A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths

Ghasimi, Salah Alden; Ramli, Rizauddin; Saibani, Nizaroyani

doi:10.1016/j.apm.2013.08.023

Cited by 27 publications

(15 citation statements)

References 28 publications

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“…The findings illustrated the effectiveness of this approach, which improve the performance and minimise the production cost within the organisation (Wong et al, 2006). Ghasimi et al (2014) introduced a novel mathematical model for three-echelon Defective Goods Supply Chain Network (DGSCN) was built to ascertain the Economic Production Quantity (EPQ) and lead time, additionally reducing all production, distribution and holding cost using JIT logistics. GA and Cplex solver were used to optimise their defective goods supply chain network model.…”

Section: An Overview Of Jit Optimisation Using Gamentioning

confidence: 99%

A Genetic Algorithm for a Simultaneous Optimisation of Cost-Risk Reduction Under a Just-in-Time Adaption

Dabee¹,

Marian²,

Amer³

2014

Journal of Computer Science

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Just-In-Time (JIT) as a lean manufacturing approach plays a significant role in minimising costs and performances of products and services supplied to the global marketplace. However, there are many potential risks that cause significant disruptions to all supply chain members. This study proposes a genetic approach for optimising a novel mathematical model for simultaneously minimising the total cost of a final product and the potential risks related to these benefits. Specifically, it demonstrates the effectiveness of a genetic algorithm in optimising the JIT model developed in our previous paper. Genetic operators adopted to improve the genetic search algorithm are introduced and discussed. Experiments are carried out to evaluate the performance of the proposed algorithm using a simplified example. Comparison of four selection methods is done to define the best method that can be used in the proposed GA. The findings demonstrate the superiority of the proposed approach in the JIT system with focus on simultaneous cost-risk reduction.

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Section: An Overview Of Jit Optimisation Using Gamentioning

confidence: 99%

A Genetic Algorithm for a Simultaneous Optimisation of Cost-Risk Reduction Under a Just-in-Time Adaption

Dabee¹,

Marian²,

Amer³

2014

Journal of Computer Science

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“…GA was combined with particle swarm optimization (PSO) for optimizing a mathematical model with a JIT system for a defective goods supply chain network [26]. Researchers proposed GA for optimizing a novel mathematical model of the defective goods supply chain network [27]. The dynamic vehicle routing problem (DVRP) and stochastic dynamic travelling salesman problem with hard time window (SDTSPTW) [11] is a core optimization task of just-in-time supply solutions.…”

Section: Content Analysismentioning

confidence: 99%

Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach

et al. 2019

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The just-in-sequence inventory strategy, as an important part of the supply chain solutions in the automotive industry, is based on feedback information from the manufacturer. The performance, reliability, availability and cost efficiency are based on the parameters of the members of the supply chain process. To increase the return on assets (ROA) of the manufacturer, the optimization of the supply process is unavoidable. Within the frame of this paper, the authors describe a flower pollination algorithm-based heuristic optimization model of just-in-sequence supply focusing on sustainability aspects, including fuel consumption and emission. After a systematic literature review, this paper introduces a mathematical model of just-in-sequence supply, including assignment and scheduling problems. The objective of the model is to determine the optimal assignment and schedule for each sequence to minimize the total purchasing cost, which allows improving cost efficiency while sustainability aspects are taken into consideration. Next, a flower pollination algorithm-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase cost-efficiency in just-in-sequence solutions.

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“…En [23] se aborda el problema de programación de tareas que pueden ser realizadas por una máquina para la fabricación de productos y en el que se debe determinar la secuencia de las tareas y también la formación de lotes de producción. En [24] se proponen AG's para reducir al mínimo los costos de producción, distribución, explotación y pedido para los productos de menor calidad en un proceso de fabricación. En el modelo propuesto se asumió que estos productos no son reparables y se venden directamente a los clientes a un precio menor.…”

Section: Procesos Scor Y Algoritmos Genéticosunclassified

Aplicaciones de inteligencia artificial en procesos de cadenas de suministros: una revisión sistemática

Ahumada

2016

Ingeniare. Rev. chil. ing.

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RESUMENUna cadena de suministro (SC) es una red de empresas que producen, venden y entregan un producto o servicio a un segmento de mercado predeterminado. No solo incluye a los fabricantes y proveedores, sino que también a transportistas, almacenes, minoristas y los propios clientes, entre otros. Debido al avance tecnológico, específicamente en áreas como las comunicaciones, procesamiento computacional, gestión y almacenamiento de información, es posible apoyar a la administración de cadenas de suministros pudiendo hacerla mas eficiente. En este contexto, la inteligencia artificial (IA) ha sido aplicada en diferentes procesos de SC pero no hay conocimiento especifico sobre cuáles técnicas de IA están siendo aplicadas (o no) en los procesos de la SC o cuáles actividades de la SC están siendo apoyadas (o no) con técnicas de IA. Con base a lo anterior, el objetivo de este trabajo fue establecer de forma empírica el aporte de la IA en procesos de la SC, para luego establecer actividades de investigación a realizarse en el futuro. Para lograr esto, se realizó una revisión sistemática considerando el modelo SCOR como referencia para procesos de una SC. Los principales resultados indican que los algoritmos genéticos y los agentes inteligentes son las técnicas más investigadas para procesos de la SC relacionados con la planificación y, en menor medida, a procesos relacionados con la entrega de productos. Además, existe una tendencia a incluir la incertidumbre como trabajo futuro en diferentes técnicas de IA de manera que los modelos se acerquen más a la realidad.Palabras clave: Inteligencia artificial, modelo SCOR, revisión sistemática, cadena de suministros. ABSTRACT A supply chain (SC) is a network of companies that produce

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A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths

Cited by 27 publications

References 28 publications

A Genetic Algorithm for a Simultaneous Optimisation of Cost-Risk Reduction Under a Just-in-Time Adaption

A Genetic Algorithm for a Simultaneous Optimisation of Cost-Risk Reduction Under a Just-in-Time Adaption

Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach

Aplicaciones de inteligencia artificial en procesos de cadenas de suministros: una revisión sistemática

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