Developing an integrated decision making model in supply chain under demand uncertainty using genetic algorithm and network data envelopment analysis

Nakhjirkan, Sara; Rafiei, Farimah Mokhatab; Kashan, Ali Husseinzadeh

doi:10.1504/ijmor.2019.096979

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…One of the most effective and popular algorithms in computer science and operations research, is the Genetic Algorithm (GA). Many researchers in the supply chain field employed the GA for the resolution of different problems (Diabat and Deskoores, 2016;Jiang et al, 2016;Hiassat et al, 2017;Nakhjirkan et al, 2019). GA refers to a metaheuristic inspired by the process of natural selection that belongs to the larger class of Evolutionary Algorithms (EAs).…”

Section: Bullwhip Effect: a Persistent Harmful Effectmentioning

confidence: 99%

Collaborative Bullwhip Effect-Oriented Bi-Objective Optimization for Inference-Based Weighted Moving Average Forecasting in Decentralized Supply Chain

Tliche¹,

Taghipour

Mahfod-Leroux

et al. 2023

International Journal of Information Systems and Supply Chain Management

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Downstream demand inference (DDI) emerged in the supply chain theory, allowing an upstream actor to infer the demand occurring at his formal downstream actor without need of information sharing. Literature showed that simultaneously minimizing the average inventory level and the bullwhip effect isn't possible. In this paper, the authors show that demand inference is not only possible between direct supply chain links, but also at any downstream level. The authors propose a bi-objective approach to reduce both performance indicators by adopting the genetic algorithm. Simulation results show that bullwhip effect can be reduced highly if specific configurations are selected from the Pareto frontier. Numerical results show that demand's time-series structure, lead-times, holding and shortage costs, don't affect the behaviour of the bullwhip effect indicator. Moreover, the sensitivity analysis show that the optimization approach is robust when faced to varied initializations. Finally, the authors conclude the paper with managerial implications in multi-level supply chains.

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Section: Bullwhip Effect: a Persistent Harmful Effectmentioning

confidence: 99%

Collaborative Bullwhip Effect-Oriented Bi-Objective Optimization for Inference-Based Weighted Moving Average Forecasting in Decentralized Supply Chain

Tliche¹,

Taghipour

Mahfod-Leroux

et al. 2023

International Journal of Information Systems and Supply Chain Management

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“…Moreover, Refs. [48,49,[59][60][61][62][63] also put forward the LIRP to describe a MESLP logistics distribution system. The work in [59] studied a class of two-echelon inventory decision problems with one depot and multiple retailers.…”

Section: Multi-echelon Single-link Problem (Meslp)mentioning

confidence: 99%

“…The image of the CCLN system is shown in Figure 4; it is a two-echelon single-link problem. The work in [62] described a MESLP supply chain that includes suppliers, business firms, DCs, and customers. Modeled after MINLP, their strategy is to use a network data envelopment analysis (NDEA) to calculate the association efficiency so that the GA would have a better selection performance.…”

Section: Multi-echelon Single-link Problem (Meslp)mentioning

confidence: 99%

Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method

et al. 2022

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A traditional logistics decision model mainly studies the location decision of logistics distribution centers, storage inventory management, vehicle scheduling, and transportation routes. The logistics location-inventory-routing problem (LIRP) is an integrated optimization of the three problems—a comprehensive optimization problem for the whole logistics system. This review paper uses the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method to review the literature on LIRP systematically. A total of 112 LIRP-related studies published between 2010 and 2021 are reviewed and classified based on 10 abstract and citation databases. The classification includes four aspects: problem characteristics, demand data types, model-based solutions, and application fields. From this systematic review, a few observations are recorded. First, the most popular problems among researchers are the multi-period multi-product problem, the multi-echelon single-link problem, and the multi-depot multi-retailer problem. Based on the objective function, the minimization of total supply chain cost is the primary concern of the LIRP literature. Researchers also favor other problem characteristics such as multi-objective programming, inventory control replenishment policy, and a homogeneous fleet of vehicles. We found that stochastic data are a common factor in an uncertain environment and have broad coverage. When dealing with the LIRP, heuristic and metaheuristic algorithms are the most widely used solution methodologies in the literature. In the application field of LIRP, the perishable products logistics network is mentioned in most applications. Finally, we discuss and emphasize the challenges of and recommendations for future work. This paper provides a systematic review of the literature on LIRP based on the PRISMA method, which contributes vital support and valuable information for researchers interested in LIRP.

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“…En los modelos tríadicos se destaca que algunos autores (25 %) tienen en cuenta el criterio ambiental, al ser un problema abordado desde el 2010. Se evidencia que es un enfoque relevante en los últimos años, siendo la disminución de las emisiones de dióxido de carbono la más frecuente, como en [72], [73], [75]. El 10 % contempla el beneficio social entre los objetivos, Zhalechian et al [66] contemplan la afectación al ambiente de los procesos y el factor de beneficio social en la creación de nuevos empleos, Eskandari et al [73] plantean un modelo basado en la CS de plaquetas y sangre, para maximizar los efectos sociales y reducir el impacto ambiental.…”

Section: Función Objetivounclassified

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

Cruz

Bernal

Castro

2022

Ing.

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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.

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Developing an integrated decision making model in supply chain under demand uncertainty using genetic algorithm and network data envelopment analysis

Cited by 6 publications

References 11 publications

Collaborative Bullwhip Effect-Oriented Bi-Objective Optimization for Inference-Based Weighted Moving Average Forecasting in Decentralized Supply Chain

Collaborative Bullwhip Effect-Oriented Bi-Objective Optimization for Inference-Based Weighted Moving Average Forecasting in Decentralized Supply Chain

Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method

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

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