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.     

Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)

In this study, a stochastic multi-objective mixed-integer mathematical programming is proposed for logistic distribution and evacuation planning during an earthquake. Decisions about the pre- and post-phases of the disaster are considered seamless. The decisions of the pre-disaster phase relate to the location of permanent relief distribution centers and the number of the commodities to be stored. The decisions of the second phase are to determine the optimal location for the establishment of temporary care centers to increase the speed of treating the injured people and the distribution of the commodities at the affected areas. Humanitarian and cost issues are considered in the proposed models through three objective functions. Several sets of constraints are also considered in the proposed model to make it flexible to handle real issues. Demands for food, blood, water, blanket, and tent are assumed to be probabilistic which are related to several complicated factors and modeled using a complicated network in this study. A simulation is setup to generate the probabilistic distribution of demands through several scenarios. The stochastic demands are assumed as inputs for the proposed stochastic multi-objective mixed integer mathematical programming model.The model is transformed to its deterministic equivalent using chance constraint programming approach. The equivalent deterministic model is solved using an efficient epsilon-constraint approach and an evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II). First several illustrative numerical examples are solved using both solution procedures. The performance of solution procedures is compared and the most efficient solution procedure, i.e., NSGA-II, is used to handle the case study of Tehran earthquake. The results are promising and show that the proposed model and the solution approach can handle the real case study in an efficient way.     

Locating a Biorefinery in Northern Spain: Decision Making and Economic Consequences

Biofuels are emerging as a prominent renewable and sustainable energy sources in developed countries. In this sense, this paper presents a case study in which a biorefinery has to be sited in Northern Spain. Thus, the strategic decision of locating such a facility is deeply investigated through strategic policy evaluation. Then, tactical decisions ranging from purchasing and transportation policies to storage protocols are carried out. Only local and limited biomass can be harvested for supplying the biorefinery through a heterogeneous vehicle fleet. Moreover two different and mutually exclusive storage strategies are evaluated: direct supply from crops to biorefinery and using intermediate-collectors. Additionally, crop exploitation factors and biorefinery sizes are used to generate several scenarios in which the strategic decision of location as well as all the tactic decisions are made. Some mixed integer linear programming models are proposed to figure out all relevant decision problems.The results suggest that the Northwest study area as the best option to locate the biorefinery and recommend the intermediate-collector storage strategy. Moreover, the key information about critical biomass, crops and times are also provided.     

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.     

基于网络均衡模型的仓库规划研究

网络均衡模型作为研究供应链的一种新的工具应用日益广泛.本文以供应链均衡模型为基础,利用网络的均衡流量建立每个仓库的决策模型,从而解决整个供应链仓库规划问题,综合提出一种新的解决仓库问题的方法,最后给出一个数值算例.通过利用网络均衡模型可以得到量化的仓库容量的解,同时又考虑自建和租赁两种情况,因此方法有很强的实用性.     

Vehicle flow formulation for two-echelon time-constrained vehicle routing problem

Two-echelon routing problems, including variants such as the two-echelon vehicle routing problem (2E-VRP) and the two-echelon location routing problem (2E-LRP), involve assignment and location decisions. However, the two-echelon time-constrained vehicle routing problem (2E-TVRP) that caters to from-linehaul-to-delivery practices does not involve assignment decisions. This routing problem variant for networks with two echelons has not yet attracted enough research interest. Localized or long-distance services suffer from the lack of the assignment decisions between satellites and customers. Therefore, the 2E-TVRP, rather than using assignment decisions, adopts time constraints to decide the routes on each of the two interacting echelons: large-capacity vehicles transport cargoes among satellites on the first echelon, and small-capacity vehicles deliver cargoes from satellites to customers on the second echelon. This study introduces a mixed integer linear programming model for the 2E-TVRP and proposes a heuristic algorithm that incorporates the savings algorithm followed by a variable neighborhood search phase. Illustrative examples are used to test the mathematical formulation and the heuristic and a case study is used to demonstrate that the heuristic can effectively solve realistic-size instances of the 2E-TVRP.     

A novel option contract integrated with supplier selection and inventory prepositioning for humanitarian relief supply chains

This paper proposes a novel two-period option contract integrated with supplier selection and inventory prepositioning. A two-stage scenario-based mixed possibilistic-stochastic programming model is developed to cope with various uncertainties. The first stage's decisions include supplier selection and capacity reservation level at each supplier/period and the level of inventory prepositioning. Furthermore, decisions regarding the time and exercised amount are made in the second stage. Applicability of the model is validated through a real case study. Finally, several sensitivity analyses are conducted to examine the effect of important parameters on the solutions to gain useful managerial insights.     

低碳经济视角下闭环供应链网络优化研究

在低碳经济背景下,研究具有多个供应商、多个制造商、多个消费者市场以及多个回收中心的闭环供应链网络。综合考虑碳减排和废旧产品回收因素,以总运营成本最小化为优化目标,建立混合整数规划模型,从而解决网络节点的建设、节点间运量、运输方式选择、减排技术投资、回收中心选址以及回收中心环保水平等供应链网络资源配置问题。通过lingo12软件对低碳闭环供应链网络进行优化模拟,得到对生产实践有指导作用的结论。     

物流配送及回收混合网络的优化模型

为提高管理物流网络的效率，基于混合整数规划的方法，建立了单产品、单周期、有能力限制的产品配送及回收混合物流网络的双层优化设计模型。双层模型从配送、回收方及客户两方的利益考虑，使得在两方相互协调的情况下最大限度地获得能使两方都满意的选址方案及配送、回收的物流分配。最后，通过具体算例验证了该模型的有效性。     

考虑物料、产能和客户优先级三重约束的订单允诺管理问题研究

客户订单允诺管理是在市场全球化的背景下．综合考虑各种资源有限性,对客户的订单给予准确的答复．因而具有很好的应用前号。文章对现有客户订单允诺模型做出改进,在供应链资源即物料和产能双重限制下,加入客户优先级约束,通过建立模型求解,给出合理的客户订单允诺机制。文中不仅定性分析客户优先级．而且将客户优先级量化为客户价值来表示,使该订单允诺管理机制更为有效．更有利于企业的长期可持续发展。     

军用仓库选址模型研究

分析了军用仓库选址的影响因素,满足军事需求的同时达到经济性要求,运用判断矩阵科学计算出军用仓库选址的一般评价指标的权重,引入供应链敏捷度作为约束条件,运用线性规划方法解决经济性要求,构建了军用仓库综合选址模型,对模型进行求解计算。     

Data center supply chain configuration design: A two-stage decision approach

Data centers are special-purpose facilities that enable customers to perform cloud based real-time online transactions and rigorous computing operations. Service levels of data center facilities are characterized by response time between query and action, which to a large extent depends on data center location and data travel distance. Another aspect of service level is resource up-time availability, which is determined by data center configuration. Data center location and configuration decisions are, therefore, of great significance to ensure uninterrupted operations in customers of manufacturing and service industries relying on cloud-based computing resources. In this study, following a grid-based location approach, we present two mixed integer linear programming models for capacitated single-source data center location-allocation problems. The first model provides optimal locations, capacities and configurations of data centers, and allocation of demands to open facilities when there is no existing facilities in the region. Our second model considers the decision problem of meeting new demand when the existing demand is met by the already opened facilities. We term these newly arrived demand as replication demand, which results either from emergence of new users of existing customers at distant locations in the future, or as a means of increasing data resilience by creating data replication as a backup. To solve the decision problem for meeting primary and replication demand optimally, we propose a two-stage decision algorithm. The algorithm provides optimal locations, capacities and configurations for new data centers, capacity addition decisions to the existing facilities and subsequent allocation of demands. Both models and solution algorithm are implemented using AMPL programming language and solved with CPLEX solver. The models are found to be scalable and capable to provide high quality solutions in reasonable time.     

基于免疫算法的后方仓库选址建模与仿真

针对当前陆军装备维修器材供应链后方仓库供应网络布局不合理、保障效益低等问题,建立后方仓库选址非线性规划模型,设计免疫算法仿真求解。以NB战区为例,验证了模型和算法的有效性,得到后方仓库最佳位置和供应网络,为全面、科学做出后方仓库选址决策供定量分析数据支撑和可视化图形参考,并与粒子群算法和遗传算法进行对比分析。     

不确定需求条件下生鲜类农产品供应链网络优化设计研究

国家一直在战略层面大力扶植农业发展,消费者对于农产品尤其是生鲜类产品品质的高标准、严要求,使得供应链网络的规划显得更为重要。文章引入时间价格因子并基于客户需求的不确定性以成本最低为目标,构建了含模糊参数的单源、单一产品、多供应商、多阶段、多客户生鲜类农产品供应链网络模糊规划模型。通过模糊数学相关理论将模型中含有不确定参数的目标函数与约束条件分别利用定理将其转化为清晰等价类。最后,应用Lingo软件求解算例,得出设施的选址以及资源合理分配的解决方案。     

对立交桥选址建模的初步探讨

本文提出了立交桥选址问题的0－1规划模型。该模型以平均车速和对路网交通均衡的作用为目标函数，考虑了立交桥选址的各项约束条件。考虑由计算机实现隐枚举法求解。在解的过程中该规模规划与交通流量分配模型不断相互作用。     

Optimizing the credit term decisions in supply chain finance

We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting.     

国际集装箱海铁联运计划优化

集装箱海铁联运具有经济、安全、效率快、运量大等突出优势,是未来集装箱多式联运的重要模式,合理优化集装箱海铁联运计划对于我国国际贸易的发展具有十分重要的意义。文中提出一个海铁联运的计划优化模型,目标函数为总成本最低。文中最后使用真实数据配合Lingo软件求解了此混合整数规划模型并得出结论。     

基于二层规划可直接利用品逆向物流网络优化

在可直接再利用品逆向物流网络现有研究的基础上,提出了一种既考虑了运输费用和固定投资费用之间的平衡（上层目标）,又考虑了客户的自由选址行为（下层目标）的二层规划模型,并给出了具体解法,进行了数值分析,得到了满意的结果。     

基于二层规划的选址库存路径问题研究

针对一个生产基地,多个配送中心和多个零售商的供应链二级分销网络,根据物流配送决策的实际过程,利用二层规划方法建立了一个选址库存路径问题模型,其中上层规划为配送中心选址模型,下层规划表示客户选择最佳配送中心、库存及路径问题,设计了双层模拟退火算法求解该模型,最后利用不同问题规模的算例将该算法与标准的模拟退火算法进行对比,结果证实改进算法的有效性。     

一种考虑供应商转换成本的多期供应商选择模型

为解决供应链中多期多种产品的供应商选择问题,对现有的供应商选择模型进行了改进,综合考虑供应商供应能力、交货提前期等限制,以及供应商转换成本、交易成本和批量库存成本对供应商选择方案的影响,建立了混合整数规划模型,以确定每个时期每种产品的最优供应商选择方案及供应量,并通过实际算例验证了模型的有效性。