1,597 research outputs found
EOQ with a correlated binomial supply
No full textThis paper considers a variant of the economic order quantity (EOQ) model under random supply. A binomial supply model is adopted where every item received is of imperfect quality with the same probability. We study the realistic case where the qualities of items in an order are correlated and draw useful insights, the most interesting of which is that correlation decreases the order size. Several practical correlation patterns are investigated and discussed. We also show that several commonly used models in the literature can be represented by an equivalent correlated binomial supply model. © 2013 Elsevier B.V. All rights reserved.Avriel M., 2003, NONLINEAR PROGRAMMIN; Babich V, 2007, MandSOM-MANUF SERV OP, V9, P123, DOI 10.1287-msom.1060.0122; Chang HC, 2004, COMPUT OPER RES, V31, P2079, DOI 10.1016-S0305-0548(03)00166-7; Cheng CS, 1997, INT J PROD RES, V35, P667, DOI 10.1080-002075497195650; Freimer M, 2006, EUR J OPER RES, V173, P241, DOI 10.1016-j.ejor.2004.11.024; GERCHAK Y, 1992, INT J PROD ECON, V26, P297, DOI 10.1016-0925-5273(92)90078-L; Goyal SK, 2002, INT J PROD ECON, V77, P85, DOI 10.1016-S0925-5273(01)00203-1; GROSFELDNIR A, 1993, MANAGE SCI, V39, P650, DOI 10.1287-mnsc.39.5.650; Huang CK, 2004, INT J PROD ECON, V91, P91, DOI 10.1016-S0925-5273(03)00220-2; Karlin S., 1958, STUDIES MATH THEORY; Khan M, 2011, INT J PROD ECON, V132, P1, DOI 10.1016-j.ijpe.2011.03.009; LEVITAN RE, 1960, MANAGE SCI, V6, P172, DOI 10.1287-mnsc.6.2.172; Maddah B, 2008, INT J PROD ECON, V112, P808, DOI 10.1016-j.ijpe.2007.07.003; Maddah B, 2010, INT J PROD ECON, V124, P340, DOI 10.1016-j.ijpe.2009.11.029; Maddah B, 2009, APPL MATH MODEL, V33, P1997, DOI 10.1016-j.apm.2008.05.009; Maddah B, 2010, COMPUT IND ENG, V58, P691, DOI 10.1016-j.cie.2010.01.014; Papachristos S, 2006, INT J PROD ECON, V100, P148, DOI 10.1016-j.ijpe.2004.11.004; PORTEUS EL, 1986, OPER RES, V34, P137, DOI 10.1287-opre.34.1.137; Purintrapiban U, 2012, COMPUT IND ENG, V62, P1093, DOI 10.1016-j.cie.2012.01.002; ROSENBLATT MJ, 1986, IIE TRANS, V18, P48, DOI 10.1080-07408178608975329; Ross Sheldon M., 1996, STOCHASTIC PROCESSES; Salameh MK, 2000, INT J PROD ECON, V64, P59, DOI 10.1016-S0925-5273(99)00044-4; Sepheri M., 1986, IIE T, V18, P63; SHIH W, 1980, INT J PROD RES, V18, P677, DOI 10.1080-00207548008919699; Silver E. A., 1976, INFOR. Canadian Journal of Operational Research and Information Processing, V14; Wright CM, 1998, OMEGA-INT J MANAGE S, V26, P29, DOI 10.1016-S0305-0483(97)00042-X; Yan XM, 2012, INT J PROD ECON, V139, P302, DOI 10.1016-j.ijpe.2012.05.013; YANO CA, 1995, OPER RES, V43, P311, DOI 10.1287-opre.43.2.311; Yu JB, 2008, INT J PROD RES, V46, P5907, DOI 10.1080-0020754070135872934
Disaggregation and consolidation of imperfect quality shipments in an extended EPQ model
No full textWe consider a standard economic production quantity (EPQ) model. Due to manufacturing variability, a fraction P of the produced inventory will have imperfect quality, where P is a random variable with a known distribution. We consider a 100percent inspection policy and further assume that the inspection rate is larger than that of production. Thus, all imperfect quality items will be detected by the end of the production cycle. For such an augmented EPQ model, we first derive the new optimal production quantity assuming that the imperfect quality items are salvaged once at the end of every production cycle. Then, we extend this base model to allow for disaggregating the shipments of imperfect quality items during a single production run. Finally, we consider aggregating (or consolidating) the shipments of imperfect items over multiple production runs. Under both scenarios we derive closed-form expressions for both the economic production quantity and the batching policy, and show that our desegregation-consolidation schemes can lead to significant cost savings over the base model. © 2010 Elsevier B.V. All rights reserved.Chan WM, 2003, PROD PLAN CONTROL, V14, P588, DOI 10.1080-09537280310001626179; Eroglu A, 2007, INT J PROD ECON, V106, P544, DOI 10.1016-j.ijpe.2006.06.015; GOSWAMI A, 1991, J OPER RES SOC, V42, P1105, DOI 10.1057-jors.1991.204; Goyal SK, 2002, INT J PROD ECON, V77, P85, DOI 10.1016-S0925-5273(01)00203-1; Hayek PA, 2001, PROD PLAN CONTROL, V12, P584, DOI 10.1080-095372801750397707; Hou KL, 2007, APPL MATH MODEL, V31, P10, DOI 10.1016-j.apm.2006.03.034; Huang CK, 2004, INT J PROD ECON, V91, P91, DOI 10.1016-S0925-5273(03)00220-2; KALRO AH, 1982, INT J PROD RES, V20, P775, DOI 10.1080-00207548208947804; Konstantaras I, 2007, INT J SYST SCI, V38, P473, DOI 10.1080-D0207720701352837; Maddah B, 2008, INT J PROD ECON, V112, P808, DOI 10.1016-j.ijpe.2007.07.003; Maddah B, 2010, INT J PROD ECON, V124, P340, DOI 10.1016-j.ijpe.2009.11.029; Maddah B, 2009, APPL MATH MODEL, V33, P1997, DOI 10.1016-j.apm.2008.05.009; Makis V, 1998, J OPER RES SOC, V49, P66, DOI 10.1057-palgrave.jors.2600484; Papachristos S, 2006, INT J PROD ECON, V100, P148, DOI 10.1016-j.ijpe.2004.11.004; PORTEUS EL, 1986, OPER RES, V34, P137, DOI 10.1287-opre.34.1.137; ROSENBLATT MJ, 1986, IIE TRANS, V18, P48, DOI 10.1080-07408178608975329; Ross Sheldon M., 1996, STOCHASTIC PROCESSES; Salameh MK, 1997, APPL MATH MODEL, V21, P85, DOI 10.1016-S0307-904X(96)00149-7; Salameh MK, 2000, INT J PROD ECON, V64, P59, DOI 10.1016-S0925-5273(99)00044-4; SHIH W, 1980, INT J PROD RES, V18, P677, DOI 10.1080-00207548008919699; YANO CA, 1995, OPER RES, V43, P311, DOI 10.1287-opre.43.2.311; Zipkin P. H., 2000, FDN INVENTORY MANAGE108
Determinants of adoption of protected designation of origin label: Evidence from the french brie cheese industry
No full textThis paper investigates empirically the determinants of agro-food firms' adoption of the Protected Designation of Origin (PDO) label. A unique dataset containing firm-level cost and production information on the French Brie cheese is used, covering the period 1980-2000. The Brie cheese data are especially relevant as PDO Brie producers have coexisted with other non-PDO producers since 1981. To evaluate the producers' incentive to opt for PDO certification, we use a structural switching regression model which incorporates cost and production structure variables. Results show that PDO certification is less attractive the higher the costs of raw materials and the greater the size of the company. PDO Brie cheese production costs are estimated to be on average 40percent higher than those for non-PDO Brie. The PDO production process could be technically inefficient when compared with the unconstrained non-PDO manufacturing; yet, PDO producers benefit from a price premium on their product which offsets their higher production cost. © 2010 The Authors. Journal compilation © 2010 The Agricultural Economics Society.Ali A. I., 1993, MEASUREMENT PRODUCTI, P120; Arfini F., 2006, CASE STUDY PARMIGIAN; BABCOCK BA, 2003, IOWA AG REV, V9; BANKER RD, 1984, MANAGE SCI, V30, P1078, DOI 10.1287-mnsc.30.9.1078; Bonnet C, 2001, EUR REV AGRIC ECON, V28, P433, DOI 10.1093-erae-28.4.433; BOWLIN WF, 1998, J COST ANAL FAL, P3; Coelli T, 1999, EUR J OPER RES, V117, P326, DOI 10.1016-S0377-2217(98)00271-9; Colinet P., 2006, CASE STUDY COMTE CHE; Collado R., 2006, CASE STUDY DEHESA EX; Fare R., 1994, PRODUCTION FRONTIERS; FARRELL MJ, 1957, J R STAT SOC SER A-G, V120, P253, DOI 10.2307-2343100; HASSAN D, 2002, CAHIERS EC SOCIOLOGI, V65, P23; Huang MY, 2002, J PROD ANAL, V18, P223, DOI 10.1023-A:1020686610802; Marette S, 2005, 05WP406 IOW STAT U C; Rosen Sherwin, 1979, J POLITICAL EC, V87, P507; Sauer J, 2006, J APPL ECON, V9, P139; SEIFORD LM, 1990, J ECONOMETRICS, V46, P7, DOI 10.1016-0304-4076(90)90045-U; van Ittersum K, 2007, J AGR ECON, V58, P1, DOI 10.1111-j.1477-9552.2007.00080.x108
- …
