We review the integration of medium-term production planning and short-term scheduling. We begin with an overview of supply chain management and the associated planning problems. Next, we formally define the production planning problem and explain why integration with scheduling leads to better solutions. We present the major modeling approaches for the integration of scheduling and planning decisions, and discuss the major solution strategies. We close with an account of the challenges and opportunities in this area.
The supply chain (SC) of a manufacturing company is a network of facilities and distribution options that performs the following functions: procurement of raw materials, transformation of raw material into finished products, and distribution of finished products to customers. The goal is to achieve high customer satisfaction level at low cost (Christopher, 1998, Chopra and Meindl, 2001 and Shapiro, 2006). Chemical supply chains in particular contain large opportunities to reduce cost: they are complex interconnected systems that change constantly, and their activities represent a significant portion of total cost to serve customers (Ferrio & Wassick, 2008).
Tayur (2003) noted that inventories in US supply chains can be reduced substantially, without affecting customer satisfaction levels, leading to significant savings. Inventory levels can be reduced if the efficiency of the SC as a whole is improved. Higher efficiency can be achieved through proper coordination of material, financial and information flows across the SC (Grossmann, 2005, Stadtler, 2005 and Varma et al., 2007). The planning problems that have to be solved to achieve this coordination cover a wide range of activities, from procurement and production to distribution and sales, and a wide range of time scales from long-term (strategic) to short-term (operational) decisions (Fig. 1).Strategic (long-term) planning determines the structure of the supply chain (e.g. facility location). Medium-term (tactical) planning is concerned with decisions such as the assignment of production targets to facilities and the transportation from facilities to warehouses to distribution centers. Finally, short-term planning is carried out on a daily or weekly basis to determine the assignment of tasks to units and the sequencing of tasks in each unit. At the production level, short-term planning is referred to as scheduling.
However, due to interconnections between different levels of the supply chain, there are numerous trade-offs between decisions made at the various nodes of the SC. To achieve globally optimal solutions therefore the interdependencies between the different planning functions should be taken into account, and planning decisions should be made simultaneously. In other words, planning problems should be integrated.
In this paper, we specifically review approaches for the integration of medium-term production planning and short-term scheduling (Shah, 2005). In Section 2, we review production planning and present the standard lot-sizing formulation that is often used in production planning systems. In the next section, we discuss why integration with scheduling is necessary, review the major approaches to process scheduling, and discuss the implementation of production planning solutions. In Sections 4 and 5, we review the different modeling approaches and discuss the main solution strategies developed to solve the integrated models effectively. We close with a discussion of open challenges in this area and some promising research directions.
This paper presented a review of modeling approaches and solution strategies for the integration of production planning and scheduling. It focused on the key concepts and advantages/disadvantages of various modeling and solution methods but also discussed some representative approaches. Clearly, there has been significant progress during the last 15 years especially in mathematical programming methods. Nevertheless, the solution of industrial problems remains a challenge.
At the same time, numerous opportunities are presented by advances in optimization solvers and decomposition methods, and increased accessibility of cheap computational components. This paper discussed how these advances can lead to modeling methods and solution strategies that can better address practical applications.
