Annual Production Budget in the Beverage Industry Luis Guimar˜ aes a , Diego Klabjan b , Bernardo Almada-Lobo a a Faculdade de Engenharia da Universidade do Porto, Porto, Portugal b Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois Abstract Driven by a real-world application in the beverage industry, this paper provides a design of a new VNS variant to tackle the annual production budget problem. The problem consists of assigning and scheduling production lots in a multi-plant environment, where each plant has a set of filling lines that bottle and pack drinks. Plans also consider final product transfers between the plants. Our algorithm fixes setup variables for family of products and determines production, inventory and transfer decisions by solving a linear programming (LP) model. As we are dealing with very large problem instances, it is inefficient and unpractical to search the entire neighborhood of the incumbent solution at each iteration of the algorithm. We explore the sensitivity analysis of the LP to guide the partial neighborhood search. Dual re-optimization is also used to speed-up the solution procedure. Tests with instances from our case study have shown that the algorithm can substantially improve the current business practice, and it is more competitive than state-of-the-art commercial solvers and other VNS variants. Keywords: Long-term production planning, Beverage industry, Large neighborhood search, Mathematical programming 1. Introduction The beverage industry is a sub-sector of the food industry, the second largest sector in the European manufacturing industry in terms of value added. It supplies a variety of products from wine, beer and spirits to mineral and sparkling water and soft drinks. Markets worldwide are strongly affected by cultural differences, especially in Europe. This effect creates the environment for the appearance of small to medium size companies that are specialised in local products and/or local brands. Nevertheless, there are a number of large multinational companies able to compete in markets across the globe offering a wide variety of products, such as soft drinks. Today’s competition in this sector leads companies to expand their product portfolio, which combined with the advanced technology present in modern production sites, raises the need for efficient production planning. Moreover, production sites in this industry tend to be geographically disperse allowing companies to satisfy local demands at lower costs. Production planning is often conducted considering only one plant at time, ignoring the potential benefits of coordination. This paper is inspired by a real industrial case from a company competing in the beer and soft drink industries. The focus is to define a long- term production plan to a series of production (filling) lines located in different plants. The scheduling of product families at each filling line is the basis for production, inventory, and transfer decisions. Transfer decisions represent movements of finished products and come from the fact that demand observed at a geographical area around each plant can be satisfied by other production sites to cope with under capacity of a given plant. Under these conditions, plants act both as production and distribution centers since warehouses are located near them and have individual demand. Decisions are traditionally made for a rolling planning horizon of 12 to 18 months with a monthly bucket. Real-world production planning problems often result in intractable models, and even simplified versions result in NP-hard problems. However, only realistic modelling of the problem features can help managers in their deci- sions, which was already pointed out as a field of future research of two previous literature reviews on production Email addresses: guimaraes.luis@fe.up.pt (Luis Guimar˜ aes), d-klabjan@northwestern.edu (Diego Klabjan), almada.lobo@fe.up.pt (Bernardo Almada-Lobo) Preprint submitted to Engineering Applications of Artificial Intelligence April 9, 2011