(472f) Resource Allocation and Campaign Planning in Pharmaceutical Plants

The pharmaceutical industry is focusing on lean and sustainable manufacturing. Costs for materials, inventory, labor, waste treatment, etc. and supply chain management are receiving greater attention. Most pharmaceutical manufacturing plants are multistage and multiproduct batch plants that produce low-volume, high-value products, each of which could involve a series of intermediates. Strict ?regulatory protocols' of the US Food and Drug Administration (FDA) constrain plant operations. One unique feature of these operations is the extensive equipment cleaning that occurs between successive batches of the same intermediate and between campaigns of different intermediates. Changeover times between intermediates can span a few weeks. Therefore, long campaigns could reduce changeover costs. However, they also increase inventory costs, as the demands of many drugs can be met by producing a few batches in a short time. Production planning of these campaigns is complicated by several factors such as production for clinical trials, introduction of new products, preventive maintenance, plant upgrades, etc. Based on our interaction with the local pharmaceutical industry, a key consideration in campaign scheduling seems to be the allocation of various resources to the multi-stage operations. The production managers very often tweak this in a heuristic fashion to influence production efficiencies or amounts. Thus, there is a need for a systematic optimization methodology and intelligent aids for assisting the campaign planning process in the pharmaceutical industry.

Shah (2004) and Verma et al. (2007) have presented excellent reviews on pharmaceutical supply chains highlighting the existing approaches, emerging research challenges, and opportunities. Sundaramoorthy and Karimi (2004) developed a multiperiod MILP model for planning campaign production and re-allocation of equipment and time for the introduction of new products. They assessed the feasibility or profitability of introducing new intermediates and outsourcing of the existing intermediates. In subsequent work (Sundaramoorthy et al., 2006) addressed global production planning in pharmaceutical industry. Suryadi and Papageorgiou (2004) proposed an integrated approach for the production, maintenance planning, and crew allocation for the maintenance along with the design of multipurpose batch plants. Thus, while campaign planning in general and in pharmaceutical industry in particular has been addressed in the literature, no work to our knowledge has addressed the effect of resource allocation on campaign planning.

In this work, we present a mathematical formulation for the integrated problem of resource allocation and campaign planning in multistage and multiproduct pharmaceutical batch plants. We consider campaign changeovers, key resources, scheduled preventive maintenance, equipment upgrades, safety stock allowance, clinical trials, and NPIs (New Product Introductions) using a reactive scheduling framework in our approach. We assume that the production manager has the freedom to allocate appropriate resources to various campaigns over time in order to address changed plant conditions, product demands, and business scenarios. The scheduling objective is to minimize the overall operating cost including those for additional resources and outsourcing. The production plan gives the campaigns, their sequence on each production line, number of batches for each campaign, resource allocation profile over time for each campaign, inventory profile, etc. To demonstrate the performance of our formulation, we consider a case study from a typical pharmaceutical plant and execute a series of scenario studies to reflect the dynamic changes in the plant and the overall supply chain.

Keywords: Campaign scheduling, multiproduct batch plants, pharmaceutical industry, MILP

References:

1. Shah, N., 2004. Pharmaceutical supply chains: key issues and strategies for optimization. Computers and Chemical Engineering 28, 929-941.

2. Varma, V. A., Reklaitis, G. V., Blau, G. E., Penky, J. F., 2007. Enterprise-wide modeling and optimization - an overview of emerging challenges and opportunities. Computers and Chemical Engineering 31, 692-711.

3. Sundaramoorthy, A., Karimi, I. A., 2004. Planning in pharmaceutical supply chains with outsourcing and new product introductions. Industrial and Engineering Chemistry Research 43, 8293-8306.

4. Sundaramoorthy, A., Xianming, S., Karimi, I. A., Srinivasan, R., Presented in PSE-2006, July 09-13. An integrated model for planning in global chemical supply chains.

5. Suryadi, H., Papageorgiou, L. G., 2004. Optimal maintenance planning and crew allocation for multipurpose batch plants. International Journal of Production Research 42, 2, 355-377