PMID- 32767212
OWN - NLM
STAT- MEDLINE
DCOM- 20201125
LR  - 20210723
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 27
IP  - 35
DP  - 2020 Dec
TI  - Mathematical modeling for green supply chain considering product recovery 
      capacity and uncertainty for demand.
PG  - 44378-44395
LID - 10.1007/s11356-020-10331-z [doi]
AB  - Competition in today's market is the most important concern of companies and 
      producers in free markets. Buyers are also looking for higher quality and lower 
      prices. Manufacturers should, therefore, reduce production costs and increase 
      budgets for research and product development. On the other hand, the limitation 
      of mineral resources in each country and in the world in general is a very 
      important factor for increasing the price of raw materials which increases the 
      cost of production of a product. In this study, a green aspect of 
      decision-making, concurrent modeling for inventory-routing, and application of 
      maximum entropy (ME) method for overcoming uncertainties of demands are applied 
      to optimize the usage of raw materials and returning of defective products to the 
      production cycle in a closed-looped supply chain under multi-period planning 
      horizon. Also, dynamic modeling is used to balance the inventory level in all 
      stages of the network that leads to optimum usage of the raw materials. For this 
      purpose, the first objective function reduces production, transportation-routing, 
      and inventory costs, and the second objective reduces greenhouse gas emissions 
      through all levels of the network. Finally, this model is solved by using the 
      exact solution method with the help of Gams software as well as the non-dominated 
      sorting genetic algorithm II (NSGAII) and multi-objective particle swarm 
      optimization (MOPSO) algorithm. Sensitivity analysis has been performed on 
      failure rates, greenhouse gas emissions during recycling and production, and the 
      optimistic-pessimistic coefficient of the ME solution method. Solution methods 
      have been compared using several criteria, and the NSGAII method has finally 
      obtained the best result. The results show that the manager should pay more costs 
      in order to prevent backorder demands. Also, collecting the more defective 
      products leads to increasing production amount since the collective products can 
      return to the production line. Finally, it is required for the managers to 
      control products' failure rate to optimize capacity usage in the model.
FAU - Mehrbakhsh, Sanaz
AU  - Mehrbakhsh S
AD  - School of Industrial Engineering, Islamic Azad University, South Tehran Branch, 
      Tehran, Iran.
FAU - Ghezavati, Vahidreza
AU  - Ghezavati V
AD  - School of Industrial Engineering, Islamic Azad University, South Tehran Branch, 
      Tehran, Iran. v_ghezavati@azad.ac.ir.
LA  - eng
PT  - Journal Article
DEP - 20200807
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - 0 (Greenhouse Gases)
SB  - IM
MH  - Algorithms
MH  - *Greenhouse Gases
MH  - *Models, Theoretical
MH  - Transportation
MH  - Uncertainty
OTO - NOTNLM
OT  - Closed-loop supply chain
OT  - Green supply chain
OT  - Greenhouse gas emissions
OT  - Maximum entropy method
OT  - Meta-heuristics
OT  - Optimistic-pessimistic approach
EDAT- 2020/08/09 06:00
MHDA- 2020/11/26 06:00
CRDT- 2020/08/09 06:00
PHST- 2020/02/27 00:00 [received]
PHST- 2020/07/30 00:00 [accepted]
PHST- 2020/08/09 06:00 [pubmed]
PHST- 2020/11/26 06:00 [medline]
PHST- 2020/08/09 06:00 [entrez]
AID - 10.1007/s11356-020-10331-z [pii]
AID - 10.1007/s11356-020-10331-z [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2020 Dec;27(35):44378-44395. doi: 
      10.1007/s11356-020-10331-z. Epub 2020 Aug 7.