In this thesis, we consider multiobjective combinatorial optimization problems. We address two main topics. We first address the polynomially solvable cases of the Traveling Salesperson Problem and the Bottleneck Traveling Salesperson Problem. We consider multiobjective versions of these problems with different combinations of objective functions, analyze their computational complexities and develop exact algorithms where possible. We next consider generating extreme supported nondominated points of multiobjective integer programming problems for any number of objective functions. We develop two algorithms for this purpose. The first one is an exact algorithm and finds all such points. The second algorithm finds only a subset of extreme supported nondominated points providing a worst case approximation for the remaining points.

In multi colony ant algorithms several colonies of ants cooperate in finding good solutions for an optimization problem. At certain time steps the colonies exchange information about good solutions. If the amount of exchanged information is not too large multi colony ant algorithms can be easily parallelized in a natural way by placing the colonies on different processors. In this paper we study the behaviour of multi colony ant algorithms with different kinds of information exchange between the colonies. Moreover we compare the behaviour of different numbers of colonies with a multi start single colony ant algorithm. As test problems we use the Traveling Salesperson problem and the Quadratic Assignment problem.

The purpose of this paper is to increase the routing efficiency of two previously separate commodity transports. By combining them in a common, multi-commodity network flow (MCNF). A Mixed Integer Linear Programming (MILP) model is used to minimize the mileage that is needed to fulfill demand in the different destinations of the transport network. Input needed for the model was mileage between destinations, which was obtained from open data. And the demand of respective commodity was received from documents and an estimation. To solve the stated problem approximations and simplifications was needed because it showed a NP-complete problem. The aim is to produce a result that shows a lower mileage than a reference measure from the present situation with separate transports. The result showed an optimized solution of 1939 km. Which was a difference of 1941 km from the reference measures, that summarized to 3880 km. Despite this the result from the model shows an effective optimization. Which makes the use of MILP for minimizing mileage inside a MCNF problem, a useful approach for solving the stated problem. / Syftet med arbetet var att effektivisera körsträckor för två tidigare separata transporter av olika produkter. Genom att kombinera dem till en gemensam transport i ett multi-commodity network flow (MCNF). Med en Mixed Integer Linear Programming (MILP) modell minimeras de körsträckor som krävs för att fylla efterfrågan i transportnätverkets adresser. In-data som krävdes för att en modell skulle kunna utföras var körsträckor mellan olika adresser, vilket hämtades från öppen data. Samt efterfrågan på produkter som erhölls från dokument och estimering.  Då problemet som skulle lösas visade på hög beräkningskomplexitet behövde ett antal approximationer och förenklingar verkställas. Målet var att visa på ett resultat där körsträckor hade förminskats relativt till ett referensmått från nuläget. Där resultatet visade på en optimerad lösning på 1939 km. Vilket var en differens på 1941 km från de referensmåttet som summerades till 3880 km. Modellens resultat visar trots det en effektiv optimering. Vilket gör att användningen av MILP för att minimera körsträckor inom MCNF problem, är ett effektivt tillvägagångssätt att lösa det motiverade problemet.