Moradi, N., Shadrokh, S. (2019). Simultaneous solution of material procurement scheduling and material allocation to warehouse using simulated annealing. Journal of Applied Research on Industrial Engineering, 6(1), 1-15. doi: 10.22105/jarie.2019.170649.1079
Nima Moradi; Shahram Shadrokh. "Simultaneous solution of material procurement scheduling and material allocation to warehouse using simulated annealing". Journal of Applied Research on Industrial Engineering, 6, 1, 2019, 1-15. doi: 10.22105/jarie.2019.170649.1079
Moradi, N., Shadrokh, S. (2019). 'Simultaneous solution of material procurement scheduling and material allocation to warehouse using simulated annealing', Journal of Applied Research on Industrial Engineering, 6(1), pp. 1-15. doi: 10.22105/jarie.2019.170649.1079
Moradi, N., Shadrokh, S. Simultaneous solution of material procurement scheduling and material allocation to warehouse using simulated annealing. Journal of Applied Research on Industrial Engineering, 2019; 6(1): 1-15. doi: 10.22105/jarie.2019.170649.1079
Simultaneous solution of material procurement scheduling and material allocation to warehouse using simulated annealing
1Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
2Department of Industrial Engineering, Sharif University of Technology, Tehran ,Iran.
Receive Date: 03 December 2018,
Revise Date: 01 March 2019,
Accept Date: 12 March 2019
Abstract
One of the classes of the project schedule is the Material Procurement Scheduling (MPS) problem, which is considered besides the material allocation to warehouse (MAW) problem recently. In the literature, the Simultaneous Solution of MPS-MAW is investigated by considering one warehouse and unlimited capacity of the warehouses most of the times. In this paper, we propose the propositional and mathematical model of the simultaneous MPS-MAW with multiple warehouses and the limited capacity at the whole of the horizon planning for each warehouse. The proposed model aims to obtain the best ordering point, selection of the best suppliers, the best activity start, and the fair material distribution to the warehouses as possible by the given objective function. The proposed model is NP-hard, so a metaheuristic namely simulated annealing is proposed to reach the acceptable but not optimal solutions in a short time. Also, to overcome the complexity of the model, the encoding of the decision variables have been done by adding the auxiliary variable. Comparing the solutions of the small problems with the exact methods shows the validation of the proposed SA. Also, the design of experiments shows the significance of the model and each SA parameters. Finally, by the optimum values of the SA parameters, the large problems have been solved at acceptable times.
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