门式起重机外文翻译.doc

外文原文（出自JOURNAL OF CONSTRUCTION ENGINEEING AND MANAGEMENT MARCH/APRIL/115-121）LOCATION OPTIMIZATION FOR A GROUP OF TOWER CRANESABSTRACT: A computerized model to optimize location of a group of tower cranes is presented. Location criteria are balanced workload, minimum likelihood of conflicts with each other, and high efficiency of operations. Three submodels are also presented. First, the initial location model classifies tasks into groups and identifies feasible location for each crane according to geometric closeness. Second, the former task groups are adjusted to yield smooth workloads and minimal conflicts. Finally, a single-tower-crane optimization model is applied crane by crane to search for optimal location in terms of minimal hook transportation time. Experimental results and the steps necessary for implementation of the model are discussed.INTRODUCTIONOn large construction projects several cranes generally undertake transportation tasks, particularly when a single crane cannot provide overall coverage of all demand and supply points, and/or when its capacity is exceeded by the needs of a tight construction schedule. Many factors influence tower crane location. In the interests of safety and efficient operation, cranes should be located as far apart as possible to avoid interference and collisions, on the condition that all planned tasks can be performed. However, this ideal situation is often difficult to achieve in practice; constrained work space and limitations of crane capacity make it inevitable that crane areas overlap. Subsequently, interference and collisions can occur even if crane jibs work at different levels. Crane position(s) tend to be determined through trial and error, based on site topography/shape and overall coverage of tasks. The alternatives for crane location can be complex, so managers remain confronted by multiple choices and little quantitative reference.Crane location models have evolved over the past 20 years. Warszawski (1973) established a time-distance formula by which quantitative evaluation of location was possible. Furusaka and Gray (1984) presented a dynamic programming model with the objective function being hire cost, but without consideration of location. Gray and Little (1985) optimized crane location in irregular-shaped buildings while Wijesundera and Harris (1986) designed a simulation model to reconstruct operation times and equipment cycles when handling concrete. Farrell and Hover (1989) developed a database with a graphical interface to assist in crane selection and location. Choi and Harris (1991) introduced another model to optimize single tower crane location by calculating total transportation times incurred. Emsley (1992) proposed several improvements to the Choi and Harris model. Apart from these algorithmic approaches, rule-based systems have also evolved to assist decisions on crane numbers and types as well as their site layout。 AssumptionsSite managers were interviewed to identify their concerns and observe current approaches to the task at hand. Further, operations were observed on 14 sites where cranes were intensively used (four in China, six in England, and four in Scotland). Time studies were carried out on four sites for six weeks, two sites for two weeks each, and two for one week each. Findings suggested inter alia that full coverage of working area, balanced workload with no interference, and ground conditions are major considerations in determining group location. Therefore, efforts were concentrated on these factors (except ground conditions because site managers can specify feasible location areas). The following four assumptions were applied to model development (detailed later):1. Geometric layout of all supply (S) and demand (D) points, together with the type and number of cranes, are predetermined.2. For each S-D pair, demand levels for transportation are known, e.g., total number of lifts, number of lifts for each batch, maximum load, unloading delays, and so on.3. The duration of construction is broadly similar over the working areas.4. The material transported between an S-D pair is handled by one crane only.MODEL DESCRIPTIONThree steps are involved in determining optimal positions for a crane group. First, a location generation model produces an approximate task group for each crane. This is then adjusted by a task assignment model. Finally, an optimization model is applied to each tower in turn to find an exact crane location for each task group.Initial Location Generation ModelLift Capacity and Feasible AreaCrane lift capacity is determined from a radius-load curve where the greater the load, the smaller the cranes operating radius. Assuming a load at supply point (S) with the weight w, its corresponding crane radius is r. A crane is therefore unable to lift a load unless it is located within a circle with radius rFig. 1(a). To deliver a load from (S) to demand point (D), the crane has to be positioned within an elliptical area (a) FIG.1. Feasible Area of Crane Location for TaskFIG. 2. Task “Closenness”enclosed by two circles, shown in Fig. 1(b). This is called the feasible task area. The size of the area is related to the distance between S and D, the weight of the load, and crane capacity. The larger the feasible area, the more easily the task can be handled.Measurement of Closeness of TasksThree geometric relationships exist for any two feasible task areas, as illustrated in Fig. 2; namely, (a) one fully enclosed by another (tasks 1 and 2); (b) two areas partly intersected (tasks 1 and 3); and (c) two areas separated (tasks 2 and 3). As indicated in cases (a) and (b), by being located in area A, a crane can handle both tasks 1 and 2, and similarly, within B, tasks 1 and 3. However, case (c) shows that tasks 2 and 3 are so far from each other that a single tower crane is unable to handle both without moving location; so more than one crane or greater lifting capacity is required. The closeness of tasks can be measured by the size of overlapping area, e.g., task 2 is closer to task 1 than task 3 because the overlapping area between tasks 1 and 2 is larger than that for 1 and 3. This concept can be extended to measure closeness of a task to a task group. For example, area C in Fig. 2(b) is a feasible area of a task group consisting of three tasks, where task 5 is said to be closer to the task group than task 4 since the overlapping area between C and D is larger than that between C and E. If task 5 is added to the group, the feasible area of the new group would be D, shown in Figure 2(c).Grouping Tasks into Separated ClassesIf no overlapping exists between feasible areas, two cranes are required to handle each task separately if no otheralternativessuch as cranes with greater lifting capacity or replanning of site layoutare allowed. Similarly, three cranes are required if there are three tasks in which any two have no overlapping areas. Generally, tasks whose feasible areas are isolated must be handled by separate cranes.These initial tasks are assigned respectively to different (crane) task groups as the first member of the group, then all other tasks are clustered according to proximity to them. Obviously, tasks furthest apart are given priority as initial tasks. When multiple choices exist, computer running time can be reduced by selecting tasks with smaller feasible areas as initial tasks. The model provides assistance in this respect by displaying graphical layout of tasks and a list of the size of feasible area for each. After assigning an initial task to a group, the model searches for the closest remaining task by checking the size of overlapping area, then places it into the task group to produce a new feasible area corresponding to the recently generated task group. The process is repeated until there are no tasks remaining having an overlapping area within the present group. Thereafter, the model switches to search for the next group from the pool of all tasks, the process being continued until all task groups have been considered. If a task fails to be assigned to a group, a message is produced to report which tasks are left so the user can supply more cranes or, alternatively, change the task layout and run the model again.Initial Crane LocationWhen task groups have been created, overlapping areas can be formed. Thus, the initial locations are automatically at the geometric centers of the common feasible areas, or anywhere specified by the user within common feasible areas.Task Assignment ModelGroup location is determined by geometric closeness. However, one crane might be overburdened while others are idle. Furthermore, cranes can often interfere with each other so task assignment is applied to those tasks that can be reached by more than one crane to minimize these possibilities.Feasible Areas from Last Three Sets of Inputshape and size of feasible areas, illustrated in Fig. 9. In this case study, from the data and graphic output, the user may become aware that optimal locations led by test sets 1, 2, and 3(Fig. 3) are the best choices (balanced workload, conflict possibility, and efficient operation). Alternatively, in connection with site conditions such as availability of space for the crane position and ground conditions for the foundation, site boundaries were restricted. Consequently, one of the cranes had to be positioned in the building. In this respect, the outcomes resulting from set 4 would be a good choice in terms of a reasonable conflict index and standard deviation of workload, provided that a climbing crane is available and the building structure is capable of supporting this kind of crane. Otherwise, set 5 results would be preferable with the stationary tower crane located in the elevator well, but at the cost of suffering the high possibility of interference and unbalanced workloadsCONCLUSIONSOverall coverage of tasks tends to be the major criterion in planning crane group location. However, this requirement may not determine optimal location. The model helps improve conventional location methods, based on the concept that the workload for each crane should be balanced, likelihood of interference minimized, and efficient operation achieved. To do this, three submodels were highlighted. First, by classifying all tasks into different task groups according to geometric closeness an overall layout is produced. Second, based on a set of points located respectively in the feasible areas (initial location), the task assignment readjusts the groups to produce new optimal task groups with smoothed workloads and least possibility of conflicts, together with feasible areas created. Finally, optimization is applied for each crane one by one to find an exact location in terms of hook transport time in three dimensions.Experimental results indicate that the model performs satisfactorily. In addition to the improvement on safety and average efficiency of all cranes, 1040% savings of total hooks transportation time can be achieved. Effort has been made to model the key criteria for locating a group of tower cranes, and two real site data have been used to test the model. However, it does not capture all the expertise and experience of site managers; other factors relating to building structure, foundation conditions, laydown spaces for materials, accessibility of adjoining properties and so on, also contribute to the problem of locations. Therefore, the final decision should be made in connection with these factors.翻译一组塔式起重机的位置优化摘要 计算机模型能使一组塔机位置更加优化。合适的位置条件能平衡工作载荷，降低塔机之间碰撞的可能性，提高工作效率。这里对三个子模型进行了介绍。首先，把初始位置模型分组，根据几何的相似性，确认每个塔机的合适位置。然后，调整前任务组的平衡工作载荷并降低碰撞的可能性。最后，运用一个单塔起重机优化模型去寻找吊钩运输时间最短的位置。本文对模型完成的实验结果和必要的步骤进行了讨论。引言在大规模的建设工程中特别是当一个单塔起动机不能全面的完成重要的任务要求时或者当塔机不能完成紧急的建设任务时通常是由几个塔机同时完成任务。影响塔机的因素很多。从操作效率和安全方面考虑，如果所有计划的任务都能执行，应将塔机尽可能的分开，避免互相干扰和碰撞。然而这种理想的情况在实践中很难成功，因为工作空间的限制和塔机的耐力有限使塔机的工作区域重叠是不可避免的。因此，即使起重机的铁臂在不同的水平工作面也会发生互相干扰和碰撞。在地形选址和全面的完成任务的基础上，通过反复实验来决定塔式起重机的合适位置。起重机位置的选择很复杂，因此，管理人员仍然面临着多样的选择和少量的定量参考。在过去的20年里，起重机位置模型逐步形成。Warszawski(1973)尝试尽可能用时间与距离来计算塔机的位置。Furusaka and Gray (1984) 提出用目标函数和被雇用成本规划动态模型，但是没考虑到位置。Gray and Little (1985)在处理不规则的混凝土建筑物时候，设置位置优化的塔式起动机。然而，Wijesundera and Harris (1986)在处理具体的任务时减少了操作时间和延长了设备使用周期时设计了一种模拟模型。Farrell and Hover (1989)开发了带有图解界面的数据库，来协助起动机的位置的选择。Choi and Harris (1991)通过计算运输所须的全部时间来提出另一种优化单塔起重机位置优化。Emsley (1992)改进了Choi and Harris提出的模型。除了在计算方法相似外，起重机的数量类型和设计系统规则也得以提高假设采访网站管理员关于他们的公司和观察到手上的工作电流的方法。另外观察起重机集中在14个操作站点的运用。(在中国是4个，在英格兰是6个，在苏格兰是4个)。研究设备放在4个站点时间为6个星期，两个站点用两个星期时间。调查结果显示尤其是在全面覆盖工作领域，没有干扰，平衡工作载荷和地面情况是决定塔机位置重要的原因。因此，重点在这些因素上（除了地面情况因为站点管理员能明确说明合适的区域位置）。下面4种假设被应用于模型发展（以后的详尽）1) 预先确定所有供应点和需求点的几何布局、起重机的类型和数量。2) 对于每个供应点和需求点，运输需求水平是已知的。例如，起重机的总数、每组起重机的数量、最大限度的装载、延迟卸货等等。3) 在建设时期和工作区域大体相同。4) 只用一个起重机运输供应点与需求点之间的物料。模型描述决定起重机理想的位置有三个位置条件。首先用位置模型产生一个相似的任务组，然后用任务分配模型调整，最后优化模型轮流并运用到每个任务组中的准确位置。初始位置生成模型起重机的起升能力和合适的区域起重机的升起能力取决于曲线的半径，负荷量越大，起重机的操作半径越小。假设供应点的负荷量是w,相应的起重机半径是r。一个起重机若不能承受装载除非它的半径在圆内（图1）。从供应点传送一个装载需求点，必须把起重机放在两个重合的椭圆区域，如图表1(b)，这是合适任务区域。区域的大小与供应点和需求点的距离、负荷量、起重机的耐力有关。合当的区域越大，越容易完成任务。相近任务的测量对于任何两种合适的任务区域存在3种几何关系，如图解2.也就是说，(a)一个图与另个图完全重合（任务1与2）。(b)两个区域部分相交（任务1与3）。（c）两个区域分开（任务2与3）。如指出的实例a与b,起重机被放在区域A中能完成任务1和任务2，同样的，在区