Decision Making Using Simulation Methods in Sustainable Transportation

Abstract

Abstract Improvement of logistics systems requires a specific objective function that may be subject to several constraints. However, for complex practical logistics systems, there is no way to grasp the system s behavior just in terms of a few equations. Simulation—especially time-sliced and discrete event simulation—can be used in such situations, as it allows for modeling the causality of systems on the basis of local logic. We illustrate the use of simulation to study practical logistics issues, such as transportation, inventory, and network structures. The analyses reported in this field mainly focus on economical improvements brought about by a reduction of traveled distance, truck load, cooling, etc. However, it turns out that in many cases these improvements also affect the other two dimensions of sustainability: The reduction of traveled distance directly leads to a decrease in energy use and pollution; and in urban areas such indicators will lead to less noise, reduced traffic jams, and as a consequence, better living conditions. This chapter discusses the simulation techniques (including the Monte Carlo technique), the introduction of environmental parameters into these models, and the combination of simulation with metaheuristics—called simheuristics—to integrate several optimization aspects. The field is illustrated with application examples involving the transportation of people, as well as the supply networks of industry and trade.