramienta contribuye a una mejora social derivada de una reducción del consumo energético y de una disminución de las emisiones contaminantes de las flotas de transporte de mercancías por carretera, que tienen un impacto a nivel local, nacional e internacional. En este sentido, la Tesis Doctoral contribuye claramente al desarrollo estratégico del sector transporte de mercancías, aumentando la eficiencia de las flotas de transporte por carretera y logrando una mayor sostenibilidad y competitividad.Nowadays, around 90% of city dwellers in the European Union are exposed to high concentrations of healthharmful pollutants, reducing the life expectancy of the population and having a large impact on the gross domestic product of European countries. Among the economic sectors, transport is presented as one of the main sources of pollution because it generates harmful levels of emissions and is responsible for up to 24% of greenhouse gases (GHG) emissions in the European Union. These emissions depend heavily on the fuel type used, the carried load, the engine technology and the total distance covered. The problem of the distribution of goods from warehouses to end users plays a central role in the logistics systems management, where the design of efficient routes is critical in reducing costs. This real-life problem is characterized by presenting a heterogeneous fleet where vehicles with different features are incorporated over the time for a better adaptation to the changing customer demands. These features include vehicles with different capacities and age, alternative fuels and motor technologies. Therefore, in the present context, environmental targets are to be added to traditional logistics strategies based on cost and time in the decision making process. The research of this Thesis has focused on the development of new mathematical models and algorithms for solving the Fixed Fleet Heterogeneous Vehicle Routing Problem with Time Windows (HVRPTW) with the additional consideration of reducing GHG and pollutants emissions. The formulation of the problem is made from two different perspectives. The first incorporates a methodology based on the estimation of the external costs of transport activities. The second perspective comprises a multiobjective optimization method with a priori articulation of preferences, in which the decision maker can establish the preferences in advance. The design of eco-efficient routes is proposed by linear mathematical programming models and is solved using quantitative techniques. These techniques include heuristics and metaheuristics that combine various advanced procedures to deal with the complexity of the problem. In particular, this Thesis describes a semi-parallel insertion heuristic and a hybrid variable neighborhood descent metaheuristic based on a tabu search algorithm for the local search and a holding list that achieves flexibility for solving any HVRPTW variant. The algorithms have been applied to benchmark problems from the scientific literature and to a real-world case that deals with a routing and scheduling problem in a service company with particular characteristics and constraints. The results show that the algorithm efficiently solves the problem addressed and it can be extended to other problem variants. The result of the Thesis is the development of a decision making process tool aimed to help in the design and control of eco-efficient routes. This tool can be integrated with the particular geographic information system (GIS) of each company, allowing the display of eco-efficient routes and assessing the economic, energy, operational and environmental impacts. Therefore, the tool will have an economic impact on the end users, with a comparison of the final routes and the results obtained from different alternatives, achieving greater competitiveness and fulfilling the sustainability commitments in the company. On the other hand, the tool globally contributes to a social improvement resulting from the fuel consumption and pollutant emissions reductions from road transport, which have an impact at local, national and international level. In this sense, the Thesis clearly contributes to the strategic development of the transport sector, increasing the efficiency of road transport fleets and achieving greater sustainability and competitiveness

Bilevel facility location problems: theory and applications.

In this doctoral thesis we focus on studying facility location problems considering customer preferences. In these problems, there is a set of customers or users who demand a service or product that must be supplied by one or more facilities. By facilities it is understood some object or structure that offers some service to customers. One of the most important assumptions is that customers have established their own preferences over the facilities and should be taken into account in the customer-facility assignment. In real life, customers choose facilities based on costs, preferences, a predetermined contract, or a loyalty coefficient, among others. That is, they are free to choose the facilities that will serve them. The situation described above is commonly modeled by bilevel programming, where the upper level corresponds to location decisions to optimize a predefined criteria, such as, minimize location and distribution costs or maximize the demand covered by the facilities; and the lower level is associated to -customer allocation- to optimize customer preferences. The hierarchy among both levels is justified because the decision taken in the upper level directly affects the decision’s space in the lower level

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio