4.2 Article

Integrated inventory and production policy for manufacturing with perishable raw materials

期刊

出版社

SPRINGER
DOI: 10.1007/s10472-021-09739-1

关键词

Inventory; Production; Non-linear inventory cost function; Hybrid intelligent algorithm

资金

  1. National Natural Science Foundation of China [71922009, 71871080, 72071056, 71690235, 71501058, 71601060]
  2. Innovative Research Groups of the National Natural Science Foundation of China [71521001]
  3. Anhui Province Natural Science Foundation [1908085MG223, 2008085QG341]
  4. Base of Introducing Talents of Discipline to Universities for Optimization and Decision-making in the Manufacturing Process of Complex Product (111 projects)
  5. Project of Key Research Institute of Humanities and Social Science in University of Anhui Province
  6. Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making(Hefei University of Technology), Ministry of Education
  7. Humboldt Research Award (Germany)

向作者/读者索取更多资源

This research focuses on the integrated inventory and production scheduling problem in a manufacturer handling perishable goods, aiming to minimize both inventory cost and production cost through an optimal schedule. It investigates both single-plant and multi-plant problems, providing solutions for each. The proposed hybrid intelligent algorithm for the multi-plant problem outperforms other algorithms in terms of effectiveness and efficiency, as demonstrated in experiments.
This research investigates an integrated inventory and production scheduling problem (IIPSP) in a manufacturer that deals with the perishable goods. The objective is to find an optimal schedule to minimize the sum of inventory cost and production cost. Both single-plant problem and multi-plant problem are investigated in this paper. For the single-plant problem, we prove that it is optimal to arrange the processing of raw materials in descending order of the value of the product of consumption rate and unit inventory cost. For the more complex multi-plant problem, we first prove that it is NP-hard, and then, we propose a hybrid intelligent algorithm to solve it. The experiments show that the proposed algorithm is superior to several other algorithms in both effectiveness and efficiency.

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