Simultaneous solution of material procurement scheduling and material allocation to warehouse using simulated annealing

Document Type: Research Paper

Authors

1 Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.

2 Department of Industrial Engineering, Sharif University of Technology, Tehran ,Iran.

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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[1]   Caron, F., Marchet, G., & Perego, A. (1998). Project logistics: integrating the procurement and construction processes. International journal of project management16(5), 311-319.

[2]   Chen, S. M., Chen, P. H., & Chang, L. M. (2012). Simulation and analytical techniques for construction resource planning and scheduling. Automation in construction21, 99-113.

[3]   Dixit, V., Srivastava, R. K., & Chaudhuri, A. (2014). Procurement scheduling for complex projects with fuzzy activity durations and lead times. Computers & industrial engineering76, 401-414.

[4]   Tabrizi, B. H., & Ghaderi, S. F. (2016). Simultaneous planning of the project scheduling and material procurement problem under the presence of multiple suppliers. Engineering optimization48(9), 1474-1490.

[5]   Tabrizi, B. H., & Ghaderi, S. F. (2016). A robust bi-objective model for concurrent planning of project scheduling and material procurement. Computers & industrial engineering98, 11-29.

[6]   Zoraghi, N., Najafi, A. A., & Akhavan Niaki, S. T. (2012). An integrated model of project scheduling and material ordering: a hybrid simulated annealing and genetic algorithm. Journal of optimization in industrial engineering5(10), 19-27.

[7]   Zoraghi, N., Shahsavar, A., & Niaki, S. T. A. (2017). A hybrid project scheduling and material ordering problem: Modeling and solution algorithms. Applied soft computing58, 700-713.

[8]   Tabrizi, B. H. (2018). Integrated planning of project scheduling and material procurement considering the environmental impacts. Computers & industrial engineering120, 103-115.

[9]   Habibi, F., Barzinpour, F., & Sadjadi, S. J. (2019). A mathematical model for project scheduling and material ordering problem with sustainability considerations: A case study in Iran. Computers & industrial engineering128, 690-710.

[10]Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science220(4598), 671-680.

[11]Hwang, C. R. (1988). Simulated annealing: theory and applications. Acta applicandae mathematicae12(1), 108-111.

[12]Ingber, L. (1993). Simulated annealing: Practice versus theory. Mathematical and computer modelling18(11), 29-57.

[13]Szu, H., & Hartley, R. (1987). Fast simulated annealing. Physics letters A122(3-4), 157-162.

[14]Khosravi, P., Alinaghian, M., Sajadi, S. M., & Babaee, E. (2015). The periodic capacitated arc routing problem with mobile disposal sites specified for waste collection. Journal of applied research on industrial engineering, 2, 64-76.

[15] Jafari, H., & Hajikhani, A. (2016). Multi objective decision making for impregnability of needle mat using design of experiment technique and respond surface methodology. Journal of applied research on industrial engineering3(1 (4)), 30-38.