ANALYSIS OF A PRODUCTION-INVENTORY MODEL WITH ERLANGIAN DEMAND ARRIVAL PROCESS

We study a production-inventory system having a machine, a storage facility. The demand for the product is governed by an Erlangian demand arrival process, where demand sizes are independent and identically distributed random variables. A two-criticalnumber policy (m, M) is used to control a machine‘s setups and shutdowns, namely, a machine is shut down whenever the inventory level reaches M, and resumes operating only when the inventory level falls below the critical number m(m ≤ M). We obtain the steady state distribution of the inventory process and some performance measures of the process.     

蓄电‒氢储混合储能系统的配电网双层优化

在“双碳”目标与清洁能源氢能利用的背景下,针对配电网配置电化学储能和氢储能系统构成混合储能系统提升电能质量的需求,建立了混合储能系统双层优化模型,上层选址定容模型综合考虑投资成本、网损成本和电压偏移,下层优化运行模型考虑混合储能系统的运行成本,并引入电压稳定性指标进行评价。求解过程中利用灵敏度分析对选址可行域进行了降维,并提出一种改进的小生境多目标粒子群算法,将小生境处理机制与外部档案选取技术、混沌变异技术相结合。利用接入新能源的IEEE33节点系统进行算例仿真。结果表明：混合储能系统容量与接入点的优化配置,可以提高系统经济性、降低全网有功网损、减小电压偏移和提高电压稳定性。     

考虑同时取送货需求带模糊容量限制的在线设施动态选址问题

共享环境下配送设施作为在线资源，其空闲容量受线下负荷影响而呈现动态可得性与波动模糊性.同时，在线资源的引入也使得城市配送中的设施选址问题由单周期决策模型转变为多周期动态决策.为此，本文构建了考虑同时取送货需求带模糊容量约束的在线设施动态选址模型，改进了五角模糊数隶属度函数用以表示模糊容量约束.在此基础上，将禁忌搜索与自适应大规模邻域搜索算法相结合，提出了邻域生成算子和新解接受准则改进方案.最后，算例结果表明该改进方案能有效提升算法全局搜索能力，降低企业在线设施动态选址成本.     

Performance Analyses of Production Inventory Systems Considering Service Time and Product Returns of Online Shopping

This paper considers a production inventory system with service time and product returns,dependent on the characteristics of online shopping behaviors. The customers arrive according to a Poisson process, in which the arriving customer requires an item in the inventory, as well as a server service that the service time is exponentially distributed. Moreover, there is one production facility to produce items according to(s, S) policy, and the production time also follows an exponential distribution. In the beginning, the stationary joint distribution is obtained in the product form for the queue length and the on-hand inventory level. Additionally, the resulting distribution is applied to derive some performance measures, which can be adopted to numerically calculate the optimal(s, S)policy. Finally, numerical analyses are processed to investigate the effects of some system parameters on the performance measures. The numerical results clearly indicate that the product return rate has a significantly monotonic effect on the optimal policy and optimal cost.     

A comparative study of the inventory system with service facility and postponed demands

In this article, we present a continuous review （s,S） inventory system with a service facility consisting of finite buffer （capacity N ） and a single server. The customers arrive according to a Poisson process. The individual customer＇s unit demand is satisfied after a random time of service, which is assumed to be exponential. When the inventory level drops to s＇an order for Q（= S-s） items is placed. The lead time of reorder is assumed to be exponential distribution. An arriving customer, who finds the buffer is full, enters into the pool of infinite size or leaves the system according to a Bernolli trial. At the time of service completion, if the buffer size drops to a preassigned level L （1 〈 L 〈 N） or below and the inventory level is above s, we select the customers from the pool according to two different policy ： in first policy, with probability p （0 〈 p 〈 1） we select the customer from the head of the pool and we place the customer at the end of the buffer; in the second policy, with p （0 〈 p 〈 1） the customer from the pool is transferred to the buffer for immediate service and after completion of his service we provide service to the customer who is in the buffer with probability one. If at a service completion epoch the buffer turns out to be empty, there is at least one customer in the pool and the inventory level is positive, then the one ahead of all waiting in the pool gets transferred to the buffer, and his service starts immediately. The joint probability distribution of the number of customers in the pool, number of customers in the buffer and the inventory level is obtained in the steady-state case. Various stationary system performance measures are computed and total expected cost rate is calculated. A comparative result of two models is illustrate numerically.     

在线销售下基于自由分布的订货与配送能力扩张决策

针对一个拥有一定配送能力的在线销售企业，研究其在不确定需求下的订货和配送能力扩张决策问题。在构建在线销售企业的期望利润函数基础上，考虑仅知需求均值和方差信息，给出了基于自由分布的在线销售企业利润鲁棒优化模型。针对所建模型，采用对偶理论将其转化为易于求解的数学规划，给出了相应的订货和配送能力扩张策略，并对相关系统参数进行了灵敏度分析。最后，通过数值计算验证了所建模型在应对需求不确定性方面的有效性。结果表明：在线销售企业的最优订货量随自身原有配送能力的增加而增加，最优配送能力增量随自身原有配送能力的增加而降低。当在线销售企业自身拥有配送能力时，其在不同分布下得到的期望利润一般均高于无配送能力的情形。特别地，将基于文中模型的鲁棒策略与需求服从正态和均匀分布下得到的最优策略及期望利润进行比较分析发现，鲁棒策略下期望利润与已知需求分布时的最优期望利润相差较小，表明基于文中方法得到的策略具有良好的鲁棒性。     

Outsourcing decision and order policy with forecast updating under capacity reservation contract

This paper investigates an optimal decision problem in a single-period, two-stage supply chain with capacity reservation contract. At the beginning of the planning horizon, the retailer, who faces stochastic demand, reserves future capacity according to his forecasting of the demand. The supplier then constructs capacity. At the beginning of the selling season, the retailer updates the demand forecasting and places an order. When the retailer＇s demand is greater than the supplier＇s capacity, the supplier can meet the retailer＇s demand by outsourcing. We analyze the optimal decision of each player in both centralized and decentralized systems. Furthermore, under the case in which demand follows a uniform distribution, we obtain the closed-form optimal strategies of each player for both centralized and decentralized systems and conduct numerical studies to reveal additional conclusions. The numerical studies show that the optimal reservation capacity for the retailer and the optimal constructing capacity for the supplier in the decentralized system are both less than the optimal constructing capacity in the centralized system. Furthermore, we also find that the profit loss due to decentralization always exists and increases in indeterminacy.     

Coordinating production and recycling decisions with stochastic demand and return

In this paper, the joint production and recycling problem is investigated for a hybrid manufacturing and remanufacturing system where brand-new products are produced in the manufacturing plant and recycled products are remanufactured into as-new products in the remanufacturing facility. Both the brand-new products and remanufactured products are used to satisfy customer demands. Returns of used products that are recycled from customers are assumed to be stochastic and nonlinearly price-dependent. A mathematical model is proposed to maximize the overall profit of the system through simultaneously optimizing the production and recycling decisions, subject to two capacity constraints — the manufacturing capacity and the remanufacturing capacity. Based on Lagrangian relaxation method, subgradient algorithm and heuristic algorithm, a solution approach is developed to solve the problem. A representative example is presented to illustrate the system, and managerial analysis indicates that the uncertainties in demand and return have much influence on the production and recycling policy. In addition, twenty randomly produced examples are solved, and computational results show that the solution approach can obtain very good solutions for all examples in reasonable time.     

COORDINATING PRODUCTION AND RECYCLING DECISIONS WITH STOCHASTIC DEMAND AND RETUR

In this paper, the joint production and recycling problem is investigated for a hybrid manufacturing and remanufacturing system where brand-new products are produced in the manufacturing plant and recycled products are remanufactured into as-new products in the remanufacturing facility. Both the brand-new products and remanufactured products are used to satisfy customer demands. Returns of used products that are recycled from customers are assumed to be stochastic and nonlinearly price-dependent. A mathematical model is proposed to maximize the overall profit of the system through simultaneously optimizing the production and recycling decisions, subject to two capacity constraints ? the manufacturing capacity and the remanufacturing capacity. Based on Lagrangian relaxation method, subgradient algorithm and heuristic algorithm, a solution approach is developed to solve the problem. A representative example is presented to illustrate the system, and managerial analysis indicates that the uncertainties in demand and return have much influence on the production and recycling policy. In addition, twenty randomly produced examples are solved, and computational results show that the solution approach can obtain very good solutions for all examples in reasonable time.     

B2C物流配送网络双目标模糊选址模型与算法

基于B2C"配送-退换同时"的物流模式及配送业务特点,集成设施选址-分配和路线优化,研究模糊需求下的B2C物流设施选址问题.针对选址-分配的模糊性和静态性、配送的确定性和动态性特征,以物流总费用为主目标函数,以配送中心流通费用、车辆派遣费用、配送费用总和为子目标函数,建立了有配送中心容量静态约束和车辆动态负载量约束的双目标模糊选址模型,设计了嵌入随机算法和禁忌搜索算法的遗传算法求解.选取合适的测试算例验证了算法的有效性,探讨了客户需求模糊区间宽度和商品退换率对物流选址结果和各项费用值的影响.实验结果表明,所设计的算法对解决这类复杂问题合理有效.客户需求模糊区间宽度与车辆利用率和车辆路线总长的波动区间、平均车辆路线总长度、配送费用正相关,且宽度较窄时,选址结果、车辆派遣费用和配送中心流通费用不变,超过一定范围,选址结果、车辆派遣费用和配送中心流通费用改变.商品退换率与流通费用和物流总费用正相关,但不会影响选址结果和其他费用.     

考虑库存损耗的单部件系统可修备件需求模型

本文研究具有库存损耗的单部件系统可修备件需求模型;采用马尔可夫到达过程(Markovian删val pcess,MAP)代替Poisson过程、更新过程等经典随机过程描述备件需求情况.假设库存备件寿命服从指数分布,故障件维修时间服从一般分布,建立了一种描述能力更强的可修备件需求模型;给出了系统的可用度、工作时间周期、停机时间周期等参数和规定条件下的两种备件需求量算法,并给出了数值算例说明所建模型的有效性.     

基于储备容量的交通网络潜在瓶颈路段识别

交通网络潜在瓶颈路段是随着需求变化将来可能会成为瓶颈的路段.准确识别出潜在瓶颈路段并进行优化管理,可以更好地适应未来交通需求的变化.基于交通网络储备容量模型,提出了交通网络潜在瓶颈路段识别方法,并设计了储备容量模型的求解算法.算例分析比较了现状需求和路网可容纳最大需求下的交通均衡流量模式.结果表明,不能简单地根据现状需求去推断潜在瓶颈路段,而论文提出的方法可以识别出路网达到最大需求时的潜在瓶颈路段.此外,利用交通网络储备容量模型设计了一种次要潜在瓶颈路段识别方法,次要潜在瓶颈路段是在优化潜在瓶颈路段之后随需求增长新出现的瓶颈路段.算例表明该识别方法具有一定的适用性.     

考虑企业生产能力储备的应急物资配置随机规划模型

需求的不确定性极大地影响着应急物资配置,易导致物资供应不足或过剩.本文引入企业生产能力差异,考虑受灾地和需求量的不确定性,以经济成本和惩罚成本最小为目标,建立基于情景的应急物资配置两阶段随机规划模型,第1阶段确定不同生产能力企业的布局和实物储备,第2阶段根据情景中受灾地需求量的不同生产并配送物资,应用样本均值逼近方法求解模型.基于2008年雨雪冰冻灾害、汶川地震、华南和中南地区洪灾,以及台风"黑格比"等自然灾害数据,设计情景进行算例分析.结果表明,储备物资生产能力,并适当考虑供给延迟和不足的惩罚,既可大幅减少实物储备,又能降低物资供应不足的风险;过度追求风险最小,需大量增加实物和生产能力储备,易造成资源浪费.     

储能产业发展带来的能源革命及其关键管理科学问题

储能产业的爆发式增长，不是一个独立的产业现象，而是以新能源利用为主要导向的能源生产和消费革命的必然要求，会导致能源供给和消费的系统性变革。在这个系统变革中，有很多管理科学的问题亟待学术界的关注和研究。讨论了多种储能发展的实际应用场景，针对共享储能的集中式储能和产销一体化的分布式储能等新兴发展的储能模式，从可行性的商业模式、中长期的规划设计、以及短期即时的运营管理等角度，提出了机制建设、容量规划、动态定价和优化调度等科学问题。     

A BI-LEVEL FORMULATION AND QUASI-NEWTON ALGORITHM FOR STOCHASTIC EQUILIBRIUM NETWORK DESIGN PROBLEM WITH ELASTIC DEMAND

1 IntroductionThe network design problem (NDP) is to select link improvements or add new links to anexisting network suCh that social welfare is maximized while according for the route choicebehavior of network users and/or changes in demand. Two sets of decision-makers with differedsobjectives are involved in the NDP. The network users individually select their routes suchthat their individual travel costs are minimized, while the planners aim to make the bestnetwork improvements for reduc…     

随机回收和有限能力下逆向供应链定价及协调

综合考虑回收量为回收价格的随机线性函数及回收商或制造商的设施有容量限制，分别针对回收商或制造商租用设施的两种情况，分析了Stackelberg博弈和集中式决策下逆向供应链中回收商的最优回收价格、制造商的最优回购价格及最优设施容量．研究表明，与集中式决策相比Stackelberg博弈情形下的系统效益非最优．然后利用收入一费用共享契约进行逆向供应链的协调．通过算例验证了该契约的有效性，并对回收敏感系数进行了灵敏度分析．     

考虑节点中断和需求波动的可靠供应链网络设计问题

本文研究了一个包含供应点、中转点和需求点的三级可靠供应链网络设计问题(RSCNDP).文章同时考虑了供应端的不确定性(节点中断的可能性)和需求端的不确定性(需求量的波动性),针对这些因素,采用情景的方法描述,并结合p-鲁棒模型的优点提出了一个新的混合整数规划模型.在模型中,α,β两参数分别表示决策者对节点中断和需求波动的风险偏好,优化的目标是在给定的风险偏好下最小化设施的固定成本和产品的运输成本.文章提出了一个由最短增广链法和遗传算法的相结合的混合智能算法来求解该模型,并结合实际进行了相应的数值分析.结论显示决策者的风险偏好对可靠供应链网络的设计会产生重大的影响,而在面临风险时,可靠的供应链网络表现要优于确定型的供应链网络,这些为企业的供应链管理决策提供了理论支撑.     

基于两个不确定参数乘积的鲁棒设施选址模型

传统设施选址往往被看作为确定问题,但实际存在需求、成本、风险等不确定因素,这些不确定因素增加了决策的困难.本文在考虑设施选址中单一不确定因素基础上,同时考虑需求和运输成本两个独立参数的不确定性,且在模型中两者为乘积形式,引入两个budget不确定集合刻画不确定性,建立一个新颖的鲁棒设施选址模型,并将非线性问题转化为易求解的鲁棒等价模型,然后通过CPLEX和MATLAB编程求解.最后,以四川西北部的汶川等13个县市的应急物资临时供应点的选址为例,确定最优的选址分配布局.结果表明,较之运输成本的不确定性,需求的不确定性影响更显著,且需求扰动对选址总成本和选址分配方案有明显的影响.决策者可根据其风险偏好程度,选择恰当的不确定水平参数组合,以获得最优的总成本和选址分配方案.     

“随机”与“择优”——超网络演化的内在驱动力

基于"随机连接"和"择优选择"的演化机制,构建了一个随机-择优混合超网络演化模型。使用Poisson过程理论和连续化方法对模型进行分析,获得超度分布的解析表达式,分析表明网络的稳态平均超度分布服从漂移的幂律分布。该模型可以退化到复杂网络和超网络中的标准模型,具有一定的普适性。通过调节机制系数,模型可以体现混合连接机制。并对3个实证数据进行了分析,该模型能有效刻画不同数据的演化机理。     

Multi-Period Optimal Capacity Strategy Based on Consumer Behavior Involved in Social Media

Social media and consumer behavior are increasingly important in business nowadays. As a new form of advertising, social media do facilitate the increase in demand and bring a challenge to manufactures. While researchers demonstrated that insufficient capacity generates the loss in the process of sales, an opposite conclusion has been obtained that the profit is larger in insufficient capacity.This study investigates this situation of a manufacturer. We develop a multi period model of insufficient capacity concerning with social media and consumer behavior. An calculation of the model indicates that a great change appears in the demand of each period. To ensure the maximum profit, the capacity of each period is computed. And the profit is almost 8 times larger than that we do not consider social media and consumer behavior. We discuss the implications of our findings for both theory and practice.