A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

Combining simulation with a GRASP metaheuristic for solving the permutation flow-shop problem with stochastic processing times

Greedy Randomized Adaptive Search Procedures (GRASP) are among the most popular metaheuristics for the solution of combinatorial optimization problems. While GRASP is a relatively simple and efficient framework to deal with deterministic problem settings, many real-life applications experience a high level of uncertainty concerning their input variables or even their optimization constraints. When properly combined with the right metaheuristic, simulation (in any of its variants) can be an effective way to cope with this uncertainty. In this paper, we present a simheuristic algorithm that integrates Monte Carlo simulation into a GRASP framework to solve the permutation flow shop problem (PFSP) with random processing times. The PFSP is a well-known problem in the supply chain management literature, but most of the existing work considers that processing times of tasks in machines are deterministic and known in advance, which in some real-life applications (e.g., project management) is an unrealistic assumption

Combining simulation with a GRASP metaheuristic for solving the permutation flow-shop problem with stochastic processing times

Greedy Randomized Adaptive Search Procedures (GRASP) are among the most popular metaheuristics for the solution of combinatorial optimization problems. While GRASP is a relatively simple and efficient framework to deal with deterministic problem settings, many real-life applications experience a high level of uncertainty concerning their input variables or even their optimization constraints. When properly combined with the right metaheuristic, simulation (in any of its variants) can be an effective way to cope with this uncertainty. In this paper, we present a simheuristic algorithm that integrates Monte Carlo simulation into a GRASP framework to solve the permutation flow shop problem (PFSP) with random processing times. The PFSP is a well-known problem in the supply chain management literature, but most of the existing work considers that processing times of tasks in machines are deterministic and known in advance, which in some real-life applications (e.g., project management) is an unrealistic assumption

Diseño de una metaheurística GRASP hibridizada con la metodología PAES y la simulación de Monte Carlo en un ambiente Flexible Flow Shop estocástico multi-objetivo

El propósito de este proyecto es estudiar un problema de programación de la producción multiobjetivo en un ambiente Flexible Flow Shop (FFS) estocástico. Los objetivos a minimizar son el valor esperado de la tardanza, la desviación estándar de la tardanza, el valor esperado del tiempo total de terminación y la desviación estándar del tiempo total de terminación. Los parámetros estocásticos son los tiempos entre fallas de las máquinas y los tiempos de reparación de las máquinas. Como método de solución, se propone una simheurística, la cual hibridiza la metaheurística GRASP con la simulación de Monte Carlo y el algoritmo PAES para obtener la frontera de Pareto. Inicialmente, se realiza un diseño experimental de la versión determinística del problema para evaluar el desempeño de la simheurística, comparando los resultados de la simheurística con el tiempo total de terminación obtenido en la programación de los trabajos con la regla de despacho FL, y la tardanza con la regla de despacho ENS2. Un segundo diseño de experimentos es diseñado para evaluar los efectos de los diferentes coeficientes de variación y la distribución de probabilidad para ambos parámetros estocásticos en las cuatro funciones objetivo del caso estocástico. Para el caso estocástico, los resultados arrojaron que ambas distribuciones de probabilidad y coeficientes de variación tienen un efecto significativo en las variables, lo que demuestra la importancia de un ajuste preciso de las distribuciones de probabilidad para obtener soluciones adecuadas.To achieve a higher level of efficiency within a manufacturing industry, the production scheduling is essential, because this process is crucial for the maximization of the business value. Currently, a big part of literature in scheduling is focused on solving a deterministic problem to minimize the makespan. Given that, realistically, the industry is exposed to random events that can affect its performance, the aim of this project is to study a multi-objective stochastic Flexible Flow Shop (FFS) environment. The objectives to minimize are expected value of tardiness, standard deviation of tardiness, expected value of total completion time (equal to flowtime due to release times are zero) and standard deviation of total completion time. The stochastics parameters are the times between failures and times to repair the machines (duration of machine breakdowns). As solution method, a simheuristic is proposed, which hybridizes the metaheuristic Greedy Randomized Adaptive Search Procedures (GRASP) with the Monte Carlo simulation and Pareto Archived Evolution Strategy (PAES) algorithm to obtain the Pareto frontier (see illustration 2). A first experimental design is done to test the simheuristic performance for the deterministic version (see illustration 1) of the problem by comparing the results of the simheuristic with the flowtime obtained by scheduling the jobs with FL dispatching rule, and the tardiness with the ENS2 dispatching rule. A second design of experiments is designed to evaluate the effects of different coefficients of variation and probability distribution of both stochastic parameters in the four objective functions of the stochastic case. To do both experimental designs 324 benchmark instances were evaluated in both cases. Results show, that for the deterministic case, the metaheuristic presents an average improvement of 3% in flowtime against FL rule, 2% in tardiness against ENS2 rule. For the stochastic case, results show that both probability distributions and coefficient of variation have a significant effect in the four response variables, which shows the importance of an accurate fitting of probability distributions to obtain adequate solutions.Ingeniero (a) IndustrialPregrad

Efficient Inventory Management of Hospital Supply Chains Using a Sim-Heuristic Approach

Inventory management is a vital section of a supply chain system. In a hospital setting, where delivering high quality patient care is a prime concern, inventory management is often overlooked. With the ever increasing demand for products, it becomes challenging to manage inventory in a dynamic facility such as a hospital. Although there is abundant research in supply chain, seldom have the proposed methods found their way into execution in actual hospital settings. Additionally, much of the literature focuses on particular aspects of the supply chain. Current methods used in practice lead to system performance that is suboptimal, resulting in too much or too short inventory in stock, overtime work to manage supplies, expedited shipments and potentially substandard quality of care delivered to patients. Having the right products available at the point-of-use is important to the efficient and effective treatment of patients. With cost and budget constraints, merely managing demand is not sufficient. There is a need to develop a system design which enables hospitals and healthcare institutions to implement and benefit from methods that have been developed or are being developed for optimal inventory management systems. In this research, we study the hospital supply chain from manufacturers/distribution centers to the point-of-use within a hospital unit, taking into account the integration and implementation of the various echelon of the supply chain system. In particular, we design and develop a sim-heuristic methodology using operations research to evaluate inventory and operational decision variables based on service level and operational costs, subject to variability in demand and lead-time. In addition, we demonstrate the capabilities and limitations of the methodology and compare alternate system configurations including a (Q, r) inventory system and Kanban system

Simheuristics to support efficient and sustainable freight transportation in smart city logistics

La logística urbana intel·ligent constitueix un factor crucial en la creació de sistemes de transport urbà eficients i sostenibles. Entre altres factors, aquests sistemes es centren en la incorporació de dades en temps real i en la creació de models de negoci col·laboratius en el transport urbà de mercaderies, considerant l’augment dels habitants en les ciutats, la creixent complexitat de les demandes dels clients i els mercats altament competitius. Això permet als que planifiquen el transport minimitzar els costos monetaris i ambientals del transport de mercaderies a les àrees metropolitanes. Molts problemes de presa de decisions en aquest context es poden formular com a problemes d’optimació combinatòria. Tot i que hi ha diferents enfocaments de resolució exacta per a trobar solucions òptimes a aquests problemes, la seva complexitat i grandària, a més de la necessitat de prendre decisions instantànies pel que fa a l’encaminament de vehicles, la programació o la situació d’instal·lacions, fa que aquestes metodologies no s’apliquin a la pràctica. A causa de la seva capacitat per a trobar solucions pseudoòptimes en gairebé temps real, els algorismes metaheurístics reben una atenció creixent dels investigadors i professionals com a alternatives eficients i fiables per a resoldre nombrosos problemes d’optimació en la creació de la logística de les ciutats intel·ligents. Malgrat el seu èxit, les tècniques metaheurístiques tradicionals no representen plenament la complexitat dels sistemes més realistes. En assumir entrades (inputs) i restriccions de problemes deterministes, la incertesa i el dinamisme experimentats en els escenaris de transport urbà queden sense explicar. Els algorismes simheurístics persegueixen superar aquests inconvenients mitjançant la integració de qualsevol tipus de simulació en processos metaheurístics per a explicar la incertesa inherent a la majoria de les aplicacions de la vida real. Aquesta tesi defineix i investiga l’ús d’algorismes simheurístics com el mètode més adequat per a resoldre problemes d’optimació derivats de la logística de les ciutats. Alguns algorismes simheurístics s’apliquen a una sèrie de problemes complexos, com la recollida de residus urbans, els problemes de disseny de la cadena de subministrament integrada i els models de transport innovadors relacionats amb la col·laboració horitzontal entre els socis de la cadena de subministrament. A més de les discussions metodològiques i la comparació d’algorismes desenvolupats amb els referents de la bibliografia acadèmica, es mostra l’aplicabilitat i l’eficiència dels algorismes simheurístics en diferents casos de gran escala.Las actividades de logística en ciudades inteligentes constituyen un factor crucial en la creación de sistemas de transporte urbano eficientes y sostenibles. Entre otros factores, estos sistemas se centran en la incorporación de datos en tiempo real y la creación de modelos empresariales colaborativos en el transporte urbano de mercancías, al tiempo que consideran el aumento del número de habitantes en las ciudades, la creciente complejidad de las demandas de los clientes y los mercados altamente competitivos. Esto permite minimizar los costes monetarios y ambientales del transporte de mercancías en las áreas metropolitanas. Muchos de los problemas de toma de decisiones en este contexto se pueden formular como problemas de optimización combinatoria. Si bien existen diferentes enfoques de resolución exacta para encontrar soluciones óptimas a tales problemas, su complejidad y tamaño, además de la necesidad de tomar decisiones instantáneas con respecto al enrutamiento, la programación o la ubicación de las instalaciones, hacen que dichas metodologías sean inaplicables en la práctica. Debido a su capacidad para encontrar soluciones pseudoóptimas casi en tiempo real, los algoritmos metaheurísticos reciben cada vez más atención por parte de investigadores y profesionales como alternativas eficientes y fiables para resolver numerosos problemas de optimización en la creación de la logística de ciudades inteligentes. A pesar de su éxito, las técnicas metaheurísticas tradicionales no representan completamente la complejidad de los sistemas más realistas. Al asumir insumos y restricciones de problemas deterministas, se ignora la incertidumbre y el dinamismo experimentados en los escenarios de transporte urbano. Los algoritmos simheurísticos persiguen superar estos inconvenientes integrando cualquier tipo de simulación en procesos metaheurísticos con el fin de considerar la incertidumbre inherente en la mayoría de las aplicaciones de la vida real. Esta tesis define e investiga el uso de algoritmos simheurísticos como método adecuado para resolver problemas de optimización que surgen en la logística de ciudades inteligentes. Se aplican algoritmos simheurísticos a una variedad de problemas complejos, incluyendo la recolección de residuos urbanos, problemas de diseño de la cadena de suministro integrada y modelos de transporte innovadores relacionados con la colaboración horizontal entre los socios de la cadena de suministro. Además de las discusiones metodológicas y la comparación de los algoritmos desarrollados con los de referencia de la bibliografía académica, se muestra la aplicabilidad y la eficiencia de los algoritmos simheurísticos en diferentes estudios de casos a gran escala.Smart city logistics are a crucial factor in the creation of efficient and sustainable urban transportation systems. Among other factors, they focus on incorporating real-time data and creating collaborative business models in urban freight transportation concepts, whilst also considering rising urban population numbers, increasingly complex customer demands, and highly competitive markets. This allows transportation planners to minimize the monetary and environmental costs of freight transportation in metropolitan areas. Many decision-making problems faced in this context can be formulated as combinatorial optimization problems. While different exact solving approaches exist to find optimal solutions to such problems, their complexity and size, in addition to the need for instantaneous decision-making regarding vehicle routing, scheduling, or facility location, make such methodologies inapplicable in practice. Due to their ability to find pseudo-optimal solutions in almost real time, metaheuristic algorithms have received increasing attention from researchers and practitioners as efficient and reliable alternatives in solving numerous optimization problems in the creation of smart city logistics. Despite their success, traditional metaheuristic techniques fail to fully represent the complexity of most realistic systems. By assuming deterministic problem inputs and constraints, the uncertainty and dynamism experienced in urban transportation scenarios are left unaccounted for. Simheuristic frameworks try to overcome these drawbacks by integrating any type of simulation into metaheuristic-driven processes to account for the inherent uncertainty in most real-life applications. This thesis defines and investigates the use of simheuristics as a method of first resort for solving optimization problems arising in smart city logistics concepts. Simheuristic algorithms are applied to a range of complex problem settings including urban waste collection, integrated supply chain design, and innovative transportation models related to horizontal collaboration among supply chain partners. In addition to methodological discussions and the comparison of developed algorithms to state-of-the-art benchmarks found in the academic literature, the applicability and efficiency of simheuristic frameworks in different large-scaled case studies are shown