application of machine learning and rfid in the stability...

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

Ehsan Mohebi and Leorey O. Marquez2014 International Workshop on Food Supply Chains4-7 November 2014, San Francisco, USA

Key Challenges to the World Food Supply Chain

Global population expected to increase to 8 billion by 2050 77% 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

Dependencies in the Australian food supply chain (DAFF, 2012)

CSIRO Digital Productivity and Services

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


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 www.foodsupplychainopt.orgCSIRO Digital Productivity and Services

RFID for Supply Chain Efficiency and Waste Reduction

• 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.

• 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.

Source: www.jesic-tech.com


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: www.tssl.com

• 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.


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 .

• RFID technology has successful applications in: access control systems, airport baggage handling, livestock management systems, automated toll collection systems, logistics and retail businesses.

Source: Fieldhouse, 2014


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


Simulated Network

Given a supply chain network with:


Supply Chain Decision-making

Problem: Find the route/vehicle decision rlk ϵ Rl

ij; k = 1,...,hij to transport product p with demand d and quality qp

d(t) at time t, from node i on level lto 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


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.


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.


Data collection for logistic regression

Minimum cost

Maximum quality

Real-time information

cost

qual

ity


Training the Kernel Logistic Regression model

Algorithm 2: (Training the KLR model at level l )Input: the simulation dataOutput: the set of decision boundaries Θ for each level l1. Select one available transportation route.2. Calculate the set Α of feasible solutions from Algorithm 1.3. 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 31.5. 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.


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 calculatedas follows:


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 {S1, S2, S3}• Quality values (related to product)• Link travel costs (c_min and c_max)• Link travel times (formulated based

on α1 to αk)• Vehicle (probability of choosing

vehicle 1 to 5: β1 to β5

• Probability of temperature failure in vehicle 1 to 5: Φ1 to Φ5


Problem 1 Connectivity

Model 1:


Numerical results


Visualization of decision making effectiveness.


Sample Problem 2

Model 2:


Problem 2 Results


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.


For inquiries, please contact:

Leorey MarquezCSIRO Digital ProductivityGate 5, 71 Normanby Road, Clayton, VictoriaAustralia 3168

Phone 61-03-9545 8258Fax 61-03-9544 1128email: [email protected]

Thank You!!


application of machine learning and rfid in the stability...

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