基于DEA的二阶段网络系统的固定成本分摊方法

结合DEA和博弈的思想研究二阶段网络系统的固定成本分摊问题,将分摊成本作为新的投入,可以证明存在某种分摊使DMU整体效率达到最优,在此基础上考虑各个DMU之间以及DMU内部之间的博弈,首先建立讨价还价乘积最大化模型,求出各DMU唯一的分摊解,然后建立DMU子系统之间的讨价还价模型,给出子系统的分摊解,最终的分摊方案满足系统效率和子系统效率为1,与现有的方法相比具有一定的优势.     

多人合作费用分摊的多目标规划解法

考虑核心是空集的情况，针对核心法的适用范围和局限性，建立了一种多人合作费用分摊的多目标规划模型，给出了求解费用分摊方案的两阶段算法。最后通过一个实例说明了该方法的可行性。     

展厅效应下双渠道制造商服务成本分摊与收益共享契约选择策略

基于对称信息框架,针对一个传统零售商和一个双渠道制造商组成的供应链系统,构建零售商实体店售前服务存在展厅效应下,制造商服务成本分摊与网络渠道收益共享契约选择策略模型.研究发现,制造商通过对上述两种不同契约的有效设计都能刺激零售商的服务动机,提高其服务水平,并进一步增加供应链各主体渠道需求和收益.对比两种不同的契约模式,发现供应链存在展厅效应时,服务成本分摊契约模式下零售商和双渠道制造商能够获得更高的收益,此时各主体更偏爱选择服务成本分摊契约模式.     

基于前景理论的两阶段DEA交叉效率评价模型

针对两阶段系统交叉效率值不唯一的问题,现有解决方法大多假设决策者完全理性,忽略了决策者心理因素对评价过程的影响。本文构建以平均值为新的参考点,分别在集中和分散决策环境下建立基于前景理论的两阶段DEA交叉效率评价模型,通过实际算例验证模型的可行性和有效性。     

二元性能相关多阶段退化系统可靠性建模与分析

针对具有两个相关性能指标的随机退化系统,考虑退化过程的多阶段特征,采用Copula函数描述性能指标间的相关关系,建立多阶段二元相关Wiener过程退化模型。给出系统可靠度的解析表达式,提出蒙特卡洛方法模拟系统可靠度的流程步骤,得到系统可靠度的数值解,并验证了解析解的正确性。最后,通过数值算例验证模型的有效性。     

基于时间的供应链物流运作能力计划模型研究

本文考察了在基于时间的多阶供应链环境下的物流运作能力计划,主要的系统成本是物流要素能力固定成本和库存成本.为了决定最优的稳定周期性的物流运作能力计划,提出了一般性的整数非线性规划公式.强调了在这样的系统运作中的开始时间的关键作用,报告了在确保在任何阶段没有缺货条件下的物流能力决策的一些有价值的结论.     

基于在险价值的物流网络规划模糊两阶段模型与精确求解方法

针对物流网络规划问题中顾客需求和运输成本的不确定性,使用在险价值量化投资风险,建立了以投资损失的在险价值最小化为目标的模糊两阶段物流网络规划模型。对于模型中不确定参数均为规则模糊数的这一类模糊两阶段规划模型,本文通过理论分析和证明将其转化为等价的确定一阶段规划模型进行求解,从而将无穷维的优化问题转化为有限维的经典优化问题,降低了计算难度且得到了模型的精确解。不同规模的数值实验证实了所提出模型及其求解方法的有效性。     

带有时间窗的生鲜物流配送路径优化研究

随着生鲜消费的日益增多,生鲜物流配送也面临着如何在快速安全的条件下满足人们对生鲜的需求,使消费者在最短的时间得到最新鲜产品的现实问题,提出带有时间窗的生鲜物流配送车辆路径问题.充分考虑配送距离、车辆固定成本、生鲜损耗等多种因素,设计以配送损耗为可变成本和车辆启动费用为固定成本之和最小的优化目标,建立带有时间窗生鲜损耗的配送模型.针对模型的特征,设计自适应遗传算法求解该模型.最后,结合仿真算例来验证模型与算法的有效性.     

具有阶段结构的竞争系统的持久性和稳定性

研究带有时滞和成长阶段的两种群竞争模型,第一个种群分成年和幼年两个阶段,第二个种群不具有阶段结构.本文证明了系统正解的有界性;利用比较原理得到了系统永久生存的充分条件;通过构造Lyapunov函数得到了系统全局渐近稳定的充分条件.     

考虑消费者低碳偏好的供应链企业减排成本分摊比较研究

在低碳环境下,研究了单一制造商和单一零售商组成的两级低碳供应链成本分摊决策问题。考虑消费者具有环保意识,因此在购买产品时会考虑产品的碳排放。分别讨论了零售商参与减排成本分摊契约和双方Nash讨价还价成本分摊契约两种形式对碳减排、产品定价和整个供应链及其成员收益的影响。研究发现,在两种契约形式中,零售商都可以实现与制造商共同降低碳排放的目标,从而促进制造商提高碳减排率,提高供应链利润。但是基于Nash讨价还价方式的减排成本分摊契约的供应链绩效高于零售商成本分摊契约下的供应链绩效。同时消费者低碳偏好增加可以激励零售商更多地分摊减排成本,在此基础上,利用Nash讨价还价模型确定了零售商为制造商提供减排成本分摊的比例范围及其最优解。最后,通过数值分析验证了减排因子对不同决策结构的定价、零售商减排分摊比例、供应链利润等决策的影响。     

The fair allocation of common fixed cost or revenue using DEA concept

The common fixed cost or revenue distribution amongst decision making units (briefly, DMUs) in an equitable way is one of the problems that can be solved by data envelopment analysis (DEA) concept. The motivation of this paper is common fixed cost or revenue allocation based on following three principles: First, allocation must be directly proportional to the elements (inputs and outputs) that are directly proportional to imposed common fixed cost or to obtained common fixed revenue. Second, allocation must be inversely proportional to the elements that are inversely proportional to common fixed cost or revenue. Finally, the elements that have no effect on common fixed cost or revenue must have no effect on allocation as well.     

Stratified two-stage sampling in domains: Sample allocation between domains, strata, and sampling stages

In the paper, formulae for optimum sample allocation between domains, strata in the domains, and sampling stages are presented for stratified two-stage sampling in domains under fixed sample size of SSUs from PSUs.     

Allocating the fixed cost as a complement of other cost inputs: A DEA approach

In cost allocation problem, traditional DEA approaches allocate the fixed cost among a group of decision making units (DMUs), and treat the allocated cost as an extra input of each DMU. If costs except for the fixed cost are regarded as inputs in the cost allocation problem, then it is obvious that the fixed cost is a complement of other inputs rather than an extra independent input. Therefore it is necessary to combine the allocated cost with other cost measures in cost allocation problem. Based on this observation, this paper investigates the relationship between the allocated cost and the DEA efficiency score and develops a DEA-based approach to allocate the fixed cost among various DMUs. An example of allocating advertising expenditure between a car manufacturer and its dealers is presented to illustrate the method proposed in this paper.     

A cost allocation problem arising in hub–spoke network systems

This paper studies a cost allocation problem arising from hub–spoke network systems. When a large-scale network is to be constructed jointly by several agents, both the optimal network design and the fair allocation of its cost are essential issues. We formulate this problem as a cooperative game and analyze the core allocation, which is a widely used solution concept. The core of this game is not necessarily non-empty as shown by an example. A reasonable scheme is to allocate the cost proportional to the flow that an agent generates. We show that, if the demand across the system has a block structure and the fixed cost is high, this cost allocation scheme belongs to the core. Numerical experiments are given with real telecommunication traffic data in order to illustrate the usefulness of our analytical findings.     

Proposing a method for fixed cost allocation using DEA based on the efficiency invariance and common set of weights principles

One of the applications of data envelopment analysis is fixed costs allocation among homogenous decision making units. In this paper, we first prove that Beasley’s method (Eur J Oper Res 147(1):198–216, 2003), whose infeasibility has been claimed by Amirteimoori and Kordrostami (Appl Math Comput 171(1):136–151, 2005), always has a feasible solution and the efficiency invariance principle does not necessarily satisfy in Amirteimoori and Kordrostami’s method (Appl Math Comput 171(1):136–151, 2005). Hence, we present two equitable methods for fixed cost allocation based on the efficiency invariance and common set of weights principles such that, if possible, they help meet these two principles. In the first method, the costs are allocated to DMU in such a way that the efficiency score of DMUs does not change, and simultaneously this allocation has the minimum distance from the allocation that has been obtained with a common set of weights. However, in the second method, the costs are allocated in such a way that input and output of all units have a common set of weights and it has the minimum distance from the allocation that satisfies the efficiency invariance principle. Moreover, both methods, consider the satisfaction of each unit of the allocated cost. Finally, the proposed method is illustrated by two real world examples.     

Cost Comparisons for Out-of-Phase Inventory Models

Inventory costs for a fixed time period have traditionally been determined by allocating total costs per cycle uniformly throughout that cycle as well as any partial cycles. This procedure for cost allocation has led to the solution of numerous inventory problems, most notable of which is the anticipated price-increase model. When comparing two out-of-phase inventory models, if costs are accounted for when they occur over a fixed planning horizon, inventory policies should be changed to reflect the impact of this different cost-allocation procedure. For the anticipated price-increase model, the ‘optimal’ order quantity as well as the implied savings in inventory costs will be different when cost models are developed based on these different cost-allocation methods. If the objective is to maximize over a fixed planning horizon the actual savings in inventory costs as they occur, the cost models presented here should be used.     

An Approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 0-1 Programming

We present a two-stage stochastic 0-1 modeling and a related algorithmic approach for Supply Chain Management under uncertainty, whose goal consists of determining the production topology, plant sizing, product selection, product allocation among plants and vendor selection for raw materials. The objective is the maximization of the expected benefit given by the product net profit over the time horizon minus the investment depreciation and operations costs. The main uncertain parameters are the product net price and demand, the raw material supply cost and the production cost. The first stage is included by the strategic decisions. The second stage is included by the tactical decisions. A tight 0-1 model for the deterministic version is presented. A splitting variable mathematical representation via scenario is presented for the stochastic version of the model. A two-stage version of a Branch and Fix Coordination (BFC) algorithmic approach is proposed for stochastic 0-1 program solving, and some computational experience is reported for cases with dozens of thousands of constraints and continuous variables and hundreds of 0-1 variables.     

Fixed cost and resource allocation based on DEA cross-efficiency

In many managerial applications, situations frequently occur when a fixed cost is used in constructing the common platform of an organization, and needs to be shared by all related entities, or decision making units (DMUs). It is of vital importance to allocate such a cost across DMUs where there is competition for resources. Data envelopment analysis (DEA) has been successfully used in cost and resource allocation problems. Whether it is a cost or resource allocation issue, one needs to consider both the competitive and cooperative situation existing among DMUs in addition to maintaining or improving efficiency. The current paper uses the cross-efficiency concept in DEA to approach cost and resource allocation problems. Because DEA cross-efficiency uses the concept of peer appraisal, it is a very reasonable and appropriate mechanism for allocating a shared resource/cost. It is shown that our proposed iterative approach is always feasible, and ensures that all DMUs become efficient after the fixed cost is allocated as an additional input measure. The cross-efficiency DEA-based iterative method is further extended into a resource-allocation setting to achieve maximization in the aggregated output change by distributing available resources. Such allocations for fixed costs and resources are more acceptable to the players involved, because the allocation results are jointly determined by all DMUs rather than a specific one. The proposed approaches are demonstrated using an existing data set that has been applied in similar studies.