Maximising manufacturing system efficiency for multi-characteristic linear assembly by using particle swarm optimisation in batch selective assembly

Quality of an assembly is mainly based on the quality of mating parts. Due to random variation in sources such as materials, machines, operators and measurements, even those mating parts manufactured by the same process vary in their dimensions. When mating parts are assembled linearly, the resulting variation will be the sum of the mating part tolerances. Many assemblies are not able to meet the assembly specification in the available assembly methods. This will decrease the manufacturing system efficiency. Batch selective assembly is helpful to keep the assembly requirement and also to increase the manufacturing system efficiency. In traditional selective assembly, the mating part population is partitioned to form selective groups, and the parts of corresponding selective groups are assembled interchangeably. After the invention of advanced dimension measuring devices and the computer, today batch selective assembly plays a vital role in the manufacturing system. In batch selective assembly, all dimensions of a batch of mating parts are measured and stored in a computer. Instead of forming selective groups, each and every part is assigned to its best matching part. In this work, a particle swarm optimisation based algorithm is proposed by applying the batch selective assembly methodology to a multi-characteristic assembly environment, to maximise the assembly efficiency and thereby maximising the manufacturing system efficiency. The proposed algorithm is tested with a set of experimental problem data sets and is found to outperform the traditional selective assembly and sequential assembly methods, in producing solutions with higher manufacturing system efficiency.     

Modelling and optimisation of hull erection process

Due to the low efficiency of hull erection, this paper applies fuzzy-timed place Petri net (P-FTPN) to model the process of hull construction. Triangular Fuzzy Number (TFN) is utilised to denote the uncertain duration. A rule of setting dummy place and a reformative Minkowski subtraction are presented. According to the model, the low efficiency attributes to the time-consuming disequilibrium of each parallel branch and the complicated interrelations within tasks. Thus, we adjust the Orientation Block, eliminate the Hatch Coaming Blocks and combine them with corresponding Compartment Blocks, repartition the complicated Bow Blocks and reduce the construction difficulty. On this basis, modularisation building and preliminary group erection are applied for partial blocks. The parallel erection strategy is achieved on the whole from serial erection strategy. The result shows the efficiency has been enhanced greatly. Finally, whether a given construction state is in rational construction scheme(s) can be identified immediately from the Reasonable Marking Graph (RMG) of the model. Then, the dynamic supervisory control to the hull erection process can be realised.     

Modelling and optimisation of a two-server queue with multiple vacations and working breakdowns

This paper presents a steady-state analysis of an M/M/2 queue with heterogeneous servers (Server 1 and Server 2). Server 1 is reliable and may leave for a vacation when the system becomes empty. Sever 2 is unreliable and may break down while serving customers. When a breakdown occurs, Server 2 reduces the service rate rather than halting service. We formulate this queueing model as a quasi birth-and-death (QBD) process, using the matrix geometric method to compute the stationary distribution of system size. We also develop several measures to evaluate the performance of the system. A cost model based on system performance measures is formulated as a heuristic cost optimisation problem subject to stability conditions. A canonical particle swarm optimisation algorithm is used to obtain numerical solutions for the approximate optimal service rates of Server 1 and Server 2. Moreover, we present numerical results showing the effects of various parameters on the approximate optimal service rates as well as a practical example illustrating the application of the proposed model.     

Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach

Traditionally, process planning and scheduling are two independent essential functions in a job shop manufacturing environment. In this paper, a unified representation model for integrated process planning and scheduling (IPPS) has been developed. Based on this model, a modern evolutionary algorithm, i.e. the particle swarm optimisation (PSO) algorithm has been employed to optimise the IPPS problem. To explore the search space comprehensively, and to avoid being trapped into local optima, the PSO algorithm has been enhanced with new operators to improve its performance and different criteria, such as makespan, total job tardiness and balanced level of machine utilisation, have been used to evaluate the job performance. To improve the flexibility and agility, a re-planning method has been developed to address the conditions of machine breakdown and new order arrival. Case studies have been used to a verify the performance and efficiency of the modified PSO algorithm under different criteria. A comparison has been made between the result of the modified PSO algorithm and those of the genetic algorithm (GA) and the simulated annealing (SA) algorithm respectively, and different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in optimising the IPPS problem.     

Cutter size optimisation and interference-free tool path generation for five-axis flank milling of centrifugal impellers

Strategies for cutter size optimisation and interference-free tool path generation are proposed for five-axis flank milling of centrifugal impellers. To increase the material removal rate and provide a stronger tool shank during flank milling, the cutter size is first maximised under a set of geometric constraints. The tool path is then globally optimised in accordance with the minimum zone criterion for the determined optimal cutter size. Aside from the local interference of the cutter with the design surface, the global interferences with the hub surface and the adjacent blade surface are also considered in the optimisation models. Interference is indicated by the signed distance from the sampled point on the blade surface to the tool envelope surface. This distance is calculated without constructing the envelope surface. On the basis of the differential property of the distance function, we choose a sequential linear programming method in implementing the optimisations. This approach applies to generic rotary cutters, such as cylindrical and conical tools. Simulations are conducted to obtain the optimal cutter size and generate an interference-free tool path for a practical impeller. Simultaneously, a software module that can generate tool envelope surfaces and verify geometric errors is used to validate the proposed method.     

Optimisation of high energy ball milling parameters to synthesize oxide dispersion strengthened Alloy 617 powder and its characterization

In the present work, ultra-fine powder of oxide dispersion strengthened Alloy 617 was synthesized by high energy ball milling. Milling parameters such as rpm and milling time were varied in the range of 500–2000 and 5–360 min, respectively. Energy applied to the powder in the milling process (Energy per unit mass per hit, E_c) was estimated using the collision model. Effect of milling parameters on the microstructure of powder and refinement of oxides was investigated using X-ray Diffraction (XRD), Scanning electron Microscopy (SEM), conventional Transmission Electron Microscopy (TEM) and High resolution Transmission Electron Microscopy (HRTEM). Desired convoluted lamellar structure with average particle size ∼33 μm was observed during milling at 1000 rpm (E_c ∼ 0.4 kJ/g.hit) for 6 h. TEM analysis of the powder showed the presence of fine oxide dispersoids in the size range 4–16 nm. HRTEM analysis substantiated the presence of fine dispersoids of size ∼4 nm and showed the presence of deformation twins in the matrix. The fine dispersoids in a nanocrystalline matrix is expected to provide superior creep strength to the material at high temperatures.     

Using the global optimisation methods to minimise the machining path length of the free-form surfaces in three-axis milling

During the machining of free-form surfaces using three-axis numerically controlled machine (NC), several parameters are chosen arbitrary and one of the most important is the feed motion direction. The main objective of this study is to minimise the machining time of complex surfaces while respecting a scallop height criteria. The analytical expression of the machining time is not known and by hypothesis, it is assumed to be proportional to the path length crossed by the cutting tool. This path length depends on the feed direction. To have an optimal feed direction at any point, the surface is divided into zones with low variation of the steepest slope direction. The optimization problem was formulated aiming at minimizing the global path length. Furthermore, a penalty reflecting the time loss due to the movement of the tool from one zone to another one is taken into account. Several heuristics are used to resolve this problem: Clarke and Wrights, Greedy randomized adaptive search procedure, Tabu search and Nearest neighbour search. An example illustrates our work by applying the different heuristics on a test surface. After simulations, the results obtained present a significant saving of paths length of 24% compared to the machining in one zone.     

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Seldom has research regarding manufacturing process modelling considered the two common types of uncertainties which are caused by randomness as in material properties and by fuzziness as in the inexact knowledge in manufacturing processes. Accuracies of process models can be downgraded if these uncertainties are ignored in the development of process models. In this paper, a hybrid swarm intelligence algorithm for developing process models which intends to achieve significant accuracies for manufacturing process modelling by addressing these two uncertainties is proposed. The hybrid swarm intelligence algorithm first applies the mechanism of particle swarm optimisation to generate structures of process models in polynomial forms, and then it applies the mechanism of fuzzy least square regression algorithm to determine fuzzy coefficients on polynomials so as to address the two uncertainties, fuzziness and randomness. Apart from addressing the two uncertainties, the common feature in manufacturing processes, nonlinearities between process parameters, which are not inevitable in manufacturing processes, can also be addressed. The effectiveness of the hybrid swarm algorithm is demonstrated by modelling of the solder paste dispensing process.     

A genetic algorithm-based optimisation model for performance parameters of manufacturing tasks in constructing virtual enterprises

The performance parameters of the manufacturing tasks (PPMT) are considered as the key parameters in constructing virtual enterprises (VE). It is difficult to determine the optimal or near-optimal values of PPMT. In this paper, the optimisation process in VE is perceived as a bidirectional optimisation process which consists of the forward and reverse process. By analysing the process, a reverse optimisation model based on vector norm theory is proposed and the target function is defined as a formula of the sum of the weighting Euclidean distance between two PPMT vectors. The existence of optimisation solution for the problem is investigated. Then an adaptive genetic algorithm based on natural number encoding is developed to solve the problem. A practical example is implemented to verify the validity of the proposed model and approach. The discussed results show that the optimal or near-optimal PPMT is helpful for the candidate enterprises selection in the process of constructing a VE.     

Using fuzzy analytic hierarchy process and particle swarm optimisation for balanced and defective supply chain problems considering WEEE/RoHS directives

This research explored problems concerning production and delivery in a green supply chain, and constructed an optimal mathematical model to provide solutions. This model incorporates WEEE and RoHS in EU directives for the selection of green partners when establishing a supply chain. The weight of each component is calculated by fuzzy analytic hierarchy process (fuzzy AHP). Previous studies suggested that a supply chain is a balanced system, however, in actual practice, there may be processing damages or delivering losses. Thus, such a supply chain with production loss is known as a ‘defective supply chain’. This research analysed the defective supply chain system to discuss its supplier selection, production, and distribution. It developed an optimal mathematical model for both balanced and defective models, and adopted particle swarm optimisation (PSO) to obtain solutions for both models. Finally, case studies for both models with quality solutions were discussed to confirm the efficiency and effectiveness of the proposed approach.     

A mathematical formulation and anarchic society optimisation algorithms for integrated scheduling of processing and transportation operations in a flow-shop environment

This paper addresses a permutation flow-shop scheduling problem where there are a finite number of transporters to carry jobs from each machine to its subsequent machine. The problem is first formulated as a mixed-integer linear programme, and then two anarchic society optimisation (ASO) algorithms are developed to solve large-sized instances of the problem. The numerical experience shows that the ASO algorithms are considerably effective and efficient. Finally, a sensitivity analysis is carried out to study the performance of the manufacturing system versus the transportation times and the number of transporters.     

Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation

This paper describes a relational database system for semi-generative process planning for sheet metal parts that emulates expert system capabilities. The system integrates a feature-based relational database for the parts, a forward chaining rule-based strategy for machine selection, both global and feature-specific execution of the rules and a graph theoretic cost optimization model for optimal process plan selection. This system, which is currently being developed for a sheet metal fabrication company, suggests that, using the experience of shopfloor personnel, an efficient integration of feature-based process planning and expert system strategies can be accomplished.     

Optimization of cutting power and power efficiency during particleboard helical milling

With the aim of providing scientific guidance for the application of spiral cutters in particleboard machining, this work studied the influence of milling parameters on milling power and power efficiency during helical milling of particleboard. And the response surface methodology was applied to optimize the milling parameters to reduce machining energy consumption and improve energy efficiency. The factors of milling depth, spindle speed and helical angle were selected as input parameters, and the mathematical models between the input parameters and the response parameters were established. Then, the significant influence of each factor and the interaction of two factors were determined by variance analysis, and the change trend of milling power and power efficiency was studied by response surface methodology. Results showed that the milling depth had the greatest impact on milling power and power efficiency, followed by the spindle speed and helical angle. An increase in the milling depth and spindle speed resulted in an increase in milling power and power efficiency, while the increased helical angle resulted in a decrease in milling power and power efficiency. The optimized values of helical angle, spindle speed and milling depth were 54°, 5650 min⁻¹ and 1.3 mm, respectively.     

Developing a novel approach to assess the efficiency of resource utilisation in organisations: a case study for an automotive supplier

There are different business processes in each organisation. Each business process utilises some resources to perform its related activities, produces specified outputs (products/services) and reaches organisational goals. In this paper, an approach is presented to assess the ability of business processes to utilise resources. To apply the presented framework, a manufacturing firm in the automotive industry was selected. Initially, four main business process groups were chosen for the assessment. Then, 15 processes of the four determined process groups and resources utilised by each process were identified. All of the recognised 19 resources were classified into six major categories, including physical, relational, organisational, informational, human and legal resources. Afterward, a hierarchical top-down analysis was performed to determine the ability of process groups and processes to utilise resource categories and resources. The results of the analysis show which resources have been strongly/weakly utilised by which business processes. In other words, by applying the suggested framework, it is possible to accurately identify the strengths and weaknesses of the resource utilisation. Therefore, the company can focus on weaknesses, prioritise them and develop improvement actions to increase the ability to utilise resources in the specified areas.     

Comparative electrochemistry of haemoglobin on the long and ball milling shortened carbon nanotubes

Short multi-walled carbon nanotubes (S-MWCNTs) were obtained by ball milling method and employed as a supporting matrix to explore a novel immobilisation and biosensing platform of redox proteins such as haemoglobin (Hb). Compared to long MWCNTs, the shortened MWCNTs-based biosensor did not show better electrocatalytic activity, which was contrary to previous theoretical expectations. The electron transfer rate constants of Hb on long and short MWCNTs modified electrodes in phosphate buffer solution were 1.08 and 0.95?s^?1, respectively. The lower electron transfer rate between haemoglobin and the electrode can be attributed to their larger resistive properties, which were confirmed by cyclic voltammetry and AC impedance.     

An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company

We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%.     

Critical parameters of mixtures of carbon dioxide and ethane

This paper deals with an evaluation of the critical parameters reported for mixtures of carbon dioxide and ethane. Equations are presented for the critical parameters as a function of the concentration.     

Grinding behavior of WO3, NiO,Fe2O3 by ultrasonic milling parameters control and preparation of nanocomposite powder

Tungsten-based alloys have been widely applied in various industries due to their excellent mechanical properties. Tungsten-based alloys have a high sintering temperature due to the high melting point of tungsten, so the coarse particles negatively affect the mechanical properties of the alloy. This problem can be solved by increasing the densification by reducing the sintering temperature and time by adding nanoparticles with high surface energy. Herein, we fabricated nanoparticle-sized metal oxides by ultrasonic milling to minimize the influx of impurities to improve the densification of tungsten alloys. The main parameters of the ultrasonic milling experiments were ball density and ball layer. Metal oxides prepared by ultrasonic milling showed an average particle size distribution of less than 200 nm, and metal composite powders prepared through subsequent hydrogen reduction also showed nanoparticle size distributions. We believe that this approach will enable the production of improved sintered tungsten-based alloys.