Solving selected logistical problems with CMPL and pyCMPL
Logistic management as planning, implementing, directing and controlling of logistical processes contains specific logistical decision problems such as Facility Location Problems, Vehicle Routing Problems or Transportation Problems [4,7]. These problems can be formulated and solved with several Operations Research approaches, whereby usually optimisation software is used. This article is intended to explain how selected logistical problems can be formulated and solved with CMPL and its Python interface pyCMPL, which belongs to optimisation software systems for linear problems [3]. As examples for logistical problems the Travelling Salesman Problem and the Capacitated Facility Location Problem will be discussed in this article. The explanations won’t be focused on detailed model discussions but the implementation of these models for realistic problems will be rather described in detail on the basis of fictitious problem scenarios located in the Russian regions of Yekaterinburg, Chelyabinsk, Kurgan and Tyumen.