Analysis of flexible decision strategies for sustainability-focused green product recovery system

Identifying and analysing key decision variables that an organisation can opt for in order to initiate resource recovery from return activities and improve overall performance is a challenge. Therefore, to assist organisations which are involved in or about to initiate an effective recovery process, this paper provides guidelines to establish flexible decision strategies using the interpretive structural modelling (ISM) approach. This study has identified interrelationships between green variables such as supplier commitment, cost, regulations, etc. and has categorised relevant enablers. Further, variables such as capacity utilisation, customer satisfaction, energy consumption reduction, etc. are recognised as outcomes or results. After statistically verifying these variables, it has been inferred that driving enablers aid in promoting performance variables, while the result variables represent outcomes achieved via the driving variables. Further, using MICMAC analysis, a graphical categorisation of the variables is done on the basis of the impact on performance. Later prioritisation of variables having high driving power and low dependence is conducted. Inputs in the proposed model are taken through a case study conducted in the paper industry. The proposed model can be quite helpful and can act as a generic tool to achieve the desired performance improvement of green product recovery systems.     

Facility location and scale optimisation in closed-loop supply chain

This paper solves a strategic-level decision problem on determining the optimal location of (re)manufacturers and logistics centres with the consideration of facility scales in an integrated closed-loop supply chain network. A two-stage stochastic mixed-integer non-linear programming model is established to minimise the fixed cost and the expected operation costs under uncertain demand and return. We develop an improved tabu search heuristic algorithm to solve the model. We also design a distance-based decision rule to validate the effectiveness of the proposed model. Numerical experiments are conducted to test the performance of the proposed model and the solution method. In addition, sensitivity analysis is provided to investigate the influences of varying inspection locations and recovery rates on the final performance.     

Integrating reverse logistics into the strategic planning of a supply chain

This paper proposes a mixed-integer programming (MIP) model for the strategic production and distribution planning of a supply chain (SC) integrating reverse logistics system. Such reverse logistics planning addresses the collection, recovery and marketing of recovered products, in addition to returned components and packing/wrapping materials. The model includes an approach that uses retail outlets as a two-way channel for marketing new products, collecting used/returned products and remarketing recovered products as a way of promoting an effective product recovery system in SC operation and optimising costs. The recovery of products/components is planned through a pool of recovery service providers (RSPs), so that maximum recovery can be ensured through combining the expertise of RSPs within optimum costs. The model follows a two-step process that addresses strategic decisions about product recovery in the first step, and the integration of the recovery process into overall SC decisions in the final step. A numerical example illustrates the applicability of the model. A sensitivity analysis has been conducted to show the effects that changes in the recovered product quantity have on the overall SC performance.     

纳滤膜系统优化设计模式探讨

纳滤膜技术在水处理领域中的规模化应用正在受到广泛关注.而纳滤膜的筛选与膜系统的优化设计是应用过程中首要解决的关键问题之一.由于纳滤膜种类繁多、对溶质截留范围宽、分离机理复杂等多变因素决定了纳滤膜系统应该采用多元化的设计模式,而不是简单模拟反渗透膜系统.选择NF270芳香族聚酰胺材料纳滤膜为例,论述了不同类型的无机盐溶液在不同压力、流量、盐度、温度、pH值等条件下,浓差极化因子对商品化纳滤膜性能的影响;并与反渗透膜性能变化规律和系统设计进行对比,给出了此类纳滤膜在一定盐度进水条件下的合理化设计建议:1)设计压力与进水盐度无关;2)系统回收率与流程长度无关;3)盐的截留率只取决于系统收率.试图为纳滤膜选择及纳滤膜系统设计新模式提供理论依据,并为提出完整的纳滤膜系统设计新模式做出初步的探索.     

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。     

摇摆结构及自复位结构研究综述

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。     

Joint decision-making on automated disassembly system scheme selection and recovery route assignment using multi-objective meta-heuristic algorithm

Green treatment on Waste Electrical and Electronic Equipmenthas increasingly attracted attention due to its significant environmental benefits and potential recovery earnings. Automated disassembly has been regarded as a powerful solution to enable more efficient recovery operations. Although numerous studies have contributed to the issues of disassembly, there are few researches that focus on decision model for selecting disassembly system scheme and recovery route in automated disassembly. In this paper, we propose a two-phase joint decision-making model to address this problem with the goal of balancing disassembly profit with environmental impact. First, we establish a multi-objective optimisation model to obtain the Pareto optimal recovery routes for each automated disassembly system scheme. Both recovery profit and energy consumption are evaluated for multi-station disassembly system. We design a multi-objective hybrid particle swarm optimisation algorithm based on symbiotic evolutionary mechanism to solve the proposed model. Then, we compare the Pareto optimal solutions of all the system schemes using a fuzzy set method and identify the best scheme. Finally, we conduct real case studies on the automated disassembly of different waste electric metres. The results demonstrate the superiority of automated disassembly and validate the effectiveness of our proposed model and algorithm.     

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.     

Planning of capacity,production and inventory decisions in a generic reverse supply chain under uncertain demand and returns

There is a growing interest for the design and operation of reverse supply chain systems due to the cost and the legislation issues. In this paper, we address the disassembly, refurbishing and production operations in a reverse supply chain setting for modular products such as computers and mobile phones considering the uncertainties in this system, which are the return amounts of the used products and demand for final products. We develop a large-scale mixed integer programming model in order to capture all characteristics of this system, and use two-stage stochastic optimisation and robust optimisation approaches to analyse the system behaviour. In the first stage, we focus on the strategic decisions about the capacities at disassembly and refurbishing sites considering different scenarios regarding the uncertainties in the system. In the second stage, we analyse the operational decisions such as production, inventory and disposal rates. We observe through our extensive numerical analysis that the randomness of demand and return values effect the performance of the system substantially and the uncertainty of the return amounts of used products is much more important than the uncertainty of demand in this system.     

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.     

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.     

基于Bertrand闭环供应链的企业价格博弈模型

设计了一个具有供应商和两个零售商组成的Bertrand双寡头价格博弈的主从闭环供应链系统,给出了分散和集中两种模式下批发价、零售价、回收价以及各方利润的具体表达式并进行了比较,研究了供应链整体利润与产品和废弃品替代率的关系。最后,在此基础上分析了分散模式下零售商和生产商的具体对策,提出了两阶段数量折扣协调策略实现帕累托改进,并求出了折扣率和补贴率的具体范围,为进一步研究更一般的闭环供应链系统打下理论基础。     

时间不确定下再利用逆向供应链激励机制研究

为提高产品回收效率,减少回收所需的时间,针对再利用逆向供应链,在委托-代理理论基础上,构造了再利用逆向供应链主从博弈模型.考虑到回收处理时间的随机性,引入奖励因子,在模型的最优性分析基础上,讨论了回收率、回收处理成本等参数对合同变量设计的影响.最后进行了算例验证.     

废旧电脑的逆向物流优化模型与应用研究

加强废旧电脑的回收控制已经成为急需解决的关键问题.分析了逆向物流的国内外研究现状,目前主要研究方向包括逆向物流网络规划、库存控制和生产计划等.通过建立电子产品制造企业供应链,分析了电子产品回收的基本原理,建立废旧电脑逆向物流的优化模型.根据模型提出了废旧电脑在珠江三角洲地区的逆向物流解决方案,计算结果表明模型合理可行,在没有政府补贴下废旧电脑的逆向物流运营是亏损的.     

电子商务环境下无缺陷退货政策

研究电子商务环境下包含一个制造商和一组顾客群的单周期供应链系统,建立了无质量缺陷的退货问题决策模型。分析顾客购买及退货的策略性决策的特点,确定了3种退货政策下,制造商的最优定价、最优供货量、最优退货价格及网络销售成本的临界值。比较分析3种退货政策的优劣及其对供应链绩效的影响。结果表明,当商品的采购/制造成本或残值较低时,零退货政策优于全额退货政策;而实行部分款额退货政策的供应链绩效优于全额退货政策。     

Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder (DAE) for learning the robust feature representation and one-class support vector machine (OCSVM) for finding the more compact decision hyperplane for intrusion detection. Specially, the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously. This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection. Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model. First, the ablation evaluation on benchmark dataset, NSL-KDD validates the design decision of the proposed model. Next, the performance evaluation on recent intrusion dataset, UNSW-NB15 signifies the stable performance of the proposed model. Finally, the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.     

Optimal configuration of remanufacturing supply network with return quality decision

This research studies the configuration problem of a remanufacturing production network together with the decision for return quality thresholds, in which, the manufacturer has multiple remanufacturing facilities to satisfy different market demands. Quality of returns is stochastic, while demand for remanufactured products is either stochastic or deterministic. The problem we considered is to determine facilities to operate, minimum quality to accept into each operating facility, return quantity and demand allocation simultaneously so that the system’s profit is maximised. The problem is formulated as a mixed integer non-linear programming model. Through the use of a numerical example, the impact of quantity of returns, total spending, quality uncertainty, demand uncertainty and transportation cost on the remanufacturing system is analysed.     

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.     

基于P2P的空间信息服务组合执行引擎

提出了一种基于P2P的空间信息服务组合执行引擎设计.引擎使用空间信息服务团体模型和服务质量参数,在P2P网络中动态地选择最佳的执行引擎,通过P2P的消息和通信机制进行协作,完成复杂的空间信息服务组合执行任务.P2P的执行机制避免了集中式执行引擎带来的网络拥塞和单点失效问题,提高了空间信息服务组合的可靠性和可用性.     

A methodology for the strategic design of reverse logistics networks and its application in the Turkish white goods industry

In this study, a multi-objective model for the reverse logistics network design (RLND) problem and a novel methodology are proposed. The proposed methodology is comprised of two stages: the centralised return centre (CRC) evaluation stage and the reverse logistics network design (RLND) stage. In the first stage an integrated ANP and fuzzy-TOPSIS methodology is utilised. In the second stage, using the CRC weights obtained in the first stage, the RLND model is solved via genetic algorithms (GAs). The proposed methodology is applied to a case from the Turkish white goods industry. The results are discussed and analysed.