Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach

In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version

Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach

In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version

SIMULATION METHODS APPLICATION FOR LPG DELIVERIES PLANNING AND SCHEDULING TO THE NETWORK OF STATIONS UNDER DEMAND UNCERTAINTY

In our paper we considered the problem of demand uncertainty and its influence on planning and scheduling of LPG deliveries. The experience of specialized transportation company in charge of LPG deliveries for the domestic supplier network under VMI approach was analyzed. High variability of distribution parameters and frequent orders modifications were observed while small stations tanks capacities comparing to high daily LPG sales volumes were considered. The combined use of simulation and optimization methods was proposed for the case of LPG distribution to the petrol stations network. The demand uncertainty at customers' stations was considered. Simulation models were assumed to be efficient for dynamic and robust delivery plans of LPG distribution. The results of computational experiments were presented for different values of coefficient of variation

Why simheuristics? : Benefits, limitations, and best practices when combining metaheuristics with simulation

Many decision-making processes in our society involve NP-hard optimization problems. The largescale, dynamism, and uncertainty of these problems constrain the potential use of stand-alone optimization methods. The same applies for isolated simulation models, which do not have the potential to find optimal solutions in a combinatorial environment. This paper discusses the utilization of modelling and solving approaches based on the integration of simulation with metaheuristics. These 'simheuristic' algorithms, which constitute a natural extension of both metaheuristics and simulation techniques, should be used as a 'first-resort' method when addressing large-scale and NP-hard optimization problems under uncertainty -which is a frequent case in real-life applications. We outline the benefits and limitations of simheuristic algorithms, provide numerical experiments that validate our arguments, review some recent publications, and outline the best practices to consider during their design and implementation stages

Diseño de una técnica de solución para el problema de construcción de horarios y programación de personal, en entornos de actividades múltiples, mediante un enfoque de simulación y optimización, considerando tiempos de traslado estocásticos y trabajadores heterogéneos

El problema de construcción de horarios y programación de personal en entornos de actividades múltiples (MCTCSP), considera un conjunto de clientes que demandan uno o más servicios en horarios y ubicaciones establecidas. El objetivo es minimizar la cantidad de trabajadores requeridos para satisfacer la demanda. Algunas aplicaciones de este problema están asociadas con la planeación de visitadores médicos y reparaciones a domicilio, entre otros. Este trabajo propone una extensión del MCTCSP contemplando trabajadores heterogéneos, es decir, con habilidades distintas y con tiempos de traslado estocásticos entre las ubicaciones geográficas de cada cliente. Se diseñó una meta heurística que resuelva la versión determinística del problema, en este caso una búsqueda Tabú, y fue integrada a un modelo de simulación de Montecarlo. En la hibridación, el modelo de simulación será usado para la construcción de la solución. En particular, se propone el uso de simheuristics. Para comparar la calidad de las soluciones se usaron cuatro indicadores de servicio, porcentaje de citas no cumplidas, total de tiempo tarde, total de tiempo ocioso y carga de trabajo. Los resultados demostraron la importancia de tener en cuenta los tiempos de traslado estocásticos en la construcción del problema, que permite tener una solución de mayor calidad en términos de los indicadores de servicio. En comparación con la Búsqueda Tabú, la simheuristic presenta una reducción del 20% en el porcentaje de citas no cumplidas, y las cargas de trabajo fueron balanceadas.The Multi-activity Combined Timetabling and Crew Scheduling Problem (MCTCSP), considers a set of clients that demand one or more Services in schedules and established locations. The objective is to minimize the amount of workers required to satisfy the demand. Some applications of this problem are associated with medical visitors schedule planning and home repair Services, etc. In this work is proposed an extension of the MCTCSP considering heterogeneous workers, with different abilities and stochastic time travel between the geographic locations of every Client. A metaheuristic was design to solve the deterministic version of the problem, in this case it was used a Tabu Search, and then a Montecarlo simulation model was integrated. In the hybridization technique, the simulation model was used for the solution construction. In particular, is proposed the use of simheuristics. To compare the quality of the Solutions it were used four Service indicators, percentage of not accomplish appointments, total late time, total idle time and workload. The results show the importance of taking into account the stochastic travel times in the construction of the solution, which allows the solution to be high quality in terms of the Service indicators. In comparison with the Tabu search, the simheuristic method presents a reduction of 20% on the percentage of unfulfilled appointments and the workload was balanced.Ingeniero (a) IndustrialPregrad

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

Inventory Routing Problem in Perishable Supply Chains: A Literature Review

Context: This paper presents a literature review of the Inventories Routing Problem (IRP) applied to supply chains of perishable products. Different approaches to solve this problem are identified and described in terms of structures, models and solution methods.  Method: A systematic literature review is conducted searching in different bibliographic databases and selecting the most relevant studies within the period 2004 to 2017. The results are analyzed so as to propose a taxonomy to classify and compare the different approaches proposed to address this problem. Results: We identified that the majority of studies consider heuristic-based algorithms to solve the problem. Because of its computational complexity the methods resort to metaheuristics and mateheuristics combined with exact methods. Regarding the application to specific supply chains of perishable products, they refer mostly to processed foods, medicines, and human blood. The constraints that differentiate this problem from other types of IRP are useful life and deterioration. Conclusions: The conditions and particularities of the supply chain of perishables products imply the need to consider new variables, parameters, constraints and objective functions; in the reviewed studies it is not clearly defined the differences involved when considering the perishability of the products in the supply chain. Future research should take into account the multiple ways in which deterioration is carried out with factors such as temperature, light, oxygen, humidity and in some cases microorganisms. Also include in the models the cold chain, hygiene standards, air pollution, emissions of greenhouse gases, generation of waste, occupation of roads and other aspects related to City Logistics and Green Logistics

El Problema de Ruteo e Inventarios en Cadenas de Suministro de Perecederos: Revisión de Literatura

Context: This paper presents a literature review of the Inventories Routing Problem (IRP) applied to supply chains of perishable products. Different approaches to solve this problem are identified and described in terms of structures, models and solution methods. Method: A systematic literature review is conducted searching in different bibliographic databases and selecting the most relevant studies within the period 2004 to 2017. The results are analyzed so as to propose a taxonomy to classify and compare the different approaches proposed to address this problem.Results: We identified that the majority of studies consider heuristic-based algorithms to solve the problem. Because of its computational complexity the methods resort to metaheuristics and mateheuristics combined with exact methods. Regarding the application to specific supply chains of perishable products, they refer mostly to processed foods, medicines, and human blood. The constraints that differentiate this problem from other types of IRP are useful life and deterioration.Conclusions: The conditions and particularities of the supply chain of perishables products imply the need to consider new variables, parameters, constraints and objective functions; in the reviewed studies it is not clearly defined the differences involved when considering the perishability of the products in the supply chain. Future research should take into account the multiple ways in which deterioration is carried out with factors such as temperature, light, oxygen, humidity and in some cases microorganisms. Also include in the models the cold chain, hygiene standards, air pollution, emissions of greenhouse gases, generation of waste, occupation of roads and other aspects related to City Logistics and Green Logistics. Contexto: Revisión de literatura del problema de ruteo e inventarios (IRP) aplicado a las cadenas de suministro de productos perecederos. Se identifican y describen los diferentes enfoques en cuanto a estructuras, modelos y métodos de solución.Método: Se realiza una revisión sistemática de la literatura en diferentes bases de datos bibliográficas y se plantea una taxonomía que clasifica las características de los estudios, lo anterior durante el período comprendido entre 2004 y 2017Resultados: Se encuentra que la mayoría de algoritmos propuestos son de carácter heurístico. Debido a complejidad computacional inherente al problema, se usan metaheurísticas y mateheurísticas combinadas con métodos exactos. Se aplican principalmente en alimentos, medicamentos y sangre humana. Las restricciones que diferencian de otros tipos de IRP son las de periodo de vida útil y deterioro.Conclusiones: Las condiciones y particularidades de la cadena de suministro de perecederos hace necesario que se planteen nuevas variables, parámetros, restricciones y funciones objetivo; por otro lado, en los estudios revisados no se establecen diferencias claras al involucrar la perecibilidad de los productos en los modelos. Las futuras investigaciones deberán tener en cuenta las múltiples maneras en las cuales se lleva a cabo el deterioro con factores como la temperatura, la luz, el oxígeno, la humedad y en algunos casos los microorganismos; asimismo, incluir en los modelos la cadena de frío, normas de higiene, contaminación del aire, emisiones de gases de efecto invernadero, generación de residuos, ocupación de vías y demás aspectos relacionados con city logistics y green logistics.

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

Inventory routing problem with backhaul considering returnable transport items collection

The Inventory Routing Problem (IRP) has been highlighted as a valuable strategy for tackling routing and inventory problems. This paper addresses the IRP but considers the forward delivery and the use of Returnable Transport Items (RTIs) in the distribution strategy. We develop an optimization model by considering inventory routing decisions with RTIs collection (backhaul customers) of a Closed-Loop Supply Chain (CLSC) within a short-term planning horizon. RTIs consider reusable packing materials such as trays, pallets, recyclable boxes, or crates. The RTIs represent an essential asset for many industries worldwide. The solution of the model allows concluding that if RTIs are considered for the distribution process, the relationship between the inventory handling costs of both the final goods and RTIs highly determines the overall performance of the logistics system under study. The obtained results show the efficiency of the proposed optimization scheme for solving the combined IRP with RTIs, which could be applied to different real industrial cases