![](bg8.png)
affected_comp(cp5364,alivestock,il),
affected_comp(cp4568,acreolfood,la),
affected_comp(cp2138,agourmet,wi),
affected_comp(cp1603,afoods,ok),
affected_comp(cp1050,atrading,ca),
affected_comp(cp3691,gustopack,il),
affected_comp(cp1981,aservice,wi),
affected_comp(cp5336,ainttrade,ca),
affected_comp(cp789,afoodsusa,fl),
affected_comp(cp3971,asausage,il),
affected_comp(cp3606,afarms,il),
affected_comp(cp5346,agrove,ga),
affected_comp(cp3753,apacking,il),
affected_comp(cp1659,aprocessor,co),
affected_comp(cp5344,aglobe,ny),
backward_trace(cp3617,il,
porkfresh,cp3572,il).
6 CONCLUSIONS
Using the case of a food recall involving pork
products, this paper demonstrates the utility of
answer set programming in identifying not only the
source of a food contamination but also the location
of contaminated products across the food chain. We
represent all possible paths of a contaminated
product across the supply chain as a sequence of
stages by which a food product evolves from raw,
unprocessed food at the farmer/grower level of the
supply chain, to a processed food ready for
consumption at the retail point-of-sale. Using rules
of inference, we then reduce the set of all possible
pathways of contamination based on information
contained in the recall. We are also able to capture
the process by which contaminated products become
ingredients in other products during sequential
stages of production. The logic-based approach
developed herein is well-suited to be used by
state
agencies charged with inspecting food production,
distribution and retail facilities in the event of a
national recall. The approach is particularly useful
for ingredient-driven contaminations in which the
contaminated product is used as an ingredient in a
broad set of secondary products.
REFERENCES
Buhr, B. L. 2003. “Traceability and information
technology in the meat supply chain: implications for
firm organization and market structure.” Journal of
Food Distribution Research, 34 (3), pp. 13–26.
Bulut, H., and Lawrence, J. D. Apr. 2008. “Meat Slaughter
and Processing Plants’ Traceability Levels: Evidence
From Iowa.” Working Paper # 08015, Working Paper
Series, Department of Economics, Iowa State
University.
Calimeri, F.; Dell’Armi, T.; Eiter, T.; Faber, W.; Gottlob,
G.; Ianni, G.; Ielpa, G.; Koch, C.; Leone, N.; Perri, S.;
Pfeifer, G.; and Polleres, A. 2002. “The dlv system.”
In Flesca, S., and Ianni, G., eds., Proceedings of the
8th European Conference on Artificial Intelligence
(JELIA 2002), pp. 537–540.
FDA Food Safety Modernization Act of 2010, S. 510,
111
th
Congress, 2
nd
Session. Signed into law by
President Barack Obama on 4 Jan. 2011.
Fritz, M., and Schiefer, G. Feb. 2009. “Tracking, tracing
and business process interests in food commodities: A
multi-level decision complexity.” International
Journal of Production Economics, 117(2), pp. 317–
329.
Fritz, M., and Schiefer, G. 2008. “Tracking and tracing in
food networks.” World Conference on Agricultural
Information and IT (IAALD AFITA WCCA 2008),
Tokyo University of Agriculture, Tokyo, Japan, 24–27
August 2008, pp. 967–972.
Gelfond, M., and Lifschitz, V. 1988. “The stable model
semantics for logic programming.” In Kowalski, R.,
and Bowen, K., eds., International Logic
Programming Conference and Symposium, pp. 1070–
1080. MIT Press.
Gelfond, M., and Lifschitz, V. 1991. “Classical negation
in logic programs and disjunctive databases.” New
Generation Computing, 9, pp. 365–385.
GS1. Mar. 2010. “Business Process and System
Requirements for Full Supply Chain Traceability.”
GS1 Global Traceability Standard, Issue 1.2.2.
Marek, V. W., and Truszczynski, M. 1999. The Logic
Programming Paradigm: a 25-Year Perspective, chap.
“Stable models and an alternative logic programming
paradigm,” pp. 375–398. Springer Verlag, Berlin.
Niemela, I., and Simons, P. 2000. Logic-Based Artificial
Intelligence, chap. “Extending the Smodels System
with Cardinality and Weight Constraints,” pp. 491–
521. Kluwer Academic Publishers.
Nogueira, M. L., and Greis, N. P. 2011. “Rule-Based
Complex Event Processing for Food Safety and Public
Health.” In Bassiliades, N., Governatori, G., and
Paschke, A., eds., Proceedings of the 5
th
International
Symposium of Rule-Based Reasoning, Programming,
and Applications (RuleML Europe 2011), Barcelona,
Spain, July 19–21, 2011. Lecture Notes in Computer
Science 6826, pp. 376–383. Springer.
Nogueira, M. L., and Greis, N. P. 2011. “Application of
Answer Set Programming for Public Health Data
Integration and Analysis.” A Min Tjoa, A. M.;
Quirchmayr, G., You, I., and Xu, L., eds., Proceedings
of the 6
th
International Availability, Reliability and
Security for Business, Enterprise and Health
Information Systems (ARES 2011), IFIP WG 8.4/8.9
Cross Domain Conference and Workshop, Vienna,
Austria, August 22–26, 2011. Lecture Notes in
Computer Science 6908, pp. 118–134. Springer.
Raschke, A.; Strich, S.; Huppke, S.; Neugebauer, M.;
Geuther, E.; Bertling, W.; Walders, B.; Reiser, C.; and
KEOD2012-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
132