交互式多准则模糊集约生产计划

本文讨论具有模糊需求量、模糊加工能力约束以及资本水平约束的多品种集约生产计划的多目标非线性整数规划模型和求解方法.通过可能性测度和必要性测度的描述,建立了最小费用和最小满意水平最大化之间平衡(最小风险方法)的目的规划模型,并提出了交互式多准则求解模型的总体步骤.􀁱     

基于模糊规划的不确定性条件下递阶生产计划模型

本文研究了一类不确定条件下的递阶生产计划（Hierarchical Production Plann ing）,简称HPP）模型问题,在综合考虑计划中各种不确定性因素的条件下,建立了一个模 糊递阶生产计划模型,并给出了一个基于模糊规划的改进算法．仿真实例说明了所建模型及 算法的有效性．     

交货期协商器及其滚动式生产计划方法

为支持制造企业与客户对订单交货期的谈判,提出交货期协商器的概念和数学模型.其主要特点有:生产计划由订单触发滚动式进行;客户可以对订货的交货期进行讨价还价;为适应制造资源的不确定性采用了模糊优化的方法.应用于某家具厂实例计算,取得了满意的结果.     

含有模糊和随机参数的混合机会约束规划模型

提出一类混合机会约束规划模型，该模型同时含有模糊和随机参数，运用随机模拟与模糊模拟相结合的技术，给出了求解该机会约束规划模型的遗传算法，通过对生产过程最优化决策的典型问题进行分析建模和数值求解，说明了该模型和算法的合理性和有效性。     

基于动态规划的生产计划优化模型研究与应用

动态规划是运筹学的一个分支,是一种多阶段决策过程,可用于解决多目标决策问题,以使系统的总效果最优.以某企业生产计划的优化安排为实例,在该企业产品的生产能力、生产成本、库存成本等约束条件下,利用动态规划方法建立生产计划安排模型,通过模型求解解决了该企业产品生产计划的优化问题,同时以精练的C++代码实现了相应程序.     

一种改进的综合生产计划动态规划优化方法

提出了一种改进的综合生产计划动态规划优化方法。以1999年甘应爱主编的《运筹学》第227~230页给出的一类综合生产计划问题为研究对象,深入分析了原综合生产计划问题、数学优化模型、动态规划求解过程、计算方法存在的不足并提出了相应的改进措施。通过案例分析验证了所提方法的有效性。     

炼油企业生产计划和能量系统集成优化研究

炼油企业通常由生产过程和公用工程两个主要部分组成。将生产计划和公用工程集成优化,不仅获得真正的全局最优解,而且克服了传统生产计划人为制定生产过程与公用工程之间中间物流（各压力等级的蒸汽,瓦斯等）价格的缺陷,避免了蒸汽减温减压和放空,以及炼厂气放空的经济损失和环境破坏。首先采用IDEF0方法建立了生产计划与公用工程的功能模型,描述了两者之间的相互关系。接着建立了生产计划与公用工程集成的混合整数线性规划模型。模型描述了装置能耗不仅与加工量相关,而且与装置的生产方案相关,更真实地反映了生产过程与能量系统之间的定量关系。模型还在全厂范围内进行各压力等级蒸汽和燃料的平衡。最后,将建立的全厂集成优化模型应用于中国北方的某大型炼油厂,验证了模型的有效性。     

基于遗传算法的多级有限能力生产计划

近年来很多学者研究了提前/拖期惩罚下的准时生产模型,但都局限于单件生产模式,关于多级装配情形的研究则很少,且均将加工能力视为常数.本文提出多级有限能力的准时化生产计划模型,并将加工能力视为变化的,最后采用遗传算法求解,计算结果证明了模型的有效性.     

CIMS环境下单元的柔性计划生产方法

本文研究了ＣＩＭＳ环境下的生产计划问题，考虑了两种市场需求驱动生产方式，一种是在计划周期内各阶段市场需求由预先订货决定，另一种是市场需求不是由预先订货决定，而是一个随机变量，但可根据统计，确认它服从某种分布规律，对于这两种生产方式，本文可给出最优的生产。     

A fuzzy mid-term single-fab production planning model

Production planning is a complicated task for a semiconductor fabrication plant because of the uncertainties in demand, product prices, cycle times, and product yields. Traditionally, mid-term production planning for a semiconductor fabrication plant is handled with MRP systems or optimized by solving LP or FLP problems. In this study, the philosophy of prioritizing demand classes with higher certainties as proposed by Leachman (1993) is applied to the FLP model of Chen and Wang (1998), and a new FLP model for planning the mid-term production of single wafer fabrication plant is constructed. Parameters in this model are given in the form of trapezoidal fuzzy numbers. Fuzzy comparison is adopted in dealing with the fuzzy objective function and expanding inequalities. The outputs are projected using Chen and Wang's fuzzy dynamic production function. The uncertain demand is classified and satisfied with four successively optimized FLP submodels according to their ascending uncertainties. Chen and Wang's example is adopted to demonstrate the proposed methodology and to make some comparisons. By moving more capacity to demand classes with higher certainties that are usually nearer and have larger discounted revenues, the proposed methodology achieves a higher value of the discounted cash flows than the two referenced models.     

Aggregate planning through the imprecise goal programming model: integration of the manager's preferences

Aggregate planning involves planning the best quantity to be produced during time periods in the medium‐range horizon at the lowest cost. Usually, the production manager seeks a plan that simultaneously optimizes several incommensurable and conflicting objectives, such as total cost, level of inventories, level of customer service, fluctuation in workforce, and utilization level of the physical facility and equipment. The goal programming (GP) model is one of the best known multi‐objective programming models that considers simultaneously several conflicting objectives to select the most satisfactory solution among a set of feasible solutions. In the production planning problem, the goals and the technological parameters are naturally imprecise. Moreover, the existing GP formulations developed in industrial engineering and aggregate production planning do not explicitly incorporate the manager's preferences. The aim of this paper is to develop a GP formulation within an imprecise environment where the concept of satisfaction function will be utilized to explicitly introduce the manager's preferences into the aggregate planning model.     

A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning

In this study a hybrid (including qualitative and quantitative objectives) fuzzy multi objective nonlinear programming (H-FMONLP) model with different goal priorities will be developed for aggregate production planning (APP) problem in a fuzzy environment. Using an interactive decision making process the proposed model tries to minimize total production costs, carrying and back ordering costs and costs of changes in workforce level (quantitative objectives) and maximize total customer satisfaction (qualitative objective) with regarding the inventory level, demand, labor level, machines capacity and warehouse space. A real-world industrial case study demonstrates applicability of proposed model to practical APP decision problems. GENOCOP III (Genetic Algorithm for Numerical Optimization of Constrained Problems) has been used to solve final crisp nonlinear programming problem.     

Multiproduct aggregate production planning with fuzzy demands and fuzzy capacities

Given the uncertain market demands and capacities in production environment, this paper discusses some practical approaches to modeling multiproduct aggregate production planning problems with fuzzy demands, fuzzy capacities, and financial constraints. By formulating the fuzzy demand, fuzzy equation, and fuzzy capacities, a fuzzy production-inventory balance equation for single period and a dynamic balance equation are formulated as fuzzy/soft equations and they represent the possibility levels of meeting the market demands. Using this formulation and interpretation, a fuzzy multiproduct aggregate production planning model is developed, and its solutions using parametric programming, best balance and interactive techniques are introduced to cater to different scenarios under various decision making preferences. Using the proposed models and techniques, first, the decision maker can select a preferred production plan with a common satisfaction level or different combinations of preferred possibility level and satisfaction levels, according to the market demands and available production capacities, and second, the obtained structure of the optimal solution can help decision maker in aggregate production planning. The decision maker can also make a preferred and reasonable production plan corresponding to one's most concerned criteria. Hence, decision makers not only can come up with a reasonable aggregate production plan with minimum efforts, but also have more choices of making a preferred aggregate plan based on his most concerned criteria. These models can effectively enhance the capability of an aggregate plan to give feasible family disaggregation plans under different scenarios with fuzzy demands and capacities. Simulation and the results of analysis on the proposed techniques are also given in detail in this paper.     

Strategic level three-stage production distribution planning with capacity expansion

In this paper, we address a strategic planning problem for a three-stage production–distribution network. The problem under consideration is a single-item, multi-supplier, multi-producer, and multi-distributor production–distribution network with deterministic demand. The objective is to minimize the costs associated with production, transportation, and inventory as well as capacity expansion costs over a given time horizon. The limitations are the production capacities of the suppliers and producers, and transportation capacities of the corresponding transportation network. On the other hand, all capacities may be increased at a fixed cost. The problem is formulated as a 0-1 mixed integer programming model. Since the problem is intractable for real life cases efficient relaxation-based heuristics are considered to obtain a good feasible solution.     

Computation of a multi-objective production planning model with probabilistic constraints

In this paper, a multi-objective production planning model has been presented for a captive plant. The model includes multi-products, multi-plants, and multi-objective with some probabilistic constraints. The probabilistic constraints have been transformed into deterministic constraints assuming the parameters as independent normal random variables. The deterministic problem has been computed with two different methods, namely weighting method and fuzzy programming method. Finally, the integral solution obtained by these two methods have been compared.     

Demand and cost forecast error sensitivity analyses in aggregate production planning by possibilistic linear programming models

A production management system contains many imprecise natures. The conventional deterministic and/or stochastic model in a computer integrated production management system (CIPMS) may not capture the imprecise natures well. This study examines how the imprecise natures in the CIPMS affect the planning results. Possibilistic linear programming models are also proposed for the aggregate production planning problem with imprecise natures. The proposed model can adequately describe the imprecise natures in a production system and, in doing so, the CIPMS can adapt to a variety of non-crisp properties in an actual system. For comparison, the classic aggregate production planning problem given by Holt, Modigliani, and Simon (HMS) is solved using the proposed possibilistic model and the crisp model of Hanssmann and Hess (HH). Perturbing the cost coefficients and the demand allows one to simulate the imprecise natures of a real world and evaluate the effect of the imprecise natures to production plans by both the possibilistic and the crisp HH approaches. Experimental results indicate that the possibilistic model does provide better plans that can tolerate a higher spectrum of imprecise properties than those obtained by the crisp HH model.