Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks

The competitive market and declined economy have increased the relevant importance of making supply chain network efficient. Up to now, this has resulted in great motivations to reduce the cost of services, and simultaneously, to improve their quality. A mere network model, as a tri-echelon, consists of Suppliers, Warehouses or Distribution Centers (DCs), and Retailer nodes. To bring it closer to reality, the majority of parameters in this network involve retailer demands, lead-time, warehouses holding and shipment costs, and also suppliers procuring and stocking costs which are all assumed to be stochastic. The aim is to determine the optimum service level so that total cost is minimized. Obtaining such conditions requires determining which supplier nodes, and which DC nodes in network should be active to satisfy the retailers’ needs, an issue which is a network optimization problem per se. The proposed supply chain network for this paper is formulated as a mixed-integer nonlinear programming, and to solve this complicated problem, since the literature for the related benchmark is poor, three numbers of genetic algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA), and Pareto Envelope-based Selection Algorithm (PESA-II) are applied and compared to validate the obtained results. The Taguchi method is also utilized for calibrating and controlling the parameters of the applied triple algorithms.     

Survivable and delay-guaranteed backbone wireless mesh network design

Backbone wireless mesh networks (BWMNs) consisting of wireless mesh routers are emerging alternatives to implementations of metropolitan area networks (MANs). In a BWMN, gateways connect to the Internet via wireline links and provide Internet access services for users. Due to the limited wireless channel bit rate, multiple gateways are usually required in a BWMN, which costs budget and takes time to set up. In this paper, we study the network topology design and the gateway arrangement so that the construction cost of a BWMN is minimal. Two algorithms, namely, the Predefined Gateway Set Algorithm (PGSA) and the Self-Constituted Gateway Algorithm (SCGA), are proposed for the BWMN design. A genetic algorithm and a proposed enhanced Djikstra's algorithm are employed to search for the low-cost network configuration with constraints such as survivability, link capacity, degree limitation and maximum tolerable delay. Computational results show that the PGSA can give an acceptable network configuration rapidly. In case the gateway cost is high, using the SCGA can lower the network construction cost at the expense of more computational time.     

不同担保模式下考虑零售商公平关切的闭环供应链博弈模型

针对由一个制造商和一个零售商构成的闭环供应链,运用Stackelberg动态博弈理论,构建不同担保模式下考虑零售商公平关切的决策博弈模型,探讨产品担保模式及零售商公平关切对闭环供应链定价及担保期决策、经济效益和环境效益的影响,分析不同担保模式的担保效率.研究结果表明:当消费者对新产品和再制造品差异化担保期较为敏感时,制造商或零售商会为再制造品提供长期担保服务;零售商公平关切会降低新产品和再制造品批发价格,其对再制造品销售价格、担保期以及供应链经济环境效益的影响与产品担保模式密切相关;两种担保模式的经济环境效益与担保成本和再制造品相对环境优势相关,当零售商担保的相对成本优势和再制造品相对环境优势显著(微弱)时,零售商(制造商)担保模式是闭环供应链利益相关者的一致选择;零售商担保模式能够减缓零售商公平关切对闭环供应链经济效益及环境效益的负面影响.     

移动群智感知网络中信息量最大化的用户选择方法研究

移动群智感知网络已经成为一种新型的感知模式,被广泛应用在环境数据收集等多种不同场景.限制群智感知系统效能的一个关键问题在于:如何在一定预算范围内选取最合适的用户来进行感知任务,从而最大化收集到数据的信息量.这其中的关键挑战包括:(1)如何定义量化评价指标衡量数据的信息量,(2)如何在无先验知识的情况下有效地学习选择每个用户的成本,(3)如何设计有效的用户选择算法,最大程度地降低算法的累计遗憾.在本文中,我们采用高斯过程建模空间环境,并且提出一个基于互信息的信息量衡量指标.为了解决第二、第三个挑战,我们提出有预算限制的多臂老虎机用户选择问题模型,并为静态和动态场景分别设计了理论可证的低累计遗憾的多轮用户选择算法.我们的理论分析和仿真实验均证实我们提出的算法能够在预算限制情况下有效地选择最有信息量的用户,与基准方法相比提升约20%.     

基于时延优化的软件定义网络控制层部署策略

当前大多数软件定义网络（SDN）中控制器的部署方案均重点考虑正常网络状态下传播时延对性能的影响,而忽略了链路故障状态下对时延的影响,针对此问题,提出了一种基于时延优化的控制层部署方案。首先,在综合考虑网络正常运行以及单链路故障等多种网络状态下的最坏情况时延最小化问题的基础上,以网络状态时延作为新的时延优化目标并建立了相应的数学模型。其次,提出了解决上述模型的两种启发式部署算法：基于贪婪算法的控制层部署算法（GA-CPA）和基于粒子群优化（PSO）算法的控制层部署算法（PSO-CPA）。最后,选取了真实网络拓扑及数据进行验证。仿真结果表明,GA-CPA和PSO-CPA两种部署算法均能在不同程度上降低网络状态时延,从而保证了大部分网络状态下的最坏情况时延维持在较低范围。     

IP／DWDM光Internet中基于软计算的智能多约束波长分配算法的研究

波长分配问题是影响IP／DwDM光Internet中网络资源利用的关键问题之一。本文提出了两种基于软计算的多限制的波长分配算法，使得波长源分配适应实际的网络特征。事实上，由于考虑到多种限制问题，包括成本，功率和网络性能等，因此采用分层解决方案。因为每一层都是NP完全问题，所以采用软计算和启发式相结合的方法进行求解，设计并实现了一个仿真软件，仿真表明该算法既是可行的又是有效的。     

An efficient online citrus counting system for large-scale unstructured orchards based on the unmanned aerial vehicle

The accurate detection and counting of fruits in natural environments are key steps for the early yield estimation of orchards and the realization of smart orchard production management. However, existing citrus counting algorithms have two primary limitations: the performance of counting algorithms needs to be improved, and their system operation efficiency is low in practical applications. Therefore, in this paper, we propose a novel end-to-end orchard fruit counting pipeline that can be used by multiple unmanned aerial vehicles (UAVs) in parallel to help overcome the above problems. First, to obtain on-board camera images online, an innovative UAV live broadcast platform was developed for the orchard scene. Second, for this challenging specific scene, a detection network named Citrus-YOLO was designed to detect fruits in the video stream in real-time. Then, the DeepSort algorithm was used to assign a specific ID to each citrus fruit in the online UAV scene and track the fruits across video frames. Finally, a nonuniform distributed counter was proposed to correct the fruit count during the tracking process, and this can significantly reduce the counting errors caused by tracking failure. This is the first work to realize online and end-to-end counting in a field orchard environment. The experimental results show that the F1 score and mean absolute percentage error of the method are 89.07% and 12.75%, respectively, indicating that the system can quickly and accurately achieve fruit counting in large-scale unstructured citrus orchards. Although our work is discussed in the context of fruit counting, it can be extended to the detection, tracking and counting of a variety of other objects of interest in UAV application scenarios     

Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty

Measuring and controlling emissions across the logistics network is an important challenge for today’s firms according to increasing concern about the environmental impact of business activities. This paper proposes a bi-objective credibility-based fuzzy mathematical programming model for designing the strategic configuration of a green logistics network under uncertain conditions. The model aims to minimize the environmental impacts and the total costs of network establishment simultaneously for the sake of providing a sensible balance between them. A popular but credible environmental impact assessment index, i.e., CO₂ equivalent index is used to model the environmental impact across the concerned logistics network. Since transportation mode and production technology play important roles on the concerned objectives, the proposed model integrates their respective decisions with those of strategic network design ones. In addition, to solve the proposed bi-objective fuzzy optimization model, an interactive fuzzy solution approach based upon credibility measure is developed. An industrial case study is also provided to show the applicability of the proposed model as well as the usefulness of its solution method.     

A particle swarm approach for optimizing a multi-stage closed loop supply chain for the solar cell industry

In order to implement sustainable strategies in a supply chain, enterprises should provide highly favorable and effective solutions for reducing carbon dioxide emissions, which brings out the issues of designing and managing a closed-loop supply chain (CLSC). This paper studies an integrated CLSC network design problem with cost and environmental concerns in the solar energy industry from sustainability perspectives. A multi-objective closed-loop supply chain design (MCSCD) model has been proposed, in consideration of many practical characteristics including flow conservation at each production/recycling unit of forward/reverse logistics (FL/RL), capacity expansion, and recycled components. A deterministic multi-objective mixed integer linear programming (MILP) model capturing the tradeoffs between the total cost and total CO₂ emissions was developed to address the multistage CSLC design problem. Subsequently, a multi-objective PSO (MOPSO) algorithm with crowding distance-based nondominated sorting approach is developed to search the near-optimal solution of the MCSCD model. The computational study shows that the proposed MOPSO algorithm is suitable and effective for solving large-scale complicated CLSC structure than the conventional branch-and-bound optimization approach. Analysis results show that an enterprise needs to apply an adequate recycling strategy or energy saving technology to achieve a better economic effectiveness if the carbon emission regulation is applied. Consequently, the Pareto optimal solution obtained from MOPSO algorithm may give the superior suggestions of CLSC design, such as factory location options, capacity expansion, technology selection, purchasing, and order fulfillment decisions in practice.     

An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

Reverse logistics consists of all operations related to the reuse of products. External suppliers are one of the important members of reverse logistics and closed loop supply chain (CLSC) networks. However in CLSC network configuration models, suppliers are assessed based on purchasing cost and other factors such as on-time delivery are ignored. In this research, a general closed loop supply chain network is examined that includes manufacturer, disassembly, refurbishing, and disposal sites. Meanwhile, it is managed by the manufacturer. We propose an integrated model which has two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Besides, a fuzzy method is designed to evaluate suppliers based on qualitative criteria. The output of this stage is the weight of each supplier according to each part. In the second phase, we propose a multi objective mixed-integer linear programming model to determine which suppliers and refurbishing sites should be selected (strategic decisions), and find out the optimal number of parts and products in CLSC network (tactical decisions). The objective functions maximize profit and weights of suppliers, and one of them minimizes defect rates. To our knowledge, this model is the first effort to consider supplier selection, order allocation, and CLSC network configuration, simultaneously. The mathematical programming model is validated through numerical analysis.     

High-performance routing in networks of workstations with irregulartopology

Networks of workstations are rapidly emerging as a cost-effective alternative to parallel computers. Switch-based interconnects with irregular topology allow the wiring flexibility, scalability, and incremental expansion capability required in this environment. However, the irregularity also makes routing and deadlock avoidance on such systems quite complicated. In current proposals, many messages are routed following nonminimal paths, increasing latency and wasting resources. In this paper, we propose two general methodologies for the design of adaptive routing algorithms for networks with irregular topology. Routing algorithms designed according to these methodologies allow messages to follow minimal paths in most cases, reducing message latency and increasing network throughput. As an example of application, we propose two adaptive routing algorithms for ANI (previously known as Autonet). They can be implemented either by duplicating physical channels or by splitting each physical channel into two virtual channels. In the former case, the implementation does not require a new switch design. It only requires changing the routing tables and adding links in parallel with existing ones, taking advantage of spare switch ports. In the latter case, a new switch design is required, but the network topology is not changed. Evaluation results for several different tapologies and message distributions show that the new routing algorithms are able to increase throughput for random traffic by a factor of up to 4 with respect to the original up*/down* algorithm, also reducing latency significantly. For other message distributions, throughput is increased more than seven times. We also show that most of the improvement comes from the use of minimal routing     

Effects of uncertainty on a tire closed-loop supply chain network

In a closed-loop supply chain (CLSC) network, there are both forward and reverse supply chains. In this research, a tire remanufacturing CLSC network is designed and optimized based on tire recovery options. The objective of the optimization model is to maximize the total profit. The optimization model includes multiple products, suppliers, plants, retailers, demand markets, and drop-off depots. The application of the model is discussed based on a realistic network in Toronto, Canada using map. In addition, a new decision tree-based methodology is provided to calculate the net present value of the problem in multiple periods under different sources of uncertainty such as demand and returns. Furthermore, the discount cash flow is considered in the methodology as a novel innovative approach. This methodology can be applied in comparing the profitability of different design options for CLSCs.     

Transport network design of integrated networks with real-time dynamic routing

Integrated real-time dynamic routing (IRR) networks provide dynamic routing features for multiple classes-of-service on an integrated transport network. In a previous Journal paper it is shown that IRR networks allow reduced network management costs since with real-time dynamic routing a number of network operations are simplified or eliminated, leading to savings in operations costs and expenses. In this paper a new algorithm is described for the transport design of IRR networks which achieves near-optimal capacity engineering. In particular, a Karmarkar Algorithm optimal solution to the linear programming flow model achieves approximately a 5 to 8 percentage point reduction in network design cost in comparison to the designs of pre-planned dynamic networks solved with heuristic design techniques. The optimization techniques described in this and the previous Journal paper attain significant capital cost reductions and network performance improvements by properly modeling the more efficient operation of IRR networks.     

PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem

Concerns over environmental degradation, legislative requirements and growing needs of business have fueled the growth of Closed Loop Supply Chains (CLSC). We consider a CLSC and address the issues of designing the network and of optimizing the distribution. Four variants of the problem are considered. The problem is modeled as an Integer Linear Program (ILP). We develop a constructive heuristic based on Vogel's approximation method–total opportunity cost method to provide good initial solutions to a priority-based simulated annealing heuristic, to accelerate its convergence. Trials on a set of hypothetical datasets have yielded encouraging results. The methodology is also tested using a case study data of a company producing electronic products. Implications for sustainability are discussed.     

演化算法中一种基于单分类的预选择策略

演化算法中,预选择算子用于为后续的环境选择过程筛选出好的潜在候选后代解.现有预选择算子大多基于适应值评估、代理模型或分类模型.由于预选择过程本质上是一个分类过程,因此基于分类的预选择过程天然适用于演化算法.先前研究工作采用二分类或多分类模型进行预选择,需预先准备“好”和“差”两组或具有区分性的多组训练样本来构建分类模型,而随着演化算法的执行,“好”解和“差”解之间的界限将愈加模糊,因此准备具有区分性的两组或多组训练样本将变得具有挑战性.为解决该问题,本文提出了一种基于单分类的预选择策略(One-class Classification based PreSelection,OCPS),首先将当前种群中的解均视为“好”类样本,之后只利用该类“好”样本构建单分类模型,然后利用构建的模型对产生的多个候选解进行标记与选择.提出的策略应用在三个代表性演化算法中,数值实验结果表明,提出的策略能够提升现有演化算法的收敛速度.     

求最大带宽路的一种新算法

带宽是网络通信中重要的性能指标。带宽资源是有限的,为了使信息在网络中尽量快地进行传输,寻找最大带宽路就是一种重要的方法。目前有两种经典的求解最大带宽路的算法:修正Dijkstra算法和修正Kruscal算法。该文提出一种新的最大带宽路算法,称为M-SPFA算法。与前两种算法相比,该算法具有更低的时间复杂度(O(m)),理解容易,实现也更加简单。     

考虑政府补贴及环境设计的再制造闭环供应链生产决策

在外包及授权再制造模式下,研究考虑政府补贴及制造商环境设计的再制造闭环供应链生产决策问题.构建4种闭环供应链决策模型,分析政府补贴及环境设计水平对制造商与再制造商竞合关系、闭环供应链绩效以及环境的影响.研究发现,制造商可以通过调节单位再制造外包费或专利许可费实现与再制造商共享政府补贴,因此政府不同补贴策略不会对闭环供应链最优生产决策及绩效产生影响.政府补贴与消费者对再制造产品的认知程度,并不总是有利于提高制造商环境设计水平,但均有助于促进再制造产品销售.制造商总是有动机进行环境设计,而再制造商只有在环境设计能为再制造产品带来更多成本节约时,才有动力接受制造商的环境设计方案.相比于授权再制造,制造商及再制造商均在外包再制造下获得更多的利润.另外,政府补贴与环境设计并不一定总能起到减少产品环境总影响程度的作用.     

Supply Chain Network Designs Developed for Deteriorating Items Under Conditions of Trade Credit and Partial Backordering

Deteriorating items, trade credit, and partial backordering are common in today’s business. However, no previous study on supply chain network design has considered these business aspects together. In this paper, we present supply chain networks designed for deteriorating items under trade credit and two conditions: (a) no shortage and (b) partial backordering of goods. We also present 2 algorithms based on nonlinear optimization that were developed in order to optimize the influence area and the joint replenishment-cycle time in the no-shortage case, and to identify the optimal shortage level in the partial-backordering case. The numerical examples presented herein illustrate how the solution procedure works. The effects of various values of the tested parameters on decisions and costs are also discussed. Our results could be used as a reference by managers when making business decisions.     

结合SURF算子和极线约束的柑橘立体图像对匹配

提出一种结合SURF算子和极线约束的立体匹配方法。对采集的双目视觉柑橘图像进行R-B分量的计算,在该分量上,采用快速hessian检测子进行特征点检测,并使用SURF描述子对检测到的特征点进行64维的特征描述。采用欧式距离和极线约束进行特征点匹配。实验表明,该方法对一幅图像对的平均处理时间为293ms,在果实被遮挡或光线变化的情况下均能较好地进行特征点提取和匹配。该方法为后续的深度信息计算提供了基础。     

Dynamic retransmission control for reliable mobile multicast

Most existing multicast protocols adopt a static retransmission scheme(unicast or multicast) to retransmit lost packets.In the mobile multicast environment,static multicast retransmission mode may lead to congestion in the receivers‘ wireless interfaces,while static unicast mode may result in great network load.Both static unicast and multicast retransmission modes will cause a performance loss.This paper logically divides the mobile multicast network into fixed and mobile parts,and focuses on the mobile part.Then this paper analyzes the retransmission costs when multicast or unicast mode is chosen.Two main parameters are used to compare their efficiencies:the average air-interface utilization of each receiver and the average network load.Based on the results of analysis,two new algorithms,called NLPA(Network Load Priority Algorithm)and AUPA(Air-interface Utilization Priority Algorithm)are presented.Finally,simulation results conclude that,with proper parameters,both NLPA and AUPA can dynamically alternate between unicast and multicast retransmission modes acording to the conditions of network and receiver,and avoid congestion in receivers‘ wireless interfaces as well as great network load,with a better use of network and terminal resources.