Planning and scheduling of green production and distribution considering several products in the chemical industry
Subject Areas :
Asghar Arabi
1
,
Hojat Nabovati
2
1 - Islamic Azad university Saveh Branch, Saveh,Iran
2 - Faculty member of Islamic Azad university Saveh branch
Keywords: Production and Green Distribution, Scheduling, MOSEO Algorithm,
Abstract :
In this research, a model has been proposed to optimize the transportation route. By considering different customers, different demands, considering different distribution points, considering different products and, different production process time of products, the current model has been able to examine the topic of vehicle routing from different aspects. These items were not available in previous studies, and other models have only generally increased profits. After solving the model by two methods, the results have shown that the planning done for vehicle routing has made a significant contribution to reducing operating costs, the route traveled, and at the same time reducing the consumption of fossil fuels. that the delivery of the consignment to the customers was within the time limit requested by the customers and exactly according to the time limit, and the products were delivered to the customers before the end of the expected deadline. Therefore, customer demand has been delivered to customers in the shortest possible way, at the lowest cost, with the lowest emissions and without shortages. Also, the results of the exact and meta-heuristic solution algorithms have shown the high adaptation of the modeling and the limitations of the model; In such a way that all the limitations of the proposed system have become feasible and the problem has been able to achieve an optimal answer in the desired period of time.
Abdi, A., Abdi, A., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2019). A set of calibrated metaheuristics to address a closed-loop supply chain network design problem under uncertainty. International Journal of Systems Science: Operations & Logistics, 1-18.
Afshari, H., Sharafi, M., ElMekkawy, T., & Peng, Q. (2014). Optimizing multi-objective dynamic facility location decisions within green distribution network design. Procedia Cirp, 17, 675-679.
Amin, S. H., & Baki, F. (2017). A facility location model for global closed-loop supply chain network design. Applied Mathematical Modelling, 41, 316-330.
Bowling, I. M., Ponce-Ortega, J. M., & El-Halwagi, M. M. (2011). Facility location and supply chain optimization for a biorefinery. Industrial & Engineering Chemistry Research, 50(10), 6276-6286.
Chen, C., Yao, B., Chen, G., & Tian, Z. (2021). A queuing–location–allocation model for designing a capacitated bus garage system. Engineering Optimization, 1-18.
Etemadnia, H., Goetz, S. J., Canning, P., & Tavallali, M. S. (2015). Optimal wholesale facilities location within the fruit and vegetables supply chain with bimodal transportation options: An LP-MIP heuristic approach. European journal of operational research, 244(2), 648-661.
Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Tavakkoli-Moghaddam, R. (2018). The social engineering optimizer (SEO). Engineering Applications of Artificial Intelligence, 72, 267-293.
Fathollahi-Fard, A. M., Ranjbar-Bourani, M., Cheikhrouhou, N., & Hajiaghaei-Keshteli, M. (2019). Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system. Computers & Industrial Engineering, 137, 106103.
Ghasemi, P., & Khalili-Damghani, K. (2021). A robust simulation-optimization approach for pre-disaster multi-period location–allocation–inventory planning. Mathematics and computers in simulation, 179, 69-95.
Golshahi-Roudbaneh, A., Hajiaghaei-Keshteli, M., & Paydar, M. M. (2017). Developing a lower bound and strong heuristics for a truck scheduling problem in a cross-docking center. Knowledge-Based Systems, 129, 17-38.
Granger, S. (2002). Social engineering fundamentals, part II: Combat strategies. Security Focus, 9.
Gulzari, A., & Tarakci, H. (2021). A healthcare location-allocation model with an application of telemedicine for an earthquake response phase. International Journal of Disaster Risk Reduction, 55, 102100.
Harrison, T. P. (2005). The practice of supply chain management: where theory and application converge: Springer Science & Business Media.
Kaya, O., & Urek, B. (2016). A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers & Operations Research, 65, 93-103.
Khorasani, S. T., & Almasifard, M. (2018). The development of a green supply chain dual-objective facility by considering different levels of uncertainty. Journal of Industrial Engineering International, 14(3), 593-602.
Li, F., Liu, T., Zhang, H., Cao, R., Ding, W., & Fasano, J. P. (2008). Distribution center location for green supply chain. Paper presented at the 2008 IEEE International Conference on Service Operations and Logistics, and Informatics.
Lin, Y., Jia, H., Yang, Y., Tian, G., Tao, F., & Ling, L. (2018). An improved artificial bee colony for facility location allocation problem of end-of-life vehicles recovery network. Journal of Cleaner Production, 205, 134-144.
Martínez, J. C. V., & Fransoo, J. C. (2017). Green facility location. In Sustainable Supply Chains (pp. 219-234): Springer.
Mousavi, S. M., Bahreininejad, A., Musa, S. N., & Yusof, F. (2017). A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. Journal of intelligent manufacturing, 28(1), 191-206.
Saadati, M., & Hosseininezhad, S. J. (2019). Designing a hub location model in a bagasse-based bioethanol supply chain network in Iran (case study: Iran sugar industry). Biomass and Bioenergy, 122, 238-256.
Sadeghi-Moghaddam, S., Hajiaghaei-Keshteli, M., & Mahmoodjanloo, M. (2017). New approaches in metaheuristics to solve the fixed charge transportation problem in a fuzzy environment. Neural Computing and Applications, 1-21.
Sarker, B. R., Wu, B., & Paudel, K. P. (2019). Modeling and optimization of a supply chain of renewable biomass and biogas: Processing plant location. Applied Energy, 239, 343-355.
Srinivasan, S., & Khan, S. H. (2018). Multi-stage manufacturing/re-manufacturing facility location and allocation model under uncertain demand and return. The International Journal of Advanced Manufacturing Technology, 94(5-8), 2847-2860.
Strong, D., Kay, M., Conner, B., Wakefield, T., & Manogharan, G. (2019). Hybrid manufacturing—Locating AM hubs using a two-stage facility location approach. Additive Manufacturing, 25, 469-476.
Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes. Retrieved from
TALAEI, M., FARHANG, M. B., PISHVAEE, M., & BOZORGI, A. A. (2015). A BI-OBJECTIVE FACILITY LOCATION MODEL FOR A GREEN CLOSED-LOOP SUPPLY CHAIN NETWORK DESIGN.
Yadegari, E., Alem-Tabriz, A., & Zandieh, M. (2019). A memetic algorithm with a novel neighborhood search and modified solution representation for closed-loop supply chain network design. Computers & Industrial Engineering, 128, 418-436.
Yousefi, K., J. Afshari, A., & Hajiaghaei-Keshteli, M. (2018). Heuristic approaches to solve the fixed-charge transportation problem with discount supposition. Journal of Industrial and Production Engineering, 35(7), 444-470.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182-214.
Zhao, X., Xia, X., Wang, L., & Yu, G. (2018). Risk-averse facility location for green closed-loop supply chain networks design under uncertainty. Sustainability, 10(11), 4072.
