The paper presents experimental results on quadratic assignment problem. The “scanning area” method formulated for radioelectronic equipment design is applied. For all more complex tests ours results are better or coincident with the ones known in literature. Conclusion concerning the effectiveness of method are given.

 We consider a generalization of the classical quadratic assignment problem, where coordinates of locations are uncertain and only upper and lower bounds are known for each coordinate. We develop a mixed integer linear programming model as a robust counterpart of the proposed uncertain model. A key challenge is that, since the uncertain model involves nonlinear objective function of the ...

Locomotive planning has been a popular application of classical optimization models for decades, but with very few success stories. There are a host of complex rules governing how locomotives should be used. In addition, it is necessary to simultaneously manage locomotive inventories by balancing the need for holding power against the need for power at other yards. At the same time, we have to ...

Electric locomotive is a kind of high power rectifier load, and its load characteristics will have a significant impact to safe, stable and economic operation of power system. This paper mainly analyses load characteristics of harmonic and reactive power for electric locomotive. Simulation model of electric locomotive is set up based on time trigger; it is so better conform to the dynamic chara...

assigning facilities to locations is one of the important problems, which significantly is influence in transportation cost reduction. in this study, we solve quadratic assignment problem (qap), using a meta-heuristic algorithm with deterministic tasks and equality in facilities and location number. it should be noted that any facility must be assign to only one location. in this paper, first o...

It was recently demonstrated that a well-known eigenvalue bound for the Quadratic Assignment Problem (QAP) actually corresponds to a semideenite programming (SDP) relaxation. However, for this bound to be computationally useful the assignment constraints of the QAP must rst be eliminated, and the bound then applied to a lower-dimensional problem. The resulting \projected eigenvalue bound" is on...

We describe a new convex quadratic programming bound for the quadratic assignment problem (QAP). The construction of the bound uses a semideenite programming representation of a basic eigenvalue bound for QAP. The new bound dominates the well-known projected eigenvalue bound, and appears to be competitive with existing bounds in the tradeoo between bound quality and computational eeort.