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.     

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.     

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.     

基于可信性理论的生产计划期望值模型

基于可信性理论,提出一类新的模糊生产计划期望值模型.然后,讨论这个模糊生产计划模型的基本性质.最后,利用这个模糊模型的基本性质我们可以把模糊生产计划期望值模型转化为一个线性规划模型并且设计相应的算法求解模糊生产计划问题的一个数值例子.     

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...     

模糊批量生产计划问题的可信性规划模型与算法

描述模糊单位利润、模糊生产能力以及模糊需求下的批量生产计划,并应用可信性规划建立了模型.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.     

带有最小风险准则的两阶段模糊产销计划模型

基于两阶段模糊优化方法建立一类带有最小风险准则的模糊产销计划模型,并设计含有逼近方法和粒子群优化算法的混合算法对提出的模型进行求解。然后,给出一个实例表明模型和算法的有效性。     

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.     

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.     

一类新的带有模糊参数的生产库存管理模型

正确有效地处理生产库存管理系统中的不确定性信息直接影响生产利润。基于模糊VaR优化准则讨论了生产工序、库存费用、缺货费用以及市场需求对生产利润的影响。同时,本文对于生产库存管理问题发展了一个新的模糊VaR优化方法。为了求解所提出的生产库存管理问题,设计了一种结合逼近方法、神经网络和遗传算法的混合智能算法。最后,给出一个带有模糊VaR准则的生产库存管理问题的数值例子表明所设计模型和算法的有效性。     

An analytical model for reverse automotive production planning and pricing

Automotive shredders need a reverse production planning strategy that includes determining at what price to purchase vehicle hulks from different sources. In this paper, we formulate the automotive reverse production planning and pricing problem in a nonlinear programming model, develop an approximate supply function for hulks when adjacent shredders price independently, and compare two hulk pricing strategies in three trends for ferrous metal and hulk prices: constant, increasing and decreasing. The case study results indicate that adjusting purchase price based on hulk composition in coordination with planning for purchasing, storing and processing can increase net revenue by 7–15%.     

Optimal Pricing and Production Planning for Multi-product Multi-period Systems with Backorders

In this paper, we develop models for production planning with coordinated dynamic pricing. The application that motivated this research is manufacturing pricing, where the products are non-perishable assets and can be stored to fulfill the future demands. We assume that the firm does not change the price list very frequently. However, the developed model and its solution strategy have the capability to handle the general case of manufacturing systems with frequent time-varying price lists. We consider a multi-product capacitated setting and introduce a demand-based model, where the demand is a function of the price. The key parts of the model are that the planning horizon is discrete-time multi-period, and backorders are allowed. As a result of this, the problem becomes a nonlinear programming problem with the nonlinearities in both the objective function and some constraints. We develop an algorithm which computes the optimal production and pricing policy on a finite time horizon. We illustrate the application of the algorithm through a detailed numerical example.     

Production planning under uncertainty in textile manufacturing

Textile manufacturing consists of yarn production, fabric formation, and finishing and dyeing stages. The subject of this paper is the yarn production planning problem, although the approach is directly applicable to the fabric production planning problem due to similarities in the respective models. Our experience at an international textile manufacturer indicates that demand uncertainty is a major challenge in developing yarn production plans. We develop a stochastic programming model that explicitly includes uncertainty in the form of discrete demand scenarios. This results in a large-scale mixed integer model that is difficult to solve with off-the-shelf commercial solvers. We develop a two-step preprocessing algorithm that improves the linear programming relaxation of the model and reduces its size, consequently improving the computational requirements. We illustrate the benefits of a stochastic programming approach over a deterministic model and share our initial application experience.     

A stochastic linear programming approach to hierarchical production planning

This paper examines production planning decisions. The process is formulated as a hierarchical production planning (HPP) model under uncertain demand. A review of HPP articles indicates that while current models do consider uncertainty as a part of their solution methods, a deficiency persists since these models fail to incorporate the uncertain demand explicitly in the formulation of the problem. A stochastic linear programming model (SLP) is proposed to better reflect reality and to provide a superior solution. The model remains computationally tractable despite the precise incorporation of uncertainty and the imposition of penalties when constraints are violated. A problem is introduced which illustrates the superiority of the proposed model over those currently being applied.     

Efficient algorithms for combined heat and power production planning under the deregulated electricity market

Combined heat and power (CHP) production is an important energy production technology that can yield much higher total energy efficiency than separate heat and power generation. In CHP production, the heat and power production follows a joint characteristic, which means that the production planning must be done in coordination. Cost-efficient operation of a CHP system can be planned by using an optimization model. A long-term planning model decomposes into thousands of hourly models. Earlier, in the regulated electric power market, the planning problem was symmetrically driven by heat and power demand. The liberalization of the power market has created an asymmetrical planning problem, where heat production responds to the demand and power production to the volatile market price. In this paper, we utilize this asymmetry to develop novel envelope-based dual algorithms for solving the hourly CHP models efficiently. The basic idea is to transform the three-dimensional characteristic operating region for heat and power production of each CHP plant into a two-dimensional envelope by taking the power price as a parameter. Then the envelopes of each plant are used for looking up the optimal solution rapidly. We propose two versions of the algorithm: the on-line envelope construction algorithm (ECON) where the envelopes are constructed for each hour based on the power price and the off-line envelope construction algorithm (ECOFF) where envelopes are pre-computed for all different power price ranges. We derive the theoretical time complexity of the two algorithms and compare their performance empirically with realistic test models against the ILOG CPLEX solver and the Power Simplex (PS) algorithm. PS is an extremely efficient specialized primal algorithm developed for the symmetrical CHP planning problem under the regulated market. On average, when reusing previous basic solutions, ECON is 603 times faster than CPLEX and 1.3 times faster than PS. ECOFF is 1860 times faster than CPLEX and four times faster than PS.     

Robust optimal dynamic production/pricing policies in a closed-loop system

Hybrid manufacturing/remanufacturing systems play a key role in implementing closed-loop production systems which have been considered due to increasingly environmental concerns and latent profit of used products. Manufacturing and remanufacturing rates, selling price of new products, and acquisition price of used products are the most critical variables to optimize in such hybrid systems. In this paper, we develop a dynamic production/pricing problem, in which decisions should be made in each period confronting with uncertain demand and return. The manufacturer is able to control the demand and return by adjusting selling price and acquisition price respectively, also she can stock inventories of used and new products to deal with uncertainties. Modeling a nominal profit maximization problem, we go through robust optimization approach to reformulate it for the uncertain case. Final robust optimization model is obtained as a quadratic programming model over discrete periods which can be solved by optimization packages of QP. A numerical example is defined and sensitivity analysis is performed on both basic parameters and parameters associated with uncertainty to create managerial views.     

A tactical model for planning the production and distribution of fresh produce

We present an integrated tactical planning model for the production and distribution of fresh produce. The main objective of the model is to maximize the revenues of a producer that has some control over the logistics decisions associated with the distribution of the crop. The model is used for making planning decisions for a large fresh produce grower in Northwestern Mexico. The decisions obtained are based on traditional factors such as price estimation and resource availability, but also on factors that are usually neglected in traditional planning models such as price dynamics, product decay, transportation and inventory costs. The model considers the perishability of the crops in two different ways, as a loss function in its objective function, and as a constraint for the storage of products. The paper presents a mixed integer programming model used to implement the problem as wells as the computational results obtained from it.     

Collaborative production planning of supply chain under price and demand uncertainty

This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.     

A robust optimization model for multi-site production planning problem in an uncertain environment

This paper addresses the multi-site production planning problem for a multinational lingerie company in Hong Kong subject to production import/export quotas imposed by regulatory requirements of different nations, the use of manufacturing factories/locations with regard to customers’ preferences, as well as production capacity, workforce level, storage space and resource conditions at the factories. In this paper, a robust optimization model is developed to solve multi-site production planning problem with uncertainty data, in which the total costs consisting of production cost, labor cost, inventory cost, and workforce changing cost are minimized. By adjusting penalty parameters, production management can determine an optimal medium-term production strategy including the production loading plan and workforce level while considering different economic growth scenarios. The robustness and effectiveness of the developed model are demonstrated by numerical results. The trade-off between solution robustness and model robustness is also analyzed.