Multiple Independent DE Optimizations to Tackle Uncertainty and Variability in Demand in Inventory Management

To determine the effectiveness of metaheuristic Differential Evolution optimization strategy for inventory management (IM) in the context of stochastic demand, this empirical study undertakes a thorough investigation. The primary objective is to discern the most effective strategy for minimizing inventory costs within the context of uncertain demand patterns. Inventory costs refer to the expenses associated with holding and managing inventory within a business. The approach combines a continuous review of IM policies with a Monte Carlo Simulation (MCS). To find the optimal solution, the study focuses on meta-heuristic approaches and compares multiple algorithms. The outcomes reveal that the Differential Evolution (DE) algorithm outperforms its counterparts in optimizing IM. To fine-tune the parameters, the study employs the Latin Hypercube Sampling (LHS) statistical method. To determine the final solution, a method is employed in this study which combines the outcomes of multiple independent DE optimizations, each initiated with different random initial conditions. This approach introduces a novel and promising dimension to the field of inventory management, offering potential enhancements in performance and cost efficiency, especially in the presence of stochastic demand patterns.Comment: 6 pages, 2 figures, 6 tables, IEEE (ICITEE 2023

Tabu search heuristic for inventory routing problem with stochastic demand and time windows

This study proposes the hybridization of tabu search (TS) and variable neighbourhood descent (VND) for solving the Inventory Routing Problems with Stochastic Demand and Time Windows (IRPSDTW). Vendor Managed Inventory (VMI) is among the most used approaches for managing supply chains comprising multiple stakeholders, and implementing VMI require addressing the Inventory Routing Problem (IRP). Considering practical constraints related to demand uncertainty and time constraint, the proposed model combines multi-item replenishment schedules with unknown demand to arrange delivery paths, where the actual demand amount is only known upon arrival at a customer location with a time limit. The proposed method starts from the initial solution that considers the time windows and uses the TS method to solve the problem. As an extension, the VND is conducted to jump the solution from its local optimal. The results show that the proposed method can solve the IRPSDTW, especially for uniformly distributed customer locations

Job shop estocástico con minimización del valor esperado del maximum lateness

The drawbacks that programming in job -shop environment imply, refer to a notorious difficulty for its resolution due to its NP-hard nature. However, the research has grown in the late years because of its constant use in manufacturing industries. According to studies, most of the research has approached the job shop scheduling through a deterministic approach. Nevertheless, real industrial environments are subject to random events as: machinery faults, maintenance duration, processing duration, enlistment times, availability times, among many others. In this project, a stochastic job shop that minimizes the expected maximum lateness is addressed. The problem consider sequence dependent setup times, and the stochastic events are machine breakdowns. To solve the problem a simheuristic approach is proposed. The simheuristic Hybridizes a tabu search algorithm with a Monte Carlo simulation. The problem was solved in three phases: Firstly, a mixed integer linear programming model was designed for the deterministic counterpart of the JSSP studied. Secondly, the meta-heuristic tabu search was designed to solving large instances of the deterministic problem. Thirdly, the simheuristic was designed and implemented to minimize the expected maximum lateness value, considering stochastic machine breakdowns. For the simheuristic designing, stochastic variables were generated: times between failures and repair times, following exponential and log-normal distributions. To generate their respective parameters [expected value (μ) and standard deviation (σ)], the mean time to repair was found (MTTR Mean Time to Repair), out of the total mean time between breakdowns. Four different variation coefficient values were proposed (0%, 5%, 10% and 15%), them being: 0% for the deterministic case and 5%, 10% and 15% for stochastic events, to calculate the (σ) in log-normal distribution. On the other hand, a simulation was performed to calculate the expected objective function. The simheuristic was firstly parametrized through an experimental design considering different tabu list sizes and number of iterations without improvement. With the generated parametrization, another computational experiment was executed for a total of 554 instances of different sizes. First, the performance of the simheuristic, for small instances, was evaluated in comparison with the simulation of optimal solutions obtained with the mathematical model. Results show that the simheuristic improves the results of simulations of the model in a 37% for 4x4 instances and in an 11% for 6x6 instances, demonstrating that the simheuristic is better than a deterministic mathematical model simulated. Additionally, the simheuristic performance was evaluated, for large instances, in comparison with the simulation of EDD dispatching rule sequences. Results show that the average improvement is 28% in log-normal distribution and 10% for exponential distribution.Ingeniero (a) IndustrialAdministrador (a) de EmpresasPregrad

Modelos logísticos estocásticos aplicados a la cadena de suministro: una revisión de la literatura

Context: The analysis of the complexity of the systems involves the evolution of the models that representation of reality, logistics has advanced from a business context to the supply chain, basic models of logistics with deterministic parameters must go represent real behavior, stochastic. In this context, the combination of inventory, location and routing models with a stochastic approach applied to supply chains appears. Method: A systematic review of the literature was developed in the bibliographic databases, ScienceDirect, ScholarGoogle, SpringerLink, Scopus, SemanticScholar, ResearchGate and Scielo, of the 72 referenced articles, 65 % between 2015 and 2019. Results: From the models identified and described, a taxonomy of the models is proposed and classified into 4 kinds, three dyadic models Location Inventory Problem (LIP), Inventory Routing Problem (IRP), Location Routing problem (LRP) and a triadic model Location Inventory Routing Problem (LIRP). The stochastic parameters used in the models, the types of models, the solution methods, the contemplated objective functions, and the number of echelons in the supply chain are established, from which taxonomies of the different types of models are proposed. Lines of work for future research is presented. Conclusions: The evolution from deterministic to stochastic models represents an increase in complexity which forces the development of new solution methods with ability to find feasible solutions. The development of models with news measurements of performance as environmental, social and humanitarian have been of recent interest. In the last period, triadic multi-product and multi-period models take on relevance.Contexto: El análisis de la complejidad de los sistemas conlleva la evolución de los modelos de representación de la realidad, la logística ha avanzado de un contexto empresarial a la cadena de suministro, los modelos básicos de logística con parámetros determinísticos requieren representar el comportamiento real estocástico. En este sentido, aparecen la combinación de los modelos de inventario, la localización y el ruteo con enfoque estocástico aplicados a cadenas de suministro. Método: Se desarrolló una revisión sistemática de la literatura en las bases de datos bibliográficas ScienceDirect, ScholarGoogle, SpringerLink, Scopus, SemanticScholar y Scielo, así como en ResearchGate. De los 79 artículos referenciados, el 65 % comprenden entre el 2015 y 2019. Resultados: Se identifican y describen los modelos, a partir de lo cual se propone una taxonomía en cuatro combinaciones, tres de modelos diádicos: LIP, IRP, LRP y un modelo tríadico: LIRP. Se identifican los parámetros estocásticos utilizados en los modelos, los tipos de modelos, los métodos de solución, las funciones objetivo contempladas y el número de eslabones de la cadena contemplados, a partir de los cuales se proponen taxonomías de los diferentes tipos de modelos. Por último, se presentan líneas de trabajo para futuras investigaciones. Conclusiones: La evolución de modelos determinísticos a estocásticos representa un incremento en la complejidad, lo que obliga a desarrollar nuevos métodos de solución con capacidad de encontrar soluciones factibles. Ha sido de reciente interés el desarrollo de modelos y problemas con medidas de desempeño ambiental, social y riesgo humanitario, en el último periodo toman relevancia modelos tríadicos multiproducto y multiperiodo

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

A biased-randomized iterated local search for the vehicle routing problem with optional backhauls

[EN] The vehicle routing problem with backhauls integrates decisions on product delivery with decisions on the collection of returnable items. In this paper, we analyze a scenario in which collection of items is optional-but subject to a penalty cost. Both transportation costs and penalties associated with non-collecting decisions are considered. A mixed-integer linear model is proposed and solved for small instances. Also, a metaheuristic algorithm combining biased randomization techniques with iterated local search is introduced for larger instances. Our approach yields cost savings and is competitive when compared to other state-of-the-art approaches.This work has been partially supported by COLCIENCIAS - Colombia, the School of Industrial Engineering of Universidad del Valle, the IoF2020, the AGAUR (2018-LLAV-00017), and the Erasmus+ Program (2018-1-ES01-KA103-049767). We also acknowledge the support of the doctoral programs at the Universitat Oberta de Catalunya and the Universidad de La Sabana.Londoño, JC.; Tordecilla, RD.; Do C. Martins, L.; Juan, AA. (2021). A biased-randomized iterated local search for the vehicle routing problem with optional backhauls. Top. 29(2):387-416. https://doi.org/10.1007/s11750-020-00558-x387416292Al Chami Z, El Flity H, Manier H, Manier MA (2018) A new metaheuristic to solve a selective pickup and delivery problem. In: 2018 4th international conference on logistics operations management (GOL), IEEE, pp 1–5Arab R, Ghaderi S, Tavakkoli-Moghaddam R (2018) Bi-objective inventory routing problem with backhauls under transportation risks: two meta-heuristics. Transportation Letters, pp 1–17Assis LP, Maravilha AL, Vivas A, Campelo F, Ramírez JA (2013) Multiobjective vehicle routing problem with fixed delivery and optional collections. 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