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In the era of climate change, industrial organizations are under increasing pressure from consumers and regulators to reduce greenhouse gas emissions. The purpose of this paper is to examine the effectiveness of product mix as a strategy to deliver the low carbon supply chain under the cap-and-trade policy.

The authors incorporate the cap-and-trade policy into the green product mix decision models by using game-theoretic approach and compare these decisions in a decentralized model and a centralized model, respectively. The research explores potential behavioral changes under the cap-and-trade in the context of a two-echelon supply chain.

The analysis results show that the channel structure has significant impact on both economic and environmental performances. An integrated supply chain generates more profits. In contrast, a decentralized supply chain has lower carbon emissions. The cap-and-trade policy makes a different impact on the economic and environmental performances of the supply chain. Balancing the trade-offs is critical to ensure the long-term sustainability.

The research offers many interesting observations with respect to the effect of product mix strategy on operational decisions and the trade-offs between costs and carbon emissions under the cap-and-trade policy. The insights derived from the analysis not only help firms to make important operational and strategic decisions to reduce carbon emissions while maintaining their economic competitiveness, but also make meaningful contribution to governments’ policy making for carbon emissions control.

This paper aims to propose a novel statistic energy analysis method with fuzzy parameters to study the dynamic and acoustic responses of coupled system with fuzzy parameters, which can expand the applied range of statistic energy analysis method in engineering to some extent.

On the basis of the property of membership level, the uncertain fuzzy parameters are expressed as the interval forms. Interval mathematics and interval expansion principle are adopted to solve the problem with interval parameters. At last, two numerical examples, which include a two-plate coupled system and a single-partition sound-insulation system, are carried out to demonstrate the feasibility and validity of the presented method.

Interval mathematics and interval expansion principle are adopted to solve the problem with interval parameters.

By integrating the interval analysis, optimization technique and Taylor expansion method, two non-probabilistic, set-theoretical statistical energy analyses are proposed for predicting the dynamical and acoustical response of the complex coupled system with uncertain parameters in high-frequency domain.

This paper is to present an experiment to verify that the motion errors of robust topology optimization results of compliant mechanisms are insensitive to load dispersion.

First, the test pieces of deterministic optimization and robust optimization results are manufactured by the combination of three-dimensional (3D) printing and casting techniques. To measure the displacement of the test piece of compliant mechanism, a displacement measurement method based on the image recognition technique is proposed in this paper.

According to the experimental data analysis, the robust topology optimization results of compliant mechanisms are less sensitive to uncertainties, comparing with the deterministic optimization results.

An experiment is presented to verify the effectiveness of robust topology optimization for compliant mechanisms. The test pieces of deterministic optimization and robust optimization results are manufactured by the combination of 3D printing and casting techniques. By comparing the experimental data, it is found that the motion errors of robust topology optimization results of compliant mechanisms are insensitive to load dispersion.

This paper seeks to fill the literature gap that lacks of exploring negotiation strategy with competing partners under asymmetric production-cost information. The purpose of this paper is to examine firms’ optimal contract negotiation strategies in buyer–supplier–supplier triads where there are concurrent negotiations between the retailer and two competing manufacturers.

The authors consider a two-echelon supply chain, in which the retailer has the option of segmented or unified negotiation policy, whereas the two competing manufacturers can withhold or share production cost information in the negotiation. Based on game theory, the authors derive the manufacturers’ optimal wholesale prices and the retailer’s optimal retail prices with eight possible scenarios. Optimal strategic choices and operational decisions are then explored through the comparative analysis of equilibriums of eight possible scenarios.

The authors find that the retailer will adopt different negotiation strategies depending on manufacturers’ decisions on sharing or withholding their production-cost information. When both manufacturers share their production-cost information, the retailer will adopt a unified negotiation policy. The high-efficiency manufacturer should adopt the same information-sharing strategy as the low-efficiency manufacturer in order to gain more profit.

The modelling helps to bring further clarity in supply chain contract negotiation by offering a conceptual framework to enhance our understanding of the effects of information-sharing strategy and negotiation policy in the negotiation process form the perspectives of all engaging parties. Managerial insights derived from the research will enable retailers and manufacturers to make informed and better strategic and operational decisions to improve market competitiveness.

There is a growing concern in recent years regarding climate change risks to real estate in the developed and developing countries. It is anticipated that the property sector could be affected by variable climate and related extremes as well as by the strategies adopted to combat greenhouse gas (GHG) emissions. This paper aims to analyse the current knowledge regarding future climate changes to understand their possible impacts on the real estate sector of Malaysia with an aim to help stakeholders to adopt necessary responses to reduce negative impacts.

Available literature is reviewed and data related to climatic influences on buildings and structures are analysed to understand the climate change impacts on real estate in Malaysia.

The study reveals that temperature in the Peninsular Malaysia will increase by 1.1 to 3.6°C, rainfall will be more variable and river discharge in some river basins will increase up to 43 per cent during the northeast monsoon season by the end of this century. These changes in turn will pose risks of property damage and increase property lifecycle costs. Furthermore, property prices and the overall growth of the property sector may be affected by the government policy of GHG emission reduction by up to 45 per cent by the year 2030. This study concludes that the property sector of Malaysia will be most affected by the implementation of GHG emission reduction policy in the short term and due to the physical risk posed by variable climate and related extremes in the long term.

The study in general will assist in guiding the operational responses of various authorities, especially in terms of those interventions aimed at climate change risk reduction in the property sector of Malaysia.

Traditional ion optics manufacturing processes are complex and costly. The purpose of this paper is to study the feasibility of using selective laser melting (SLM) to produce additively manufactured ion optics.

An SLM machine was used to generate Ti6Al4V screen grids. The output was separated through wire cutting from the build platform and studied through a scanning electron microscope. To increase the geometrical accuracy of the original grid, samples consisting of nine-aperture arrays were fabricated with different parameter combinations, increasing the energy density. An empirical method to correlate the energy density applied in the fabrication process with the dimensional accuracy of the hole array positioning was developed through the analysis of multiple samples.

The SLM machine generated grids with optimal microstructure, the apertures fell within the specified tolerances and tolerances of slightly less than 10 µm can be guaranteed for the hole array positioning. The grids’ upper surfaces presented good-quality surface finish, and the lower surface quality was acceptable when the wire cutting process that separated the grid from the build platform performed slowly. Regardless of the build strategy, the stresses generated in the separation process caused the warping of the ion optic, so a flattening operation was necessary in all cases.

This research proved that SLM is a viable solution for ion optics fabrication, faster (less than 24 h) and less expensive (order of US$300) than traditional fabrication methods (with fabrication times from 24 to more than 400 h and costs from US$500 to US$5,000, depending on the material, size and shape).

The representation of Heterogeneous Object (HO) is divided into two categories: Data model (DM) and material evaluation paradigm (MEP). A hybrid methodology with geometry model and volumetric dataset to represent heterogeneous properties is proposed in this paper. Geometry model of an object can guarantee the accuracy of the final HO slices; and volumetric dataset lends the flexible manipulability and other advantages to HO representation. Two MEPs, namely distance field (DF) based and Fixed Reference Features & Active Grading Source(s) (FRF&AGS) are presented to facilitate the process of HO representation according to the designer)s input parameters. The DM can be modified interactively with users until the final satisfactory result is obtained. In this paper, a scheme of HO slicing is described. In this method, we utilize the slices contour of geometrical model as constraint to reconstruct the HO slices, which can theoretically achieve the same accuracy with the geometrical shape. Some examples of Heterogeneous object represented with our scheme are provided.

The purpose of this paper is to focus on the influences of the uncertain dynamic responses on the reconstruction of loads.

Based on the assumption of unknown-but-bounded (UBB) noise, a time-domain approach to estimate the uncertain time-dependent external loads is presented by combining the inverse system method in modern control theory and interval analysis in interval mathematics. Inspired by the concept of set membership identification in control theory, an interval analysis model of external loads time history, which is indeed a region or feasible set containing all possible loads being consistent with the bounded structural acceleration responses is established and further solved by two interval algorithms.

Unlike traditional loads identification methods which only give a point estimation, an interval estimation of external loads time history, which is a region containing all the possible loads being consistent with the uncertain structural responses, is determined. The correlation characteristics among the responses of acceleration, velocity, and displacement are also discussed in consideration of the UBB uncertainty.

For one hand, the solution of the inverse problem in original system is transformed to the solution of the direct problem in inverse system; for another, the authors deal with the uncertainty by use of interval analysis method, and the identified interval process, which contains any possible external loads time history being consistent with the bounded structural responses can be approximately obtained.

Although big data analytics has reaped great business rewards, big data system design and integration still face challenges resulting from the demanding environment, including challenges involving variety, uncertainty, and complexity. These characteristics in big data systems demand flexible and agile integration architectures. Furthermore, a formal model is needed to support design and verification. The purpose of this paper is to resolve the two problems with a collective intelligence (CI) model.

In the conceptual CI framework as proposed by Schut (2010), a CI design should be comprised of a general model, which has formal form for verification and validation, and also a specific model, which is an implementable system architecture. After analyzing the requirements of system integration in big data environments, the authors apply the CI framework to resolve the integration problem. In the model instantiation, the authors use multi-agent paradigm as the specific model, and the hierarchical colored Petri Net (PN) as the general model.

First, multi-agent paradigm is a good implementation for reuse and integration of big data analytics modules in an agile and loosely coupled method. Second, the PN models provide effective simulation results in the system design period. It gives advice on business process design and workload balance control. Third, the CI framework provides an incrementally build and deployed method for system integration. It is especially suitable to the dynamic data analytics environment. These findings have both theoretical and managerial implications.

In this paper, the authors propose a CI framework, which includes both practical architectures and theoretical foundations, to solve the system integration problem in big data environment. It provides a new point of view to dynamically integrate large-scale modules in an organization. This paper also has practical suggestions for Chief Technical Officers, who want to employ big data technologies in their companies.