TY - JOUR
T1 - Multiscale production routing in multicommodity supply chains with complex production facilities
AU - Zhang, Qi
AU - Sundaramoorthy, Arul
AU - Grossmann, Ignacio E.
AU - Pinto, Jose M.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - In this work, we introduce the multiscale production routing problem (MPRP), which considers the coordination of production, inventory, distribution, and routing decisions in multicommodity supply chains with complex continuous production facilities. We propose an MILP model involving two different time grids. While a detailed mode-based production scheduling model captures all critical operational constraints on the fine time grid, vehicle routing is considered in each time period of the coarse time grid. In order to solve large instances of the MPRP, we propose an iterative MILP-based heuristic approach that solves the MILP model with a restricted set of candidate routes at each iteration and dynamically updates the set of candidate routes for the next iteration. The results of an extensive computational study show that the proposed algorithm finds high-quality solutions in reasonable computation times, and in large instances, it significantly outperforms a standard two-phase heuristic approach and a solution strategy involving a one-time heuristic pre-generation of candidate routes. Similar results are achieved in an industrial case study, which considers a real-world industrial gas supply chain.
AB - In this work, we introduce the multiscale production routing problem (MPRP), which considers the coordination of production, inventory, distribution, and routing decisions in multicommodity supply chains with complex continuous production facilities. We propose an MILP model involving two different time grids. While a detailed mode-based production scheduling model captures all critical operational constraints on the fine time grid, vehicle routing is considered in each time period of the coarse time grid. In order to solve large instances of the MPRP, we propose an iterative MILP-based heuristic approach that solves the MILP model with a restricted set of candidate routes at each iteration and dynamically updates the set of candidate routes for the next iteration. The results of an extensive computational study show that the proposed algorithm finds high-quality solutions in reasonable computation times, and in large instances, it significantly outperforms a standard two-phase heuristic approach and a solution strategy involving a one-time heuristic pre-generation of candidate routes. Similar results are achieved in an industrial case study, which considers a real-world industrial gas supply chain.
KW - MILP-based heuristic
KW - Multiscale optimization
KW - Production routing
KW - Production scheduling
KW - Supply chain management
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U2 - 10.1016/j.cor.2016.11.001
DO - 10.1016/j.cor.2016.11.001
M3 - Article
AN - SCOPUS:84998667842
VL - 79
SP - 207
EP - 222
JO - Computers and Operations Research
JF - Computers and Operations Research
SN - 0305-0548
ER -