Application of Machine Learning and RFID in the Stability Optimization of Perishable Foods

Transcription

1 Application of Machine Learning and RFID in the Stability Optimization of Perishable Foods Ehsan Mohebi and Leorey O. Marquez 2014 International Workshop on Food Supply Chains 4-7 November 2014, San Francisco, USA

2 Key Challenges to the World Food Supply Chain Dependencies in the Australian food supply chain (DAFF, 2012) Global population expected to increase to 8 billion by % increase in global demand for food from 2007 to 2050 Food production under threat from climate change, competing land uses, erosion, salinity, and diminishing supplies of clean water Food losses and waste amount to 1.3 billion tons/yr ~ 1/3 of production Focus supply chains on increased efficiency and waste reduction

3 Centre for Food & Beverage Supply Chain Optimisation Australian Research Council (ARC) funding Participating Organisations University of Newcastle, CSIRO, U.T. Sydney, Georgia Tech, Coca Cola Amatil (Australia), NSW Dep. Primary Industries, Batlow Fruit Co-op., Sanitarium Areas of Interest Vendor managed inventory grocery industry Optimised Market Selection & Product Timing Grape Harvest Sequencing Disaster Management and Recovery for Food Supply Chains Land Use & Food Supply Chains Edible Films & Coatings Agri-Food Waste Utilisation Distribution planning for Beverages Waste-to-Energy projects in the rice industry

4 Industrial Transformation Training Centre (ITTC) ITTC will train the next generation of multidisciplinary researchers capable of designing, building, and managing safe, sustainable, resilient and cost-effective food supply chains. Projects Sustainable Food Chains: Reducing electricity consumption & fuels use in cold chains. Reducing Food Miles. Optimising trade off between food quality & supply chain efficiency Post-Harvest Science and Technology: Use of controlled atmosphere storage. Novel handling and packaging techniques. Cold Food Supply Chains: Design optimisation of cost-effective cold chains. Preservative efforts for more efficient transportation and storage of fresh products. Scholarships/Fellowships (3) Postdoctoral Research Fellowships (10) PhD Scholarships Details in

5 RFID for Supply Chain Efficiency and Waste Reduction RFIDs can allow the stakeholders in the food supply chain to manage customer demand and adjust the production process in real-time to improve whole of system efficiency and reduce wastage. An RFID system consists of: tags, readers, and middleware. A tag is usually a microchip with an antenna. The tag keeps and transmits data to a reader, which is an electronic device used to wirelessly communicate information from the tag to a back-end database. Source:

6 RFID-based Monitoring Systems An RFID-based monitoring system can provide real time information to transporters, managers, and distributors in the supply chain. Data from the RFID and the sensor network allow for the quality of goods to be consistently and accurately monitored in-transit at the carton or pallet level at every point in the logistic chain. Source: Aside from monitoring environmental data, RFID tags uniquely identify goods for traceability throughout the logistics chain while also providing receivers the ability to more easily identify and manage product grading and recalls.

7 RFID and Sensor Networks RFID tags are robust, can work under extreme temperatures and different pressures, it can be detected at distances of more than 100 m, and many tags can be read simultaneously. Middleware systems such as Sense-T data platform enable collection of key environmental attributes from supplier/producer in real-time to prove performance through to the market. Source: Fieldhouse, 2014 RFID technology has successful applications in: access control systems, airport baggage handling, livestock management systems, automated toll collection systems, logistics and retail businesses.

8 Simulation Study Demonstrate how online information provided by RFID can be integrated into a decision support system to increase network efficiency and maximise quality in a supply chain. Simulated supply chain consists of: Products with quantity demanded and set quality Product processing nodes at different levels Transport links with vehicles, costs, delays Stochastic environmental factors Travel costs increase with decreasing travel times Product quality deteriorates with increasing travel times and delays RFID information: {quality delivered, delivery date, vehicle used} Objective: Deliver quantity demanded of products at required destinations with minimum transport costs while maximising product quality

9 Simulated Network Given a supply chain network with:

10 Supply Chain Decision-making Problem: Find the route/vehicle decision r l k ϵ R l ij; k = 1,...,h ij to transport product p with demand d and quality q p d(t) at time t, from node i on level l to node j at level l+1 Strategy: If the quality of the product is higher than a threshold then the route with less cost (but high travel time) will be selected. Otherwise the one with less travel time (high cost) will be selected Trade off between cost minimization and quality maximization Quality and cost functions for routes

11 Information Flow Warning Module: introduces unexpected delays in routes and temperature fluctuations due to vehicle storage system malfunctions. Quality forecasting: the quality of products at the next level is forecasted and a penalty applied if the delivered quality of the product at the next level is less than the forecast.

12 Generating the simulation data Algorithm 1: (Simulation process of demand d at level l ) 1. Select one available transportation route. 2. Calculate transportation time. 3. (Warning modules) Calculate delay time and quality loss of the selected system. 4. (Forecasting) Calculate forecasted quality based on the real time information. 5. Calculate the total elapsed time and the quality of demand d. 6. Calculate the cost penalty function and the total cost. 7. Collect the simulation results for further investigation. 8. If all transportation routes have been evaluated terminate, otherwise go to Step 1.

13 Data collection for logistic regression quality Maximum quality cost Minimum cost Real-time information

14 Training the Kernel Logistic Regression model Algorithm 2: (Training the KLR model at level l ) Input: the simulation data Output: the set of decision boundaries Θ for each level l 1. Select one available transportation route. 2. Calculate the set Α of feasible solutions from Algorithm Calculate the new set Ξ of the solutions using the specified real time data. 4. Find the decision boundary from Θ solving the log-likelihood problem in equation Add the decision boundary to the set of decision boundaries for level l. 6. If all transportation routes have been evaluated terminate. Otherwise go to Step 1.

15 Integration of KLR with the simulation model First we find the solution to the following problem At the level l of the simulation model the best transportation system is calculated as follows:

16 Sample Problem 1 One Product (t_max, q_min and q_max) 4 Levels (3 nodes, 3 nodes, 4 nodes, 5 nodes) 3 connections matrices {S 1, S 2, S 3 } Quality values (related to product) Link travel costs (c_min and c_max) Link travel times (formulated based on α 1 to α k ) Vehicle (probability of choosing vehicle 1 to 5: β 1 to β 5 Probability of temperature failure in vehicle 1 to 5: Φ 1 to Φ 5

17 Problem 1 Connectivity Model 1:

18 Numerical results

19 Visualization of decision making effectiveness.

20 Sample Problem 2 Model 2:

21 Problem 2 Results

22 Conclusion We demonstrate the integration of RFID technology and machine learning to optimize the quality of the products at the delivery stage with minimum cost in perishable foods supply chain. The simulation procedure provides the information about the best decision, which minimize the cost and maximize the quality with respect to real time situations. Based on these information, at each node of the model a kernel logistic regression (KLR) is adopted to optimize decision makings based on real time information (RFID) of the further demands at that node. The results show that the integrated KLR supply chain model improves the cost of handling of demands by 15% in the first model and 21% in the second model. Furthermore, in the first and the second model, the proposed real time food quality control improves the quality of the products by 14% and 5%, respectively.

23 Thank You!! For inquiries, please contact: Leorey Marquez CSIRO Digital Productivity Gate 5, 71 Normanby Road, Clayton, Victoria Australia 3168 Phone Fax