Steel construction materials are very common use for factory and high building in nearly years. Generally speaking the steel construction material is refers has the fixed cross section the steel products. Like rolling H, the T section, the light channel steel, the angle steel and so on all are. But their order characteristic for depends on the length order, also can be different regarding the different length its unit weight price. Manages in not the easy situation in the present enterprise, saved the material cost already to change can it be that is often important, All company shall consider for its huge material storage. Regarding the company huge stock information bank system in, frequently the existence permits the material which how long year hasn't used, finally really very is big by the waste treatment regarding company's loss. So material cutting plane is an important work for those fabrication company. A good cutting plane is directly effect the used percentage, material cost, transportation cost, moving efficiency are also an important effects points, Therefore a good combination for cutting plan can obvious lower material cost, also good help for cutting efficiency, The research of Tabu search shall be use to steel construction material combination and becoming an good effective calculation method to reach three purpose as follow: 1.Considers the multi-objective of material fixed number of years, the spillage of material and the material cost, that material best arrangement combination solution or approximate best solution. 2.Develop algorithm for actuality using in materials combination so all fabrication company can use this method to reduce their cost. 3.Collection all remain material and make it together to save moving time. The result showed obtains the solution by the Tabu Search method to be possible the effective reduced material purchase quantity; also remainder centralism also has the very good improvement.

This research aims to improve the performance of the service of a Chemotherapy Treatment Unit by reducing the waiting time of patients within the unit. In order to fulfill the objective, initially, the chemotherapy treatment unit is deduced as an identical parallel machines scheduling problem with unequal release time and single resource. A mathematical model is developed to generate the optimum schedule. Afterwards, a Tabu search (TS) algorithm is developed. The performance of the TS algorithm is evaluated by comparing results with the mathematical model and the best results of benchmark problems reported in the literature. Later on, an additional resource is considered which converted the problem into a dual resources scheduling problem. Three approaches are proposed to solve this problem; namely, heuristics, a Tabu search algorithm with heuristic (TSHu), and Tabu search algorithm for dual resources (TSD).

This research aims to improve the performance of the service of a Chemotherapy Treatment Unit by reducing the waiting time of patients within the unit. In order to fulfill the objective, initially, the chemotherapy treatment unit is deduced as an identical parallel machines scheduling problem with unequal release time and single resource. A mathematical model is developed to generate the optimum schedule. Afterwards, a Tabu search (TS) algorithm is developed. The performance of the TS algorithm is evaluated by comparing results with the mathematical model and the best results of benchmark problems reported in the literature. Later on, an additional resource is considered which converted the problem into a dual resources scheduling problem. Three approaches are proposed to solve this problem; namely, heuristics, a Tabu search algorithm with heuristic (TSHu), and Tabu search algorithm for dual resources (TSD).

No description available.

The flow shop scheduling problem is a classical combinatorial problem being studied for years. The focus of this research is to study two variants of the flow shop scheduling problem in order to minimize makespan by scheduling n jobs on m machines. A solution approach is developed for the modified flow shop problem with due dates and release times. This algorithm is an attempt to contribute to the limited literature for the problem. Another tabu search-based solution approach is developed to solve the classical flow shop scheduling problem. This meta-heuristic (called 3XTS) allows an efficient search of the neighboring solutions leading to a fast solution procedure. Several control parameters affecting the quality of the algorithm are experimentally tested, and certain rules are established for different problem instances. The 3XTS is compared to another tabu search method (that seems to be a champion) in terms of solution quality and computation time.

No description available.

The purpose of this thesis is to develop a heuristic solution for the static problem of resequencing unblocked vehicles as a part of an ongoing research project at Daimler AG. The target client of this project is Mercedes-Benz Cars. An unblocked vehicle is defined as a vehicle that for some reason could not be processed in its given time slot but at a later point in time needs to be inserted into the production sequence. Work overload is defined as work that the worker is unable to finish prior to reaching the station border. The resequencing problem can be described as finding new positions for a set of unblocked vehicles in a sequence of previously not blocked vehicles, such that the new sequence containing the previously not blocked vehicles and the additional unblocked vehicles causes as little work overload as possible. A decision has to be made in real-time, forcing the solution method to return a solution within a cycle time. Today, Mercedes-Benz Cars uses the sequencing approach “car sequencing”. This approach relies on so called spacing constraints, which basically means, trying to distribute work intensive vehicles as evenly as possible over the planning horizon and thereby enabling a hopefully smooth production. The car sequencing approach needs limited information. The difficulty is to find spacing constraints that fits the high level of product customization characterizing a modern car manufacturer. To overcome these difficulties, a new approach is being considered, namely the mixed-model sequencing, which takes more detailed data into account than the car sequencing approach but on the other hand is more costly in terms of computation. To this end, a simple but promising tabu search scheme was developed, that for many instances was able to find the optimal solution in less than 30 seconds of computing time and that also clearly outperformed all benchmark heuristics.

El presente trabajo de título tuvo como objetivo el diseño, desarrollo y evaluación de una herramienta que permitiera apoyar la toma de decisiones respecto de la programación de operaciones en cada una de las dos plantas con que actualmente cuenta una compañía siderúrgica. Se buscó de esta forma encontrar la secuencia en que debieran ejecutarse los distintos trabajos mensuales, minimizando los tiempos de producción así como los tiempos de retrasos en las fechas de entrega. La investigación se centró, por razones de tiempo, únicamente en el proceso de laminación de la compañía, que es aquel en donde se le da la forma final a los productos siderúrgicos mediante deformación termomecánica. Esta elección no fue al azar y se tomó considerando el hecho de que dicho proceso es hasta el día de hoy el principal “cuello de botella” y por lo tanto representaba las mayores oportunidades de ganancia. Ahora bien, fue posible demostrar que el problema pertenece a la clase NP-Hard por lo que no se conocen algoritmos capaces de resolverlo en un tiempo polinomial. Como consecuencia, y dado que el tamaño de la instancia es relativamente grande, se hizo necesario incorporar enfoques heurísticos que permitieran obtener resultados suficientemente buenos en un tiempo de computación razonable. Es así como se optó por un algortimo de Búsqueda Tabú. La elección se basó principalmente en los buenos resultados reportados en la literatura para otros problemas de programación de operaciones (Lin y Ying, 2006; Gupta y Smith, 2007 y Valente y Alves, 2008). Gran parte del desempeño de este tipo de heurística depende de dos elementos: la solución inicial y la metodología de generación de vecindades. Es por ello que la estrategia seguida consistió en evaluar un amplio espectro de las técnicas más utilizadas para tales fines, escogiendo finalmente aquella combinación que presentó un mejor desempeño. Los resultados obtenidos muestran que la aplicación de la heurística propuesta a instancias reales permite obtener reducciones importantes en comparación a la situación actual: un 7% promedio en los tiempos de producción y disminuciones promedio del 35% en lo que se refiere a los tiempos de retraso. Por otra parte, se observa una dramática caída del 82% promedio en lo que al tiempo necesario para determinar la programación respecta. Finalmente, cabe destacar que la investigación realizada sugiere también que aún hay espacio para futuras mejoras, por lo que se recomienda dar continuidad al estudio y en lo posible ampliarlo a otros procesos de la cadena productiva.

In this thesis there is a set of waste disposal facilities, a set of customers at which waste is collected and an unlimited number of homogeneous vehicles based at a single depot. Empty vehicles leave the depot and collect waste from customers, emptying themselves at the waste disposal facilities as and when necessary. Vehicles return to the depot empty. We take into consideration time windows associated with customers, disposal facilities and the depot. We also have a driver rest period. The problem is solved heuristically. A neighbour set is defined for each customer as the set of customers that are close, but with compatible time windows. This thesis uses six different procedures to obtain initial solutions for the problem. Then, the initial solutions from these procedures are improved in terms of the distance travelled using our phase 1 and phase 2 procedures, whereas we reduce the number of vehicles used using our vehicle reduction (VR) procedure. In a further attempt to improve the solutions three metaheuristic algorithms are presented, namely tabu search (TS), variable neighbourhood search (VNS) and variable neighbourhood tabu search (VNTS). Moreover, we present a modified disposal facility positioning (DFP), reverse order and change tracking procedures. Using all these procedures presented in the thesis, four solution procedures are reported for the two benchmark problem sets, namely waste collection vehicle routing problems with time windows (VRPTW) and multi-depot vehicle routing problem with inter-depot routes (MDVRPI). Our solutions for the waste collection VRPTW problems are compared with the solutions from Kim et al (2006), and our solutions for the MDVRPI problems are compared with Crevier et al (2007). Computational results for the waste collection VRPTW problems indicate that our algorithms produce better quality solutions than Kim et al (2006) in terms of both distance travelled and number of vehicles used. However for the MDVRPI problems, solutions from Crevier et al (2007) outperform our solutions.

This study aims to answer whether a heuristic that trades cycles between the tours in a solution would show good results when trying to solve the Weighted k-Chinese Postman Problem for undirected graphs, of varying size, representing neighbourhoods in Sweden.A tabu search heuristic was implemented with each iteration consisting of giving a cycle from the most expensive tour to the cheapest. The heuristic performed increasingly well for graphs of increasing size, although the solution quality decreased when increasing the number of tours to be used in the solution. It is suspected that the cause for this behavior is due to the heuristic only giving cycles from the most expensive tour, not considering trading cycles from other tours in the solution. It is believed that a heuristic considering more than only the most expensive tour when trading cycles would produce even better solutions.