随着人民生活水平的不断提升，消费水平的不断升级，人们对生鲜产品的品质要求越来越高。而我国分散经营的特点导致生鲜损耗大，“冷处理”率低的现实情况严重阻碍了生鲜的高效高品质的流通。如今，随着移动预冷库技术的不断发展，在“田间地头”通过租赁移动预冷库来实现产地生鲜预冷成为一个有效的提升生鲜品质的手段。在此情况下，如何进行合理的移动预冷库租赁规模决策，并集成预冷库的选址与运输作业，建立高效协调的生鲜采后预冷与运输体系，成为生鲜前端供应链邻域的重点问题。基于此，本文从鲜果种植采摘合作社的角度出发，以具有高价值的樱桃为现实案例，以实现合作社整体成本最小化为研究目标，以考虑鲜果采摘量不确定性下的移动预冷库租赁－选址－路径优化为研究问题，以选址机会成本与运输成本的效益背反为关键点，运用随机规划、蒙特卡洛仿真、分支定界法、启发式算法等理论方法，开展对生鲜移动预冷库的租赁决策与成本分析、分区域的移动预冷库选址－路径优化模型与算法的研究。具体的研究内容如下：

1. 针对生鲜采摘过程中存在着明显的产量的不确定性以及周期性的问题，通过建模求解与仿真求解并行的方法，研究了单个采摘区域的移动预冷库的最优租赁规模决策与成本分析问题。首先，构建了考虑租赁折扣系数的随机规划模型，并将非线性模型进行线性转化。随后运用求解器以及蒙特卡洛仿真算法分别对模型进行求解，实现两种方法的双向验证。最后，通过算例的求解与分析，证明了模型的正确性与仿真算法的可靠性，为移动预冷库的后续选址与路径优化提供了信息支撑。
2. 针对生鲜在产地进行预冷后，需要及时的将预冷后的生鲜产品通过冷链运输到冷藏中心的背景，以及生鲜采摘种植合作社需要对多个采摘区域进行联合优化的现实情况，研究了分区域的选址－路径联合优化问题。首先，建立了多周期的选址－路径规划模型。其次，通过加入有效不等式来提高CPLEX求解器的求解效率，验证了模型的正确性。随后构建了基于MIP的模拟退火算法(MIP-SA)进行问题的求解。最后，通过设立算例验证了模型的正确性与算法的有效性。
3. 以山东省枣庄市某樱桃采摘合作社为研究案例，进行实际问题的解决。首先，对所需数据进行调研。随后，根据本文所提出的两阶段方法，对问题进行分析与求解。接着，通过对相关因素的灵敏度分析，探讨了不同因素变化对决策的影响。最后，从管理者的视角和政府的视角提出了相应的管理见解与意见。

本论文的研究为实现租赁移动预冷库与选址－运输的优化决策提供了新的思路，提出了面对采摘量不确定性情况下的移动预冷库租赁决策方法、选址－路径联合优化方法，为推动移动预冷库有效落地施实奠定了理论基础和数据支撑，为合作社的决策者提供了分析和决策方法。为实现提升鲜果预冷率降低鲜果损耗、促进产地预冷体系建设、推进乡村振兴战略提供了管理意见。

With the continuous improvement of people's living standard, the consumption level has been upgraded. People's demand for quality of fresh products is getting higher and higher. The decentralized operation in China has led to a large loss of fresh produce, and the low "cold treatment" rate has seriously hindered the efficient and high quality circulation of fresh produce. Nowadays, with the continuous development of mobile pre-cooling technology, it has become an effective means to improve the quality of agri-food by renting mobile pre-cooling storage in the "field" to realize the pre-cooling of fresh produce at origin. In this case, how to make a reasonable mobile pre-cooling storage rental scale decision. And integrate the site selection and transportation operation of the pre-cooling storage warehouse to establish an efficient and coordinated post-harvest pre-cooling and transportation system for fresh fruits, which becomes a key issue in the front-end supply chain field of fresh fruits. Based on this, this work starts from the perspective of a fresh products growing and picking cooperative. Cherries with high value are used as a realistic case. The research objective is to achieve the overall cost minimization of the cooperative. Considering the mobile pre-cooled storage lease sizing-location-routing optimization under the uncertainty of fresh products picking volume as the research problem. Take the benefit backward of opportunity cost of site selection and transportation cost as the key point. Using theoretical methods such as stochastic programming, Monte Carlo simulation, branch-and-bound method, and heuristic algorithm, this work carried out the research on the leasing decision and cost analysis of fresh picking mobile pre-cooling warehouse, and the model and algorithm of mobile pre-cooling storage lease sizing-location-routing optimization in sub-region. The specific research in detail are as follows:

(1) In view of the obvious uncertainty and periodicity in the fresh picking process, the optimal leasing size decision and cost analysis of mobile pre-cooling storage for a single picking area are studied by the parallel method of modeling solution and simulation solution. First, a stochastic programming model considering the lease discount factor is constructed, and the nonlinear model is linearly transformed. Then the linear model is solved by using CPLEX solver, and a Monte Carlo simulation algorithm is constructed for a more intuitive solution analysis to realize the two-way verification of the two methods. Finally, the correctness of the model and the reliability of the simulation algorithm are evaluated through the solution and analysis of the instances, which provide information support for the subsequent site selection and path optimization of the mobile pre-cooling warehouse.

(2)The location routing problem of sub-region is studied for the background that the pre-cooled fresh products need to be transported to the cold storage center through the cold chain in time after the pre-cooling at the origin, and the reality that the fresh picking and planting cooperatives need to jointly optimize multiple picking areas. Firstly, a multi-cycle location routing model is established. Second, the correctness of the model was verified by adding an effective inequality to improve the solving efficiency of the CPLEX solver. Subsequently, the MIP-based simulated annealing algorithm (MIP-SA) is constructed for problem solving. Finally, the correctness of the model and the effectiveness of the algorithm are verified by setting up an arithmetic example.

(3) Combining with the above research, a cherry picking cooperative in Zaozhuang City, Shandong Province, is used as a research case to solve the practical problem.First, the required data are investigated. Subsequently, the problem is analyzed and solved according to the two-stage approach proposed in this work. Then, the impact of different factor changes on decision making is explored through sensitivity analysis of relevant factors. Finally, corresponding management insights and opinions are presented from the perspective of managers and from the perspective of the government.

The research of this work provides new ideas to realize the optimal decision of leasing mobile pre-cooling storage and site-transportation, and proposes a mobile pre-cooling storage leasing decision method and a joint site-path optimization method in the face of uncertainty of harvesting volume, which lays a theoretical foundation and data support to promote the effective implementation of mobile pre-cooling storage and provides a cooperative It provides analysis and decision-making methods for decision makers of cooperatives. It provides management advice to improve the pre-cooling rate of fresh fruits and reduce the loss of fresh fruits, promote the construction of the pre-cooling system of origin, and promote the rural revitalization strategy.

[1]冯雅蓉．预冷方式对红玛瑙樱桃果实冷藏期间真菌多样性的影响［J］．食品科学，2020，41（11）：214-221．

[2]葛显龙，张雅婷．基于前摄性调度的生鲜物流集货路径优化［J］．系统工程，2020，38（06）：70-80．

[3]何朋飞，李静．考虑降雨量不确定的稻麦收割机多类型配置优化［J］．中国农业大学学报，2018，23（03）：122-130．

[4]李玉民，王新露，郭晓燕．基于集合覆盖模型的农产品冷链网络布局［J］．中国农业大学学报，2017，22（09）：212-220．

[5]马祖军，王一然．考虑生鲜农产品“最先一公里”损耗的预冷站布局优化［J/OL］．中国管理科学：1-11［2023-04-06］．DOI:10.16381/j.cnki.issn1003-207x.2021.2618.

[6]邱若臻，张多琦，孙艺萌，关志民．供需不确定条件下基于CVaR的零售商库存鲁棒优化模型［J］．中国管理科学，2020，28（12）：98-107．DOI:10.16381/j.cnki.issn1003-207x.2018.1013.

[7]王丹，张静．基于甜樱桃“最初一公里”小型移动预冷机的研制及应用［J］．农产品加工，2022（04）：73-75+78．DOI:10.16693/j.cnki.1671-9646(X).2022.02.051.

[8]王馨渝，安容慧，赵安琪，韩颖，胡花丽，李鹏霞，颜廷才，李国锋．真空预冷过程中喷雾补水处理对低温流通及货架期上海青品质的影响［J/OL］．食品科学：1-14［2022-07-03］．http://kns.cnki.net/kcms/detail/11.2206.ts.20220622.0853.012.html

[9]吴迪．丹东市圣野浆果专业合作社草莓集货路径优化研究［D］．沈阳农业大学，2020．DOI:10.27327/d.cnki.gshnu.2020.000833.

[10]薛靖文，刘升．延迟预冷对芥蓝冷链过程品质和营养的影响［J/OL］．甘肃农业大学学报：1-9［2022-07-03］．http://kns.cnki.net/kcms/detail/62.1055.S.20220531.1112.004.html

[11]杨天阳，田长青，刘树森．生鲜农产品冷链储运技术装备发展研究［J］．中国工程科学，2021，23（04）：37-44.

[12]朱宝琳，薛林，戢守峰，邱若臻．零售商风险规避下基于联合契约的双渠道供应链库存决策模型［J］．系统管理学报，2022，31（02）：217-229.

[13]张驰怡. 基于双层规划的多类型预冷设施选址-路径研究［D］．大连理工大学，2021．DOI:10.26991/d.cnki.gdllu.2021.001974.

[14]张喜才，霍迪．中国生鲜农产品冷链物流薄弱环节梳理及对策研究［J］．农业经济与管理，2021（03）：93-102．

[15]ALBAYATI O A Z, KUMAR R, CHAUHAN G. Forced Air Precooling Studies of Perishable Food Products[J]. International Journal of Food Engineering, 2007, 3(6).

[16]ALMEIDA F S, SILVEIRA F H, VISACRO S. Stopping criterion for Monte Carlo method-based simulations of the lightning performance of transmission lines[J]. Electric Power Systems Research, 2023, 214:108797.

[17]AHUMADA O, VILLALOBOS J R. Operational model for planning the harvest and distribution of perishable agricultural products[J]. International Journal of Production Economics, 2011, 133(2):677-687.

[18]ALVAREZ A, CORDEAU J-F, JANS R, et al. Inventory routing under stochastic supply and demand[J]. Omega, 2021, 102.

[19]AMBAW A, MUKAMA M, OPARA U L. Analysis of the effects of package design on the rate and uniformity of cooling of stacked pomegranates: Numerical and experimental studies[J]. Computers and Electronics in Agriculture, 2017, 136:13-24.

[20]AN R, LUO S, ZHOU H, et al. Effects of hydrogen-rich water combined with vacuum precooling on the senescence and antioxidant capacity of pakchoi (Brassica rapa subsp. Chinensis)[J]. Scientia Horticulturae, 2021, 289.

[21]BENGTSSON J, OLHAGER J. The impact of the product mix on the value of flexibility[J]. Omega, 2002, 30(4):265-273.

[22]DAROUDI S, KAZEMIPOOR H, NAJAFI E, et al. The minimum latency in location routing fuzzy inventory problem for perishable multi-product materials[J]. Applied Soft Computing, 2021, 110.

[23]DE PONTI T, RIJK B, VAN ITTERSUM M K. The crop yield gap between organic and conventional agriculture[J]. Agricultural Systems, 2012, 108:1-9.

[24]DUAN Y, WANG G-B, FAWOLE O A, et al. Postharvest precooling of fruit and vegetables: A review[J]. Trends in Food Science & Technology, 2020, 100:278-291.

[25]FERRER J-C, MAC CAWLEY A, MATURANA S, et al. An optimization approach for scheduling wine grape harvest operations[J]. International Journal of Production Economics, 2008, 112(2):985-999.

[26]FUJIWARA O , PERERA U L J S R . EOQ models for continuously deteriorating products using linear and exponential penalty costs[J]. European Journal of Operational Research, 1993, 70(1):104-114.

[27]GAUDER D, BOTT A, GOLZ J, et al. Simulation uncertainty determination of single flank rolling tests using monte carlo simulation and skin model shapes for zero defect manufacturing of micro gears[J]. Computers in Industry, 2023, 146.

[28]HALVORSEN-WEARE E E, FAGERHOLT K, R6NNQVIST M. Vessel routing and scheduling under uncertainty in the liquefied natural gas business[J]. Computers & Industrial Engineering, 2013, 64(1):290-301.

[29]HANSEN O, TRANSCHEL S, FRIEDRICH H. Replenishment strategies for lost sales inventory systems of perishables under demand and lead time uncertainty[J]. European Journal of Operational Research, 2023, 308(2):661-675.

[30]HE P, LI J. The two-echelon multi-trip vehicle routing problem with dynamic satellites for crop harvesting and transportation[J]. Applied Soft Computing, 2019, 77: 387-398.

[31]HIASSAT A, DIABAT A, RAHWAN I. A genetic algorithm approach for location-inventory-routing problem with perishable products[J]. Journal of Manufacturing Systems, 2017, 42:93-103.

[32]JEON D, LIM M K, PENG Z, et al. Got organic milk? Joint inventory model with supply uncertainties and partial substitution[J]. Operations Research Letters, 2021, 49(5): 663-670.

[33]JIA B, LIU F, YUAN S, et al. The effect of alternating ventilation on forced air pre-cooling of cherries [J]. International Journal of Food Engineering, 2021, 17(6):423-433.{Wang, 2021 #10}

[34]JUNG J Y, BLAU G, PEKNY J F, et al. A simulation based optimization approach to supply chain management under demand uncertainty[J]. Computers & chemical engineering, 2004, 28(10): 2087-2106.

[35]KITINOJA L, THOMPSON J F. Pre-cooling systems for small-scale producers[J]. Stewart Postharvest Review, 2010, 6(2): 1-14.

[36]KOFJAČ D, KLJAJIĆ M, REJEC V. The anticipative concept in warehouse optimization using simulation in an uncertain environment[J]. European Journal of Operational Research, 2009, 193(3): 660-669.

[37]LAMSAL K, JONES P C, THOMAS B W. Harvest logistics in agricultural systems with multiple, independent producers and no on-farm storage[J]. Computers & Industrial Engineering, 2016, 91: 129-138.

[38]LI X, WU W, LI K, et al. Experimental study on a wet precooling system for fruit and vegetables with ice slurry[J]. International Journal of Refrigeration, 2022, 133: 9-18.

[39]LIU A, ZHU Q, XU L, et al. Sustainable supply chain management for perishable products in emerging markets: An integrated location-inventory-routing model[J]. Transportation Research Part E-Logistics and Transportation Review, 2021, 150.

[40]MAKULE E, DIMOSO N, TASSOU S A. Precooling and Cold Storage Methods for Fruits and Vegetables in Sub-Saharan Africa-A Review[J]. Horticulturae, 2022, 8(9).

[41]MANGANARIS G A, ILIAS I F, VASILAKAKIS M, et al. The effect of hydrocooling on ripening related quality attributes and cell wall physicochemical properties of sweet cherry fruit (Prunus avium L.)[J]. International Journal of Refrigeration, 2007, 30(8): 1386-1392.

[42]MANSOURI S A, AHMARINEJAD A, ANSARIAN M, et al. Stochastic planning and operation of energy hubs considering demand response programs using Benders decomposition approach[J]. International Journal of Electrical Power & Energy Systems, 2020, 120.

[43]PARTOVI F, SEIFBARGHY M, ESMAEILI M. Revised solution technique for a bi-level location-inventory-routing problem under uncertainty of demand and perishability of products[J]. Applied Soft Computing, 2023, 133.

[44]PLESSEN M G . Coupling of crop assignment and vehicle routing for harvest planning in agriculture[J]. Artificial Intelligence in Agriculture, 2019, 2(C):99-109.

[45]QIU R, SUN Y, SUN M. A distributionally robust optimization approach for multi-product inventory decisions with budget constraint and demand and yield uncertainties[J]. Computers & Operations Research, 2021, 126.

[46]RAFIE-MAJD Z, PASANDIDEH S H R, NADERI B. Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm[J]. Computers & Chemical Engineering, 2018, 109:9-22.

[47]RAHBARI M, RAZAVI HAJIAGHA S H, AMOOZAD MAHDIRAJI H, et al. A novel location-inventory-routing problem in a two-stage red meat supply chain with logistic decisions: evidence from an emerging economy[J]. Kybernetes, 2022, 51(4):1498-1531.

[48]SALHI S, NAGY G. A cluster insertion heuristic for single and multiple depot vehicle routing problems with backhauling[J]. Journal of the operational Research Society, 1999, 50(10): 1034-1042.

[49]SHOKOUHIFAR M, SABBAGHI M M, PILEVARI N. Inventory management in blood supply chain considering fuzzy supply demand uncertainties and lateral transshipment[J]. Transfusion and Apheresis Science, 2021, 60(3).

[50]SONG L, WU Z. An integrated approach for optimizing location-inventory and location-inventory-routing problem for perishable products[J]. International Journal of Transportation Science and Technology, 2023, 12(1): 148-172.

[51]THOMPSON S D, DAVIS W J. An integrated approach for modeling uncertainty in aggregate production planning[J]. IEEE transactions on systems, man, and cybernetics, 1990, 20(5): 1000-1012.

[52]TRANSCHEL S, HANSEN O. Supply planning and inventory control of perishable products under lead-time uncertainty and service level constraints[J]. Procedia Manufacturing, 2019, 39: 1666-1672.

[53]WANG C, LIU Y, YANG G. Adaptive distributionally robust hub location and routing problem with a third-party logistics strategy[J]. Socio-Economic Planning Sciences, 2023: 101563.

[54]WANG N, KAN A, MAO S, et al. Study on heat and mass transfer of sugarcane stem during vacuum pre-cooling [J]. Journal of Food Engineering, 2021, 292.

[55]WANG Y, WANG N, HAN P. Maritime location inventory routing problem for island supply chain network under periodic freight demand[J]. Computers & Operations Research, 2023, 149.

[56]WU W, ZHOU W, LIN Y, et al. A hybrid metaheuristic algorithm for location inventory routing problem with time windows and fuel consumption[J]. Expert systems with applications, 2021, 166.

[57]YAVARI M, ENJAVI H, GERAELI M. Demand management to cope with routes disruptions in location-inventory-routing problem for perishable products[J]. Research in Transportation Business and Management, 2020, 37.

[58]华经情报网. 2023年中国生鲜零售行业发展分析，线下渠道占比逐渐提升[EB/OL]. [2023-02-28]. https://baijiahao.baidu.com/s?id=1759039142255851750&wfr=spider&for=pc.