Combining heuristics with simulation and fuzzy logic to solve a flexible-size location routing problem under uncertainty

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033). In addition, it has received the support of the Doctoral School at the Universitat Oberta de Catalunya (Spain) and the Universidad de La Sabana (INGPhD-12-2020).Peer ReviewedPostprint (published version

Optimizing fire station locations for the Istanbul metropolitan municipality

Copyright @ 2013 INFORMSThe Istanbul Metropolitan Municipality (IMM) seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire station’s receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbul’s road network, and solve two location models—set-covering and maximal-covering—as what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the city’s fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years

Optimizing fire station locations for the Istanbul metropolitan municipality

Copyright @ 2013 INFORMSThe Istanbul Metropolitan Municipality (IMM) seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire station’s receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbul’s road network, and solve two location models—set-covering and maximal-covering—as what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the city’s fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years

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

Sustainability assessment of biomass-based energy supply chain using multi-objective optimization model

In recent years, population growth and lifestyle changes have led to an increase in energy consumption worldwide. Providing energy from fossil fuels has negative consequences, such as energy supply constraints and overall greenhouse gas emissions. As the world continues to evolve, reducing dependence on fossil fuels and finding alternative energy sources becomes increasingly urgent. Renewable energy sources are the best way for all countries to reduce reliance on fossil fuels while reducing pollution. Biomass as a renewable energy source is an alternative energy source that can meet energy needs and contribute to global warming and climate change reduction. Among the many renewable energy options, biomass energy has found a wide range of application areas due to its resource diversity and easy availability from various sources all year round. The supply assurance of such energy sources is based on a sustainable and effective supply chain. Simultaneous improvement of the biomass-based supply chain's economic, environmental and social performance is a key factor for optimum network design. This study has suggested a multi-objective goal programming (MOGP) model to optimize a multi-stage biomass-based sustainable renewable energy supply chain network design. The proposed MOGP model represents decisions regarding the optimal number, locations, size of processing facilities and warehouses, and amounts of biomass and final products transported between the locations. The proposed model has been applied to a real-world case study in Istanbul. In addition, sensitivity analysis has been conducted to analyze the effects of biomass availability, processing capacity, storage capacity, electricity generation capacity, and the weight of the goals on the solutions. To realize sensitivity analysis related to the importance of goals, for the first time in the literature, this study employed a spherical fuzzy set-based analytic hierarchy method to determine the weights of goals

Network Flexibility for Recourse Considerations in Bi-Criteria Facility Location

What is the best set of facility location decisions for the establishment of a logistics network when it is uncertain how a company’s distribution strategy will evolve? What is the best configuration of a distribution network that will most likely have to be altered in the future? Today’s business environment is turbulent, and operating conditions for firms can take a turn for the worse at any moment. This fact can and often does influence companies to occasionally expand or contract their distribution networks. For most companies operating in this chaotic business environment, there is a continuous struggle between staying cost efficient and supplying adequate service. Establishing a distribution network which is flexible or easily adaptable is the key to survival under these conditions. This research begins to address the problem of locating facilities in a logistics network in the face of an evolving strategic focus through the implicit consideration of the uncertainty of parameters. The trade-off of cost and customer service is thoroughly examined in a series of multi-criteria location problems. Modeling techniques for incorporating service restrictions for facility location in strategic network design are investigated. A flexibility metric is derived for the purposes of quantifying the similarity of a set of non-dominated solutions in strategic network design. Finally, a multi-objective greedy random adaptive search (MOG) metaheuristic is applied to solve a series of bi-criteria, multi-level facility location problems

A study on the optimal aircraft location for human organ transportation activities

Abstract The donation-transplant network's complexity lies in the need to reconcile standardized processes and high levels of urgency and uncertainty due to organs' perishability and location. Both punctuality and reliability of air transportation service are crucial to ensure the safe outcome of the transplant. To this scope, an Integer Linear Programming (ILP) model is here proposed to determine the optimal distribution of aircraft in a given set of hubs and under the demand extracted from the Italian transplant database. This is an application of uncapacitated facility location problems, where aircraft are facilities to be located and organ transportation requests represent the demand. Two scenarios (two hubs versus three hubs) are tested under the performance point of view and over different time periods to assess the influence of variations in demand pattern and time period length on the solution

On the medication distribution system for home health care through convenience stores, lockers, and home delivery.

Medication distribution service can be delivered based on a combination of home delivery and customer pickup. That is, medications are delivered either to customers' homes directly or to the pickup facilities (e.g. lockers) close to customers' homes. In Taiwan, there are more than 11,000 convenience stores that provide a 24-h service for customers to pick up the ordered items from e-commerce, which is unique to the world. In the medication distribution system, convenience stores can provide a unique opportunity for customers to more conveniently collect medications at stores, and also can reduce the operating cost for a logistics company providing the medication delivery service. Therefore, this work proposes a medication distribution system through convenience stores, lockers, and home delivery. Under this system, this work investigates how to simultaneously determine employment of convenience store chains, the convenience store locations to be visited, locations of lockers, vehicle routes for convenience stores and lockers, and vehicle routes for customers' homes, so that the total operating cost is minimized. This work further proposes a genetic algorithm to solve the medication distribution problem. Through simulation, the experimental results show that the proposed algorithm is able to solve the problem efficiently