A scheduling model for multirobot assembly cells

We propose a mixed 0–1 linear programming model for repetitive scheduling of multirobot assembly and machining cells. The approach adopted is monolithic as opposed to hierarchical to avoid system suboptimization. The model permits any number of alternative ways (or modes) to perform each operation. A mode of an operation is determined by the required resources (facilities) and the duration of their use. The model incorporates facility changeover times. Robot collisions are avoided. Several objective functions are formulated to support different purposes. The scheduling problem of a multirobot assembly cell is formulated and solved by using commercially available mathematical programming software. Solutions under four different objective functions are reported.Acknowledging the complexity and considerable size of the formulation required, we prescribe and illustrate specific methods to achieve size reduction. Finally, for successful use of our model, an information processing schema is offered as a general guidance to help data management needed by the model.     

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.     

Selection of government supervision mode of PPP projects during the operation stage

During the operation stage of public-private partnership (PPP) projects, investors may engage in opportunistic behaviour in pursuit of their own profits. In order to curb this kind of behaviour, this article analyses the selection of government supervision mode based on evolutionary game theory taking the perspective of government supervision. The results show that government supervision mode is closely related to the probability of identifying investors’ speculative behaviour through outcome-oriented supervision. When the probability of identifying such behaviour is relatively high, the equilibrium strategy of investors and governmental supervision institutions is (not to behave opportunistically, outcome-oriented supervision). In contrast, if the probability is relatively low, there is no set of evolutionarily stable strategies (ESS); rather, a periodic behavioural pattern is formed. In this scenario, the strategies ultimately chosen by both sides relate to initial states and the payoffs of the options. Furthermore, determinants and some recommendations for government supervision are proposed, providing a reference for efficient governance.     

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.     

Automated trade‐off between time and cost in planning repetitive construction projects

An automated model is developed to support the optimization of the planning and scheduling of repetitive construction projects. The model provides the capability of optimizing two important objectives commonly sought in scheduling repetitive construction projects: minimizing project duration; and minimizing project cost. The model performs this multi‐objective optimization using a genetic algorithm approach. The output of the model is a set of optimal solutions that represent the trade‐off between time and cost in planning repetitive construction projects. Furthermore, the model can be utilized to find a single scheduling solution that provides the minimum overall project cost by simply adding project indirect cost to the obtained project direct cost for each of the obtained scheduling solutions on the Pareto optimal curve. Other important time‐related costs are also considered in the model including: early completion incentives, late completion penalties and lane rental costs. Providing the planners of repetitive construction projects with an automated set of optimal time–cost trade‐off solutions should contribute to cost‐effective and speedy delivery of this type of construction project. An application example is analysed to illustrate the use of the model and demonstrate its capabilities in generating optimal trade‐off solutions between minimizing the project time and cost for repetitive construction projects.     

A common network flow formulation for several civil engineering problems

This paper discusses a common network flow problem that arises in four areas of civil engineering. The problem is one of identifying arc flows that simultaneously satisfy the circuit and node law constraints for a given network. Applications and computational procedures in the areas of elastic structural analysis, hydraulic network analysis, traffic equilibrium and construction task scheduling are described. The interpretation of decision variables and mathematical functions will vary depending on the application, but the general priblem formulation and solution technique for each application remains essentially unchanged. Analogies between the different applications may enhance the conceptual understanding and intuition of engineers working in each area.     

Optimisation model to schedule railway track renewal operations: a life-cycle cost approach

Besides high initial construction costs, ballasted railway tracks also have high investment requirements, related to maintenance and renewal (M&R) works. Decision support tools for railway track components that optimise these works are increasingly gaining in importance. This paper presents an optimisation model that integrates ballast, rail and sleeper degradation models in a mixed integer linear programming model. This model links the decisions to renew these components with their condition and takes advantage of the integrated planning of renewal works to minimise the railway track life-cycle cost (LCC). The practical utility of the model is illustrated with a case study involving the Portuguese Lisbon–Porto line. The results indicate a reduction in track renewal cost if the grouping of components, track segments and time interval for renewal operations are optimised. Furthermore, this paper demonstrates that possible annual budget restrictions for railway track M&R operations can have an important influence on the railway track LCC.     

可靠度区间维护与调度集成优化

针对柔性作业车间生产过程中的预防性维护方案难以有效预测设备故障造成的维护过量和不足等问题进行了研究.为了有效应对这种情况,提出了基于设备健康状态的可靠度区间维护与鲁棒性调度生产集成优化模型.以最小化最大完工时间最小和工序偏差度最小为优化目标,应用多目标遗传算法NRGA对模型求解,针对柔性作业车间特点采用四层编码方案.最...     

多项目调度问题研究

现有多项目调度研究一般都基于不切实际的假定——资源在多个项目间进行传递时不需要花费额外的时间和成本。在对传递时间进行分析的基础上,建立了考虑资源传递时间的多项目调度问题的数学模型,并针对模型设计了结合并行调度的混合遗传算法,在保证项目任务紧前关系与资源约束的条件下,能够合理优化多项目的资源分配,从而有效地缩短多项目的总工期,通过实例验证了考虑传递时间的必要性,同时实例也证明了算法的有效性。