Concentrator utilisation under geological uncertainty

Mineral concentrators are designed from preliminary geological data, and are therefore subject to geological uncertainty. This uncertainty has a demonstrable impact on the mining lifecycle, and has motivated stochastic strategic mine planning algorithms. The current paper describes how these algorithms may be extended to consider mineral processing operations in conjunction with open-pit mining operations. In particular, the paper describes an effective way to incorporate variable concentrator feed grade into these algorithms, which is the first step toward more elaborate representations of mineral concentrators. The resulting framework will be able to connect geological data to alternate configurations of the downstream resources, hence comparing the benefit of each configuration, and the corresponding capital investments. Sample computations are presented, comparing the net present value and utilisation for several different mineral processing capacities.     

Lifetime determination of tetragonal zirconia under static loading using the Constant Stress Rate method

Subcritical crack growth is a phenomenon which limits service time of a ceramic material. It is especially prevalent for oxides, because this phenomenon is attributed to the activity of water at the crack tip of the material and can be caused even by water present as a humidity in the air (Salem and Jenkins, 2002; Michalske and Freiman, 1983). It is very important to determine its lifetime at the setting loads with high probabilities of survival. The Constant Stress Rate method gives results that are sufficient for lifetime predictions. Estimations are based on n parameter which results from the slope of the strength vs. stress rate dependence. Only a conversion from dynamic to static conditions has to be done (Wojteczko et al., 2016). The attempts were made at different stress rates on sintered samples with pre-existing flaws and in two environments − air and water. Tetragonal zirconia was the tested material. Biaxial loading method was used for strength measurements. Microstructural and fractographic observations were made using the scanning electron microscope.     

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.     

Blood supply chain network design considering blood group compatibility under uncertainty

This paper addresses the design of a blood supply chain (SC) network considering blood group compatibility. To this aim, a bi-objective mathematical programming model is developed which minimises the total cost as well as the maximum unsatisfied demand. Due to uncertain nature of some input parameters, two novel robust possibilistic programming models are proposed based on credibility measure. The data of a real case study are then used to illustrate the applicability and performance of the proposed models as well as validating the proposed robust possibilistic programming approach. The obtained results show the superiority of the developed models and significant cost savings compared to current existed blood SC network.     

Multistage PRIM: patient rule induction method for optimisation of a multistage manufacturing process

Industries such as automotive, LCD, PDP, semiconductor and steel produce products through multistage manufacturing processes. In a multistage manufacturing process, performances of stages are not independent. Therefore, the relationship between stages should be considered when optimising the multistage manufacturing process. This study proposes a new procedure of optimising a multistage manufacturing process, called multistage PRIM (patient rule induction method). Multistage PRIM extends the scope of process optimisation from a single stage to the multistage process, and it can use the information encapsulated in the relationship between stages when maximising each stage's performance. A case study in a multistage steel manufacturing process is conducted to illustrate the proposed procedure.     

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.