Strategies for evaluating performance of flexibility in product recovery system

In view of the increasing business opportunities with changing customer attitudes and stricter legislations, the handling of returns has become a daunting challenge. The need for decision models for evaluating return performance has been observed in the academia and the corporate world. To improve return system performance, integrated flexible reverse enterprise systems have attracted attention from researchers as well as practitioners. This paper addresses these critical issues and proposes a novel integrated and Flexible recovery system decision model. The proposed model aims to facilitate enterprises in assessing their product recovery system capability, and in improving overall performance. The proposed model is a natural extension of several well-grounded policies for conventional reverse supply chains and can be verified on a simulation platform.     

Heuristic algorithms for minimising total recovery cost of end-of-life products under quality constraints

Recently, the optimisation of end-of-life (EOL) product recovery processes has been highlighted. At the inspection phase after disassembly, each part can have various recovery options such as reuse, reconditioning, remanufacturing, and disposal. Depending on the selected options of parts, the values of recovered products that are made by reassembling parts will be different. Hence, it is important to decide appropriate recovery options of parts at the treatment of EOL products, in order to maximise the values of recovered products. To this end, this study deals with a decision making problem to select the best recovery options of parts for minimising the total recovery cost of products under quality constraints. This problem is formulated with a mixed integer nonlinear programming model and heuristic search algorithms are proposed to resolve it. A case study for a turbocharger product is introduced with computational experiments of the proposed algorithms.     

A quantitative and simulation model for managing sudden supply delay with fuzzy demand and safety stock

In this paper, a recovery model is developed for managing sudden supply delays that affect retailers’ economic order quantity model. For this, a mathematical model is developed that considers fuzzy demand and safety stock, and generates a recovery plan for a finite future period immediately after a sudden supply delay. An efficient heuristic solution is developed that generates the recovery plan after a sudden supply delay. An experiment with scenario-based analysis is conducted to test our heuristic and to analyse the results. To assess the quality and consistency of solutions, the performance of the proposed heuristic is compared with the performance of the generalised reduced gradient method, which is widely applied in constrained mathematical programming. A simulation model is also designed to bring the recovery model closer to real-world processes. Several numerical examples are presented and a sensitivity analysis is performed to demonstrate the effects of various parameters on the performance of the heuristic method. The results show that safety stock plays an important role in recovery from sudden supply delays, and there is a trade-off between backorder and lost sales costs in the recovery plan. With the help of the proposed model, supply chain decision-makers can make accurate and prompt decision regarding recovery plans in case of sudden supply delay.     

纳米材料在提高原油采收率中的研究进展

近年来,油田采油过程对驱油材料的性能提出了更高的要求,使用纳米材料是从微观角度提升驱油剂性能的重要方法。研究表明,对纳米材料进行改性处理,如表面接枝改性、构造核-壳分子结构等,以及将纳米材料与其他驱油材料(如聚合物、表面活性剂等)复配,可从降低油水界面张力、乳化原油降低原油粘度、改善岩石表面润湿性、稳定泡沫等方面推动原油流动,可见纳米材料在提高原油采收率方面具有重要意义。综述了近几年国内外纳米SiO₂、纳米TiO₂、纳米纤维素、聚合物纳米微球、纳米石墨烯等纳米材料在驱油材料中应用的研究进展,解析了其研发中的瓶颈问题及今后的研发方向,评述了各种纳米材料提高原油采收率、抗温、抗盐等性能,以期为相关研究提供借鉴。     

基于客户感知价值的客户细分研究

基于客户感知价值进行了客户细分的理论和实证研究,以物流公共信息平台为例提出依据客户感知价值进行客户细分的一般流程：首先利用文献查阅与问卷调查方法确定客户感知价值的驱动因素,接着利用因子分析法对驱动因素变量降维处理,然后利用聚类方法识别客户,再利用决策树方法寻找客户分类规则并分析各类客户特征。最后依据客户特征将物流公共信息平台的客户分为4类：高价值需求客户、中等价值需求客户、低价值需求客户与高性能需求客户,研究结果可以为不同企业实施客户细分提供借鉴和参考。     

Global supplier selection: a fuzzy-AHP approach

Global supplier selection has a critical effect on the competitiveness of the entire supply chain network. Research results indicate that the supplier selection process appears to be the most significant variable in deciding the success of the supply chain. It helps in achieving high quality products at lower cost with higher customer satisfaction. Apart from the common criteria such as cost and quality, this paper also discusses some of the important decision variables which can play a critical role in case of the international sourcing. The importance of the political-economic situation, geographical location, infrastructure, financial background, performance history, risk factors, etc., have also been pointed out in particularly in the case of global supplier selection. Supplier selection problem related to the global sourcing is more complex than the general domestic sourcing and as a result it needs more critical analysis, which could not be found properly in past available literatures. This paper discusses the fuzzy based Analytic Hierarchy Process (fuzzy-AHP) to efficiently tackle both quantitative and qualitative decision factors involved in selection of global supplier in current business scenario. The fuzzy-AHP is an efficient tool to tackle the fuzziness of the data involved in deciding the preferences of the different decision variables involved in the process of global supplier selection. The triangular fuzzy numbers are used to transform the linguistic comparison of the different decision criteria, sub-criteria and performance of the alternative suppliers. The pairwise comparison matrices help in deciding the synthetic extent value of each comparison and finally, the priority weights of one alternative over another are decided in this paper. An example from a manufacturing industry searching for the global supplier for a critical component is used to demonstrate the effective implementation procedure of proposed fuzzy-AHP technique. The proposed model can provide the guidelines and directions for the decision makers to effectively select their global suppliers in the current competitive business scenario.     

A two-stage robust programming approach to demand-driven disassembly planning for a closed-loop supply chain system

The closed-loop supply chain system, which integrates forward and reverse logistics, is a desirable policy for retaining recoverable resources and extending the life cycles of products. In this study, we propose a methodology to contend with a demand-driven disassembly planning problem under a closed-loop supply chain system. A two-stage robust programming model is developed correspondingly, such that multiple products with a hierarchical product's structure are disassembled to satisfy uncertain demands in multiple periods. The objective of the model is to determine a robust decision for recycle volume and timing of each type of end-of-life (EOL) product, as well as recovery strategies. The results provide two-stage decisions by considering future scenarios of periodic demands at the beginning of a planning horizon. The first-stage decision is to determine a compromise solution that is close to the optimal solution for every scenario while retaining a certain level of infeasibility of constraints, such as unsatisfied demand. Afterward, when the outcome of a scenario has been realised, the second-stage decision, such as, inventory volume, is conducted to become a buffer for mitigating uncertain impacts. Furthermore, the computational results confirm the trade-off relationship between solution robustness and model robustness, which are core results of the robust model apart from expected profit. The different types of decision makers’ preferences toward risk can be accounted for to determine a compromise robust solution.     

Exhaust gas energy recovery system of pneumatic driving automotive engine

Almost the same quantity to net output work of energy has been carried out and wasted by exhaust gas in typical automotive engine. Recovering the energy from exhaust gas and converting to mechanical energy will dramatically increase the heat efficiency and decrease the fuel consumption. With the increasing demand of fuel conservation, exhaust gas energy recovery technologies have been a hot topic. At present, many researches have been focused on heating or cooling the cab, mechanical energy using, and thermo-electronic converting. Unfortunately, the complicated transmission of mechanical energy using and the depressed efficiency of thermo-electronic converting restrict their widely applying. In this paper, a kind of pneumatic driving automotive engine exhaust gas energy recovery system, in which highly compressed air acts as energy storing and converting carrier, has been established. Pneumatic driving motor can produce moderate speed and high torque output, which is compatible for engine using. The feasibility has been certificated by GT-Power simulation and laboratory testes. The technologies about increasing recovery efficiency have been discussed in detail. The results demonstrated that the in parallel exhaust gas energy recovery system, which is similar to the compound turbo-charger structure can recovery 8 to 10 percent of rated power output. At last, a comprehensive system, which includes Rankine cycle based power wheel cycle unit etc, has been introduced.     

Impact of sustainable manufacturing practices on consumer perception and revenue growth: an emerging economy perspective

The view that adopting an environmental perspective on operations can lead to improved operations has become commonplace over the past decade. The implication is that any operational system that has minimised inefficiencies is also more environmentally sustainable. In the first phase of our research, we conducted a field research of the green manufacturing techniques in place in two world-class, ISO 14001-certified paint manufacturing firms in India. This phase of the study was undertaken to ascertain the cost benefits that these firms enjoy due to green manufacturing and recycling. Based on the field work in these two companies, a conceptual framework was proposed to investigate any relationship between the consumer's attitude towards the environment and the perceived image of a company that was environmentally conscious. Subsequently, a survey was administered to understand the impact of green marketing on the decision of consumers to buy paint manufactured by a firm that stringently regulates its hazardous waste. Results indicate that there is a strong correlation between the environmental concern of the survey respondent and the perception that the respondent forms of the firm that has implemented green manufacturing techniques. There is also a strong correlation between the decision to buy a green product and the respondent's concern for the environment as well as the perception formed of the firm. Further research is required to substantiate the cost benefits of green manufacturing.     

基于NSGA-II的城市桥梁系统震后可恢复性分析与优化

城市桥梁是经济和社会发展的重要基础设施,特别是重要桥梁以及由其构成的网络在灾难发生后如何快速恢复,对社会安定和地区发展十分重要。近年来抗震韧性倍受关注,如何考虑结构可恢复性并将韧性纳入桥梁系统抗震性能综合评估和优化,还存在很多问题尚待研究。该文定量分析桥梁震后恢复过程、剩余功能和修复时间与韧性指标间的联系,建立城市桥梁系统性能评估方法。由桥梁不同损伤状态计算不同恢复过程的剩余功能、修复时间、可恢复性和震后经济损失;对震后恢复过程的功能函数模型给出建议,量化不同震后恢复策略的影响;将韧性纳入城市桥梁系统抗震性能框架中,提出城市桥梁系统震后可恢复性评估框架,对风险高低不同采用不同的修复过程;对震后经济损失、可恢复性和震前修/改造成本采用多目标优化算法NSGA-II求解震前提升韧性优化策略,用于抗震加固措施的选取。通过案例给出了方法流程,也为城市桥梁系统地震韧性提升提供了新思路。     

Scheduling of recovery actions in the supply chain with resilience analysis considerations

Supply chain engineering models with resilience considerations have been mostly focused on disruption impact quantification within one analysis layer, such as supply chain design or planning. Performance impact of disruptions has been typically analysed without scheduling of recovery actions. Taking into account schedule recovery actions and their duration times, this study extends the existing literature to supply chain scheduling and resilience analysis by an explicit integration of the optimal schedule recovery policy and supply chain resilience. In particular, we compute a schedule optimal control policy and analyse the performance of this policy by varying the perturbation vector and representing the outcomes of variations in the form of an attainable set. We propose a scheduling model that considers the coordination of recovery actions in the supply chain. Further, we suggest a resilience index by using the notion of attainable sets. The attainable sets are known in control theory; their calculation is based on the schedule control model results and the minimax regret approach for continuous time parameters given by intervals. We show that the proposed indicator can be used to estimate the impact of disruption and recovery dynamics on the achievement of planned performance in the supply chain.     

Supply chain resilience: model development and empirical analysis

The purpose of this study is to develop a hierarchy-based model for supply chain resilience (SCRES), explaining the dynamics between various enablers and validating the model empirically. Literature review and a survey identified the enablers. Interpretive structural modelling (ISM) is used to analyse the levels of relationships among enablers. Based on their driving power and dependence, these enablers are also classified into different categories. Structural equation modelling is used to validate the hierarchical SCRES model and test the path analytical model. The study provides empirical justification for a framework that identifies 13 key enablers of resilient supply chain practices and describes the relationship among them using ISM. It also classifies them using Matrix of Cross Impact Multiplications Applied to Classification analysis on the basis of their driver power and dependence. The key finding is that using the proposed model, organisations can enhance their resilience potential by modifying their strategic assets. The model was tested using rigorous statistical tests including convergent validity, discriminant validity and reliability. The holistic view offered by the proposed model depicts the relationship among enablers to achieve SCRES.     

Influence of impeller speed and cell volume on coal flotation performance in a self-aerating flotation machine

The effect of impeller speed and cell volume on batch coal flotation performance in a self-aerating flotation machine is studied in the present investigation. Factorial designed experiments are carried out to establish the relationships between the manipulated variables (impeller speed, and cell volume, and their interactions) with the separation indicators (yield, ash, combustible recovery and entrained water recovery). Correlation coefficient values are used to select the best fit model, and an analysis of variance to identify the statistically significant components of the model. The results indicated that both the impeller speed and cell volume significantly influence the performance of coal flotation. Further, the influence of these variables and their significance is discussed by flotation rate constant.     

An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products

There is a strong need for recovery decision-making for end-of-life (EOL) products to satisfy sustainable manufacturing requirements. This paper develops and tests a profit maximisation model by simultaneously integrating recovery option selection and disassembly planning. The proposed model considers the quality of EOL components. This paper utilises an integrated method of multi-target reverse recursion and partial topological sorting to generate a feasible EOL solution that also reduces the complexity of genetic constraints handling. In order to determine recovery options, disassembly level and disassembly sequence simultaneously, this paper develops an improved co-evolutionary algorithm (ICA) to search for an optimal EOL solution. The proposed algorithm adopts the evolutionary mechanism of localised interaction and endosymbiotic competition. Further, an advanced local search operator is introduced to improve convergence performance, and a global disturbance strategy is also suggested to prevent premature convergence. Finally, this paper conducts a series of computational experiments under various scenarios to validate the meta-heuristic integrated decision-making model proposed and the superiority of the developed ICA. The results show that the proposed approach offers a strong and flexible decision support tool for intelligent recovery management in a ubiquitous information environment. We discuss the theoretical and practical contributions of this paper and implications for future research.     

An integrated ISM fuzzy MICMAC approach for modelling the supply chain knowledge flow enablers

The aim of this study is to identify supply chain knowledge flow enablers (SCKFEs) to inspect interrelationships among these enablers and classify these enablers into driving power and dependence power using an integrated interpretive structural modelling (ISM) and fuzzy Matriced Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) methodology. While the ISM methodology analyses the interactions among the SCKFEs, fuzzy MICMAC analysis is employed to obtain insights into the dependencies among the SCKFEs. A total of 34 SCKFEs were identified through the literature review and expert opinion. As an example, an Indian manufacturing organisation is selected that is willing to adopt the successful knowledge flow for improving supply chain (SC) performance to overcome the intense competition among the SC versus SC. The research shows SCKFEs having high driving power and low dependence have strategic importance because of their driving nature, while the SCKFEs having high dependence and low driving power are more performance orientated. Therefore, it is the responsibility of SC executives to address the high driving power SCKFEs for the enhancement of SC performance. This categorisation provides a useful tool to top management to differentiate between independent and dependent SCKFEs and their mutual relationships, helping them focus on those key SCKFEs that are most significant. This gives a clear picture to SC practitioners and decision-makers about number of SCKFEs, interrelationship and dependencies existing among them.     

Quality-driven recovery decisions for used components in reverse logistics

Reverse logistics has emerged as a promising strategy for enhancing environmental sustainability through remanufacturing, reusing, or recycling used components. It is crucial to pursue quality-driven decision-making for component recovery because quality is a dominant factor for component salvage value and its recoverability. To maximise the profit from component recovery, a quality-driven decision model was proposed in this study. Remaining useful life (RUL) was utilised as a measure of quality in the proposed model, where conditional RUL distribution was predicted by utilising both the failure data and condition monitoring data based on a proportional hazard model. Under RUL uncertainty, an interval decision-making approach was developed to suggest recovery strategies for the decision-makers to identify a satisfactory solution according to their risk preferences. Compared to the existing approaches for quality-driven recovery decision-making based on RUL prediction, this work provides a more accurate and powerful approach to managing and mitigating decision risk. Numerical experiments demonstrated the effectiveness and superiority of the proposed model.     

Chasing the silver bullet: measuring driver fatigue using simple and complex tasks

Driver fatigue remains a significant cause of motor-vehicle accidents worldwide. New technologies are increasingly utilised to improve road safety, but there are no effective on-road measures for fatigue. While simulated driving tasks are sensitive, and simple performance tasks have been used in industrial fatigue management systems (FMS) to quantify risk, little is known about the relationship between such measures. Establishing a simple, on-road measure of fatigue, as a fitness-to-drive tool, is an important issue for road safety and accident prevention, particularly as many fatigue related accidents are preventable. This study aimed to measure fatigue-related performance decrements using a simple task (reaction time - RT) and a complex task (driving simulation), and to determine the potential for a link between such measures, thus improving FMS success. Fifteen volunteer participants (7 m, 8 f) aged 22-56 years (mean 33.6 years), underwent 26 h of supervised wakefulness before an 8h recovery sleep opportunity. Participants were tested using a 30-min interactive driving simulation test, bracketed by a 10-min psychomotor vigilance task (PVT) at 4, 8, 18 and 24h of wakefulness, and following recovery sleep. Extended wakefulness caused significant decrements in PVT and driving performance. Although these measures are clearly linked, our analyses suggest that driving simulation cannot be replaced by a simple PVT. Further research is needed to closely examine links between performance measures, and to facilitate accurate management of fitness to drive, which requires more complex assessments of performance than RT alone.     

双效溴化锂吸收式热泵余热回收系统数值模拟研究

溴化锂吸收式热泵机组可以有效回收利用工业和建筑中的各种形式低温余热,提高余热资源回收率,但设备参数对热泵性能影响很大。因此本文基于温度对口和梯级利用的原则,对蒸汽型双效溴化锂吸收式热泵机组内传热部件进行热力及传热分析,通过质量和能量守恒建立热泵机组数学模型,分析热网供水温度、蒸发器进口低温余热水温度和驱动热源温度这三个外部因素的变化对系统性能的影响。研究结果表明:热网供水温度在49℃左右,热泵系统COP最佳为2.67;蒸发器进口低温余热水温度在47℃左右时,热泵系统COP最佳为2.67;随着驱动热源温度的上升,热泵系统的COP呈上升趋势。为吸收式热泵实际运行过程中,合理设置设备参数提高热泵性能提供指导。     

Efficient waste-to-energy system as a contribution to clean technologies

This paper deals with the problem of efficient energy utilization in the field of thermal processing of waste (waste-to-energy). An up-to-date incineration plant cannot be considered as only waste disposal facility. The waste combustion (incineration) processes are accompanied by release of large amount of energy, that shall be effectively utilized (e.g. steam production and its export to consumers, power generation, cogeneration). In addition to the main purpose of incineration, i.e. treating the specified amount of waste, waste-to-energy systems are able to some extent substitute conventional energy production plants fired by fossil fuel and thus to contribute to solving global environmental problems. For example, methodology presented in Reference Document on the Best Available Techniques for Waste Incineration (BREF) can be used for assessment of plant performance from the view of energy utilization. This approach is based on evaluation of criterion Plant Efficiency. An existing incineration plant has been evaluated and results are presented and discussed. In the future, it has been expected that evaluation like this will be obligatory for every plant. The arrival of more and more sweeping environmental limits represents a driving force for retrofit. At the same time maximum heat recovery and efficient energy utilization should be required. Influence of different operational modes (type of waste incinerated, combustion temperature, air-preheating etc.) and lay-out of heat recovery system (e.g. cogeneration) on plant performance has been investigated. A mathematical model based on combination of basic auxiliary operations and simple thermodynamic models of heat engines has been created with the aid of a specific computational tool. Its conception is based on both requirements and experience coming from industry. The most serious problem of effectively running incineration plants consists in economical utilization of energy produced. Examples of integration into existing energy systems are shown and discussed. An erratum to this article can be found at     

Analysis and optimization of clutch judder based on a hybrid uncertain model with random and interval variables

Clutch judder has serious impacts on the noise, vibration and harshness performance. In this article, a simplified dynamic model with nonlinear friction torque is developed to simulate clutch judder, and the stability and dynamic response of the clutch are analysed. The real part of the judder modal eigenvalue, the moment when the clutch enters the stick state and the fluctuation level of the driving part of the clutch are treated as the evaluation indices. An uncertain hybrid model with random and interval variables is used to describe the uncertainty of parameters and a hybrid perturbation vertex method is formulated to compute the uncertainty. Furthermore, parameters with high sensitivities are used as design variables and uncertainty-based optimization is conducted to reduce clutch judder. The optimization results strongly validate that the proposed method is very effective in improving the robustness of the clutch judder performance.