TY - JOUR
T1 - Integrated production planning and warehouse storage assignment problem
T2 - An IoT assisted case
AU - Zhang, Guoqing
AU - Shang, Xiaoting
AU - Alawneh, Fawzat
AU - Yang, Yiqin
AU - Nishi, Tatsushi
N1 - Funding Information:
This research was mainly supported by the Natural Sciences and Engineering Research Council of Canada Discovery grant (RGPIN-2014-03594, RGPIN-2019-07115). The first author also would like to acknowledge the research support from JSPS Fellowship (S17088, L12542). The research of the fifthauthor was supported by JSPS KAKENHI (A, 18H03826).
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/4
Y1 - 2021/4
N2 - This study is motivated by a real-world problem in a food company, where production planning is restricted by the available warehouse space for the finished goods. A novel integrated strategy that combines production planning with a randomized storage assignment policy is presented. The strategy takes advantage of greater visibility and traceability of items provided by IoT-enabled tracking systems in order to increase space utilization. An integer linear programming model is developed to formulate the strategy to minimize the total cost of production and warehouse operations. Our model is the first dynamic model for a randomized storage assignment policy. The model's feasibility, complexity, and its lower bound are presented. A heuristic algorithm is developed to obtain the near-optimal solution for the large-scale real-world problem. Based on numerical experiments, comparisons between our solutions and the solutions to model with a dedicated storage policy are also presented. The results show that the integrated strategy with a randomized storage policy can significantly reduce the total cost (up to 16.84% with an average of 9.95%) and increase space utilization (up to 26.1% with an average of 14.8%), compared to the strategy with a dedicated policy. Such results provide evidence that may justify the cost of applying the new technologies, such as IoT-enabled tracking systems, in warehouse management.
AB - This study is motivated by a real-world problem in a food company, where production planning is restricted by the available warehouse space for the finished goods. A novel integrated strategy that combines production planning with a randomized storage assignment policy is presented. The strategy takes advantage of greater visibility and traceability of items provided by IoT-enabled tracking systems in order to increase space utilization. An integer linear programming model is developed to formulate the strategy to minimize the total cost of production and warehouse operations. Our model is the first dynamic model for a randomized storage assignment policy. The model's feasibility, complexity, and its lower bound are presented. A heuristic algorithm is developed to obtain the near-optimal solution for the large-scale real-world problem. Based on numerical experiments, comparisons between our solutions and the solutions to model with a dedicated storage policy are also presented. The results show that the integrated strategy with a randomized storage policy can significantly reduce the total cost (up to 16.84% with an average of 9.95%) and increase space utilization (up to 26.1% with an average of 14.8%), compared to the strategy with a dedicated policy. Such results provide evidence that may justify the cost of applying the new technologies, such as IoT-enabled tracking systems, in warehouse management.
KW - Heuristic algorithm
KW - Integer linear programming
KW - Production planning
KW - Randomized policy
KW - Warehouse storage assignment
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U2 - 10.1016/j.ijpe.2021.108058
DO - 10.1016/j.ijpe.2021.108058
M3 - Article
AN - SCOPUS:85101336442
SN - 0925-5273
VL - 234
JO - International Journal of Production Economics
JF - International Journal of Production Economics
M1 - 108058
ER -