Production and Operations Management: Models and Algorithms

This chapter intends to give an overview of the literature on dynamic lot-sizing models and stochastic transshipment models. These two types of models are used as a basis for developing models with substitution in the following chapters. Section 2.1 contains a classification of models for dynamic lot-sizing / production planning, and selected models. In Sect. 2.2, we give a brief overview of available methods for solving deterministic dynamic lot-sizing problems modeled using mixed-integer linear programming (MILP). Section 2.3 introduces transshipment problems and presents a classification scheme for transshipment models. Section 2.4 reviews selected solution approaches that can be applied to stochastic inventory control models such as transshipment problems. Dynamic lot-sizing models and transshipment models are linked to certain planning tasks in an Advanced Planning System (APS): In the conceptual framework of Advanced Planning and the Supply Chain Planning (SCP) matrix (Fleischmann et al., 2005, p. 87) that is shown in Fig. 2.1, the combined lot-sizing and scheduling models considered in this work cover the planning tasks lot-sizing and machine scheduling. In addition, they are linked to the topic short-term sales planning, as a Capable-To-Promise (CTP) logic (Fleischmann et al., 2005, p. 91, Kilger and Schneeweiß, 2005, p. 185) could make use of the models to check whether customer orders could be fulfilled. Also, some lot-sizing models include supplier selection, capacity planning and other mid-term/tactical planning tasks in addition to shortterm planning tasks. Transshipment models are used to optimize short-term planning tasks related to warehouse replenishment, transport planning, and short-term sales planning: Transshipments are executed to fulfill customer demands in case of local stock-outs. These replenishments from warehouses on the same echelon have to be implemented using available transportation capacities. Thus, in the software architecture of an APS (a generic, idealized architecture is shown in Fig. 2.2), the lot-sizing models considered in this work will most likely be used for optimization in a Production Planning and/or Scheduling software module. Transshipment models would be used for the optimization of operational decisionmaking in the Transport Planning and Demand Fulfilment & Available-To-Promise (ATP) software modules. For details on the functionalities and architectures of APS, the reader is referred to Meyr et al. (2005a,b).