Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses

Abstract

To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality. We develop a stochastic programming model that exploits historical product quality delivery data to produce slaughterhouse allocation plans with reduced levels of uncertainty in received livestock quality. The allocation plans generated by this model fulfill demand for multiple quality features at separate slaughterhouses under prescribed service levels while minimizing transportation costs. We test the model on real world problem instances generated from a data set provided by an industrial partner. Results show that historical farmer delivery data can be used to reduce uncertainty in quality of animals to be delivered to slaughterhouses.

Green closed-loop supply chain network design considering cost control and CO2 emission

“…Ultimately, the decision-maker chooses the best or feasible solution with respect to the objective and the Pareto solutions. Compared with stochastic opportunity constraint programming (Sch€ on and K€ onig, 2018; Rijpkema et al, 2016), the scenario-based method is more convenient for dealing with the uncertain environment (Freeman et al, 2015;Vrakopoulou et al, 2019). Therefore, we employ the scenario-based method to cope with the uncertain demand.…”

A multi-objective approach under uncertainty for designing a green meat supply chain network

“…These objectives have focused on different concerns such as maximization of quality and the safety of products (James et al, 2006;Ahumada and Villalobos, 2009;Akkerman et al, 2010;Rong et al, 2011;Soysal et al, 2012;Rijpkema et al, 2016), or minimization of the total network cost (Villegas et al, 2006;Bhattacharya and Bandyopadhyay, 2010;Cheshmehgaz et al, 2013;Mogale et al, 2018). Paksoy et al (2012) proposed a multi-objective linear programming formulation to minimize the total transportation cost between different echelons of the supply chain network for vegetable oils.…”

Bi-criterion procedures to support logistics decision making: cost and uncertainty

“…Japan prefers fat meat, Greece prefers light and lean carcasses). Rijpkema et al [2013] describe an allocation problem where a decision maker is confronted with variability in delivered quality. For this problem, a model is presented where one of the objectives is to minimize the expected shortage with respect to a demanded product quality, whereas the other objective is the minimization of transportation cost.…”

“…For this planning problem, Rijpkema et al [2013] present a stochastic programming model to reduce service level violations in supplied livestock quality. In this paper we do not consider service levels, but instead focus on minimizing the expected shortfall from demanded quality.…”