Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. dual sourcing

This paper presents a stochastic mixed integer programming approach to integrated supplier selection and customer order scheduling in the presence of supply chain disruption risks, for a single or dual sourcing strategy. The suppliers are assumed to be located in two different geographical regions: in the producer's region (domestic suppliers) and outside the producer's region (foreign suppliers). The supplies are subject to independent random local disruptions that are uniquely associated with a particular supplier and to random semi-global (regional) disruptions that may result in disruption of all suppliers in the same geographical region simultaneously. The domestic suppliers are relatively reliable but more expensive, while the foreign suppliers offer competitive prices, however material flows from these suppliers are more exposed to unexpected disruptions. Given a set of customer orders for products, the decision maker needs to decide which single supplier or which two different suppliers, one from each region, to select for purchasing parts required to complete the customer orders and how to schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The problem objective is either to minimize total cost or to maximize customer service level. The obtained combinatorial stochastic optimization problem will be formulated as a mixed integer program with conditional value-at-risk as a risk measure. The risk-neutral and risk-averse solutions that optimize, respectively average and worst-case performance of a supply chain are compared for a single and dual sourcing strategy and for the two different objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.     

Keep Your Friends Close? Supply Chain Design and Disruption Risk

In this article, we evaluate the relationship between supply chain design decisions and supply chain disruption risk. We explore two supply chain design strategies: (i) the dispersion of supply chain partners to reduce supply chain disruption risk versus (ii) the co‐location of supply chain partners to reduce supply chain disruption risk. In addition, we assess supply chain disruption risk from three perspectives: the inbound material flow from the supplier (supply side), the internal production processes (internal), and the outbound material flow to the customer (customer side) as a disruption can occur at any of these locations. We measure disruption risk in terms of stoppages in flows, reductions in flow, close calls (disruptions that were prevented at the last minute), disruption duration (time until normal operation flow was restored), and the spread of disruptions all the way through the supply chain. We use seemingly unrelated regression (SUR) to analyze our data, finding that lead times, especially supply side lead times, are significantly associated with higher levels of supply chain disruption risk. We find co‐location with suppliers appears to have beneficial effects to the reduction of disruption duration, and, overall supply side factors have a higher impact when it comes to supply chain disruption risk than comparable customer side factors.     

基于P-中位模型的网络关键设施识别问题的算法设计与实现

在网络服务系统中,存在由于各种人为因素(恐怖行为、黑客袭击等)导致网络设施服务中断的情况.为抵御有预谋的攻击,需要更加重视如何识别网络系统中的关键设施.结合P-中位选址模型,以设施失效对网络系统运行效率影响最大化为目标,给出针对基于P-中位模型的网络关键设施识别问题(即R-中断模型),并针对该模型提出贪婪搜索、邻域搜索和禁忌搜索3种算法.结合Galvo、Europe 150 和USA 263 等大型的测试实例,对上述算法进行比较分析,得出禁忌搜索算法最有效的结论.最后,结合Europe 150 数据的例子比较了P-中位问题与R-中断问题,认为在选址决策中事先考虑到人为攻击导致的中断问题可以增加网络的抗攻击能力,减少损失.     

有模糊时间窗的车辆调度组合干扰管理研究

研究带有模糊时间窗的车辆调度组合干扰管理模型及其混合遗传算法.采用时间窗模糊化处理方法,定义客户满意度函数,根据干扰管理思想对车辆调度中组合性干扰事件进行分析,从配送路径、配送成本和客户满意度三个方面进行干扰辨识与度量,建立基于模糊时间窗的车辆调度组合干扰管理模型;构造模型求解的混合遗传算法,将最佳客户插入规则与遗传算法结合,同时在算法中嵌入模糊优化程序以处理问题的模糊特征;进行数值实验,实验结果验证了模型与算法的有效性.     

Robust Resource Allocation Decisions in Resource‐Constrained Projects*

The well‐known deterministic resource‐constrained project scheduling problem involves the determination of a predictive schedule (baseline schedule or pre‐schedule) of the project activities that satisfies the finish–start precedence relations and the renewable resource constraints under the objective of minimizing the project duration. This baseline schedule serves as a baseline for the execution of the project. During execution, however, the project can be subject to several types of disruptions that may disturb the baseline schedule. Management must then rely on a reactive scheduling procedure for revising or reoptimizing the baseline schedule. The objective of our research is to develop procedures for allocating resources to the activities of a given baseline schedule in order to maximize its stability in the presence of activity duration variability. We propose three integer programming–based heuristics and one constructive procedure for resource allocation. We derive lower bounds for schedule stability and report on computational results obtained on a set of benchmark problems.     

Locating Facilities in the Presence of Disruptions and Incomplete Information*

In this article, we analyze a location model where facilities may be subject to disruptions. Customers do not have advance information about whether a given facility is operational or not, and thus may have to visit several facilities before finding an operational one. The objective is to locate a set of facilities to minimize the total expected cost of customer travel. We decompose the total cost into travel, reliability, and information components. This decomposition allows us to put a value on the advance information about the states of facilities and compare it to the reliability and travel cost components, which allows a decision maker to evaluate which part of the system would benefit the most from improvements. The structure of optimal solutions is analyzed, with two interesting effects identified: facility centralization and co‐location; both effects appear to be stronger than in the complete information case, where the status of each facility is known in advance.     

Selection of supply portfolio under disruption risks

This paper deals with the optimal selection of supply portfolio in a make-to-order environment in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order to minimize total cost and mitigate the impact of disruption risks. The selection of suppliers and allocation of orders is based on price and quality of purchased parts and reliability of delivery. The two types of disruption scenarios are considered: scenarios with independent local disruptions of each supplier and scenarios with local and global disruptions that may result in all suppliers disruption simultaneously. The problem is formulated as a single- or bi-objective mixed integer program and a value-at-risk and conditional value-at-risk approach is applied to control the risk of supply disruptions. The proposed portfolio approach is capable of optimizing the supply portfolio by calculating value-at-risk of cost per part and minimizing expected worst-case cost per part simultaneously. Numerical examples are presented and some computational results are reported.     

加工能力受限的单机干扰管理研究

针对单机环境下最小化加权折扣加工时间和的排序问题,研究如何应对可预见的干扰事件。由于干扰事件使得机器加工能力受限,初始最优加工时间表不再可行,采用外包的方式来进行干扰管理。构建了排序模型,同时考虑原目标和与初始计划偏离的扰动目标,选择外包工件集并对所有工件进行重排序。为了求解得到的双目标排序问题,基于理想点法设计了一种动态规划算法和量子遗传算法相结合的算法。最后通过一个数值算例说明,该排序模型对于求解加工能力受限的单机干扰管理问题是有效的。     

供应链应急援助的CVaR模型

企业间通过应急援助方式共同应对突发事件是供应链应急管理的常用合作策略,而应急合作目标是防止突发事件下的损失失去控制.本文引入CVaR来刻画企业在突发事件下的应急目标,进而建立供应链应急援助的决策模型,分析了供应商和零售商遭遇突发事件时的应急援助状况并给出了在一定置信水平控制下的最优援助额.研究表明:CVaR方法能够恰当...     

Responsive contingency planning in supply risk management by considering congestion effects

Contingency rerouting is known as a cost-effective risk management strategy for major disruptions such as earthquakes and natural disasters. The objective of this paper is to develop a decision-making tool to determine the appropriate response speed of a volume-flexible backup supplier to improve the supply chain responsiveness. We propose a mixed integer programming (MIP)-based capacity planning tool which generates the contingency plan of the supply chain subject to random disruptions. In order to make an accurate decision, the impact of critical operational characteristics such as response time and congestion are considered in a disruption scenario. The appropriate response speed is selected through a decision tree analysis by minimizing the expected supply chain costs. The selection is made with respect to three different attitudes of the decision maker towards risk. In order to evaluate the impact of the different failure and recovery probabilities over the selection process, a sensitivity analysis is presented. The results show that considering congestion is especially critical for risk-neutral decision makers in mitigating against disruptions.     

Mitigating disruptions in a multi-echelon supply chain using adaptive ordering

Supply chains often experience significant economic losses from disruptions such as facility breakdowns, transportation mishaps, natural calamities, and intentional attacks. To help respond and recover from a disruption, we investigate adjustments in order activity across four echelons including assembly. Simulation experiments reveal that the impact of a disruption depends on its location, with costlier and longer lasting impacts occurring from disruptions at echelons close to ultimate consumption. Cost functions based on system inventory and service can be quite ill-behaved in these complex problem settings. Expediting, an adaptive ordering approach often used to mitigate disruptions, can trigger unintended bullwhip effects, and hurt rather than help overall performance. As an alternative to expediting interventions, dynamic order-up-to policies show promise as an adaptive mitigation tool. We also find benefits in the dynamic policies from incorporating a metaheuristic parameter search over multiple echelons, yielding significantly better solution quality than embedded unimodal search.     

Building up Resilience in a Pharmaceutical Supply Chain through Inventory,Dual Sourcing and Agility Capacity

This paper is inspired by a risk management problem faced by a leading pharmaceutical company. Key operational risk mitigation measures include Risk Mitigation Inventory (RMI), Dual Sourcing and Agility Capacity. We study the relationship between these three measures by modeling the drug manufacturing firm that is exposed to supply chain disruption risk. The firm determines optimal RMI levels for assumed Dual Sourcing and Agility Capacity. We quantify the decrease in RMI levels in the presence of Dual Sourcing and Agility Capacity. Furthermore, using an example, we analyze RMI, Dual Sourcing and Agility Capacity decisions jointly. It turns out that RMI and Agility Capacity can be substitutes as long as no Dual Source is available. Once the Dual Source is available, Agility Capacity and Dual Sourcing appear to be substitutes. We further show that for long disruption times, the optimal Dual Source production rate may decrease in the disruption time. Within our modeling framework, we introduce an operational metric that quantifies Supply Chain Resilience. Supply chain disruptions can have a severe business impact and need to be managed appropriately.     

An integrated supplier selection and inventory problem with multi-sourcing and lateral transshipments

In this research, we consider the supplier selection problem of a firm offering a single product via multiple warehouses. The warehouses face stationary, stochastic demand and replenish their inventory via multiple suppliers, to be determined from a set of candidates, with varying price, capacity, quality, and disruption characteristics. Additionally, the warehouses may simultaneously replenish their inventory from other warehouses proactively. With these characteristics, the problem is a multi-sourcing, supplier selection, and inventory problem with lateral transshipments. Even though the benefits of multi-sourcing and lateral transshipments have been presented in the literature individually to mitigate risks associated with uncertain demand and disrupted supply, the intertwined sourcing and inventory decisions under these settings have not been investigated from a quantitative perspective. We develop a decomposition based heuristic algorithm, powered with simulation. While the decomposition based heuristic determines a solution with supplier selection and inventory decisions, the simulation model evaluates the objective function value corresponding to each generated solution. Experimental results show, contrary to the existing literature, inferior decisions may result when considering the selection of suppliers solely on unit and/or contractual costs. We also evaluate the impact of multi-sourcing with rare but long disruptions compared to frequent but short ones.     

A Two-Phase Exact Algorithm for MAX-SAT and Weighted MAX-SAT Problems

We describe a two-phase algorithm for MAX-SAT and weighted MAX-SAT problems. In the first phase, we use the GSAT heuristic to find a good solution to the problem. In the second phase, we use an enumeration procedure based on the Davis-Putnam-Loveland algorithm, to find a provably optimal solution. The first heuristic stage improves the performance of the algorithm by obtaining an upper bound on the minimum number of unsatisfied clauses that can be used in pruning branches of the search tree.We compare our algorithm with an integer programming branch-and-cut algorithm. Our implementation of the two-phase algorithm is faster than the integer programming approach on many problems. However, the integer programming approach is more effective than the two-phase algorithm on some classes of problems, including MAX-2-SAT problems.     

Price competition,cost and demand disruptions and coordination of a supply chain with one manufacturer and two competing retailers

This paper studies the coordination of a supply chain with one manufacturer and two competing retailers after the production cost of the manufacturer was disrupted. We consider two coordination mechanisms: an all-unit quantity discount and an incremental quantity discount. For each mechanism, we develop the conditions under which the supply chain is coordinated and discuss how the cost disruption may affect the coordination mechanisms. For the all-unit quantity discount scheme, we find that the manufacturer charges the lower-cost retailer for a lower unit wholesale price in order to induce him to order more products. If the costs of two retailers have a remarkable difference, then the all-unit quantity discount scheme cannot coordinate the supply chain with disruptions. While the cost disruption may affect the wholesale prices, order quantities as well as retail prices, it is optimal for the supply chain to keep the original coordination mechanism if the production cost change is sufficiently small. The model is also extended to the case with both cost and demand disruptions. The equilibrium strategies of the retailers are investigated when the manufacturer cannot timely react to the disruptions such that she has to keep the original mechanism. We illustrate the results by numerical examples.     

Computational risk management techniques for fixed charge network flow problems with uncertain arc failures

We consider a formulation for the fixed charge network flow (FCNF) problem subject to multiple uncertain arc failures, which aims to provide a robust optimal flow assignment in the sense of restricting potential losses using Conditional Value-at-Risk (CVaR). We show that a heuristic algorithm referred to as Adaptive Dynamic Cost Updating Procedure (ADCUP) previously developed for the deterministic FCNF problem can be extended to the considered problem under uncertainty and produce high-quality heuristic solutions for large problem instances. The reported computational experiments demonstrate that the described procedure can successfully tackle both the uncertainty considerations and the large size of the networks. High-quality heuristic solutions for problem instances with up to approximately 200,000 arcs have been identified in a reasonable time.     

Parallel-machine scheduling of deteriorating jobs with potential machine disruptions

We consider parallel-machine scheduling of deteriorating jobs in a disruptive environment in which some of the machines will become unavailable due to potential disruptions. This means that a disruption to some of the machines may occur at a particular time, which will last for a period of time with a certain probability. If a job is disrupted during processing by a disrupted machine and it does not need (needs) to re-start after the machine becomes available again, it is called the resumable (non-resumable) case. By deteriorating jobs, we mean that the actual processing time of a job grows when it is scheduled for processing later because the machine efficiency deteriorates over time due to machine usage and aging. However, a repaired machine will return to its original state of efficiency. We consider two cases, namely performing maintenance immediately on the disrupted machine when a disruption occurs and not performing machine maintenance. In each case, the objective is to determine the optimal schedule to minimize the expected total completion time of the jobs in both non-resumable and resumable cases. We determine the computational complexity status of various cases of the problem, and provide pseudo-polynomial-time solution algorithms and fully polynomial-time approximation schemes for them, if viable.     

Supply Disruptions,Heterogeneous Beliefs,and Production Efficiencies

Recent years have witnessed the pervasive supply disruptions and their impacts on supply chain performance. In this study, we investigate the optimal procurement design with supply disruptions and heterogeneous beliefs between the buyer and the supplier. We examine the impact of information asymmetry on the supplier's belief, the control right of the backup production, and the verifiability of supply disruption. The belief heterogeneity creates speculative gains and losses because the buyer and the supplier hold different estimates of the disruption probability. We demonstrate that the buyer's incentive to exploit this belief heterogeneity leads to real production inefficiencies in different scenarios. The production efficiency is not necessarily improved with more transparent information. Moreover, a very pessimistic supplier may have no incentive to invest in improving the reliability even if this is costless, and the supplier may produce more when the expected production cost becomes higher. When the buyer sees some value in using the supplier's estimate to update his own belief, we find that the main results hold unless the buyer completely abandons his belief.     

基于免疫遗传算法和列生成的多项目人力资源调度研究

主要研究列生成法求解带有人力资源约束的多项目多模式进度管理问题。首先根据问题建立了相应的数学模型,模型中考虑了多种约束,如项目对人员能力、水平的不同要求,目标为满足约束的条件下成本最小化,其中包含固定和可变两类成本。模型分解后,按照列生成法流程求解。由于问题的复杂性,采用启发式算法求解每个子问题:首先由基于优先原则的启发式方法给出问题的初始解,再由免疫遗传算法寻优。通过数值实验分析了算法性能、模型改进情况,不同优先原则组合对目标成本和各项目间时间分配的影响。     

Stages of Supply Chain Disruption Response: Direct,Constraining, and Mediating Factors for Impact Mitigation

It is well established that supply chain disruptions can have a severe negative impact on firms and general wisdom suggests that this impact can be mitigated by quick responses. Aside from a few anecdotes, however, little is known about the decision‐making process that leads to speedy responses and about its impeding and supporting antecedents. Using the organizational information‐processing perspective, this empirical study unravels the disruption management process along a sequence of four stages—disruption recognition, disruption diagnosis, response development, and response implementation—and hypothesizes constraining and mediating effects of these stages. The findings contribute to an improved understanding of the role that the decision stages play in mitigating supply chain disruptions, and confirm the prediction that the speed with which information is processed and the stages are worked through positively affects supply chain performance. In addition, the findings suggest that one of the stages, diagnosis, acts as a constraining factor to the other stages. The stages also play a mediating role between the impact that the disruption has and a firm's readiness (prior to a disruption), dependence on a key supplier, and supply chain complexity. This provides guidance to decision makers in the application of resources both prior to a negative event and during a disruption recovery.