Task assignment optimization in SAP Extended WarehouseManagement

Nowadays in the world of mass consumption there is big demand for distributioncenters of bigger size. Managing such a center is a very complex and difficult taskregarding to the different processes and factors in a usual warehouse when we want tominimize the labor costs. Most of the workers’ working time is spent with travelingbetween source and destination points which cause deadheading. Even if a worker knowsthe structure of a warehouse well and because of that he or she can find the shortest pathbetween two points, it is still not guaranteed that there won’t be long traveling timebetween the locations of two consecutive tasks. We need optimal assignments betweentasks and workers.In the scientific literature Generalized Assignment Problem (GAP) is a wellknownproblem which deals with the assignment of m workers to n tasks consideringseveral constraints. The primary purpose of my thesis project was to choose a heuristics(genetic algorithm, tabu search or ant colony optimization) to be implemented into SAPExtended Warehouse Management (SAP EWM) by with task assignment will be moreeffective between tasks and resources.After system analysis I had to realize that due different constraints and businessdemands only 1:1 assingments are allowed in SAP EWM. Because of that I had to use adifferent and simpler approach – instead of the introduced heuristics – which could gainbetter assignments during the test phase in several cases. In the thesis I described indetails what ware the most important questions and problems which emerged during theplanning of my optimized assignment method.

Statistical bound of genetic solutions to quadratic assignment problems

Quadratic assignment problems (QAPs) are commonly solved by heuristic methods, where the optimum is sought iteratively. Heuristics are known to provide good solutions but the quality of the solutions, i.e., the confidence interval of the solution is unknown. This paper uses statistical optimum estimation techniques (SOETs) to assess the quality of Genetic algorithm solutions for QAPs. We examine the functioning of different SOETs regarding biasness, coverage rate and length of interval, and then we compare the SOET lower bound with deterministic ones. The commonly used deterministic bounds are confined to only a few algorithms. We show that, the Jackknife estimators have better performance than Weibull estimators, and when the number of heuristic solutions is as large as 100, higher order JK-estimators perform better than lower order ones. Compared with the deterministic bounds, the SOET lower bound performs significantly better than most deterministic lower bounds and is comparable with the best deterministic ones. 

A Beam Search Method to Solve the Problem of Assignment Cells to Switches in a Cellular Mobile Network

Assigning cells to switches in a cellular mobile network is known as an NP-hard optimization problem. This means that the alternative for the solution of this type of problem is the use of heuristic methods, because they allow the discovery of a good solution in a very satisfactory computational time. This paper proposes a Beam Search method to solve the problem of assignment cell in cellular mobile networks. Some modifications in this algorithm are also presented, which allows its parallel application. Computational results obtained from several tests confirm the effectiveness of this approach and provide good solutions for large scale problems.

Assignment of chromosome rearrangements between X chromosomes of human and cattle by laser microdissection and Zoo-FISH

Cross-species fluorescence in-situ hybridization (Zoo-FISH) was performed on cattle metaphase spreads using Homo sapiens X chromosome (HSAX) painting probes specific for the p- and q-arms to identify the cytogenetic location of a chromosome breakpoint between HSAX and the Bos taurus X chromosome (BTAX). The existence of a breakpoint is strongly suggested by recent radiation hybrid and FISH mapping results. Hybridization probes were generated by microdissection of HSAX p- and q-arms using the contact-free technology of Laser Microdissection and Pressure Catapulting (LMPC), amplification of the isolated chromosome material by DOP-PCR, and labelling of the PCR products with digoxigenin in a secondary PCR. Independent Zoo-FISH of the two painting probes on bovine metaphase chromosomes (detected by antidigoxigenin-fluorescein) resulted in clear hybridization signals on BTAX. A breakpoint was identified between HSAXp and HSAXq on BTAX, and narrowed down between the G-bands BTAXq25 and BTAXq26. The assumed centromere transposition between HSAX and BTAX associated with the rearranged chromosome segments is supported by cytogenetic assignments of the genes BGN and G6PD to BTAX.