This paper introduces a bi-objective winner-determination problem and presents a multiobjectivegenetic algorithm to solve it. The problem examined arises in the procurement of transportation contractsvia combinatorial auctions. It is modeled as an extension to the set-covering problem and...

The paper presents a tree search algorithm for the three-dimensional container loading problem(3D-CLP). The 3D-CLP is the problem of loading a subset of a given set of rectangularboxes into a rectangular container so that the packed volume is maximized. The method hastwo variants: the packing...

Given a set of rectangular pieces and a rectangular container, the two-dimensional knapsackproblem (2D-KP) consists of orthogonally packing a subset of the pieces within the container suchthat the sum of the values of the packed pieces is maximized. If the value of a piece is given by itsarea...

This paper presents a new algorithm for the dynamic Multi-Level Ca-pacitated Lot Sizing Problem with Setup Carry-Overs (MLCLSP-L). TheMLCLSP-L is a big-bucket model that allows the production of any num-ber of products within a period, but it incorporates partial sequencing of theproduction...

This paper presents an optimization-based solution approach for the dynamicmulti-level capacitated lot sizing problem (MLCLSP) with positive leadtimes. The key idea is to solve a series of mixed-integer programs in an iterativefix-and-optimize algorithm. Each of these programs is optimized over...

This paper examines numerically the impact of a negative exogenousshock to marginal productivity (such as ecological government regulationthat becomes eective at some point in time) in an endogenousnite time growth model with sluggish reallocation of human capital.The policy can be anticipated...

In this paper we propose a hybrid ACO approach to solve a full truckload transportation problem. Hybridization is achieved through the use of a problem specific heuristic.(...)

In this paper we propose an unsupervised voting-merging scheme that is capable of clustering data sets, and also of finding the number of clusters existing in them. The voting part of the algorithm allows us to combine several runs of clustering algorithms resulting in a common partition.(...)

We consider job shop scheduling problems with release and due-dates, as well as various tardiness objectives. To date, no efficient general-purpose heuristics have been developed for these problems. genetic algorithms can be applied almost directly, but come along with apparent weaknesses.