Capacitated Remanufacturing Inventory Model Considering Backorder: A Case Study of Indonesian Reverse Logistics

Masudin, Ilyas; Jannah, Fathihah Raudhattul; Utama, Dana Marsetiya; Restuputri, Dian Palupi

doi:10.1109/access.2019.2944135

Cited by 11 publications

(5 citation statements)

References 43 publications

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“…For example, both the sales margin and the warehousing and inventory cost can be calculated in an item-dependent way (i.e., for each item separately) to improve its accuracy. Furthermore, the compensations of customers for their pending orders can be incorporated [67]. Finally, it might be promising to evaluate the performance of genetic algorithm-derived classifiers using other advanced optimization models such as salp swarm algorithm or grey wolf optimizer.…”

Section: Discussionmentioning

confidence: 99%

A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics

Hájek

Abedin

2020

IEEE Access

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Inventory backorder prediction is widely recognized as an important component of inventory models. However, backorder prediction is traditionally based on stochastic approximation, thus neglecting the substantial amount of useful information hidden in historical inventory data. To provide those inventory models with a big data-driven backorder prediction, we propose a machine learning model equipped with an undersampling procedure to maximize the expected profit of backorder decisions. This is achieved by integrating the proposed profit-based measure into the prediction model and optimizing the decision threshold to identify the optimal backorder strategy. We show that the proposed inventory backorder prediction model shows better prediction and profit function performance than the state-of-the-art machine learning methods used for large imbalanced data. Notably, the proposed model is computationally effective and robust to variation in both warehousing/inventory cost and sales margin. In addition, the model predicts both major (non-backorder items) and minor (backorder items) classes in a benchmark dataset. INDEX TERMS Big data, inventory backorder, machine learning, prediction.

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Section: Discussionmentioning

confidence: 99%

A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics

Hájek

Abedin

2020

IEEE Access

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“…In addition, Sarkar et al [18] show that the carbon emission parameter affects the total cost, so that based on the sensitivity results, this parameter is in an equilibrium position. Another parameter in the study of Sarkar et al [18] and Masudin et al [74], namely the production level, shows that the production level parameter's sensitivity increases gradually with the total cost. Saga et al (2019) found a sensitivity test on energy loss related to carbon emissions released to increase the total cost.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Production-Distribution Model Considering Traceability and Carbon Emission: A Case Study of the Indonesian Canned Fish Food Industry

et al. 2021

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Background: Traceability systems and carbon emissions are two important factors involved in production and distribution activities. The involvement of these two factors in production and distribution activities along the supply chain will ensure the safety and quality of food through the manufacture, packaging and distribution of products with minimal costs and in an environmentally friendly way. Objective: This study aimed to develop a model of canned fish food production and distribution integration by considering traceability and carbon emissions to minimize total costs. Method: A mixed-integer linear programming (MILP) approach was used to develop mathematical models and the optimal solution of the model created was obtained using an open-source spreadsheet solver program. Results: The results show that the proposed models produce the minimum total production and distribution cost with high traceability and low carbon emissions. Conclusions: The sensitivity analysis from this study shows that there is a significant relationship between production, carbon emissions, and the total cost of production-distribution. Moreover, it was concluded that the production level, carbon emission level, and emission threshold can have a significant influence in the generation of the total carbon emissions.

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“…They consider remanufacturing and recycling processes in a decentralized closed-loop supply chain system. Other studies developed an integrative production and distribution in closed-loop supply chain networks considering reverse logistics such as reuse (Ech-Charrat et al ., 2017), redistribution (Zahedi et al ., 2021) and recycling (Masudin et al ., 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Model of multiperiod production-distribution for closed-loop supply chain considering carbon emission and traceability for agri-food products

Yuniarti

Masudin

Rusdiansyah

et al. 2023

IJIEOM

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PurposeThis study aimed to develop the integration of the multiperiod production-distribution model in a closed-loop supply chain involving carbon emission and traceability. The developed model was for agricultural food (agri-food) products, considering the reverse flow of food waste from the disposal center (composting center) to producers.FindingsThe results indicate that integrating the production and distribution model considering food waste recycling provides low carbon emissions in lower total costs. The sensitivity analysis also found that there are trade-offs between production and distribution rate and food waste levels on carbon emission and traceability.Research limitations/implicationsThis study focuses on the mathematical modeling of a multiperiod production-distribution formulation for a closed-loop supply chain.Originality/valueThe model of the agri-food closed-loop supply chain in this study that considers food recycling and carbon emissions would help stakeholders involved in the agri-food supply chain to reduce food waste and carbon emissions.

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Capacitated Remanufacturing Inventory Model Considering Backorder: A Case Study of Indonesian Reverse Logistics

Cited by 11 publications

References 43 publications

A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics

A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics

Production-Distribution Model Considering Traceability and Carbon Emission: A Case Study of the Indonesian Canned Fish Food Industry

Model of multiperiod production-distribution for closed-loop supply chain considering carbon emission and traceability for agri-food products

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