How to carry out assembly line–cell conversion? A discussion based on factor analysis of system performance improvements

The line–cell (or line–seru) conversion is an innovation of assembly systems that has received less attention. Its essence is dismantling an assembly conveyor line and adopting a mini-assembly unit, called seru (or cell). In this paper, we discuss how to do such line–cell conversions, especially focusing on assembly cell formation (ACF) and assembly cell loading (ACL). We perform 64 arrays of full factorial experiment analysis that incorporate three factors: work stations, product types, and product lot sizes. We construct a two-objective line–cell conversion model that minimises the total throughput time (TTPT) and the total labour hours (TLH). Three non-dominated solutions obtained from the two-objective model are used to evaluate the performance of the line–cell conversion. By investigating the experimental results of the ACF and the ACL, we summarise several managerial insights that could be used to help successful line–cell conversions.     

Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge

Particle swarm optimisation (PSO) is an evolutionary metaheuristic inspired by the swarming behaviour observed in flocks of birds. The applications of PSO to solve multi-objective discrete optimisation problems are not widespread. This paper presents a PSO algorithm with negative knowledge (PSONK) to solve multi-objective two-sided mixed-model assembly line balancing problems. Instead of modelling the positions of particles in an absolute manner as in traditional PSO, PSONK employs the knowledge of the relative positions of different particles in generating new solutions. The knowledge of the poor solutions is also utilised to avoid the pairs of adjacent tasks appearing in the poor solutions from being selected as part of new solution strings in the next generation. Much of the effective concept of Pareto optimality is exercised to allow the conflicting objectives to be optimised simultaneously. Experimental results clearly show that PSONK is a competitive and promising algorithm. In addition, when a local search scheme (2-Opt) is embedded into PSONK (called M-PSONK), improved Pareto frontiers (compared to those of PSONK) are attained, but longer computation times are required.     

Sensitivity aware NSGA-II based Pareto front generation for the optimal sizing of analog circuits

This paper deals with multiobjective analog circuit optimization taking into consideration performance sensitivity vis-a-vis parameters' variations. It mainly considers improving computation time of the inloop optimization approaches by including sensitivity considerations in the Pareto front generation process, not as a constraint, but by involving it within the used metaheuristic evolution process. Different approaches are proposed and compared. NSGA-II metaheuristic is considered. The proposed sensitivity aware approaches are showcased via two analog circuits, namely, a second generation CMOS current conveyor and a CMOS voltage follower. We show that the proposed ideas considerably alleviate the long computation time of the process and improve the quality of the generated front, as well.     

Optimised design of energy efficient building façades via Evolutionary Neural Networks

Buildings are required to be more and more energy efficient, in order to comply with restrictive requirements of building regulations and energy certifications. Optimisation algorithms have shown to be effective in identifying good solutions for the design of efficient building services. In this article Evolutionary Neural Network Design (ENN-Design) has been adopted to drive the design of a typical façade module for an office building. This application is significant, since façades play a major role in the definition of the energy performance of buildings. Both single-objective and multi-objective optimisations have been carried out. The aim of the article is to introduce an innovative approach for improving the performance of building envelopes by means of a reasonable amount of calculation time.     

基于混沌粒子群优化算法的NOMA功率分配研究

非正交多址接入(NOMA)技术使多个用户共享一个时频资源块,显著地提高了频谱利用率,已成为5G多址技术的研究热点。针对NOMA系统能量效率最大化问题,对非正交多址接入技术中功率分配进行了研究,提出了基于混沌粒子群优化算法的NOMA功率分配方案。建立了能量效率最大化的NOMA优化模型,利用混沌粒子群优化算法对系统功率进行分配,使NOMA系统的能量效率得到优化。仿真结果表明,当发射功率为36 dBm时,系统能量效率最大,且与传统粒子群算法相比所需迭代次数更少,获得的能量效率更优。     

Experimental investigation and optimisation in drilling of GFRP composites

Glass fibre-reinforced polymer (GFRP) composite materials are one of the important materials and are economic alternative to engineering materials because of their superior properties. This paper presents an effective approach for the optimisation of drilling parameters with multiple performance characteristics based on the Tagugch’s method with grey relational analysis. Taguchi’s L₁₆, 4-level orthogonal array has been used for the experimentation. The drilling parameters such as spindle speed and feed rate are optimised with consideration of multiple performance characteristics, such as thrust force, workpiece surface roughness and delamination factor. Response table and response graph are used for the analysis. The analysis of grey relational grade indicates that feed rate is the more influential parameter than spindle speed. The results indicate that the performance of drilling process can be improved effectively through this approach.     

Global supply chain planning for pharmaceuticals

The shortening of patent life periods, generic competition and public health policies, among other factors, have changed the operating context of the pharmaceutical industry. In this work we address a dynamic allocation/planning problem that optimises the global supply chain planning of a pharmaceutical company, from production stages at primary and secondary sites to product distribution to markets. The model explores different production and distribution costs and tax rates at different locations in order to maximise the company's net profit value (NPV).Large instances of the model are not solvable in realistic time scales, so two decomposition algorithms were developed. In the first method, the supply chain is decomposed into independent primary and secondary subproblems, and each of them is optimised separately. The second algorithm is a temporal decomposition, where the main problem is separated into several independent subproblems, one per each time period. These algorithms enable the solution of large instances of the problem in reasonable time with good quality results.     

Antoni Gaudí and Frei Otto: Essential Precursors to the Parametricism Manifesto

As Senior Architect to the Basilica of the Sagrada Família in Barcelona, Mark Burry has been ‘thinking parametrically’ for almost his entire career. Here he describes how his longstanding role overseeing the completion of Antoni Gaudí's masterpiece has afforded unique insights into the work of a great geometer and parametric thinker. Burry places the contribution of Gaudí alongside that of Frei Otto – the other eminent 20th-century Proto-Parametricist.     

Active–reactive power approaches for optimal placement of charge stations in power systems

Electric Vehicles (EVs) have been suggested as alternatives to conventional vehicles for reducing petrol consumption and carbon dioxide (CO₂) emissions. When a large number of EVs connect to the grid, they can cause a large amount of power loss. Where to install multiple charge stations in the grid, so as to mitigate losses caused by EVs when providing energy to those EVs, is becoming vitally important. In this paper, a distribution test-network model is described. A new analytical method is proposed, using the stations’ cooperation in terms of optimal active and reactive power dispatch as well as power flow analysis for locating the optimal placement of charge stations, so as to reduce power losses. This method is compared with the previously developed current density method for single charge stations using system simulation results. It was demonstrated that the methods proposed in this paper are more accurate than the current density method, and that 17% of the average active power loss can be saved for three different types of load profile. In addition, 27% of the average active power loss was saved by installing two charge stations rather than no charge stations in the test-line. It is shown that this could represent a 2.6% annual yield above inflation for investing in installing and running such charge stations.     

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.