Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm

S Safaei, P Ghasemi, F Goodarzian… - … Science and Pollution …, 2022 - Springer
Demand plays a vital role in designing every closed-loop supply chain network in today's
world. The flow of materials and commodities in the opposite direction of the standard supply …

[PDF][PDF] Designing a New Multi-Echelon Multi-Period Closed-Loop Supply Chain Network by Forecasting Demand Using Time Series Model: A Genetic Algorithm

S Safaei, P Ghasemi, F Goodarzian, M Momenitabar - scholar.archive.org
In the closed-loop supply chain, demand plays a critical role. The flow of materials and 17
commodities in the opposite direction of the normal chain is inevitable too. So, in this paper …

Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm

S Safaei, P Ghasemi, F Goodarzian… - Environmental …, 2022 - search.proquest.com
Demand plays a vital role in designing every closed-loop supply chain network in today's
world. The flow of materials and commodities in the opposite direction of the standard supply …

[引用][C] Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm

S Safaei, P Ghasemi, F Goodarzian… - Environmental …, 2022 - ui.adsabs.harvard.edu
Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting
demand using time series model: a genetic algorithm - NASA/ADS Now on home page ads …

[PDF][PDF] Designing a new multi‑echelon multi‑period closed‑loop supply chain network by forecasting demand using time series model: a genetic algorithm

S Safaei, P Ghasemi, F Goodarzian, M Momenitabar - researchgate.net
Demand plays a vital role in designing every closed-loop supply chain network in today's
world. The flow of materials and commodities in the opposite direction of the standard supply …

Designing a New Multi-Echelon Multi-Period Closed-Loop Supply Chain Network by Forecasting Demand Using Time Series Model: A Genetic Algorithm

S Safaei, P Ghasemi, F Goodarzian, M Momenitabar - 2022 - researchsquare.com
In the closed-loop supply chain, demand plays a critical role. The flow of materials and
commodities in the opposite direction of the normal chain is inevitable too. So, in this paper …

Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm.

S Safaei, P Ghasemi, F Goodarzian… - … Science and Pollution …, 2022 - europepmc.org
Demand plays a vital role in designing every closed-loop supply chain network in today's
world. The flow of materials and commodities in the opposite direction of the standard supply …

Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm.

S Safaei, P Ghasemi, F Goodarzian… - … Science & Pollution …, 2022 - search.ebscohost.com
Demand plays a vital role in designing every closed-loop supply chain network in today's
world. The flow of materials and commodities in the opposite direction of the standard supply …

Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm

S Safaei, P Ghasemi, F Goodarzian… - Environmental …, 2022 - pubmed.ncbi.nlm.nih.gov
Demand plays a vital role in designing every closed-loop supply chain network in today's
world. The flow of materials and commodities in the opposite direction of the standard supply …

Designing a New Multi-Echelon Multi-Period Closed-Loop Supply Chain Network by Forecasting Demand Using Time Series Model: A Genetic Algorithm

S Safaei, P Ghasemi, F Goodarzian, M Momenitabar - 2022 - europepmc.org
In the closed-loop supply chain, demand plays a critical role. The flow of materials and
commodities in the opposite direction of the normal chain is inevitable too. So, in this paper …