(136a) Novel Approaches for the Integration of Supply Chain Planning and Scheduling

Supply chain decisions are hierarchically organized in strategic, tactical, and operational decisions. In practice, these planning processes are frequently conducted independently with minimal exchange of information between them, thereby leading to suboptimal solutions. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas[1]. Optimization methods for the integration of strategic and tactical decisions are well established[2]. Nevertheless, the integration of tactical and operational decisions has not been solved[3],[4]. There are at least three shortcomings that need to be addressed to create integrated planning and scheduling systems that can be used in real-world applications: 1) the models proposed in previous work are too simplistic, assuming a mid-term planning level setting inventory targets for the scheduling; 2) they aim to solve the scheduling model in the horizon of the planning model, and 3) there are no efficient algorithms capable of handling industrial-sized problems in reasonable amount of time.

In this work, we present an integrated formulation of short term multisite planning with batch scheduling using the UOPSS modeling framework[5]. The planning level considers an (s,S) inventory policy for all inventories in the system[6], and distinguishes between make-to-order and make-to-stock products[7]. The scheduling level receives planned orders, corresponding to a mix of inventory replenishment orders and customer orders, to determine the optimal production schedule. The scheduling objective includes minimization of priority-dependent backlog and changeovers. To address the difference in timescales we present an objective scaling approach. In the proposed scaling method the objective function of the model with the largest horizon (planning) is downscaled to make it comparable to the objective of the model of the shorter horizon (scheduling). The method is evaluated through simulation, and compared with traditional integration methods: full-space solution, and representative day approach. Results on industrially sized problems clearly indicate significantly improved solutions with the proposed approach. To address the computational complexity added by the integration of planning and scheduling, Benders decomposition with mixed-integer subproblems inspired by the stochastic programming literature[8],[9], and cross decomposition[10] methods, have also been developed to effectively address the integration problem.

References

[1] Brunaud, B. and Grossmann, I.E., 2017. Perspectives in multilevel decision-making in the process industry. Frontiers of Engineering Management, 4(3), pp.256-270.

[2] Barbosa-Póvoa, A.P., 2012. Progresses and challenges in process industry supply chains optimization. Current Opinion in Chemical Engineering, 1(4), pp.446-452.

[3] Maravelias, C.T. and Sung, C., 2009. Integration of production planning and scheduling: Overview, challenges and opportunities. Computers & Chemical Engineering, 33(12), pp.1919-1930.

[4] Garcia, D.J. and You, F., 2015. Supply chain design and optimization: Challenges and opportunities. Computers & Chemical Engineering, 81, pp.153-170.

[5] Zyngier, D. and Kelly, J.D., 2012, June. UOPSS: a new paradigm for modeling production planning & scheduling systems. In Symposium on Computer Aided Process Engineering (Vol. 17, p. 20).

[6] Brunaud, B., Lainez-Aguirre, J.M., Pinto, J.M. and Grossmann, I.E., 2017. Mixed-integer Models for Simultaneous Optimization of Inventory Policies and Supply Chain Planning. In Computer Aided Chemical Engineering (Vol. 40, pp. 1255-1260).

[7] Soman, C.A., van Donk, D.P. and Gaalman, G.J., 2007. Capacitated planning and scheduling for combined make-to-order and make-to-stock production in the food industry: An illustrative case study. International Journal of Production Economics, 108(1-2), pp.191-199.

[8] Zou, J., Ahmed, S. and Sun, X.A., 2017. Stochastic dual dynamic integer programming. Mathematical Programming, pp.1-42.

[9] Gade, D., Küçükyavuz, S. and Sen, S., 2014. Decomposition algorithms with parametric gomory cuts for two-stage stochastic integer programs. Mathematical Programming, 144(1-2), pp.39-64.

[10] Mitra, S., Garcia-Herreros, P. and Grossmann, I.E., 2014. A novel cross-decomposition multi-cut scheme for two-stage stochastic programming. In Computer Aided Chemical Engineering (Vol. 33, pp. 241-246).