Optimising a modular expansion of a wastewater treatment plant

In the design of a wastewater treatment plant, engineers must estimate the future population that the plant will need to serve. When the population growth is uncertain, modular design can reduce up-front capital costs, operating costs and total expected present value costs. Models based on real option theory can provide insights to engineers/decision-makers that will not arise from standard net present value analysis. We consider a municipality faced with a plant expansion decision required to meet the demand of significant, uncertain growth. We present a model that optimises the size of the plant and its maximum modular size. The optimisation requires that multiple solutions be calculated at the decision time for the staged expansion. We utilise a moment matching technique to approximate the solution of the Asian-like option. The results show that as the uncertainty increases, a modular plant can save significant total costs for the municipality.     

A simulation optimisation tool for planning of low-income housing projects

Construction of low-income housing projects is a recurring process and is associated with uncertainties that arise from the unavailability of resources. This paper presents a case study that discusses how computer simulation and optimisation are used to aid government agencies and/or contractors in planning of such projects. It illustrates the optimisation of project objectives, taking into consideration the interaction amongst involved resources. As such, total duration and the associated total costs, including direct and indirect costs, can be estimated and optimised. One Youth Habitation project that is executed in 6th of October City in Egypt is analysed in a step-by-step procedure to demonstrate the capability of proposed computer simulation and optimisation prototype (named LIHouse{_}Sim) in the modelling construction of low-income housing projects using bearing block walls with hollow core technique. The presented tool proves its practicality to contractors in estimating the time and costs of the recurring process of low-income housing construction, considering complex interdependencies between construction resources and the uncertainties associated with construction activities. The LIHouse{_}Sim prototype is used to perform a wide analysis for the alternative of the effective optimisation criteria in the bearing block walls/hollow core technique and for the genetic algorithm optimisation approach elements.     

供热系统优化规划方法研究

提出了适用于供热系统的两级优化规划方法，即第一级的布局优化和第二级管网的多目标优化方法。运用最优化理论建立的布局优化模型和管网系统多目标优化模型及采用的求解方法，适用于所有具有“管一站”网络特征的系统。     

Multi-disciplinary collaborative building design—A comparative study between multi-agent systems and multi-disciplinary optimisation approaches

The importance of collaboration between different design disciplines has been widely recognised by the building industry. Traditionally this is achieved through physical meetings between the representatives of different design groups. However, this is facing more challenges when dealing with large and complex design problems. Various approaches have been introduced to tackle this problem, such as multi-agent systems (MAS) and the multi-disciplinary design optimisation (MDO) approaches. The former represents an advanced, ICT-based framework which facilitates collaborative design through communication, data and knowledge sharing and negotiation while the latter represents a theoretical modelling approach which facilitates collaborative design through a thorough analysis of the technical problems. By comparing the strengths and weaknesses of these approaches in facilitating collaborative design, this paper provides a roadmap study for the development of collaborative building design.     

多关节测量臂的小生境混沌优化校准

多关节测量臂是一种便携式的坐标测量设备,它由一系列的旋转关节组成。为了提高多关节测量臂的测量精度和可重复性水平,必须对其运动学参数进行校准。首先,利用小生境的混沌优化算法提出了一种新的运动学校准方法以及一种混合目标的运动学校准函数,它考虑了诸如单点测量实验、容积性测量实验等多种性能测量实验的实验结果,然后,采用Levenberg Marquardt(L-M)算法和小生境混沌优化算法应用于运动学参数校准。小生境混沌优化算法显示出了优于L-M算法的性能。实验结果表明:使用NCOA算法校准后,测量误差的标准差始终优于LMA算法,并且校准前后多关节测量臂的测量精度提高了40倍。采用L-M算法和小生境混沌优化算法应用于运动学参数校准。小生境混沌优化算法显示出了优于L-M算法的性能。     

Proximal schemes for parabolic optimal control problems with sparsity promoting cost functionals

Fast first-order proximal methods that solve linear and bilinear parabolic optimal control problems with a sparsity cost functional are discussed. Weak convergence of these methods is proved and, for benchmarking purposes, the proposed inexact proximal schemes are compared to an inexact semi-smooth Newton method. Results of numerical experiments are presented to demonstrate the computational effectiveness of proximal schemes and to validate the theoretical estimates.     

Asynchronous optimisation with the use of a cascade search algorithm

This paper introduces the development of an asynchronous approach coupled with a cascade optimisation algorithm. The approach incorporates concepts of asynchronous Markov processes and introduces a search process that is benefiting from distributed computing infrastructures. The algorithm uses concepts of partitions and pools to store intermediate solutions and corresponding objectives. Population inflections are performed periodically to ensure that Markov processes, still independent and asynchronous, make arbitrary use of intermediate solutions. Tested against complex optimisation problems and in comparison with commonly used Tabu Search, the asynchronous cascade algorithm demonstrates a significant potential in distributed operations with favourable comparisons drawn against synchronous and quasi-asynchronous versions of conventional algorithms.     

Cycle time-oriented mid-term production planning for semiconductor wafer fabrication

Wafers are produced in an environment with uncertain demand and failure-prone machines. Production planners have to react to changes of both machine availability and target output, and revise plans appropriately. The scientific community mostly proposes WIP-oriented mid-term production planning to solve this problem. In such approaches, production is planned by defining targets for throughput rates and buffer levels of selected operations. In industrial practice, however, cycle time-oriented planning is often preferred over WIP-oriented planning. We therefore propose a new linear programming formulation, which facilitates cycle time-oriented mid-term production planning in wafer fabrication. This approach plans production by defining release quantities and target cycle times up to selected operations. It allows a seamless integration with the subordinate scheduling level. Here, least slack first scheduling translates target cycle times into lot priorities. We evaluate our new methodology in a comprehensive simulation study. The results suggest that cycle time-oriented mid-term production planning can both increase service level and reduce cycle time compared to WIP-oriented planning. Further, it requires less modelling effort and generates plans, which are easier to comprehend by human planners.