多级供应链系统成本分析随机网络模型及算法研究

供应链成本管理是企业战略管理的核心组成部分。为研究多级供应链网络系统的成本组成及其分布特征、分析各节点企业的成本管理对多级供应链网络系统的影响、找到供应链系统成本管理中的薄弱环节和关键企业,本文构建了多级供应链系统成本的随机网络分析模型。首先研究了模型的结构性质特征,给出成本分布特征的解析算法。然后扩展模型,分别从系统成本类型构成(生产成本、库存成本和物流成本)和系统对企业成本波动的灵敏度两个角度深入研究多级供应链网络成本问题。数值算例分析结果说明了多级供应链网络系统成本分析模型和相关算法的有效性和实用性。     

Modeling a low-carbon garment supply chain

This article attempts to introduce indirect carbon emission and trade-credit concept in a network optimisation model for sustainable supply chain. The proposed model optimises total cost, total direct carbon emission, total indirect emission in the form of embodied carbon footprint of the raw material and total trade-credit amount over the purchased item in a supply chain. The model calculates the total cost by considering purchasing cost, logistics cost, handling cost and manufacturing cost. It attempts to measure the direct emission involved in manufacturing and logistics operations. The model has the capability to consider dissimilar trucks used for transportation according to their operating cost and carbon emission. Multi-objective goal programming is applied to deal with four objectives to find a tradeoff among these objectives. The result suggests that managers should capture the direct as well as the indirect emission which helps in arriving at appropriate strategy for a sustainable supply chain. The effectiveness of the proposed model is demonstrated through a case of a garment supply chain. This model also supports in deciding appropriate goal for carbon emission, supply chain costs, etc.     

A sustainable second-generation biodiesel supply chain network design problem under risk

This paper presents a multi-objective possibilistic programming model to design a second-generation biodiesel supply chain network under risk. The proposed model minimizes the total costs of biodiesel supply chain from feedstock supply centers to customer centers besides minimizing the environmental impact (EI) of all involved processes under a well-to-wheel perspective. Non-edible feedstocks are considered for biodiesel production. Variable cultivation cost of non-edible feedstock is assumed to be non-linear and dependent upon the amount of cultivated area. New formulation of possibilistic programming method is developed which is able to minimize the total mean and risk values of problems with possibilistic-based uncertainty. To solve the proposed multi-objective model, a hybrid solution approach based on flexible lexicographic and augmented ɛ-constraint methods is proposed which is capable to find appropriate efficient solutions from the Pareto-optimal set. The performance of the proposed possibilistic programming method as well as the developed solution approach are evaluated and validated through conducting a real case study in Iran. The outcome of this study demonstrates that high investment cost is required for improving the environmental impact and risk of sustainable biodiesel supply chain network design under risk. Decision maker preferences are required for suitable trade-off among total costs, risk values and environmental impact.     

非平稳需求下考虑碳配额的供应链选址-库存模型与算法研究

针对非平稳需求下考虑碳配额的多期、多需求情景的三级供应链选址-库存问题,构建了库存策略(t,s,S)下供应链运营期望收益最大化的两阶段选址-库存随机优化模型,依据供应链企业不同着眼点下的决策流程,提出了一种三步骤的分层级启发式算法,该算法包含了选址导向和需求导向的两种子问题序贯求解模式。数值算例验证了在不同问题规模及需求类型下算法求解的有效性,同时分析了供应链网络设计、各成本占比和运营收益对不同供应链成本结构、需求不确定性与碳配额的敏感性,并给出了管理上的启示。     

Bounds of Relative Regret Limit in p‐Robust Supply Chain Network Design

This research studies the p‐robust supply chain network design with uncertain demand and cost scenarios. The optimal design integrates the supplier selection together with the facility location and capacity problem. We provide a new framework to obtain the relative regret limit, which is critical in the robust supply chain design but is assumed to be a known value in the existing literature. We obtain lower and upper bounds for relative regret limit and obtain a sequence of optimal solutions for series relative regret limits between the upper and lower bounds. An algorithm for p‐robust supply chain network design is provided. A series of numerical examples are designed to find the properties of the bottleneck scenarios. A scenario with low probability and a low optimal objective function value for the scenario has a greater chance of being a bottleneck. To focus only on the influence from the relative regret, we also introduce three separate new objective functions in p‐robust design. The proposed new theories and approaches provide a sequence of options for decision makers to reduce the marketing risks effectively in supply chain network design.     

分销网络优化模型及算法研究

为了优化网络结构,寻求最佳配送策略,最终找出成本最小的供应链,针对需求拖动式供应链中,多供应商、多产品、多客户分销配送网络的优化设计问题,在考虑需求分配的情况下,提出了分销配送网络的优化模型。为了求解优化模型,提出了基于混合遗传算法求解混合0-1整数规划问题的算法,它是用遗传算法搜索0-1变量的最优解,将其他算法融入遗传算法中,对非0-1变量进行求解的一种算法。最后通过两个算例进行了仿真实验,初步验证了优化模型和算法的有效性。     

Sustainable supply chain network design: A case of the wine industry in Australia

This paper comes at a time when many companies and people are increasingly facing various global problems linked to sustainability challenges, and when the literature is still scarce on research incorporating all three dimensions of sustainability in supply chain analytical models. The paper conducts a case-based modeling study to address this gap in the area of supply chain network design. We investigate the wine supply chain and propose a generic model for sustainable wine supply chain network design encompassing economic, environmental and social objectives. The case company is a real large-sized wine company located in Australia, for which a customized model is provided. Both models are formulated as a multi-objective mixed-integer program and solved using the augmented ϵ-constraint method by CPLEX. Social implications of the feasible scenarios are examined through introducing social impact coefficients. Non-dominated solutions are obtained and some balanced scenarios are proposed. The results show trade-offs between the objectives, yet more interestingly demonstrate how large is the gap between the existing supply chain configuration and the proposed scenarios in terms of supply chain cost and emissions.     

考虑技术投资的供应链网络下可交易污染排放许可均衡问题

构建了包含多污染物、多污染接收点的供应链网络,考虑了生产技术和减排技术投资,对可交易的污染排放许可下的供应链网络进行了均衡分析,给出了变分不等式框架,建立了均衡模型。模型明确地处理了空间差异,同时保证了通过初始污染排放许可分配就可达到指定的环境标准。提出了模型的求解算法,最后利用提出的模型及算法对算例进行了计算,得到利润最大化下供应链网络中各工厂的产品生产量、排放量、污染排放许可的均衡分配额和污染排放许可的交易价格、技术最优投资额,并分析了成本函数参数以及工厂数量的变化对生产技术投资和减排技术投资的影响。     

Using branch-and-price approach to solve the directed network design problem with relays

We present node-arc and arc-path formulations, and develop a branch-and-price approach for the directed network design problem with relays (DNDR). The DNDR problem can be used to model many network design problems in transportation, service, and telecommunication system, where relay points are necessary. The DNDR problem consists of introducing a subset of arcs and locating relays on a subset of nodes such that in the resulting network, the total cost (arc cost plus relay cost) is minimized, and there exists a directed path linking the origin and destination of each commodity, in which the distances between the origin and the first relay, any two consecutive relays, and the last relay and the destination do not exceed a predefined distance limit. With the node-arc formulation, we can directly solve small DNDR instances using mixed integer programming solver. With the arc-path formulation, we design a branch-and-price approach, which is a variant of branch-and-bound with bounds provided by solving linear programs using column generation at each node of the branch-and-bound tree. We design two methods to efficiently price out columns and present computational results on a set of 290 generated instances. Results demonstrate that our proposed branch-and-price approach is a computationally efficient procedure for solving the DNDR problem.     

Integrating performance and risk aspects of supply chain design processes

Abstract

Supply chain design is a complex and relatively poorly structured process, involving choosing many decisional parameters and it usually requires consideration of numerous sources of uncertainty. Many conventional processes of supply chain design involve taking a deterministic approach, using point estimates, on important measures of supply chain effectiveness such as cost, quality, delivery reliability and service levels. Supply chain disruptions are often separately considered as risks, both in the research literature and in practice, meaning that a purely traditional risk management and risk minimization approach is taken. We have developed and applied an approach that combines the intellect and experience of the supply chain designer with the power of evaluation provided by a Monte Carlo simulation model, which uses decision analysis techniques to explicitly incorporate the full spectrum of uncertain quantities across the set of alternative supply chain designs being considered. After defining and setting out the general decision variables and uncertainty factors for 16 distinct supply chain design decision categories, we then apply that approach to combine the decision-makers’ heuristics with the probabilistic modeling approach, iteratively, to achieve the best of both elements of such an approach. This novel approach to fully integrating performance and risk elements of supply chain designs is then illustrated with a case study. Finally, we call for further developmental research and field work to refine this approach.     

基于分布式工厂的供应链二级分销网络生产计划优化模型

从供应链的集成和协作角度出发,提出了在多工厂、多分销商条件下二级分销网络生产计划制定的双层规划模型,模型考虑了二级网络中制造商和分销商之间的信息共享,从而降低了整个链上的费用。同时设计了启发式求解算法,并给出了一个数值例子。     

Reliability evaluation of a multicast over coded packet networks

Network coding is a generalization of conventional routing methods that allows a network node to code information flows before forwarding them. While it has been theoretically proved that network coding can achieve maximum network throughput, theoretical results usually do not consider the stochastic nature in information processing and transmission, especially when the capacity of each arc becomes stochastic due to failure, attacks, or maintenance. Hence, the reliability measurement of network coding becomes an important issue to evaluate the performance of the network under various system settings. In this paper, we present analytical expressions to measure the reliability of multicast communications in coded networks, where network coding is most promising. We define the probability that a multicast rate can be transmitted through a coded packet network under a total transmission cost constraint as the reliability metric. To do this, we first introduce an exact mathematical formulation to construct multicast connections over coded packet networks under a limited transmission cost. We then propose an algorithm based on minimal paths to calculate the reliability measurement of multicast connections and analyze the complexity of the algorithm. Our results show that the reliability of multicast routing with network coding improved significantly compared to the case of multicast routing without network coding.     

考虑不确定履约率的全局鲁棒供应组合决策研究

生产和采购的全球化为供应链企业带来了更多的复杂性与不确定性。收集全面信息和使用有效的工具是控制决策风险、减少不确定性危害的有效途径。在供应商选择与订单分配管理中,供应商履约率是影响企业供应组合决策的主要因素,如何对其进行定量刻画是迫切需要解决的问题。已有的研究通常是由期望方法或鲁棒方法来刻画履约率。然而,期望履约率法往往忽略了履约率的波动性,从而造成需求损失;而鲁棒履约率法通常存在过于保守、退化过于迅速两个缺点,使企业承担不必要的成本。本文研究供应商选择与订单分配决策问题,使用全局鲁棒优化的两个不确定集合来刻画供应商履约率,同时给出了这一问题的确定性鲁棒表达和一种多项式时间求解算法。最后,通过仿真实验证实本文的全局鲁棒优化模型可克服期望模型和一般鲁棒模型的缺点。本文提出的算法能有效求解本文全局鲁棒优化等价确定性问题,为企业的供应商选择和订单分配决策提供更为灵活精确的辅助工具。     

A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models

This study proposes a framework for the main parties of a sustainable supply chain network considering lot-sizing impact with quantity discounts under disruption risk among the first studies. The proposed problem differs from most studies considering supplier selection and order allocation in this area. First, regarding the concept of the triple bottom line, total cost, environmental emissions, and job opportunities are considered to cover the criteria of sustainability. Second, the application of this supply chain network is transformer production. Third, applying an economic order quantity model lets our model have a smart inventory plan to control the uncertainties. Most significantly, we present both centralized and decentralized optimization models to cope with the considered problem. The proposed centralized model focuses on pricing and inventory decisions of a supply chain network with a focus on supplier selection and order allocation parts. This model is formulated by a scenario-based stochastic mixed-integer non-linear programming approach. Our second model focuses on the competition of suppliers based on the price of products with regard to sustainability. In this regard, a Stackelberg game model is developed. Based on this comparison, we can see that the sum of the costs for both levels is lower than the cost without the bi-level approach. However, the computational time for the bi-level approach is more than for the centralized model. This means that the proposed optimization model can better solve our problem to achieve a better solution than the centralized optimization model. However, obtaining this better answer also requires more processing time. To address both optimization models, a hybrid bio-inspired metaheuristic as the hybrid of imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) is utilized. The proposed algorithm is compared with its individuals. All employed optimizers have been tuned by the Taguchi method and validated by an exact solver in small sizes. Numerical results show that striking similarities are observed between the results of the algorithms, but the standard deviations of PSO and ICA–PSO show better behavior. Furthermore, while PSO consumes less time among the metaheuristics, the proposed hybrid metaheuristic named ICA–PSO shows more time computations in all small instances. Finally, the provided results confirm the efficiency and the performance of the proposed framework and the proposed hybrid metaheuristic algorithm.

     

具有遗憾值约束的鲁棒供应链网络设计模型研究

考虑不确定性环境,研究战略层次的供应链网络鲁棒设计问题,目标是设计参数发生摄动时,供应链性能能够保持稳健性。基于鲁棒解的定义,建立从上游供应商选择到下游设施选址-需求分配的供应链网络设计鲁棒优化模型;提出确定遗憾值限定系数上限和下限的方法,允许决策者调节鲁棒水平,选择多种供应链网络结构;通过模型分解与协调,设计了供应链节点配置的禁忌搜索算法。算例的计算结果表明了禁忌搜索算法具有良好的收敛特性,以及在处理大规模问题上的优越性;同时也反映了利用鲁棒优化模型进行供应链网络设计,可以有效规避投资风险。     

基于混合差分进化算法的联合补货-配送优化模型

本文综合考虑联合补货与配送决策,研究了随机需求、允许缺货环境下多企业多产品联合补货与配送集成优化模型,设计了混合差分进化算法(Hybrid Differential Evolution, HDE)对该模型进行求解,同时通过算例与遗传算法、标准的DE算法进行了比较,证实HDE算法高效且稳定;另外,设计了一个先补货再配送的两阶段优化模型,对比优化结果发现采用供应链协同时补货成本较高,配送成本较低,且总成本较低。最后,对相关参数进行了敏感性分析,发现需求率和库存维持成本的变动对总成本的影响远远大过次要订货成本对总成本的影响。     

具有批量折扣的供应物流网络优化遗传算法研究

针对具有批量折扣和转运的供应链优化问题的特征,提出供应物流网络概念及其优化参数:节点、线路和流量;根据包括采购和物流的供应总成本函数最小化,建立供应物流网络优化问题的数学模型;设计此类优化问题的遗传算法,通过仿真算例,说明和验证遗传算法对该问题具有很好的可行性和实用性.     

基于双层规划的供应链二级分销网络优化设计模型

从供应链集成的角度出发,利用双层规划模型描述了二级分销网络优化问题,充分考虑了网络决策部门及客户双方的自身及共同利益。同时设计了启发式求解算法,最后用一简单实例验证了模型及其算法的有效性。     

考虑供应风险和生产成本不确定性的供应链动态决策研究

考虑到供应风险和生产成本的不确定性,本文研究了买方或卖方实施努力既可能改善供应可靠性,同时又可能降低生产成本的供应链最优决策问题。分别构建了制造商努力和供应商努力的两阶段动态决策模型。分析了提升供应可靠性和降低成本的概率对两种模型中的最优采购数量、最优努力程度和上下游期望收益以及供应链期望收益的影响。同时在两种模型的比较中发现,制造商的最优努力与供应商的最优努力之间的差异与收益分享系数有关。相同努力效果下,制造商努力时的最优采购数量总是不小于供应商努力时的最优采购数量。相比供应商努力,制造商努力更有利于增加其自身的期望收益。然而,供应商以及整个供应链的期望收益却可能在供应商努力时更高。     

供应链分销系统双层优化模型

分销渠道决策在整个供应链管理中非常关键,因为它直接影响着其它的市场决策. 从供 应链集成的角度出发,利用双层规划模型描述了二级分销网络优化问题,充分考虑了网络决策 部门及客户双方的自身及共同利益. 同时设计了启发式求解算法,最后用简单算例验证了模型 及其算法的有效性.