Production decisions in a supply chain environment aim at determining the most effective wayto use resources for the production of items while satisfying customers’ requirements. Thesedecisions are generally separated into two levels: a planning level and a scheduling level. At theplanning level, the objective is to determine a production plan, i.e. production quantities forevery period of the horizon that satisfy the demand and minimize the different costs (productioncost, setup cost, logistics such as holding cost, etc.). These production quantities correspond tothe sizes of the lots processed in the shop floor. At the scheduling level, these lots aresequenced on the resources what means that the starting and completion times are computedfor each lot.In theory as well as in practice, the tactical (planning) and operational (scheduling) decisionlevels are most of the time considered sequentially. However, scheduling largely depends onthe production quantities computed at the planning level and ignoring scheduling constraints inthe determined plan always leads to inconsistent decisions. Integrating planning and schedulingis therefore important for effectively managing logistics and production operations. This isparticularly true in a supply chain context where suppliers and customers tend to work moreclosely in order to avoid stock-outs and unnecessary inventories.The purpose of project IALOM is to design innovative approaches, in the form of solutionalgorithms for solving in an integrated way planning and scheduling problems in a multi-siteenvironment, each site being a multi-machine work centre (job shop, flow shop, open shop,multiprocessor job shop, etc.). Moreover, logistics and supply chain-related constraints will beintegrated to the planning models by taking the interdependencies between productionquantities in different sites into account. This requires the development of new models andoptimization approaches as superior alternatives to the classical hierarchical approach.The scientific originalities and challenges of IALOM are therefore multiple:• A first challenge is the development of optimization techniques for large and complexproblems. These techniques should provide optimized solutions in reasonablecomputational times.• A second challenge lies in the analysis and optimization of multi-level problems.Understanding and designing effective models is difficult and crucial to ensure therelevance of the optimization process.• A third challenge is related to the type of optimization problems that need to be solved,since they combine large continuous and discrete variables. These mixed integerproblems are known to be very difficult to solve with traditional approaches. Indeed,optimization methods used in practice for production planning problems are usuallybased on standard commercial solvers or greedy heuristics. Except general branch andcut methods that are integrated in standard solvers, advanced operational researchmethods (Lagrangian relaxation, column generation, specialized cuts, meta- heuristics,etc.) are generally not developed.The main expected results from project IALOM are complete and innovative productionplanning and scheduling solution algorithms based on novel operational research optimizationtechniques that will provide flexible and robust production plans. Those algorithms could be thebases for further software development, after extensive experimentation phases.