Toward a coordination of inventory and distribution schedules for blood in disasters

The need for efficient blood supply is of more significance in the event of disasters, when there is a lack of coordination between distribution and inventory management. The recent earthquake in Kermanshah province in Iran is among such cases that confirmed the need for coordinating such schedules. In this respect, a two-stage stochastic programming (SP) approach is presented for planning supply of blood after disasters that can assist in inventory decisions under hybrid uncertainty, minimizing the shortage and wastages. The uncertainty stems from imprecise parameters and scenario variability, and a robust-fuzzy-stochastic programming (RFSP) approach is devised to hedge against the uncertainty. The perishability of blood, the substitutability of blood groups, and the age-based characteristic of blood are taken into account to make the model more reliable. The compromise programming is applied to solve the multi-objective model. The results illustrate that the RFSP model can make a reasonable trade-off between mean value, feasibility robustness, and optimality robustness, which results in a robust and reliable solution under disastrous conditions.     

Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories

Demand uncertainty coupled with the short lifespan of itemized platelets has caused a significant shortage at public therapeutic centers. In this regard, this paper presents a multi-objective robust stochastic mixed integer linear programming model for an integrated platelet supply chain considering unidirectional lateral transshipment between hospitals and clinics. In the proposed model, platelets are divided into typical, irradiated and washed platelets with lifespan differentiated for various types of patients. This model evaluates the risk of inappropriate production by concentrating on three different procedures for extracting platelet, including apheresis, platelet rich plasma and buffy coat methods. In this investigation, staff errors, laboratory hygiene and device failure, as well as donors' blood contamination, are considered for obtaining the function of inappropriate production rate. In our model, the logistics costs are considered as the first objective and the maximum unsatisfied demand as the second objective, both also to be minimized. To tackle demand uncertainty, a robust stochastic approach is then developed. Afterwards, to create a more realistic model, the Monte Carlo sampling approach is used for generating required scenarios. Finally, some managerial insights are provided through a real case study to evaluate the applicability of the presented model. The results indicate that the proposed model not only improves the demand satisfaction but also reduces the risk of itemized platelets wastage.     

A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study

In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.     

Integrated manufacturing/distribution planning decisions with multiple imprecise goals in an uncertain environment

This work presents a possibilistic linear programming (PLP) method for solving the integrated manufacturing/distribution planning decision (MDPD) problems with multiple imprecise goals in supply chains under an uncertain environment. The imprecise PLP model designed here aims to simultaneously minimize total net costs and total delivery time with reference to available supply, capacities, labor levels, quota flexibility and cost budget constraints at each source, as well as forecast demand and warehouse space at each destination. The proposed method achieves greater computational efficiency by employing the simplified triangular distribution to represent imprecise numbers. An industrial case is used to demonstrate the feasibility of applying the proposed method to a real MDPD problem. Overall, the proposed PLP method provides a practical means of solving the multi-objective MDPD problems in an uncertain environment, and can effectively improve manufacturer/ distributor relationships in a supply chain.     

Bracing for demand shocks: An experimental investigation

We investigate inventory ordering decisions when decision makers anticipated a demand shock. Decision makers anticipating an event have been shown to brace for an uncertain negative outcome by overestimating the likelihood of that event. Decision makers faced with a spike in demand may incur increased holding costs because they may brace, exhibiting a judgment bias, and consequently a decision bias by over-ordering inventory. Three studies span conditions of uncertainty regarding the timing and magnitude of a demand shock: Employing three between-subjects experiments, Study 1 investigates behavior when decision makers were faced with uncertainty in timing and in magnitude of demand at the most elemental level, manipulating holding and stock out costs. The three experimental tasks feature uncertainty about the magnitude of demand (Experiment 1.1), uncertainty about the timing of demand (Experiment 1.2), and uncertainty about both the magnitude and timing of demand (Experiment 1.3). Study 2 uses a dynamic, multi-period replenishment task and a between-subjects manipulation regarding the uncertainty of timing and magnitude of a demand shock. Study 3 also employs a multi-period decision environment, but compares behavior under a demand shock condition with that in a condition featuring only random variability. The collective results from the three studies identify a bias toward over-ordering in response to a demand shock, relative to the optimal orders. The between-subjects manipulations in Study 2 points toward a possible remedy as we found that providing information concerning the timing and magnitude of a shock ameliorated the bias. The primary revelation was that decision makers had more difficulty dealing with uncertain timing than with uncertain magnitude of demand. One implication is that it is particularly critical for retailers to carefully plan and manage how they share information with upstream supply chain partners regarding when they plan to introduce store-level promotions.     

政企双层规划下考虑碳配额的供应链网络优化

构建了政企互动决策下考虑碳配额的双层规划模型,随后采用多目标粒子群优化算法求解模型,最后解析碳配额和需求不确定置信水平变化对供应链网络Pareto最优解集的影响。研究表明政企互动决策过程中存在最优碳配额,且随着政府部门根据双寡头制造商的反馈合理调整碳配额,上层规划模型中政府部门考虑双寡头制造商反馈的调整系数增大,双寡头制造商能够引导其它供应链企业减少间接碳排放,导致供应链网络总成本和碳排放持续减少。随着市场需求不确定置信水平升高,需求预测的精度降低,各设施间流量增加,需要增设备选设施,供应链网络总成本和碳排放也随之增加。     

供应不确定下基于Supply-hub的供应商选择和库存协同决策

针对由供应商、Supply-hub和制造商组成的三级供应链的多产品供应商选择和库存协同问题,考虑供应商存在延迟交付风险、供应数量约束,以供应链运作成本最小为目标,构建整数规划模型。分析结果表明:在一定的条件下,总存在最优的供应商选择方案和库存策略;当供应商的延迟交付概率增加,供应链期望成本上涨;当供应商供应数量无限制时,减少最优订货批量可以弱化供应不确定性增加造成的影响;当供应商选择方案不变,订货提前期增加,供应链的运作成本以及最优订货批量不变;一种零部件提前期的变化不影响其他零部件的供应商选择和库存策略。     

供应链——营销一体化随机模型研究

供应链活动的一体化已成为大多数行业的必需的竞争力。文章研究单工厂、多产品、多周期、多分销中心供应链网络的供应链—营销一体化问题。提出了利用随机混合整数规划方法,建立了一体化模型。随机模型解决了需求的不确定性问题。同时,模型优化了系统的生产批量、车辆调度和库存。数值实例展示了模型的应用,并用优化软件“What’sBest!”求出了结果。最后指出模型还可以在很多方面进行扩展。     

供应链环境下库存的模糊多目标优化策略

由于供应链中产品订货提前期的不确定性以及决策者对目标偏好和产品价格的模糊性,本文讨论了多级供应链库存控制的多个不可公度的模糊多目标决策问题,采用模糊数来描述产品市场价格和目标权重的模糊性,并利用交互式多目标线形加权优化算法对模型进行了分析和求解,从而使企业和供应链在库存管理方面获得双赢。     

需求不确定条件下低碳供应链网络设计研究

不同的碳排放约束会对供应链网络设计的选址及工厂环保等级的选择产生影响。文中综合考虑环境投资决策、网点选择以及路径的选择等问题,以总运作成本、生产和运输途中碳排放量最小为目标,建立不同需求情境下、需求不确定的多产品双目标混合整数规划模型,结合ε约束法设计了基于情景的求解算法。最后通过算例分析,验证模型和算法的有效性。     

一类供应链分销网络的双层多目标规划模型

以多生产商、多分销中心的供应链分销网络为基础,建立了多目标双层机会约束规划模型。基于协同供应链管理思想,充分考虑了多生产厂商相互制约以及决策信息的不完整性、市场需求不确定性,探讨如何实现整体成本的最小化。最后,结合一种算法给出了该模型的求解方法,并结合一个实例证明算法的有效性。     

需求确定型多级库存优化模型研究

多级库存管理对于企业来说具有重要的意义,在需求确定的情形下对库存模型进行了分析,建立了基于确定需求的多级库存数学模型,包括一个单目标模型和一个多目标模型,使供应链的效率得到了有效提高。     

基于物流服务供应链模式的供应商选择研究

针对物流服务供应链中集成商对功能型物流服务提供商的评估选择问题,描述了物流服务的采购流程。通过建立多目标规划的数学模型,引入最大满意度的求解方法,探讨了物流服务集成商对功能型物流服务提供商的选择与优化组合问题。最后通过对算例的分析,证明了模型的有效性和实用性。     

基于收入共享契约的供应链协调研究

主要研究了收入共享契约对供应链的协调。在市场需求不确定下,分别建立了对称信息下与非对称信息下的供应链收入共享契约协调模型,分析了在此模型下供应商与零售商的决策行为,及其参数的约束条件。由此,得到了供应链的最佳利润。最后,通过算例分析,验证了收入共享契约对供应链的协调作用。     

Environmental uncertainty, supply chain relationship quality and performance

Environmental uncertainty is a fact of life in today's supply chains. In this paper we develop a model of environmental uncertainty, supply chain (SC) relationship quality and SC performance. We use data from the electronics sector in Ireland to test our model. Our results provide mixed support for the model, with the moderating role of both demand and supply uncertainty being supported, but technological uncertainty not supported. We reflect on these findings and suggest a research agenda based on our results.     

基于信息共享的供应链双源渠道Stackelberg博弈分析

对于双源渠道供应链系统中零售商和制造商的信息共享问题,引入市场需求不确定信息作为信息共享的重要影响因素,利用Stackelberg主从对策,构建了四个考虑信息共享的双源渠道供应链Stackelberg博弈模型,分析了零售商和制造商在是否分享市场不确定信息和是否运用于商务活动的博弈过程。研究结果显示:零售商和制造商都会尽可能去获取市场需求不确定信息,并且制造商会把此信息充分运用到电子商务市场来提高自己以及供应链整体利益。     

A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms

With the discovery of the COVID-19 vaccine, what has always been worrying the decision-makers is related to the distribution management, the vaccination centers' location, and the inventory control of all types of vaccines. As the COVID-19 vaccine is highly demanded, planning for its fair distribution is a must. University is one of the most densely populated areas in a city, so it is critical to vaccinate university students so that the spread of this virus is curbed. As a result, in the present study, a new stochastic multi-objective, multi-period, and multi-commodity simulation-optimization model has been developed for the COVID-19 vaccine's production, distribution, location, allocation, and inventory control decisions. In this study, the proposed supply chain network includes four echelons of manufacturers, hospitals, vaccination centers, and volunteer vaccine students. Vaccine manufacturers send the vaccines to the vaccination centers and hospitals after production. The students with a history of special diseases such as heart disease, corticosteroids, blood clots, etc. are vaccinated in hospitals because of accessing more medical care, and the rest of the students are vaccinated in the vaccination centers. Then, a system dynamic structure of the prevalence of COVID -19 in universities is developed and the vaccine demand is estimated using simulation, in which the demand enters the mathematical model as a given stochastic parameter. Thus, the model pursues some goals, namely, to minimize supply chain costs, maximize student desirability for vaccination, and maximize justice in vaccine distribution. To solve the proposed model, Variable Neighborhood Search (VNS) and Whale Optimization Algorithm (WOA) algorithms are used. In terms of novelties, the most important novelties in the simulation model are considering the virtual education and exerted quarantine effect on estimating the number of the vaccines. In terms of the mathematical model, one of the remarkable contributions is paying attention to social distancing while receiving the injection and the possibility of the injection during working and non-working hours, and regarding the novelties in the solution methodology, a new heuristic method based on a meta-heuristic algorithm called Modified WOA with VNS (MVWOA) is developed. In terms of the performance metrics and the CPU time, the MOWOA is discovered with a superior performance than other given algorithms. Moreover, regarding the data, a case study related to the COVID-19 pandemic period in Tehran/Iran is provided to validate the proposed algorithm. The outcomes indicate that with the demand increase, the costs increase sharply while the vaccination desirability for students decreases with a slight slope.     

A grid-based collaborative supply chain with multi-product multi-period production–distribution

A collaborative supply chain seeks to coordinate its members to produce and distribute products along the chain for minimum overall costs to satisfy customer demand. An integrated multi-period, multi-product and multi-stage supply chain model is developed. A collaborative supply chain grid (CSCG) system gathers necessary information on operations and resources from members of the supply chain. It then guides the members on ordering decisions for a minimum overall cost, while continuously re-directing them as new data becomes available. An actual CSCG system has been implemented in a collaborative auto industry with diverse and dynamic membership, yielding an approximately 26% reduction in total supply chain costs.     

Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19

As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.     

基于Floyd算法的供应链网络路径研究

21世纪的竞争不是企业与企业之间的竞争，而是供应链之间的竞争。运输系统是供应链中一个重要的子系统，运输路径的选择是否合理直接影响供应链的运作成本、速度和效益。文中考虑在拉动式生产的背景下，当销售商提出需求订单的时候，最快的选择相应的制造商和配送中心，使得供应链的总运输成本最小化的问题，利用floyd算法研究三级供应链的运作模式，理论证明其可行性，并通过算例分析验证了此优化算法使得供应链运输成本最小化。