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A robust optimization model for agile and build-to-order supply chain planning under uncertainties

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Abstract

Supply chain planning as one of the most important processes within the supply chain management concept, has a great impact on firms’ success or failure. This paper considers a supply chain planning problem of an agile manufacturing company operating in a build-to-order environment under various kinds of uncertainty. An integrated optimization approach of procurement, production and distribution costs associated with the supply chain members has been taken into account. A robust optimization scenario-based approach is used to absorb the influence of uncertain parameters and variables. The formulation is a robust optimization model with the objective of minimizing the expected total supply chain cost while maintaining customer service level. The developed multi-product, multi-period, multi-echelon robust mixed-integer linear programming model is then solved using the CPLEX optimization studio and guidance related to future areas of research is given.

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Author information

Authors and Affiliations

  1. Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia

    Morteza Lalmazloumian & Kuan Yew Wong

  2. Department of Business and Economics, University of Southern Denmark, Odense, Denmark

    Kannan Govindan

  3. Department of Mechanical & Manufacturing Engineering, Aalborg University, 2450, Copenhagen, Denmark

    Devika Kannan

Authors
  1. Morteza Lalmazloumian
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  2. Kuan Yew Wong
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  3. Kannan Govindan
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  4. Devika Kannan
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Corresponding author

Correspondence to Kannan Govindan.

Appendix

Appendix

The demand data under different scenarios in each time period are shown in Tables 18, 19, 20 and 21. Parameters such as raw material supply cost, component fabrication cost and product assembly cost in both regular time and overtime, transportation cost, inventory holding cost, backorder cost, fabrication capacity and production capacity in both regular time and overtime, fabrication and assembly process time, procurement lead time, maximum supply and inventory capacity, minimum allowable demand fulfillment rate, etc are also shown in Tables 22–28. It should be mentioned that the data related to demand, and regular time and overtime capacities are shown for every planning period while the other data are assumed to be similar throughout the entire planning horizon.

Table 18 Market demand for customer zone 1 under each scenario/\(D_{pct}^{s}\)
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Table 19 Market demand for customer zone 2 under each scenario/\(D_{pct}^{s}\)
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Table 20 Market demand for customer zone 3 under each scenario/\(D_{pct}^{s}\)
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Table 21 Market demand for customer zone 4 under each scenario/\(D_{pct}^{s}\)
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Table 22 Final product related data
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Table 23 Final product transportation cost and inventory capacity
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Table 24 Raw material related data
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Table 25 Component related data
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Table 26 Raw material transportation cost
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Table 27 Raw material usage, component usage and production process time
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Table 28 Available regular time, overtime, procurement lead time and demand fulfillment
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Lalmazloumian, M., Wong, K.Y., Govindan, K. et al. A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Ann Oper Res 240, 435–470 (2016). https://doi.org/10.1007/s10479-013-1421-5

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