Development of Integrated Multi-objective Green Supply Chain Scheduling Model: Production, Distribution and Heterogeneous Vehicle Routing with Customer Time Windows

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 New Business Department, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran

Abstract

Objective: In this study, different ways of delivering goods to customers are created and therefore the vehicle routing decisions are added to this issue. In this case, customers must be divided into clusters and each customer assigned to one means of transportation in order to minimize the cost of orders between customers. In this study, the problem of integrated supply chain scheduling is determined by timely delivery of orders, scheduling orders on a machine in a manufacturing system and batch shipment, allocation to multiple heterogeneous transport modes according to capacity, and finally order delivery. To customers in the time window, it aims to minimize the total cost of distributing orders and the constant and variable costs of fuel and carbon emissions of the vehicle and the total time delay of customer orders.
Methods: The problem programming model is a mathematical model of complex nonlinear integer and has been used for solving multi-objective meta-algorithms MOPSO and NSGA-II.
Results: The results show that NSGA-II algorithm performs well.
Conclusion: This research reduces the costs of production, distribution, inventory maintenance and fuel consumption. It can also help reduce product inventory and maintenance costs.

Keywords

References

Ahmadizar, F., & Farhadi, S. (2015). Single-machine batch delivery scheduling with job release dates, due windows and earliness, tardiness, holding and delivery costs. Computers & Operations Research, 53, 194-205.
Ai, T. J., & Kachitvichyanukul, V. (2009). A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Computers & Operations Research, 36(5), 1693-1702.
Asgharizadeh, E., Jafarnejad, A., Zandieh, M., & Joybar, S. (2017). Explaining the model of traffic modeling in vehicle routing problems based on the green transportation paradigm (Case study: Zamzam Company). Journal of Industrial Management, 9 (2), 217-244.
 (in Persian)
Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). IEEE.
Bashiri, M., Jalili, M. (2014). Genetic algorithm in single-objective and multi-objective space (concepts and tools). Tehran: Shahed University Press. (in Persian)
Chen, Z. L. (1996). Scheduling and common due date assignment with earliness-tardiness penalties and batch delivery costs. European Journal of Operational Research, 93(1), 49-60.
Chen, Z. L., & Vairaktarakis, G. L. (2005). Integrated scheduling of production and distribution operations. Management Science, 51(4), 614-628.
Cheng, T. C. E., & Kahlbacher, H. G. (1993). Scheduling with delivery and earliness penalties. Asia-Pacific Journal of Operational Research, 10(2), 145-152.
Cheng, T. E., Gordon, V. S., & Kovalyov, M. Y. (1996). Single machine scheduling with batch deliveries. European Journal of Operational Research, 94(2), 277-283.
Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (Vol. 5, pp. 79-104). New York: Springer.
Deb, K., & Pratap, A. (2002). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization, NSGA-II.
Demir, E., Bektaş, T., & Laporte, G. (2012). An adaptive large neighborhood search heuristic for the pollution-routing problem. European Journal of Operational Research, 223(2), 346-359.
Dondo, R., & Cerdá, J. (2015). The heterogeneous vehicle routing and truck scheduling problem in a multi-door cross-dock system. Computers & Chemical Engineering, 76, 42-62.
Dong, J., Zhang, A., Chen, Y., & Yang, Q. (2013). Approximation algorithms for two-machine open shop scheduling with batch and delivery coordination. Theoretical Computer Science, 491, 94-102
Eberhart, R., & Kennedy, J. (1995, November). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942-1948).
Fahimnia, B., Farahani, R. Z., Marian, R., & Luong, L. (2013). A review and critique on integrated production–distribution planning models and techniques. Journal of Manufacturing Systems, 32(1), 1-19.
Feng, X., & Zheng, F. (2013, December). Integrated Job Scheduling with Parallel-Batch Processing and Batch Deliveries. In International Conference on Combinatorial Optimization and Applications (pp. 72-83). Springer, Cham.
Geismar, H. N., Laporte, G., Lei, L., & Sriskandarajah, C. (2008). The integrated production and transportation scheduling problem for a product with a short lifespan. INFORMS Journal on Computing, 20(1), 21-33.
Gharaei, A., & Jolai, F. (2018). A multi-agent approach to the integrated production scheduling and distribution problem in multi-factory supply chain. Applied Soft Computing, 65, 577-589.
Goksal, F. P., Karaoglan, I., & Altiparmak, F. (2013). A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery. Computers & Industrial Engineering, 65(1), 39-53.
Golden, B., Assad, A., Levy, L., & Gheysens, F. (1984). The fleet size and mix vehicle routing problem. Computers & Operations Research, 11(1), 49-66.
Gu, J., Gu, M., & Gu, X. (2015). A mutualism quantum genetic algorithm to optimize the flow shop scheduling with pickup and delivery considerations. Mathematical Problems in Engineering, 2015. https://doi.org/10.1155/2015/387082.
Hajian, S., Afshar Kazemi, M. A., Seyed Hosseini, S. M., Toloui Ashlaghi, A. (2019). Presenting a multi-objective model for the location-routing-inventory problem in the multi-cycle green supply chain network of multi-period and multi-product for perishable goods. Journal of Industrial Management, 11 (1), 83-110. (in Persian)
Hall, N. G., & Potts, C. N. (2003). Supply chain scheduling: Batching and delivery. Operations Research, 51(4), 566-584
Hamidinia, A., Khakabimamaghani, S., Mazdeh, M. M., & Jafari, M. (2012). A genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system. Computers & Industrial Engineering, 62(1), 29-38.
Herrmann, J. W., & Lee, C. Y. (1993). On scheduling to minimize earliness-tardiness and batch delivery costs with a common due date. European Journal of Operational Research, 70(3), 272-288.
Ji, M., He, Y., & Cheng, T. E. (2007). Batch delivery scheduling with batch delivery cost on a single machine. European journal of operational research, 176(2), 745-755.
Kalyanmoy, D. (2001). Multi objective optimization using evolutionary algorithms (pp. 124-124). John Wiley and Sons.
 Karimi, N., & Davoudpour, H. (2017). Integrated production and delivery scheduling for multi-factory supply chain with stage-dependent inventory holding cost. Computational and Applied Mathematics, 36(4), 1529-1544.
 Kim, E. S., & Oron, D. (2013). Coordinating multi-location production and customer delivery. Optimization Letters, 7(1), 39-50.
 Kirkpatrick, S., Gelatt, C. D., & Vecchi, M.P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.
Kumar, R. S., Kondapaneni, K., Dixit, V., Goswami, A., Thakur, L. S., & Tiwari, M. K. (2016). Multi-objective modeling of production and pollution routing problem with time window: A self-learning particle swarm optimization approach. Computers & Industrial Engineering, 99, 29-40.
Lee, C. Y., & Chen, Z. L. (2001). Machine scheduling with transportation considerations. Journal of scheduling, 4(1), 3-24.
Li, C. L., & Vairaktarakis, G. (2007). Coordinating production and distribution of jobs with bundling operations. IIE transactions, 39(2), 203-215.
Li, G., Lu, X., & Liu, P. (2016). The coordination of single-machine scheduling with availability constraints and delivery. Journal of Industrial & Management Optimization, 12(2), 757-770.
Li, J., Wang, D., & Zhang, J. (2018). Heterogeneous fixed fleet vehicle routing problem based on fuel and carbon emissions. Journal of Cleaner Production, 201, 896-908
Mavrotas, G., & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652-9669.
Mazdeh, M. M., & Rostami, M. (2014). A branch-and-bound algorithm for two-machine flow-shop scheduling problems with batch delivery costs. International Journal of Systems Science: Operations & Logistics, 1(2), 94-104.
Mazdeh, M. M., Zaerpour, F., Zareei, A., & Hajinezhad, A. (2010). Parallel machines scheduling to minimize job tardiness and machine deteriorating cost with deteriorating jobs. Applied Mathematical Modelling, 34(6), 1498-1510
Mazdeh, M., Heydari, M., & Karamouzian, A. (2016). An integrated model of scheduling, batch delivery and supplier selection in a make-to-order manufacturing system. Decision Science Letters, 5(2), 189-200.
Moons, S., Ramaekers, K., Caris, A., & Arda, Y. (2017). Integrating production scheduling and vehicle routing decisions at the operational decision level: a review and discussion. Computers & Industrial Engineering, 104, 224-245.
Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390.
Noroozi, A., Mazdeh, M. M., Heydari, M., & Rasti-Barzoki, M. (2018). Coordinating order acceptance and integrated production-distribution scheduling with batch delivery considering Third Party Logistics distribution. Journal of manufacturing systems, 46, 29-45.
Pundoor, G., & Chen, Z. L. (2005). Scheduling a production–distribution system to optimize the tradeoff between delivery tardiness and distribution cost. Naval Research Logistics (NRL), 52(6), 571-589.
Rahimi, S., Abdollahpouri, A., & Moradi, P. (2018). A multi-objective particle swarm optimization algorithm for community detection in complex networks. Swarm and Evolutionary Computation, 39, 297-309.
Rasti-Barzoki, M., & Hejazi, S. R. (2015). Pseudo-polynomial dynamic programming for an integrated due date assignment, resource allocation, production, and distribution scheduling model in supply chain scheduling. Applied Mathematical Modelling, 39(12), 3280-3289.
Sadeghi Moghadam, M.R., Momeni, M., Nalchiger, S. (2010). Integrated Supply Chain Planning, Production and Distribution Using Genetic Algorithm. Journal of Industrial Management, 1 (2), 71-88. (in Persian)
Schott, J. R. (1995). Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization (No. AFIT/CI/CIA-95-039). Air Force Inst of Tech Wright-Patterson Afb Oh.
Seifi, M., & Tavakkoli-Moghaddam, R. (2008). A new bi-objective model for a multi-mode resource-constrained project scheduling problem with discounted cash flows and four payment models. International Journal of Engineering, 21(4), 347-360.
Shahbandarzadeh, H., Najmi, M.H., Ataiee, A. (2017). Provide a mathematical model based on the problem of routing a capacity vehicle with time windows for waste collection. Journal of Industrial Management, 9 (1), 147-166. (in Persian)
Solomon, M. M. (1986). On the worst-case performance of some heuristics for the vehicle routing and scheduling problem with time window constraints. Networks, 16(2), 161-174.
Sterna, M. (2011). A survey of scheduling problems with late work criteria. Omega, 39(2), 120-129.
Tan, K. C., Lee, T. H., & Khor, E. F. (2002). Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons. Artificial intelligence review, 17(4), 251-290.
Toth, P., & Vigo, D. (Eds.). (2002). The vehicle routing problem. Society for Industrial and Applied Mathematics.
Wang, G., & Cheng, T. E. (2000). Parallel machine scheduling with batch delivery costs. International Journal of Production Economics, 68(2), 177-183.
Wang, H., & Lee, C. Y. (2005). Production and transport logistics scheduling with two transport mode choices. Naval Research Logistics (NRL), 52(8), 796-809
Xiang, W., & Lee, H. P. (2008, June). Ant colony intelligence in agent coordination for dynamic manufacturing scheduling. In 2008 7th World Congress on Intelligent Control and Automation (pp. 3527-3532). IEEE.
 Yin, Y., Cheng, T. C. E., Cheng, S. R., & Wu, C. C. (2013). Single-machine batch delivery scheduling with an assignable common due date and controllable processing times. Computers & Industrial Engineering, 65(4), 652-662
Zarei, H., & Rasti-Barzoki, M. (2018). Mathematical programming and three metaheuristic algorithms for a bi-objective supply chain scheduling problem. Neural Computing and Applications, 1-21.
Zhong, X. and Jiang, D. (2015). Parallel machine scheduling with batch delivery to two buyers. Mathematical Problems in Engineering.
Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary computation, 8(2), 173-195.
Industrial Management Journal
Volume 12, Issue 1
2020
Pages 47-81

Files

Share

How to cite

Statistics