新冠肺炎疫情条件下的企业复工复产优化规划方法

doi:10.15960/j.cnki.issn.1007-6093.2020.03.003

Abstract

Abstract: The outbreak of the novel coronavirus pneumonia (COVID-19) has caused a great impact on the whole economic and social development. It is an important challenge for local governments to plan the production resumption of enterprises without relaxing the epidemic prevention and control. Based on the experiences of Zhejiang Province in overall planning of epidemic prevention and control and economic development, in this paper, we formulate a production resumption planning problem, which selects a subset of enterprises from a large number of candidates that apply for production resumption and determines their order of resumption under epidemics, so as to satisfy the social demand for industrial capacities as much as possible without violating the constraints such as epidemic spreading risk. To efficiently solve this problem, we propose an improved tabu search algorithm, which uses a greedy strategy to construct an initial solution and continually explores a better solution based on variable neighborhood search. Computational results on enterprise production resumption planning in several regions demonstrates the efficiency of our method.

Key words: production resumption, epidemic prevention and control, optimization, integer programming, tabu search

CLC Number:

O221.4
O229

ZHENG Yujun, WU Chenxin, CHEN Enfu, LU Xueqin, ZHANG Minxia. An optimization method for production resumption planning under COVID-19 Epidemic[J]. Operations Research Transactions, 2020, 24(3): 43-56.

References

[1] 浙江日报. 我省召开疫情防控工作第二十九场新闻发布会提高产能恢复率引才招商"云上见"[N], 2020-02-27[2020-03-17]. http://www.zj.gov.cn/art/2020/2/27/art_1228996602_42013277.html.
[2] Hung Y F, Leachman R C. A production planning methodology for semiconductor manufacturing based on iterative simulation and linear programming calculations[J]. IEEE Transactions on Semiconductor Manufacturing, 1996, 9(2):257-269.
[3] 黄贵东, 丁文英. 基于线性规划的生产计划优化研究[J]. 物流技术, 2006, 2:83-84
[4] Sawik T. Integer programming approach to production scheduling for make-to-order manufacturing[J]. Mathematical and Computer Modelling, 2005, 41(1), 99-118.
[5] de Kruijff J T, Hurkens C A J, de Kok T G. Integer programming models for mid-term production planning for high-tech low-volume supply chains[J]. European Journal of Operational Research, 2018, 269(3):984-997.
[6] Chen K, Ji P. A mixed integer programming model for advanced planning and scheduling (APS)[J]. European Journal of Operational Research, 2007, 181(1):515-522.
[7] Pochet Y, Wolsey L A. Production Planning by Mixed Integer Programming[M]. New York:Springer, 2006.
[8] 陈可嘉, 叶舒婷. 面向供应链的高级计划与排程的混合整数规划模型[J]. 中国机械工程, 2012, 23(14):1688-1692.
[9] Fleten S E, Kristoffersen T K. Short-term hydropower production planning by stochastic programming[J]. Computers & Operations Research, 2008, 35(8):2656-2671.
[10] Kazemi Zanjani M, Nourelfath M, Ait-Kadi D. A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand[J]. International Journal of Production Research, 2010, 48(16):4701-4723.
[11] Jamalnia A, Soukhakian M A. A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning[J]. Computers & Industrial Engineering, 2009, 56(4):1474-1486.
[12] da Silva A F, Marins F A S. A fuzzy goal programming model for solving aggregate productionplanning problems under uncertainty:A case study in a brazilian sugar mill[J]. Energy Economics, 2014, 45:196-204.
[13] Garey M R, Johnson D S. Computers and Intractability:A Guide to the Theory of NPCompleteness[M]. San Francisco:W.H. Freeman, 1979.
[14] Glover F. Tabu search-part I[J]. ORSA Journal on Computing, 1989, 1(3):190-206.
[15] Glover F. Tabu search-part Ⅱ[J]. ORSA Journal on Computing, 1990, 2(1):4-32.
[16] Zhu S, Srebric J, Spengler J D, et al. An advanced numerical model for the assessment of airborne transmission of influenza in bus microenvironments[J]. Building and Environment, 2012, 47:67-75.
[17] 张玉, 刘新新, 蔡传锋, 等. 基于交通网络传递的传染病风险传播模型研究[J]. 计算机与数字工程, 2017, 45(12):2359-2363.
[18] Song Q, Zheng Y J, Shen W G, et al. Tridirectional transfer learning for predicting gastric cancer morbidity[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, doi:10.1109/TNNLS.2020.2979486.
[19] 王霞, 唐三一, 陈勇, 等. 新型冠状病毒肺炎疫情下武汉及周边地区何时复工? 数据驱动的网络模型分析[J]. 中国科学:数学, 2020, 50:1-10.
[20] Song Q, Zheng Y, Huang Y, et al. Emergency drug procurement planning based on big-data driven morbidity prediction[J]. IEEE Transactions on Industrial Informatics, 2019, 15(12):6379-6388.
[21] Hansen P, Mladenović N, Todosijević R, et al. Variable neighborhood search:basics and variants[J]. EURO Journal on Computational Optimization, 2017, 5(3):423-454.

An optimization method for production resumption planning under COVID-19 Epidemic

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