Diseño y desarrollo de un modelo y planificación óptima para la responsabilidad de la cadena de suministro con el medio ambiente

La gestión responsable de los flujos de retorno de productos en entornos de producción e inventario es un requisito cada vez mayor para las empresas. Esto puede atribuirse a motivaciones económicas, ambientales y / o regulatorias. El modelado matemático detales sistemas ha ayudado a los procesos de toma de decisiones y ha proporcionado una mejor comprensión del comportamiento de dichos entornos de producción e inventario. Este artículo revisa la literatura sobre el modelado de sistemas de inventario de logística inversa que se basan en la orden económica / cantidad de producción (EOQ / EPQ) y la configuración del tamaño de lote económico conjunto (JELS) para analizar sistemáticamente las matemáticas involucradas en la captura de las principales característicasde los procesos relacionados. La literatura se examina y clasifica de acuerdo con los problemas específicos que se enfrentan y los supuestos del modelo. Se presta especial atención a las cuestiones ambientales. Hay indicios de la necesidad de que las matemáticas de los modelos de logística inversa sigan las tendencias actuales en los modelos de cadena de suministro y inventario "ecológicos". La modelización de la eliminación de residuos, las emisiones de gases de efecto invernadero y el consumo de energía durante la producción se considera la prioridad más urgente para el futuro de los modelos de logística inversa. Se presenta un ejemplo ilustrativo para modelar modelos de inventario de logística inversa con implicaciones ambientale

Diseño y desarrollo de un modelo y planificación óptima para la responsabilidad de la cadena de suministro con el medio ambiente

The responsible management of product return flows in production and inventory environments is a rapidly increasing requirement for companies. This can be attributed to economic, environmental and/or regulatory motivations. Mathematical modeling of such systems has assisted decision-making processes and provided a better understanding of the behavior of such production and inventory environments. This paper reviews the literature on the modeling of reverse logistics inventory systems based on the economic order/production quantity (EOQ/EPQ) and the joint economic lot size (JELS) settings to systematically analyze the mathematics involved in capturing the main characteristics of related processes. The literature is surveyed and classified according to the specific issues faced and modeling assumptions. Special attention is given to environmental issues. There are indications of the need for reverse logistics models' mathematics to follow current trends in ‘greening’ inventory and supply-chain models. The modeling of waste disposal, greenhouse-gas emissions, and energy consumption during production is considered as the most pressing priority for the future of reverse logistics models. An illustrative example for modeling reverse logistics inventory models with environmental implications is presented.La gestión responsable de los flujos de retorno de productos en entornos de producción e inventario es un requisito cada vez mayor para las empresas. Esto puede atribuirse a motivaciones económicas, ambientales y / o regulatorias. El modelado matemático de tales sistemas ha ayudado a los procesos de toma de decisiones y ha proporcionado una mejor comprensión del comportamiento de dichos entornos de producción e inventario. Este artículo revisa la literatura sobre el modelado de sistemas de inventario de logística inversa que se basan en la orden económica / cantidad de producción (EOQ / EPQ) y la configuración del tamaño de lote económico conjunto (JELS) para analizar sistemáticamente las matemáticas involucradas en la captura de las principales características de los procesos relacionados. La literatura se examina y clasifica de acuerdo con los problemas específicos que se enfrentan y los supuestos del modelo. Se presta especial atención a las cuestiones ambientales. Hay indicios de la necesidad de que las matemáticas de los modelos de logística inversa sigan las tendencias actuales en los modelos de cadena de suministro y inventario "ecológicos". La modelización de la eliminación de residuos, las emisiones de gases de efecto invernadero y el consumo de energía durante la producción se considera la prioridad más urgente para el futuro de los modelos de logística inversa. Se presenta un ejemplo ilustrativo para modelar modelos de inventario de logística inversa con implicaciones ambientales

Review Bidang Kajian Model Persediaan pada Reverse Logistics dan Sistem Rantai Pasok Siklus Tertutup

Penelitian terkait pengelolaan persediaan barang bekas pakai pada logistik terbalik atau reverse logistics (RL) mengalami perkembangan yang cukup signifikan beberapa tahun terakhir. Beberapa isu kemudian menjadi tantangan baru bagi para peneliti untuk terus mengembakan model persediaan pada bidang kajian terkait sehingga dapat menjawab kebutuhan dari industri yang riil. Pada penelitian ini, dilakukan pembahasan terkait perkembangan pada ranah penelitian pemodelan persediaan pada RL dan sistem rantai pasok siklus tertutup atau closed-loop supply chain (CLSC). Tujuan dari penelitian ini adalah memberikan gambaran terkait perkembangan model persediaan pada bidang RL dan CLSC, sehingga dapat diidentifikasi peluang penelitian baru dalam hal model persediaan lot sizing untuk dapat diimplementasikan secara praktis pada industri yang ril

Impact of Quality Grading and Uncertainty on Recovery Behaviour in a Remanufacturing Environment

This research considers a remanufacturing enterprise that constitutes a stage in a closed loop supply chain. Each returned item is precisely tested and assigned a quality grade between zero and a hundred. Consequently, acceptance to the facility, acquisition price and remanufacturing cost are all quality dependants. The research implements the newsvendor modeling techniques to model the system when a single remanufacturing facility satisfies a single market\u27s demand or when multiple remanufacturing facilities satisfy multiple markets\u27 demand. Thus, non-linear programming or mixed integer non- linear programming models are proposed to maximize the total profit by selecting facilities to operate, optimal minimum quality to accept into each operating facility and market\u27s demand to satisfy from each operating facility. Returns\u27 quality is considered to be stochastic, while markets\u27 demand could be either stochastic or deterministic. The impact of changing returns, quality and demand uncertainties, and transportation cost on remanufacturing systems are studied

Optimal Decision Making for Capacitated Reverse Logistics Networks with Quality Variations

Increasing concerns about the environmental impact of production, product take-back laws and dwindling natural resources have heightened the need to address the impact of disposing end-of-life (EOL) products. To cope this challenge, manufacturers have integrated reverse logistics into their supply chain or chosen to outsource product recovery activities to third party firms. The uncertain quality of returns as well as uncertainty in return flow limit the effectiveness of planning, control and monitoring of reverse logistics networks. In addition, there are different recovery routes for each returned product such as reuse, repair, disassembling, remanufacturing and recycling. To determine the most profitable option for EOL product management, remanufacturers must consider the quality of returns and other limitations such as inventory size, demand and quantity of returns. The work in this dissertation addresses these pertinent aspects using two models that have been motivated by two remanufacturing facilities whereby there are uncertainties in the quality and quantity of return and capacitated inventories. In the first case, a disposition decision making model is developed for a remanufacturing process in which the inventory capacity of recoverable returns is limited and where there\u27s a constant demand to be met, for remanufactured products that meet a minimum quality threshold. It is assumed that the quality of returns is uncertain and remanufacturing cost is dependent on the quality grade. In this model, remanufacturing takes place when there is demand for remanufactured products. Accepted returns that meet the minimum quality threshold undergo the remanufacturing processes, and any unacceptable returns are salvaged. A continuous time Markov chain (CTMC) is presented as the modeling approach. The Matrix-Geometric solution methodology is applied to evaluate several key performance metrics for this system, to result in the optimal disposition policy. The numerical study shows an intricate trade-off between the acceptable quality threshold value and the recoverable product inventory capacity. Particularly, there are periodic system starvation whenever there is a mis-match between these two system metrics. In addition, the sensitivity analysis indicates that changes to the demand rate for remanufactured products necessitates the need to re-evaluate the existing system configuration. In the second case, a general framework is presented for a third party remanufacturer, where the remanufacturer has the alternative of salvaging EOL products and supplying parts to external suppliers, or remanufacture the disassembled parts to \u27as new\u27 conditions. The remanufacturing processes of reusable products and parts is studied in the context of other process variables such as the cost and demand of remanufactured products and parts. The goal of this model is to determine the return quality thresholds for a multi-product, multi-period remanufacturing setting. The problem is formulated as a mixed integer non-linear programming (MINLP) problem, which involves a discretization technique that turns the problem turns into a quadratic mixed integer programming (QMIP) problem. Finally, a numerical analysis using a personal computer (PC) remanufacturing facility data is used to test the extent to which the minimum acceptance quality threshold is dependent on the inventory level capacities of the EOL product management sites, varying operational costs and the upper bound of disposal rate