Enhanced heuristic for finance-based scheduling of construction projects

Typically, construction contractors operate under cash-constrained operating conditions. The lag between the time when contractors spend money to accomplish work on site and the time when payments are actually made by clients, which partially compensate contractors for the accomplished work, constantly creates a finance deficit. Contractors often supplement finance deficits using external funds procured through establishing credit-line bank accounts which typically allow contractors to withdraw cash up to specified credit limits. This makes the task of project scheduling considering the constraints of specified finance very important for financial and operational planning. This scheduling concept and technique are referred to as finance-based scheduling. An enhanced heuristic is proposed to devise finance-based schedules of multiple projects within contractors’ portfolios. The enhancement is achieved by replacing the exhaustive enumeration technique employed in the heuristic to specify activities’ start times with a polynomial shifting algorithm. This enhancement resulted in a substantial reduction in the number of solutions explored before a feasible solution is encountered. The enhanced heuristic was validated through comparison with the integer programming technique using 240 problems of randomly generated networks of sizes that range from 30 to 240 activities. Further, it was proved that the enhanced heuristic can be easily scaled up to handle portfolios of multiple large-size projects.     

Precedence relationship options for the resource levelling problem using a genetic algorithm

The resource levelling problem (RLP) arises in project scheduling where the intention is to reduce the fluctuation of the resource demand. The solutions of the RLP are achieved through the rearrangement of the project activities under a constraint of preselected precedence relationships. To relieve the constraint, a new concept of the RLP with relationship options is initiated. An activity of the project network can have one or more alternative types of relationship with other activities as appropriate. An alternative relationship provides more float time and allows new possibilities for the arrangement of efficient patterns of the project resource demand. The scheduling problem model was formulated by using mathematical equations on spreadsheet software and solved by using the genetic algorithm based optimization. The prototype was tested in two different project instances. The test results demonstrated that this new model could calculate and arrange the project schedules for all selected alternative types of relationships. The model with relationship options provided consistent results for efficient resource utilization schedules, which were better than the one without relationship options. These schedules could decrease the resource demand fluctuation and the maximum resource demand level. This new model of the RLP with relationship options not only provides the additional flexibility to level the resource demands but also determines suitable types of relationships for the project activities.     

A numerical analysis method for computing schedule and resource forecasting for industrial projects

The derivation of schedule curves and resource histograms from manhours is traditionally carried out on spreadsheet sytems; this method proves tedious and becomes complex during the performance of ‘what-if’ analysis. Consequently, a new method has been developed to derive schedule curves and resource histograms from manhours and to improve the efficiency of the computations and the accuracy of the results. The new method is based on numerical analysis techniques, tested with historical data from industrial projects and used in the oil and petrochemical industry. Results obtained using the new method correlate closely to results calculated by a traditional spreadsheet method. The new method proves to be valuable to project management at the beginning of projects where the availability of scheduling data is limited and during the execution of projects to perform ‘what-if’ analyses.     

程控交换机与生产调度机组网改造设计

介绍了程控交换机与生产调度机组网改造设计的方案,从物理组网与中继管理数据配置两个方面论述了改造方案实施的方式,指出采用数字中继组网后,提高了系统的安全可靠性,达到了预期的改造目标。     

ERP系统在铁路道岔制造企业中的应用研究

为提高企业核心竞争力以应对市场竞争需要,引进了Oracle ERP管理系统,通过需求分析、系统规划,方案设计、系统实现等实施过程,在企业中建立了扁平化管理、运营组织模式,同时通过对ERP系统的生产管理模块进行深层次开发,实现了生产计划的高级排产及生产过程的精益化管理,达到了信息流与物流的统一。     

Surrogate-based optimization of a periodic rescheduling algorithm

Periodic rescheduling is an iterative method for real-time decision-making on industrial process operations. The design of such methods involves high-level when-to-schedule and how-to-schedule decisions, the optimal choices of which depend on the operating environment. The evaluation of the choices typically requires computationally costly simulation of the process, which—if not sufficiently efficient—may result in a failure to deploy the system in practice. We propose the continuous control parameter choices, such as the re-optimization frequency and horizon length, to be determined using surrogate-based optimization. We demonstrate the method on real-time rebalancing of a bike sharing system. Our results on three test cases indicate that the method is useful in reducing the computational cost of optimizing an online algorithm in comparison to the full factorial sampling.     

A nested online scheduling and nonlinear model predictive control framework for multi-product continuous systems

In the pursuit of integrated scheduling and control frameworks for chemical processes, it is important to develop accurate integrated models and computational strategies such that optimal decisions can be made in a dynamic environment. In this study, a recently developed switched system formulation that integrates scheduling and control decisions is extended to closed-loop operation embedded with nonlinear model predictive control (NMPC). The resulting framework is a nested online scheduling and control loop that allows to obtain fast and accurate solutions as no model reduction is needed and no integer variables are involved in the formulations. In the outer loop, the integrated model is solved to calculate an optimal product switching sequence such that the process economics is optimized, whereas in the inner loop, an NMPC implements the scheduling decisions. The proposed scheme was tested on two multi-product continuous systems. Unexpected large disturbances and rush orders were handled effectively.     

[车间调度]求解带有阻塞限制的HFSP的MILP模型与改进回溯搜索算法

针对带有阻塞限制的不相关并行机混合流水车间调度问题,以最小化最长完工时间为目标,依据不同的建模思想,建立了求解该问题的4个混合整数线性规划(MILP)模型;鉴于混合整数线性规划不适合求解中大规模问题,提出了一种改进的回溯搜索算法以求解中大规模问题,在该算法中,引入了轮盘赌选择策略以及变邻域搜索算法,以提高算法的收敛速度以及局部搜索能力。最后,对所提MILP模型以及算法进行了对比分析,通过对具体实例的求解验证了所提MILP模型以及算法的有效性及优越性。     

Optimization models for shale gas water management

There are four key aspects for water use in hydraulic fracturing, including source water acquisition, wastewater production, reuse and recycle, and subsequent transportation, storage, and disposal. Water use life cycle is optimized for wellpads through a discrete‐time two‐stage stochastic mixed‐integer linear programming model under uncertain availability of water. The objective is to minimize expected transportation, treatment, storage, and disposal cost while accounting for the revenue from gas production. Assuming freshwater sources, river withdrawal data, location of wellpads, and treatment facilities are given, the goal is to determine an optimal fracturing schedule in coordination with water transportation, and its treatment and reuse. The proposed models consider a long‐time horizon and multiple scenarios from historical data. Two examples representative of the Marcellus Shale play are presented to illustrate the effectiveness of the formulation, and to identify optimization opportunities that can improve both the environmental impact and economical use of water. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3490–3501, 2014     

Refinery scheduling with varying crude: A deep belief network classification and multimodel approach

In model‐based refinery scheduling, the varying composition of the crude being refined is a major challenge, especially for those reaction processes. A classification based, multimodel approach is proposed to handle the frequently varying crude. The idea is to build a scheduling model for each type of feed crude, and the type can be determined using an online classifier. The recently emerged deep belief network is introduced to develop the classifier, which provides more accurate classification than the traditional neural network. The proposed method is demonstrated through modeling a fluidized catalytic cracking unit (the mostly affected by varying crude), and then the scheduling of a refinery that was carefully simulated to mimic the actual operation of a refinery in northern China. The results reveal that the multimodel approach is effective in handling varying crude. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2525–2532, 2014