Quasi on-line scheduling procedures for flexible manufacturing systems †

This paper presents three quasi on-line scheduling procedures for FMSs consisting of work stations, transport devices, and operators. In the scheduling, different types of decisions are taken to perform a particular operation, i.e. the selection of (a) a work station, (b) a transport device and (c) an operator. Further, (d) the scheduling sequence of the operations has to be determined. The three developed procedures differ in the way these four decision problems are solved hierarchically. Several dispatching rules (SPT, SPT.TOT, SPT/ TOT and EFTA) are available to solve the last mentioned decision problem. Limited buffer capacities in an FMS may cause deadlock in the procedures as well as in practice. The scheduling procedures involve a buffer handling method to avoid deadlock. A case study is presented to demonstrate the three procedures and to show some of its properties. Based on simulation tests, some conclusions are drawn about the performance of the scheduling procedures and the various dispatching rules.     

Machine grouping problem in cellular manufacturing systems— an integer programming approach

In this paper a methodology is proposed to group the machines in cellular manufacturing systems based on the tooling requirements of the parts, toolings available on the machines and the processing times. Two 0-1 integer programming formulations are proposed. These formulations assume that the part families are known. The first formulation groups the machines based on the compatibility of parts with machines. The second formulation groups the machines in order to minimise the cost of allocating the machines and the cost of intercell movement. These formulations take into account the limitations on the number of machines in a group and the number of machines available of a particular type. The application of these formulations is illustrated using an example.     

Optimality criteria-based topology optimization of a bi-material model for acoustic–structural coupled systems

This article investigates topology optimization of a bi-material model for acoustic–structural coupled systems. The design variables are volume fractions of inclusion material in a bi-material model constructed by the microstructure-based design domain method (MDDM). The design objective is the minimization of sound pressure level (SPL) in an interior acoustic medium. Sensitivities of SPL with respect to topological design variables are derived concretely by the adjoint method. A relaxed form of optimality criteria (OC) is developed for solving the acoustic–structural coupled optimization problem to find the optimum bi-material distribution. Based on OC and the adjoint method, a topology optimization method to deal with large calculations in acoustic–structural coupled problems is proposed. Numerical examples are given to illustrate the applications of topology optimization for a bi-material plate under a low single-frequency excitation and an aerospace structure under a low frequency-band excitation, and to prove the efficiency of the adjoint method and the relaxed form of OC.     

A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning

Nowadays in Supply Chain (SC) networks, a high level of risk comes from SC partners. An effective risk management process becomes as a consequence mandatory, especially at the tactical planning level. The aim of this article is to present a risk-oriented integrated procurement–production approach for tactical planning in a multi-echelon SC network involving multiple suppliers, multiple parallel manufacturing plants, multiple subcontractors and several customers. An originality of the work is to combine an analytical model allowing to build feasible scenarios and a multi-criteria approach for assessing these scenarios. The literature has mainly addressed the problem through cost or profit-based optimisation and seldom considers more qualitative yet important criteria linked to risk, like trust in the supplier, flexibility or resilience. Unlike the traditional approaches, we present a method evaluating each possible supply scenario through performance-based and risk-based decision criteria, involving both qualitative and quantitative factors, in order to clearly separate the performance of a scenario and the risk taken if it is adopted. Since the decision-maker often cannot provide crisp values for some critical data, fuzzy sets theory is suggested in order to model vague information based on subjective expertise. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution is used to determine both the performance and risk measures correlated to each possible tactical plan. The applicability and tractability of the proposed approach is shown on an illustrative example and a sensitivity analysis is performed to investigate the influence of criteria weights on the selection of the procurement–production plan.     

Intelligent and integrated RFID (Ⅱ-RFID) system for improving traceability in manufacturing

In the wake of globalization,many modern manufacturing companies in Norway have come under intense pressure caused by increased competition,stricter government regulation,and customer demand for higher value at low cost in a short time.Manufacturing companies need traceability,which means a real-time view into thenproduction processes and operations.Radio frequency identification(RFID) technology enables manufacturing companies to gain instant traceability and visibility because it handles manufactured goods,materials and processes transparently.RFID has become an important driver in manufacturing and supply chain activities.However,there is still a challenge in effectively deploying RFID in manufacturing.This paper describes the importance for Norwegian manufacturing companies to implement RFID technology,and shows how the intelligent and integrated RFID(n-RFID) system,which has been developed in the Knowledge Discovery Laboratory of Norwegian University of Science and Technology,provides instant traceability and visibility into manufacturing processes.It supports the Norwegian manufacturing industries survive and thrive in global competition.The future research work will focus on the field of RFID data mining to support decision-making process in manufacturing.     

Set-up and production planning in hybrid manufacturing–remanufacturing systems with large returns

Hybrid systems that use both raw materials and returned products in the production process are considered. The system contains one facility, and undergoes set-ups each time it switches between two production modes. In particular, we address systems engaged mainly in remanufacturing and having a large percentage of return. This situation is encountered in companies with mature remanufacturing channels. The targeted application area is comprised of hybrid systems that uses leasing as a business model with manufacturing serving to attenuate return uncertainty. To evaluate the system performance, we take into account manufacturing and remanufacturing costs, holding costs in serviceable and return inventories, backlog and set-up costs. Our analysis of hybrid systems with high return levels reveals features that are peculiar to such systems and that differentiate them from systems with lower return rates. We first present analytical solutions for optimal production and set-up schedule, and determine the possible cycle shapes for reliable systems. Optimal policies contain intervals of manufacturing and remanufacturing at maximal rate, and intervals of on-demand and on-return remanufacturing. Failure-prone systems are studied next, using the formalism of stochastic dynamic programming. Optimal policies give rise to the trajectories converging to the patterns similar to the analytically calculated cycles.     

Production control of hybrid manufacturing–remanufacturing systems under demand and return variations

Hybrid manufacturing systems that use both raw materials and returned products as a supply for their production process are considered. Specifically, the system under study contains two machines: one uses raw materials for manufacturing, while another utilises end-of-life products returned from the market for remanufacturing. Machines are failure-prone, demand and return rates fluctuate in time reflecting market behaviour due to economical, seasonal and environmental changes. The system performance is characterised by a long-term discounted cost that integrates several partial costs (those of manufacturing, remanufacturing, disposal, holding costs in serviceable and return inventories). Optimisation of the hybrid system behaviour requires to determine the combined manufacturing, remanufactruring and disposal policy, withstanding machine failures under dynamic market conditions. Optimality conditions in the form of Hamilton–Jacoby–Bellman equations are obtained and a novel numerical approach, based on the estimation of value function timederivative, is proposed in order to deal with demand and return variations. Extensive simulations are performed to address the numerous scenarios corresponding to evolving relationship between manufacturing capacities and varying demand and return levels. Simulation results show that the optimal policies have an important property of anticipating the future changes in the demand and return, and making the timely decisions relevant to these changes.     

Organic host–guest systems for blue emission

The photoluminescence (PL) quantum yields (QYs) and lifetimes of the 1,6-diphenyl-1,3,5-hexatriene (DPH) blue emitter and of the acceptor–donor system, DPH and p-terphenyl (P3), are investigated embedded in the nanochannels of a perhydrotriphenylene (PHTP) matrix. In the co-inclusion system, light emission takes place from DPH, due to the efficient resonant energy transfer (RET) from the P3 donor molecules. The host matrix stabilizes the guest molecules against photooxidation degradation. The largest quantum yield, up to 100%, has been found in the co-inclusion compound (IC), where excitations are localized at the emissive acceptor sites.     

A new trust region–sequential quadratic programming approach for nonlinear systems based on nonlinear model predictive control

In this article, a superlinearly convergent trust region–sequential quadratic programming approach is first proposed, developed and investigated for nonlinear systems based on nonlinear model predictive control. The method incorporates a combination algorithm that allows both the trust region technique and the sequential quadratic programming method to be used. If the attempted search of the trust region method is not accepted, the line search rule will be adopted for the next iteration. Also, having to resolve the quadratic programming subproblem for nonlinear constrained optimization problems is avoided. This gives the potential for fast convergence in the neighbourhood of an optimal solution. Moreover, additional characteristics of the algorithm are that each quadratic programming subproblem is regularized and the quadratic programming subproblem always has a consistent point. The main result is illustrated on a nonlinear system with a variable parameter and a bipedal walking robot system through simulations and is utilized to achieve rapidly stability. Numerical results show that the trust region–sequential quadratic programming algorithm is feasible and effective for a nonlinear system with a variable parameter and a bipedal walking robot system. Therefore, the simulation results demonstrate the usefulness of the trust region–sequential quadratic programming approach with nonlinear model predictive control for real-time control systems.     

Microstructural topology optimization for minimizing the sound pressure level of structural–acoustic coupled systems with multi-scale random parameters

The main aim of this article is to present a robust microstructural topology optimization methodology for structural–acoustic coupled systems with multi-scale random parameters. During the microstructural topology optimization, both the uncertainty at the macro-scale, which comes from the physical parameters of the acoustic medium or the external load, and the uncertainty existing in the constituent material properties of the microstructure at the micro-scale are considered as random parameters. A homogenization-based probabilistic finite element method (HPFEM) is first developed for quantifying the structural–acoustic system with multi-scale random parameters. The use of the HPFEM transforms the problem of microstructural topology optimization with multi-scale random parameters to an augmented deterministic microstructural topology optimization problem. This provides a computationally cheap alternative to Monte Carlo-based optimization algorithms. A numerical example of a hexahedral box is given to demonstrate the efficiency of the proposed method.     

Multi-expansion modal reduction: A pragmatic semi–a priori model order reduction approach for nonlinear structural dynamics

A novel model order reduction scheme for nonlinear finite element (NL-FE) structural dynamics models is proposed in this work, which enables an overall accelerated model evaluation. The proposed multi-expansion modal (MEM) reduction employs a set of modal bases evaluated for a number of representative statically deformed configurations, combined with these static deformation patterns, to obtain a reduced order basis (ROB). This ROB is then exploited in an a priori element sampling to obtain a hyper-reduced model based on the energy-conserving sampling and weighting approach. The proposed element sampling scheme uses a nonnegative L₁ optimization of the element weights where an equality between the projected and hyper-reduced linearized stiffness matrix or static deformation forces is added. A procedure and several guidelines on the reduced order model setup and evaluation are presented, with specific attention to how the presented approach allows to exploit existing NL-FE frameworks. The MEM approach is validated numerically on an academic cantilever beam model and is shown to provide good accuracy and consistent convergence properties.     

Deadlock avoidance algorithm for flexible manufacturing systems by calculating effective free space of circuits†

Modern flexible manufacturing systems (FMS) are highly automated and flexible in which raw parts of various types are processed concurrently. Deadlock issue arises easily in these systems due to shared equipment usage and high production flexibility. It was indicated in Wysk et al . (Wysk, R., Yang, N. and Joshi, S., Detection of deadlocks in flexible manufacturing cells. IEEE Trans. Rob. Auto., 1991, 7, 853–859.) that when a manufacturing system is modelled by a digraph, existence of circuits in such a graph is a necessary condition for deadlock. Deadlock avoidance for systems with free choice in part routing has been well studied in the Petri net formalism, however, we have not found that it has been studied systematically in the digraph formalism. Because of choices introduced, part flow dynamics become much more complex. Based on our previous work Zhang et al . (Zhang, W., Judd, R.P. and Paul P., Evaluating order of circuits for deadlock avoidance in a flexible manufacturing system. Int. J. Prod. Res., 2006, 44, 5247–5259.), this paper presents a deadlock avoidance algorithm for FMS, which allows free choices in part routing by calculation of effective free space of circuits of the digraph model. The algorithm is highly permissive since the effective free space calculation captures more parts flow dynamics, especially when there exist multiple knots in the digraph model. And it runs in polynomial time once the set of circuits of the digraph is computed offline. Simulation results on selected examples are given.     

PVD thermal barrier coating systems for Mo–Si–B alloys

Abstract

Oxidation protective layers with chemical compositions of Mo–70Al, Mo–46Si–24B, Mo–37Si–15B and Mo–47Si–24Al (at.-%) were deposited on Mo–9Si–8B specimens by magnetron sputtering. After pre-oxidation of the coated samples, ceramic topcoats of yttria partially stabilized zirconia (YSZ) and gadolinium zirconate (GZO) were applied using electron-beam physical vapour deposition. Both as-deposited YSZ and GZO topcoats exhibited good adhesion to the pre-oxidised bond coats. The different thermal barrier coating (TBC) systems were exposed to air at 1000 °C for periods between 20 and 100 h. The YSZ topcoat was tightly-adherent to the borosilicate scale grown on the Mo–46Si–24B bond coat after 20 h of exposure. Similar results were obtained for GZO topcoats deposited on Mo–46Si–24B and Mo–37Si–15B bond coats. The TBC system consisting of GZO topcoat and Mo–47Si–24Al bond coat, which formed a mixed scale of silica and mullite-like oxides, survived 100 h at 1000 °C. However, after this exposure time, the bond coats were approaching their lifetime due to the low layer thickness (5–10 μm). Oxidation of the Mo–Si–B substrate at unprotected areas around the suspension hole of the samples caused severe deterioration of the Mo–70Al bond coat and substantial degradation of the outer region of the GZO topcoat due to chemical reactions with MoO₃.     

A primary influence vertex approach to identify driving factors in complex integrated agri-industrial systems – an example from sugarcane supply and processing systems

Integrated agri-industrial systems (IAISs), such as sugarcane supply and processing systems, are complex systems and hence generally difficult to understand and manage. The large number factors in IAISs coupled with the complex interrelationships among the factors make it challenging to identify the points of intervention for improving their overall performance. Several approaches, such as the network theory and the Theory of Constraints have been used to identify important factors in systems with variations in success. This paper demonstrates a primary influence vertex approach for identifying and ranking the factors that drive the performance of IAISs. The approach is based on comprehensive causal network analyses and was tested in four relatively diverse large-scale sugarcane milling operations in South Africa. Results from the analyses were found to be consistent with the literature and external knowledge of the milling areas as at the time of the study. It is concluded that the approach can proffer a sound basis from which deeper rooted problems in systems can be identified on an ongoing basis. It is, however, recommended that the approach should be systematically compared with other relevant methods that are used to analyse complex systems.     

Study of microalgae cultivation systems based on integrated analytic hierarchy process–life cycle optimization

Optimization of sustainable process plant configurations requires the use of systematic assessment methods based on the usage of natural resources, release of pollutants and generation of environmental impact. This paper presents an integrated life cycle optimization framework for the synthesis of microalgae cultivation systems, using a multiple objective linear program formulation; in the model, individual objective functions are aggregated and weighted using the analytic hierarchy process. Four different cultivation alternatives were used as case study to demonstrate the capability of this formulated integrated model. The model takes into account three main environmental criteria in assessing different cultivation alternatives, namely energy, water (direct and indirect water) and carbon footprints. It is determined in the case study that the open pond cultivation system is preferred compared to other alternatives.     

Hybrid silica–polymer structure for integrated optical waveguides with new potentialities

We report some features of a new waveguide structure in integrated optic providing new potentialities. We propose to use specific polymers as overlayer of ridge silica waveguides. This method can be used to finely tune the superstrate refractive index in order to adjust performances of components. In this paper, we give as examples, the auto-stabilization of integrated Bragg gratings and also, first experiments to realize thermo-optic directional couplers which can be achieved by combining these two materials in the waveguide structure.     

A dimension reduction approach for conditional Kaplan–Meier estimators

Many quantities of interest in survival analysis are smooth, closed-form functionals of the law of the observations. For instance, the conditional law of a lifetime of interest under random right censoring, and the conditional probability of being cured. In such cases, one can easily derive nonparametric estimators for the quantities of interest by plugging-into the functional the nonparametric estimators of the law of the observations. However, with multivariate covariates, the nonparametric estimation suffers from the curse of dimensionality. Here, a new dimension reduction approach for survival analysis is proposed and investigated in the right-censored lifetime case. First, we consider a single-index hypothesis on the conditional law of the observations and propose a \(\sqrt{n}-\)asymptotically normal semiparametric estimator. Next, we apply the smooth functionals to this estimator. This results in semiparametric estimators of the quantities of interest that avoid the curse of dimensionality. Confidence regions for the index and the functional of interest are built by bootstrap. The new methodology allows to test the dimension reduction assumption, can be extended to other dimension reduction methods and can be applied to closed-form functionals of more general censoring mechanisms.     

Improvement in dynamic properties of laminated MWCNT-polystyrene composite beams via an integrated numerical–experimental approach

A new concept for the optimization of dynamic behavior of laminated nanocomposites is introduced where fiber orientation factor in continuous fiber-reinforced composites is replaced by different wt.% of carbon nanotubes (CNTs) in each layer. First, at a design concept level, an optimum distribution of multi-walled CNTs (MWCNTs) through the thickness of a typical cantilever beam is sought to achieve its highest fundamental natural frequency for a given weight percent of MWCNTs. This is done using a finite element (FE) model in ABAQUS along with a user-defined Python code. Next, based on the obtained optimum distribution, actual laminated MWCNT/polystyrene (PS) composite beams were fabricated and their effective stiffness, fundamental natural frequencies and damping ratios were measured through static deflection and free vibration tests. It was found that the optimum distribution of MWCNTs resulted in an increase of 21.9% and 10.4% in the effective Young’s modulus and the fundamental damped natural frequency values, respectively, which were almost two-fold higher than those of a beam with a uniform MWCNT distribution. In addition, compared to a pure polymer beam, 38.9% and 27.8% improvements in the damping ratio of the uniformly and optimally distributed MWCNT polymer composite beams were achieved.     

A method for identifying customer orientations and requirements for product–service systems design

For manufacturers, developing product–service systems (PSSs) is getting more important because of the trends of servitisation and creating social value. A PSS is a social system where multiple actors mutually provide products and services. A PSS design, therefore, must take into account various actors as customers. However, existing methods provide an insufficient solution as to how various customers should be handled in an analysis to identify and accommodate various customer preferences and requirements. To tackle this issue, this article proposes a new method of identifying customers’ orientations and requirements for PSS design. The proposed method employs a combination of topic analysis, persona and scenario approaches. The effectiveness of the method is demonstrated with its application to an urban development case. Through the demonstration, its practical benefits are concluded as follows: consistent and logical results of requirement analysis and insights into a new market for manufacturers.