A genetic algorithm for the optimisation of assembly sequences

This paper describes a Genetic Algorithm (GA) designed to optimise the Assembly Sequence Planning Problem (ASPP), an extremely diverse, large scale and highly constrained combinatorial problem. The modelling of the ASPP problem, which has to be able to encode any industrial-size product with realistic constraints, and the GA have been designed to accommodate any type of assembly plan and component. A number of specific modelling issues necessary for understanding the manner in which the algorithm works and how it relates to real-life problems, are succinctly presented, as they have to be taken into account/adapted/solved prior to Solving and Optimising (S/O) the problem. The GA has a classical structure but modified genetic operators, to avoid the combinatorial explosion. It works only with feasible assembly sequences and has the ability to search the entire solution space of full-scale, unabridged problems of industrial size. A case study illustrates the application of the proposed GA for a 25-components product.     

Response time variability

Response time variability is a new optimization problem with a broad range of applications and a distinctive number of theoretic flavour. The problem occurs whenever events, jobs, clients or products need to be sequenced so as to minimize the variability of time for which they wait for the next turn in obtaining the resources necessary for their advance. The problem has numerous real-life applications. We study its computational complexity, present efficiency, polynomial time algorithms for some cases, and the NP-hardness proof for a general problem. We propose a position exchange heuristic and apply it to improve the total response time variability of an initial sequence. The latter is the optimum bottleneck sequence, Webster or Jefferson sequence of the apportionment, or a random sequence. We report on computational experiments with the heuristic.     

Waste-to-energy based greenhouse heating: exploring viability conditions through optimisation models

An optimisation approach is proposed in order to assess the technical and economic feasibility of a renewable-energy-based greenhouse in North-Eastern Italy. A floor heating system that can exploit a low-temperature heat flow coming from the condenser of a waste-to-energy plant is chosen for the greenhouse and designed developing a non-linear optimisation model, solved by a genetic algorithm. In order to determine under what conditions the combination of a floor-heating-based greenhouse with a waste-to-energy plant can be profitable, a mixed integer optimisation model is introduced to allow selection of the minimum cost fuel solution as a function of different design variables of the greenhouse, such as indoor temperature settings and floor area. The ranges within which the renewable energy solution can lead to significant savings in comparison to traditional fossil fuel are identified both from the point of view of costs and of environmental impact. Furthermore, the sales prices for waste heat that would make investment in the renewable energy solution attractive for potential entrepreneurs are given.     

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.     

Screening model for assessing ‘cost’ of minimum flow requirements in the Svartå River,Sweden

The minimum flow requirements in the Svartå River in Sweden are directed at maintaining fishlife and providing suitable dilution for waste flows. The implications of varying the minimum flow requirements in the river are examined using a mixed integer optimisation model. The model is formulated as a modified method-of-weights technique with the economic issues of hydro-electricity generation, irrigation and urban water supply placed in the objective function and the minimum flows specified within the constraint set. The integer component of the model is required to model the operating policy at the major flow regulation facility in the system and the restricted validity of the irrigation permits. Application of the model shows that in dry years where competition between minimum flow levels and the other economic uses, is most intense, the levels achieved by the various economic objectives are only slightly reduced even with significant increases in the minimum flow requirements. Variations in minimum flow requirements of up to 45% only produce changes of 10% or less in the economic objectives. The lack of sensitivity of the objective levels is due primarily to the level of control exerted indirectly on the whole system in dry years by the release regulation policy and the restricted validity of the irrigation permits. In normal to wet years these policies are not as restrictive and more choice is available. In such years, however, there is generally sufficient water to satisfy all requirements and allocation is not a critical issue. The model itself is formulated generally so that a range of scenarios beyond those examined specifically in the paper can be considered.     

Modelling of the mist formation in a segmented grinding wheel system

It has been found that using a segmented grinding wheel with a fluid chamber can significantly minimise the quantity of coolant while improving the ground surface integrity. The present investigation aims to explore the fluid flow mechanism in such a wheel system. To this end, the Weber theory for Newtonian jet instability was applied to quantitatively determine the contribution of coolant flow rate to mist and ligament modes. A semi-analytical model was then developed to predict the mist flow rate by taking into account both the grinding parameters and fluid properties. It was shown that the model prediction was in good agreement with experimental measurements. Because of the comprehensive integration of variables in the formulation, the model provides a good fundamental understanding of the mist formation and offers a practical guideline for the selection and use coolant in minimising the mist flow rate.     

Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners’ preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors – by helping them to monitor, refine, and improve e-learning modules and courses according to the learners’ behaviour.     

无约束非线性lp问题的区间极大熵方法

针对信号处理、系统识别等领域中涉及到的无约束非线性lp问题,为减小由于二进制编码的舍入误差对该问题计算结果的影响,对求解该问题的极大熵方法进行了区间扩张.证明了区间扩张后的极大熵函数至少具有二阶收敛性,并设计了具有多项式时间复杂度的区间算法进行求解,举例进行了数值计算.数值计算结果显示,该区间算法可靠,计算结果与区间扩张前相比,结果更加精确.     

一种求解约束优化问题的改进型免疫克隆算法

针对约束优化问题提出一种基于精英库机制的改进型免疫克隆优化算法ICOAEB(Immune clonal optimization algorithm based on elite bank)。该算法利用精英库机制动态存储迭代过程中父代优势个体,实现优秀个体的多代记忆,从而提高算法寻优能力;并利用灾变算子扰动算法运行过程从而摆脱迭代缓慢的状态,避免局部收敛。通过对五个约束优化函数的测试,实验结果表明ICOAEB的求解精度和稳定性较高,可以较好地解决约束优化问题。最后针对影响算法性能的两项重要参数选择问题给出了相关的实验及分析。