طراحی شبکه لجستیک پایدار تحت عدم قطعیت

Logistics network design problems that take into account the facility locations and the shipment of the product flows have been extensively tackled for decades. Recently due to the increase in stringent pressures from environmental and social requirements, more and more manufacturers have adopted the practice of using returned products and incorporated product recovery activities into the production. Consequently, a focus on logistics network design is a step towards the broader adoption and development of sustainability, which concerns not only the economic aspects but also how logistics network will affect other aspects of human life, such as the environment and sustainability of natural resources. Sustainability stretches the concept of logistics network design to look at optimizing operations from a broader perspective—the entire production system and postproduction stewardship as opposed to just the production of a specific product (Linton et al., 2007). Implementation of sustainable logistics operations requires setting up additional appropriate logistics infrastructure for the arising flows of used and recovered products, which adds an additional level of complexity to traditional logistics network design. Physical location, facilities and transportation links need to be chosen to transfer forward products from manufacturers to customers and to convey used products from their former users to manufacturers for the purpose of recovery or safe disposal. Thus, the network design issues in the sustainable logistics system involve two categories with respect to the material flows: forward product flow and returned product flow. For the traditional forward logistics environments, a number of standard mixed integer linear programming (MILP) approaches have been developed that are commonly recognized (Mirchandani and Francis, 1989). For the reverse logistics context, a standard set of model has not yet been established. Spengler et al. (1997) developed an MILP model for recycling of industrial byproducts. The model was based on the multi-level capacitated facility location problem modified for the special problem structure. Jayaraman et al. (1999) analyzed the logistics network of an electronic equipment remanufacturing company in the USA. A single period MILP model based on a multi-product capacitated warehouse location model was developed. Shih (2001) proposed a new MILP model to optimize the infrastructure design and the reverse network flow for the recovery of electrical appliances and computers. As summarized from the aforementioned discussion, most studies of the existing network models only consider a single flow such as forward or reverse flow, whereas the activities of reverse logistics may have strong influence on the operations of forward logistics such as the occupancy of the storage spaces and transportation capacity. Fleischmann et al. (2001) developed an MILP model to analyze the impact of product recovery on sustainable logistics network design. Ko and Park (2005) also proposed an MILP model to illustrate the impact of an integrated solution on the sustainable logistics network design. However, both the aforementioned research assumed that the operational characteristics of, and hence the design parameters for, the sustainable logistics network were deterministic. In practice, the characteristics of sustainable logistics network include considerable system uncertainty. Both markets for forward products and supply of used products by customers typically involve many unknowns (Corbett and Klassen, 2006). For the studies of logistics network planning problem under uncertainty, Li et al. (2009) developed a hybrid simulation optimization method for production planning of dedicated remanufacturing under uncertainty. Rastogi et al. (2010) studied the supply network capacity planning for semiconductor manufacturing by considering the uncertainty of the future demand. In this paper, a two-stage stochastic programming model is proposed to explicitly account for the design of sustainable logistics network under uncertainty. A solution approach integrating the sample average approximation scheme with an importance sampling strategy is developed to solve a case study with a large number of scenarios.

Logistics network design problems that take into account the facility locations and the shipment of the product flows have been extensively tackled for decades. Recently due to the increase in stringent pressures from environmental and social requirements, more and more manufacturers have adopted the practice of using returned products and incorporated product recovery activities into the production. Consequently, a focus on logistics network design is a step towards the broader adoption and development of sustainability, which concerns not only the economic aspects but also how logistics network will affect other aspects of human life, such as the environment and sustainability of natural resources. Sustainability stretches the concept of logistics network design to look at optimizing operations from a broader perspective—the entire production system and postproduction stewardship as opposed to just the production of a specific product (Linton et al., 2007). Implementation of sustainable logistics operations requires setting up additional appropriate logistics infrastructure for the arising flows of used and recovered products, which adds an additional level of complexity to traditional logistics network design. Physical location, facilities and transportation links need to be chosen to transfer forward products from manufacturers to customers and to convey used products from their former users to manufacturers for the purpose of recovery or safe disposal. Thus, the network design issues in the sustainable logistics system involve two categories with respect to the material flows: forward product flow and returned product flow. For the traditional forward logistics environments, a number of standard mixed integer linear programming (MILP) approaches have been developed that are commonly recognized (Mirchandani and Francis, 1989). For the reverse logistics context, a standard set of model has not yet been established. Spengler et al. (1997) developed an MILP model for recycling of industrial byproducts. The model was based on the multi-level capacitated facility location problem modified for the special problem structure. Jayaraman et al. (1999) analyzed the logistics network of an electronic equipment remanufacturing company in the USA. A single period MILP model based on a multi-product capacitated warehouse location model was developed. Shih (2001) proposed a new MILP model to optimize the infrastructure design and the reverse network flow for the recovery of electrical appliances and computers. As summarized from the aforementioned discussion, most studies of the existing network models only consider a single flow such as forward or reverse flow, whereas the activities of reverse logistics may have strong influence on the operations of forward logistics such as the occupancy of the storage spaces and transportation capacity. Fleischmann et al. (2001) developed an MILP model to analyze the impact of product recovery on sustainable logistics network design. Ko and Park (2005) also proposed an MILP model to illustrate the impact of an integrated solution on the sustainable logistics network design. However, both the aforementioned research assumed that the operational characteristics of, and hence the design parameters for, the sustainable logistics network were deterministic. In practice, the characteristics of sustainable logistics network include considerable system uncertainty. Both markets for forward products and supply of used products by customers typically involve many unknowns (Corbett and Klassen, 2006). For the studies of logistics network planning problem under uncertainty, Li et al. (2009) developed a hybrid simulation optimization method for production planning of dedicated remanufacturing under uncertainty. Rastogi et al. (2010) studied the supply network capacity planning for semiconductor manufacturing by considering the uncertainty of the future demand. In this paper, a two-stage stochastic programming model is proposed to explicitly account for the design of sustainable logistics network under uncertainty. A solution approach integrating the sample average approximation scheme with an importance sampling strategy is developed to solve a case study with a large number of scenarios.