In this paper, a multiple objective model to large-scale and long-term industrial energy supply chain scheduling problems is considered. The problems include the allocation of a number of fossil, peat, and wood-waste fuel procurement chains to an energy plant during different periods. This decision environment is further complicated by sequence-dependent procurement chains for forest fuels. A dynamic linear programming model can be efficiently used for modelling energy flows in fuel procurement planning. However, due to the complex nature of the problem, the resulting model cannot be directly used to solve the combined heat and electricity production problem in a manner that is relevant to the energy industry. Therefore, this approach was used with a multiple objective programming model to better describe the combinatorial complexity of the scheduling task. The properties of this methodology are discussed and four examples of how the model works based on real-world data and optional peat fuel tax, feed-in tariff of electricity and energy efficiency constraints are presented. The energy industry as a whole is subject to policy decisions regarding renewable energy production and energy efficiency regulation. These decisions should be made on the basis of comprehensive techno-economic analysis using local energy supply chain models. (C) 2011 Elsevier Ltd. All rights reserved.