AOR 食品饮料工业中的计算物流：建模与应用进展

Call for Papers

Special issue on Computational Logistics in the Food and Drink Industry:

Advances in Modelling and Applications

Aims and scope:

Computational logistics uses models and algorithms, including machine learning and deep learning, to execute complex logistics/supply chain network designs. The computational logistics models and algorithms (Kannan et al., 2009) facilitate solutions to large scale problems to achieve improved accuracy and efficiency, higher productivity in enterprises, improved product visibility, lower inventory levels, higher resource utilization, improved transportation efficiency, risk assessment (Chang et al., 2017), and improved logistics resiliency, etc. In reality, optimal and efficient network design from farm to shelf drives the enterprises to adopt sustainable development goals through computational logistics models (examples: Unilever, Tesco, etc.). Appropriate implementation of the computational models can even scale down environmental impact (Koh et al., 2013) of food and drink supply chains significantly (Validi et al, 2018).

Over the years food and drink supply chain/logistics networks have been facing tremendous pressure to conform to the requirements of distribution, waste reduction, food and packaging quality, sustainability in food production, and many more (Yakovleva et al., 2012; Kaur and Singh, 2018; Zhong et al., 2017). Appropriate implementation of computational logistics can facilitate the food and drink industry to reduce waste from various sources, reduce carbon footprint, and improve resource efficiency and the energy-water-food nexus (Bieber et al., 2018; Kibler et al., 2018). For example, in the United Kingdom, the use of computational logistics has successfully enabled implementation of the three phases of the Courtauld Commitment programme (WRAP, 2019) (examples: Cadbury, Mars, Nestlé, Heinz, Premier Foods, etc.).

Several examples elucidate that computational logistics acts as an enabler to complex food and drink supply chain networks for efficient resource optimization, cost reduction, and carbon footprint reduction within a circular economy. For example, Glanbia plc, one of Ireland’s largest dairy processing companies selling products (e.g., Yoplait, Avonmore, and Kilmeaden) successfully reduced the number of distribution routes from 28 to 23, enabling substantial savings in delivery costs and carbon footprint through Paragon’s optimization solution (Paragon, 2010). Similarly, Greggs, the UK’s leading bakery food-on-the-go retailer, is using Paragon’s optimization solution to enhance its distribution operation performance (Paragon, 2018). Several other leading enterprises in the food and drink industry, including ASDA, Sainsbury's, Fuller's, Tesco and AB Agri, are using computational logistics solutions. Nestlé USA is using ‘FICO® Xpress Optimization’ – a computational optimization solver – to optimize redeployment of inventory from one distribution center to another (FICO, 2017), resulting in savings on transportation and production costs.

The computational logistics solutions combine advanced large-scale optimization techniques, (meta)heuristics and rule-based approaches, agent-based modelling, and simulation combined with digitalization tools for upstream and downstream sides of the logistics networks, including the distribution side.

Driven by the growing needs of the computational logistics in the food and drink industry in the context of the sustainable development goals (e.g. SDG #12) of the United Nations and Courtauld Commitment 2025, this special issue of the Annals of Operations Researchis intended to include answers to the following research questions:

(a) What are the computational logistics models that can successfully drive the energy-water-food nexus in multi-echelon food and drink supply chain/logistics networks?

(b) How can computational logistics models be used to improve operational efficiencies of multi-echelon food and drink supply chain/logistics networks by optimizing resources, minimizing wastes from various corners of the networks, and reducing the carbon footprint?

(c) What are the roles of the computational logistics optimizers for enhancing the energy efficacy in multi-echelon food and drink supply chain/logistics networks?

Computational logistics models and algorithms:

This special issue invites novel and high-quality research articles on the development of new computational logistics models and algorithms, including computational decision-support tools, in the food and drink industry. Original research articles that have not been published or considered for publication elsewhere may include the following topics with specific applications to food and drink supply chain/logistics networks.

computational aspects of large scale optimization
nature inspired computation
multi-objective optimization
combinatorial optimization
hybrid methods
neuro computing
probabilistic computing
evolutionary computing (including meta-heuristic optimization algorithms like particle swarm optimization and genetic algorithm etc.)
agent-based modelling
machine learning and deep learning etc.

Application areas:

Areas include but are not limited to the following application areas considering any of the aspects of the above three research questions and models/algorithms.

waste reduction from food manufacturing, distribution, packaging, and consumption
sustainable practices in multi-echelon food and drink supply chain/logistics network design
design of multi-echelon cold chain networks
carbon footprint / carbon emission, carbon cap, carbon trade and taxes in food and drink supply chain/logistics networks
inventory-transportation problems for food and drink supply chain/logistics networks
closed loop computational logistics models in food and drink supply chain / logistics networks
complex food and drink distribution system including vehicle routing and location routing
risk, uncertainties, cost efficiency, and resiliency in food and drink supply chain / logistics networks
resource efficient food and drink supply chains considering energy-water-food nexus
food and drink supply chain performance benchmarking
simulations in food and drink supply chain network
emerging computational logistics models in the era of digitalization (e.g., Industry 4.0, Supply Chain 4.0), circular economy, and deep learning.

The submission deadline is September 30, 2020

Please select article type: S.I.: Computational Logistics in Food and Drink Industry

Instructions for authors can be found at

http://www.springer.com/business/operations+research/journal/10479

Contributions arising from papers given at a conference should be substantially extended, and should cite the conference paper where appropriate.

Manuscript submission: https://www.editorialmanager.com/anor/default.aspx

References

Bieber, N., Ker, J.H., Wang, X., Triantafyllidis, C., van Dam, K.H., Koppelaar, Rembrandt H.E.M. and Shah, N. (2018). Sustainable planning of the energy-water-food nexus using decision making tools. Energy Policy, 113, 584-607.

Chang, Y., Erera, A.L. and White, C.C. (2017). Risk assessment of deliberate contamination of food production facilities. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(3), 381-393.

FICO (2017). Nestlé USA Applies Optimization Models to Save on Transportation and Production Costs. Available at: https://community.fico.com/servlet/JiveServlet/downloadBody/3642-102-3-4899/FICO_Nestle%CC%81_Applies_Optimization_ModelCase%20Study.pdf, Accessed: 8 January 2019.

Kannan, G., Haq, A.N. and Devika, M. (2009). Analysis of closed loop supply chain using genetic algorithm and particle swarm optimization. International Journal of Production Research, 47(5), 1175-1200.

Kaur, H. and Singh, S.P. (2018). Heuristic modeling for sustainable procurement and logistics in a supply chain using big data. Computers & Operations Research, 98, 301-321.

Kibler, K.M., Reinhart, D., Hawkins, C., Motlagh, A.M., Wright, J. (2018). Food waste and the food-energy-water nexus: a review of food waste management alternatives. Waste Management, 74, 52-62.

Koh, S.C.L., Genovese, A., Acquaye, A.A., Barratt, P., Rana, N., Kuylenstierna, J. and Gibbs, D. (2013). Decarbonising product supply chains: design and development of an integrated evidence-based decision support system – the supply chain environmental analysis tool (SCEnAT). International Journal of Production Research, 51(7), 2092-2109.

Paragon (2010). Glanbia saves 16% in six months with Paragon routing software. Available at: https://www.paragonrouting.com/about/news/glanbia-saves-16-six-months-paragonrouting-software, Accessed: 8 January 2019.

Paragon (2018), Greggs optimises multi-drop distribution operation with Paragon's routing and scheduling software. Available at: https://www.paragonrouting.com/about/news/greggs-optimises-multi-drop-distributionoperation-paragons-routing-and-scheduling-software, Accessed: 8 January 2019.

Validi, S., Bhattacharya, A. and Byrne, P.J. (2018). Sustainable distribution system design: a two-phase DoE-guided meta-heuristic solution approach for a three-echelon bi-objective AHP-integrated location-routing model. Annals of Operations Research. DOI: https://doi.org/10.1007/s10479-018-2887-y.

Waste and Resources Action Programme (2019). Available at: http://www.wrap.org.uk, Accessed: 9 January 2019.

Yakovleva, N., Sarkis, J. and Sloan, T. (2012). Sustainable benchmarking of supply chains: the case of the food industry. International Journal of Production Research, 50(5), 1297-1317.

Zhong, R., Xu, X. and Wang, L. (2017). Food supply chain management: systems, implementations, and future research. Industrial Management & Data Systems, 117(9), 2085-2114.

Guest Editors:

Dr Arijit Bhattacharya (Managing Guest Editor)

Norwich Business School, University of East Anglia

Norwich NR4 7TJ, United Kingdom.

e-mail: arijit.bhattacharya2005@gmail.com;A.Bhattacharya@uea.ac.uk

Dr Sachin Kumar Mangla

Plymouth Business School, University of Plymouth

Plymouth, PL48AA

United Kingdom.

e-mail: sachinmangl@gmail.com; sachin.kumar@plymouth.ac.uk

Professor Alessio Ishizaka

Portsmouth Business School, University of Portsmouth, PO13DE, United Kingdom.

e-mail: alessio.ishizaka@port.ac.uk

Dr Sunil Luthra

Department of Mechanical Engineering

Government Engineering College

Nilokheri-132117, Haryana, India.

e-mail: sunilluthra1977@gmail.com

为食品饮料工业计算物流：模型与应用进展征集论文

目标与范围：

计算物流使用模型和算法，包括机器学习和深度学习，来执行复杂的物流/供应链网络设计。计算物流模型和算法（Kannan等，2009）有助于解决大规模问题，以实现更高的准确性和效率、更高的企业生产力、更高的产品可见性、更低的库存水平、更高的资源利用率、更高的运输效率、风险评估（Chang等，2017）和更高的物流弹性等。在现实中，从农场到货架的优化和高效的网络设计促使企业通过计算物流模型采取可持续发展目标（例如：联合利华、乐购等）。适当实施计算模型甚至可以显著降低食品和饮料供应链（Validi等，2018）的环境影响（Koh等，2013）。

多年来，食品和饮料供应链/物流网络一直面临着巨大的压力，需要符合配送、减少浪费、食品和包装质量、食品生产的可持续性等要求（Yakovleva等，2012；Kaur和Singh，2018；Zhong等，2017）。适当实施计算物流可以促进食品和饮料行业减少各种来源的废物，减少碳足迹，提高资源效率和能源-水-食物关系（Bieber等，2018；Kibler等，2018）。例如，在英国，计算物流的使用成功地实现了考陶尔德承诺计划（WRAP，2019年）的三个阶段（例如：Cadbury, Mars, Nestlé, Heinz, Premier Foods, 等）。

几个例子说明，计算物流作为一个复杂食品和饮料供应链网络的赋能者，在循环经济中有效地优化资源、降低成本和减少碳足迹。例如，爱尔兰最大的乳制品加工公司之一的格兰比亚公司（Glanbia plc）成功地将配送路线从28条减少到23条，通过Paragon的优化解决方案，实现了配送成本和碳排放量的大幅节约（Paragon，2010）。同样，英国领先的即时面包零售商格雷格斯（Greggs）正在使用Paragon的优化解决方案来提高其分销运营绩效（Paragon，2018）。食品和饮料行业的其他几家领先企业，包括ASDA、Sainsbury's、Fuller's、Tesco和AB Agri，都在使用计算物流解决方案。雀巢美国正在使用“FICO®Xpress Optimization”（一种计算优化解决方案）来优化从一个配送中心到另一个配送中心的库存重新调配（FICO，2017），从而节省运输和生产成本。

计算物流解决方案结合了先进的大规模优化技术（元）启发式和基于规则的方法、基于agent的建模和仿真，并结合了物流网络上游和下游侧（包括配送侧）的数字化工具。

在联合国可持续发展目标（如SDG #12）和《考陶尔德承诺2025》（Courtauld Commitment 2025）的背景下，食品和饮料行业对计算物流的需求日益增长，《运筹学年鉴》的这期特刊旨在回答以下研究问题：

（a）在多级食品饮料供应链/物流网络中，哪些计算物流模型能够成功地推动能源-水-食品的联系？

（b）如何使用计算物流模型，通过优化资源、最小化来自网络各个角落的废物和减少碳足迹，来提高多级食品和饮料供应链/物流网络的运营效率？

（c）在多级食品饮料供应链/物流网络中，计算物流优化器在提高能源效率方面的作用是什么？

计算物流模型和算法：

本期特刊邀请新的和高质量的关于开发新的计算物流模型和算法的研究论文，包括在食品和饮料行业的计算决策支持工具。尚未出版或考虑在其他地方发表的原创研究文章可能包括将其具体应用于食品和饮料供应链/物流网络的以下主题。

大规模优化的计算问题
自然启发计算
多目标优化
组合优化
混合方法
神经算法
概率算法
进化算法(包括元启发式优化算法，如粒子群优化和遗传算法等)
基于agent的建模
机器学习和深度学习等。

应用领域：

考虑到上述三个研究问题和模型/算法的任何方面，这些领域包括但不限于以下应用领域。

减少食品制造、分销、包装和消费产生的废物
多级食品和饮料供应链/物流网络设计的可持续实践
多级冷链网络设计
食品饮料供应链/物流网络中的碳足迹/碳排放、碳总量、碳交易和税收
食品和饮料供应链/物流网络的库存运输问题
食品饮料供应链/物流网络中的闭环计算物流模型
复杂的食品和饮料配送系统，包括车辆路线和位置路线
食品和饮料供应链/物流网络中的风险、不确定性、成本效率和弹性
考虑能源-水-食物关系的资源节约型食品和饮料供应链
食品饮料供应链绩效基准
食品饮料供应链仿真
数字化时代（如工业4.0、供应链4.0）新兴的计算物流模型、循环经济和深度学习

作者指南可在以下网址找到：

http://www.springer.com/business/operations+research/journal/10479

在会议上提交的文件所产生的稿件应大幅度增加，并应酌情引用会议文件。

稿件提交：https://www.editorialmanager.com/anor/default.aspx

参考文献

Chang, Y., Erera, A.L. and White, C.C. (2017). Risk assessment of deliberate contamination of food production facilities. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(3), 381-393.

Kaur, H. and Singh, S.P. (2018). Heuristic modeling for sustainable procurement and logistics in a supply chain using big data. Computers & Operations Research, 98, 301-321.

Kibler, K.M., Reinhart, D., Hawkins, C., Motlagh, A.M., Wright, J. (2018). Food waste and the food-energy-water nexus: a review of food waste management alternatives. Waste Management, 74, 52-62.

Waste and Resources Action Programme (2019). Available at: http://www.wrap.org.uk, Accessed: 9 January 2019.

Yakovleva, N., Sarkis, J. and Sloan, T. (2012). Sustainable benchmarking of supply chains: the case of the food industry. International Journal of Production Research, 50(5), 1297-1317.

Zhong, R., Xu, X. and Wang, L. (2017). Food supply chain management: systems, implementations, and future research. Industrial Management & Data Systems, 117(9), 2085-2114.

特邀编辑：

Arijit Bhattacharya博士（常务特邀编辑）

东英吉利大学诺维奇商学院

诺维奇NR4 7TJ，英国

电子邮件：: arijit.bhattacharya2005@gmail.com;A.Bhattacharya@uea.ac.uk

Sachin Kumar Mangla博士

普利茅斯大学普利茅斯商学院

普利茅斯，PL48AA

英国

电子邮件：sachinmangl@gmail.com; sachin.kumar@plymouth.ac.uk

Alessio Ishizaka教授

朴茨茅斯商学院，朴茨茅斯大学，PO13DE，英国

电子邮件：alessio.ishizaka@port.ac.uk

Sunil Luthra博士

机械工程系

政府工程学院

Nilokheri-132117，哈里亚纳邦，印度

电子邮件：sunilluthra1977@gmail.com