Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics

242 páginasTransportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution –e.g., a solution with the minimum cost or the maximum profit– is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems’ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima –por ejemplo, una solución con el mínimo costo o la máxima utilidad– es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente, si el mundo real no proporciona suficiente información para estimar de manera confiable una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con métodos aproximados y exactos para resolver problemas de T&L considerando niveles de decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último, se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los problemas abordados.Doctorado en Logística y Gestión de Cadenas de SuministrosDoctor en Logística y Gestión de Cadenas de Suministro

Mixing quantitative and qualitative methods for sustainable transportation in Smart Cities

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Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Contributions to sustainable urban transport : decision support for alternative mobility and logistics concepts

Increasing transport activities in cities are a substantial driver for congestion and pollution, influencing urban populations’ health and quality of life. These effects are consequences of ongoing urbanization in combination with rising individual demand for mobility, goods, and services. With the goal of increased environmental sustainability in urban areas, city authorities and politics aim for reduced traffic and minimized transport emissions. To support more efficient and sustainable urban transport, this cumulative dissertation focuses on alternative transport concepts. For this purpose, scientific methods and models of the interdisciplinary information systems domain combined with elements of operations research, transportation, and logistics are developed and investigated in multiple research contributions. Different transport concepts are examined in terms of optimization and acceptance to provide decision support for relevant stakeholders. In more detail, the overarching topic of urban transport in this dissertation is divided into the complexes urban mobility (part A) in terms of passenger transport and urban logistics (part B) with a focus on the delivery of goods and services. Within part A, approaches to carsharing optimization are presented at various planning levels. Furthermore, the user acceptance of ridepooling is investigated. Part B outlines several optimization models for alternative urban parcel and e-grocery delivery concepts by proposing different network structures and transport vehicles. Conducted surveys on intentional use of urban logistics concepts give valuable hints to providers and decision makers. The introduced approaches with their corresponding results provide target-oriented support to facilitate decision making based on quantitative data. Due to the continuous growth of urban transport, the relevance of decision support in this regard, but also the understanding of the key drivers for people to use certain services will further increase in the future. By providing decision support for urban mobility as well as urban logistics concepts, this dissertation contributes to enhanced economic, social, and environmental sustainability in urban areas

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Consolidation of Urban Freight Transport – Models and Algorithms

Urban freight transport is an indispensable component of economic and social life in cities. Compared to other types of transport, however, it contributes disproportionately to the negative impacts of traffic. As a result, urban freight transport is closely linked to social, environmental, and economic challenges. Managing urban freight transport and addressing these issues poses challenges not only for local city administrations but also for companies, such as logistics service providers (LSPs). Numerous policy measures and company-driven initiatives exist in the area of urban freight transport to overcome these challenges. One central approach is the consolidation of urban freight transport. This dissertation focuses on urban consolidation centers (UCCs) which are a widely studied and applied measure in urban freight transport. The fundamental idea of UCCs is to consolidate freight transport across companies in logistics facilities close to an urban area in order to increase the efficiency of vehicles delivering goods within the urban area. Although the concept has been researched and tested for several decades and it was shown that it can reduce the negative externalities of freight transport in cities, in practice many UCCs struggle with a lack of business participation and financial difficulties. This dissertation is primarily focused on the costs and savings associated with the use of UCCs from the perspective of LSPs. The cost-effectiveness of UCC use, which is also referred to as cost attractiveness, can be seen as a crucial condition for LSPs to be interested in using UCC systems. The overall objective of this dissertation is two-fold. First, it aims to develop models to provide decision support for evaluating the cost-effectiveness of using UCCs. Second, it aims to analyze the impacts of urban freight transport regulations and operational characteristics on the cost attractiveness of using UCCs from the perspective of LSPs. In this context, a distinction is made between UCCs that are jointly operated by a group of LSPs and UCCs that are operated by third parties who offer their urban transport service for a fee. The main body of this dissertation is based on three research papers. The first paper focuses on jointly-operated UCCs that are operated by a group of cooperating LSPs. It presents a simulation model to analyze the financial impacts on LSPs participating in such a scheme. In doing so, a particular focus is placed on urban freight transport regulations. A case study is used to analyze the operation of a jointly-operated UCC for scenarios involving three freight transport regulations. The second and third papers take on a different perspective on UCCs by focusing on third-party operated UCCs. In contrast to the first paper, the second and third papers present an evaluation approach in which the decision to use UCCs is integrated with the vehicle route planning of LSPs. In addition to addressing the basic version of this integrated routing problem, known as the vehicle routing problem with transshipment facilities (VRPTF), the second paper presents problem extensions that incorporate time windows, fleet size and mix decisions, and refined objective functions. To heuristically solve the basic problem and the new problem variants, an adaptive large neighborhood search (ALNS) heuristic with embedded local search heuristic and set partitioning problem (SPP) is presented. Furthermore, various factors influencing the cost attractiveness of UCCs, including time windows and usage fees, are analyzed using a real-world case study. The third paper extends the work of the second paper and incorporates daily and entrance-based city toll schemes and enables multi-trip routing. A mixed-integer linear programming (MILP) formulation of the resulting problem is proposed, as well as an ALNS solution heuristic. Moreover, a real-world case study with three European cities is used to analyze the impact of the two city toll systems in different operational contexts

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Space mission risk, sustainability and supply chain: review, multi-objective optimization model and practical approach

This paper investigates the convergence of risk, sustainability, and supply chain in space missions, including a review of fundamental concepts, the introduction of a multi-objective conceptual optimization model, and the presentation of a practical approach. Risks associated with space missions include technical, human, launch, space environment, mission design, budgetary, and political risks. Sustainability considerations must be incorporated into mission planning and execution to ensure the long-term viability of space exploration. The study emphasizes the importance of considering environmental sustainability, resource use, ethical concerns, long-term planning, international collaboration, and public outreach in space missions. It emphasizes the significance of reducing negative environmental consequences, increasing resource use efficiency, and making responsible and ethical actions. The paper offers a multi-objective optimization conceptual model that may be used to evaluate and choose sustainable space mission tactics. This approach considers a variety of elements, including environmental effects, resource utilization, mission cost, and advantages for society. It provides a systematic decision-making approach that examines trade-offs between different criteria and identifies optimal conceptual model solutions that balance risk, sustainability, and supply chain objectives. A practical approach is also offered to demonstrate the use of the multi-criteria optimization conceptual model in a space mission scenario. The practical approach demonstrates how the model can aid in the development of mission strategies that minimize risks, maximize resource consumption, and fit with sustainability goals. Overall, this paper delivers a multi-criteria optimization conceptual model and provides a space mission planning practical approach, as well as an overview of the interaction between risk, sustainability, and supply chain in space mission organization, planning, and execution.This research was partially supported by the AGH University of Science and Technology, Kraków, Poland (16.16.200.396) and the financial aid of the Polish Ministry of Science and Higher Education (MNISW) grants (N N519 405934; 6459/B/T02/2011/40) and the Polish National Science Centre (NCN) research grant (DEC-2013/11/B/ST8/04458). Moreover, I appreciate the support of the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T; RED2022-134703-T; PID2019-111100RB-C22/AEI/10.13039/501100011033). Additionally, I acknowledge the support from the Public University of Navarra, Pamplona, Spain and the University of California at Berkeley, USA. The research was also partially supported by the European Union Horizon 2020 research and innovation program under Marie-Skłodowska Curie, No: 101034285

Last mile delivery in the retail sector in an urban context

Last mile delivery (LMD) is a critical yet ambiguous stage of every supply chain. Previous studies have indicated that LMD is one of the most expensive, inefficient and polluting stages of the supply chain yet, despite its importance, the concept of LMD remains unclear in both academic and industry contexts. The use of different phrases, unclear boundaries and uncertain definitions and structures cause LMD to remain unclear. Thus, this study aims to demystify the basic understanding of LMD in terms of terminology, definition, scope, dimensions and structures. It then aims to introduce an initiative to improve the performance of LMD. A systematic literature review and content analysis are used to clarify the definition, dominant terminology and boundary of LMD, and investigate how the literature addresses these. The study then uses the ontology concept to discover and classify the LMD component, which provides a framework for extracting potential problems, solutions and structures for LMD. The proposed ontology is also used to map the LMD literature and identify the gaps in the literature. Using the proposed ontology, LMD is categorised into 40 structures that are employed to discover the structure of LMD used by major retailers and third-party logistics in the city of Melbourne. The results indicate that warehouses and distribution centres are the most common places that the investigated companies used to begin LMD. The results also indicate that the LMD process is usually finalised at stores in the business-to-business (B2B) context, while it is finalised at consignees location in the business-to-consumer (B2C) context. The companies investigated in this study mostly prepared the orders at factories, warehouses or distribution centres in the B2B context and prepared orders at stores in the B2C context. Considering these findings, along with coopetition strategy, this study develops an initiative to improve LMD performance. This study proposes a conceptual model for collaboration in the form of coopetition between retailers and logistics providers, and develops mathematical models to evaluate and optimise the initiative. The conceptual model is formed based on sharing 'empty running vehicles' between different delivery networks to decrease the cost and lead-time of delivery simultaneously. A mixed-integer linear programming model solved by genetic algorithm is developed to discover the optimised vehicle-sharing combinations. The results indicate that the proposed model with coopetition decreases delivery cost and lead-time by 60% and 56%, respectively. The results also indicate that the model reduces travelling distance by 66%, which contributes positively to environmental effects. The scenarios with and without coopetition strategy are then compared using real data from the city of Melbourne, which confirms the improvements of the proposed model with coopetition. The results of a case study show that the LMD model with coopetition strategy reduces cost, lead-time and travelling distance by 55%, 46% and 64%, respectively, which is almost similar to the results of random instance sets. This thesis makes significant theoretical and practical contributions in relation to LMD and employing coopetition strategy in this area. This thesis provides a conclusion regarding the domain terminology, definition and scope of LMD, and presents classified components and structures of LMD, which help create a common understanding among people working and studying in this field. This study presents an LMD model with coopetition among carriers sharing empty running vehicles, which decreases cost, lead-time, travelling distance and the number of vehicles required. The implementation of the proposed model on a large scale can reduce congestion and improve the sustainability aspects of deliveries in cities. The results of this study encourage decision makers in government authorities to identify empty running vehicles in cities and facilitate collaboration among different networks and companies. Moreover, LMD stakeholders such as residents, authorities and end consumers may enjoy the benefits of the proposed coopetition model without being involved in the coopetition practice directly. A shorter time for receiving parcels and lower price of service are the potential benefits experienced by end consumers, while reduced traffic and reduced negative environmental effects are the potential advantages for residents and government authorities. An initiative two-echelon vehicle routing problem (VRP) model is presented to simultaneously minimise lead-time and cost in this study, which has not previously been presented in the LMD context. Moreover, the proposed two-echelon VRP model can be used in other contexts and disciplines