An important issue in supply chain management is how to measure performance. Traditionally, companies will try to adjust the various parameters under their control in order to simultaneously maximize profit (or minimize costs) and optimize customer ser- vice. Unfortunately, these two objectives are frequently in confict, and devising a single performance measure that weighs the two objectives in a satisfactory way is a challenge. An added complication arises if we wish to incorporate the environmental impact of transport activities into our model.

The objective of the project is to investigate the feasibility of building multi-objective optimization (MOO) decision support tools for strategic and operational supply chain management, with a special focus on environmental issues. The aim is to explore a wide range of approaches to multi-objective optimization, from the traditional "ε-constraint method, which transforms the problem into a single objective optimization, to the latest multi-objective evolutionary algorithms (MOEA), which generate a large number of non-dominated solutions, simultaneously on different supply chain models. Also new approaches that are based on MOEAs, but constrain the size of the solution set will be investigated. Particular interest will be the close integration of transport management within supply chains, with a view to having more control over the environmental impact of goods transport.