两阶段模糊生产计划期望值模型

在现实的生产系统中,生产计划问题常常是-个确定的线性规划问题.但是,在许多的实际情况中,由于生产系统中不确定性因素的影响,带有常系数的线性规划模型不能合理地描述现实的决策环境.为了准确有效地描述生产决策环境,本文提出一类新的带有模糊参数的两阶段生产计划期望值模型并且讨论模型的一些基本性质.然后,讨论补偿函数的逼近并且设计-个基了:逼近方法、神经网络和遗传算法的启发式算法来求解这个两阶段模糊生产计划模型.最后,给出一个数值例子来表明所设计算法的可行性和有效性.     

带有模糊参数的运输期望值模型

基于可信性理论,将提出一类带有模糊参数的运输期望值模型.然后,讨论模糊运输期望值模型的基本性质.最后,给出一个数值例子来表明所设计模型的实用性.     

基于可信性理论的两阶段生产计划模型

为了准确有效地处理农业生产中的不确定性因素,基于可信性理论和两阶段模糊优化方法提出一类新的带有最小风险准则的两阶段模糊农业生产计划模型.然后,讨论可信性函数的逼近方法并且设计一个基于逼近方法、神经网络和模拟退火的启发式算法来求解这个两阶段模糊农业生产计划最小风险模型.最后,给出一个数值例子来表明所设计算法的可行性和有效性.     

带有模糊参数的农业生产计划模型

在现实的生产系统中, 由于材料价格, 产品价格, 市场需求以及劳动者能力等不确定因素的影响, 生产计划问题常常是一个不确定规划问题. 因此, 带有常系数的生产计划模型不能准确有效的描述生产决策环境. 基于可信性理论, 本文将提出一类新的带有模糊参数的生产计划模型. 然后, 我们讨论了可信性函数的逼近并且设计一个基于逼近方法、神经网络和遗传算法的启发式算法来求解这个模糊生产计划问题. 最后, 给出了一个数值例子来表明所设计算法的可行性和有效性.     

两阶段模糊运输期望值模型及其逼近方法

基于可信性理论和两阶段模糊优化方法,提出一类带有模糊参数的两阶段运输期望值模型.由于提出运输问题包含带有无限支撑的模糊变量系数,因此它是一个无限堆的优化问题.然后,讨论两阶段模糊运输期望值问题的逼近方法并且将逼近方法嵌套到遗传算法中产生一个基于遗传算法的逼近方法求解提出的两阶段模糊运输期望值问题.最后,给出一个数值例子...     

基于可信性理论的运输机会约束模型

基于可信性理论,将提出一类带有模糊参数的运输计划机会约束模型.然后,讨论可信性函数的逼近方法并且设计一个基于逼近方法、神经网络和遗传算法的启发式算法来求解这个模糊运输计划机会约束模型.最后,给出一个数值例子来表明所设计算法的实用性和有效性.     

带有模糊约束的房地产投资随机期望值模型

基于可信性理论,提出一类新的带有模糊约束的房地产投资随机期望值模型来处理房地产经济中的不确定性信息.另一方面,通过目标函数和可信性函数的一些性质将提出的房地产投资问题转化为一个等价的线性形式,从而可以利用经典的线性规划算法进行求解.最后,给出一个房地产投资问题的实例并通过Lindo软件进行求解.     

2-型模糊环境下的能源系统优化

基于模糊可能性理论,建立2-型模糊环境下的能源分配优化模型,其中各种类型能源的成本用2-型模糊变量刻画.用均值简约方法简约2-型模糊成本,建立广义期望值意义下的模糊能源分配优化模型.当成本用相互独立的三角2-型模糊变量刻画时,所建立的模糊能源分配优化模型可以转化为等价的参数线性规划.最后提供一个数值例子表明建模思想.     

带有方案偏好关系的模糊多属性决策方法

针对属性值为一般模糊变量、属性权重信息完全未知但已知方案偏好关系的模糊多属性决策问题，给出了决策方法。首先通过求解一个模糊期望值模型来确定属性的权重，然后基于简单加权平均法则来计算各方案的模糊综合评价值,再根据比较模糊变量大小的期望值方法来对方案进行排序,最后给出一个算例。     

带有方案优先序的模糊多属性决策方法

针对属性值为模糊变量,属性权重完全未知但已知方案优先序的模糊多属性决策问题给出一种新的决策方法.该方法通过建立一个线性目标规划模型来确定属性的权重,再基于简单加权平均法则来计算各方案的模糊综合属性值,然后根据比较模糊变量大小的期望值方法对方案进行排序.最后给出了应用实例.     

具有惩罚因子的模糊集约生产计划

研究基于模糊环境下的集约生产计划问题,并设计了带有惩罚因子的模糊优化模型,以实现生产费用和惩罚费用之和最小.通过模糊变量和模糊等式定义的描述,简化了模型,并给出机会约束规划方法进行模型求解的整体步骤.通过仿真结果和灵敏度分析,表明模型和方法的有效性,并为决策者在模糊环境下的决策提供支持.     

A stochastic programming approach for planning horizons of infinite horizon capacity planning problems

Planning horizon is a key issue in production planning. Different from previous approaches based on Markov Decision Processes, we study the planning horizon of capacity planning problems within the framework of stochastic programming. We first consider an infinite horizon stochastic capacity planning model involving a single resource, linear cost structure, and discrete distributions for general stochastic cost and demand data (non-Markovian and non-stationary). We give sufficient conditions for the existence of an optimal solution. Furthermore, we study the monotonicity property of the finite horizon approximation of the original problem. We show that, the optimal objective value and solution of the finite horizon approximation problem will converge to the optimal objective value and solution of the infinite horizon problem, when the time horizon goes to infinity. These convergence results, together with the integrality of decision variables, imply the existence of a planning horizon. We also develop a useful formula to calculate an upper bound on the planning horizon. Then by decomposition, we show the existence of a planning horizon for a class of very general stochastic capacity planning problems, which have complicated decision structure.     

Two-stage fuzzy production planning expected value model and its approximation method

This work develops a novel two-stage fuzzy optimization method for solving the multi-product multi-period (MPMP) production planning problem, in which the market demands and some of the inventory costs are assumed to be uncertainty and characterized by fuzzy variables with known possibility distributions. Some basic properties about the MPMP production planning problem are discussed. Since the fuzzy market demands and inventory costs usually have infinite supports, the proposed two-stage fuzzy MPMP production planning problem is an infinite-dimensional optimization problem that cannot be solved directly by conventional numerical solution methods. To overcome this difficulty, this paper adopts an approximation method (AM) to turn the original two-stage fuzzy MPMP production planning problem into a finite-dimensional optimization problem. The convergence about the AM is discussed to ensure the solution quality. After that, we design a heuristic algorithm, which combines the AM and simulated annealing (SA) algorithm, to solve the proposed two-stage fuzzy MPMP production planning problem. Finally, one real case study about a furniture manufacturing company is presented to illustrate the effectiveness and feasibility of the proposed modeling idea and designed algorithm.     

Proactive data envelopment analysis: Effective production and capacity expansion in stochastic environments

Demand fluctuations that cause variations in output levels will affect a firm’s technical inefficiency. To assess this demand effect, a demand-truncated production function is developed and an “effectiveness” measure is proposed. Often a firm can adjust some input resources influencing the output level in an attempt to match demand. We propose a short-run capacity planning method, termed proactive data envelopment analysis, which quantifies the effectiveness of a firm’s production system under demand uncertainty. Using a stochastic programming DEA approach, we improve upon short-run capacity expansion planning models by accounting for the decreasing marginal benefit of inputs and estimating the expected value of effectiveness, given demand. The law of diminishing marginal returns is an important property of production function; however, constant marginal productivity is usually assumed for capacity expansion problems resulting in biased capacity estimates. Applying the proposed model in an empirical study of convenience stores in Japan demonstrates the actionable advice the model provides about the levels of variable inputs in uncertain demand environments. We conclude that the method is most suitable for characterizing production systems with perishable goods or service systems that cannot store inventories.     

Efficiency Analysis and Ranking of DMUs with Fuzzy Data

In this paper, a fuzzy version of CCR model (Charnes, Cooper and Rhodes (1978)) with asymmetrical triangular fuzzy number is presented and a procedure is suggested for its solution. The basic idea is to transform the fuzzy CCR model into a crisp linear programming problem by applying an alternative -cut approach. Thereby, the problem is converted to an interval programming. In this method, instead of comparing the equality (or inequality) of two intervals, a variable is defined in the interval, not only satisfies the set of constraints, but also maximizes the efficiency value. We also propose a ranking method for fuzzy DMUs using presented fuzzy DEA approach. To demonstrate the concept, numerical examples are solved and solutions are compared with Guo and Tanaka (2001).     

Modeling fuzzy multi-period production planning and sourcing problem with credibility service levels

A great deal of research has been done on production planning and sourcing problems, most of which concern deterministic or stochastic demand and cost situations and single period systems. In this paper, we consider a new class of multi-period production planning and sourcing problem with credibility service levels, in which a manufacturer has a number of plants and subcontractors and has to meet the product demand according to the credibility service levels set by its customers. In the proposed problem, demands and costs are uncertain and assumed to be fuzzy variables with known possibility distributions. The objective of the problem is to minimize the total expected cost, including the expected value of the sum of the inventory holding and production cost in the planning horizon. Because the proposed problem is too complex to apply conventional optimization algorithms, we suggest an approximation approach (AA) to evaluate the objective function. After that, two algorithms are designed to solve the proposed production planning problem. The first is a PSO algorithm combining the AA, and the second is a hybrid PSO algorithm integrating the AA, neural network (NN) and PSO. Finally, one numerical example is provided to compare the effectiveness of the proposed two algorithms.     

模糊批量生产计划问题的机会约束规划

描述了模糊单位利润、模糊生产能力以及模糊需求下的批量生产计划,并应用模糊机会约束规划规划建立了模型.当模糊变量是梯形模糊数时,我们将模糊模型转化为确定意义下的模型.为了求解优化模型,我们设计了基于模糊模拟的遗传算法.最后,通过一个数值例子说明算法的有效性.     

Aggregate production planning for process industries under oligopolistic competition

We consider a competitive version of the traditional aggregate production planning model with capacity constraints. In the general case, multiple products are produced by a few competing producers (oligopoly) with limited capacities. Production quantities, prices and consequently profits depend on production and allocation decisions of each producer. In addition, there is competition for the raw material whose supplies are limited, and where prices reflect these limitations. Such situations have recently occurred in several process industry settings including petro-refining, petrochemicals, basic chemicals, cement, fertilizers, pharmaceuticals, rubber, paper, food processing and metals. We use a successive “Bertrand–Cournot” framework to address this problem and to determine optimal production quantities, prices and profits at the producers and at the raw material supplier. Our analysis allows a new way to understand and evaluate the marginal value of additional capacity when there is competition for the market and raw materials.