随机和模糊约束下的扩展型集约生产计划方法

针对不确定环境下具有模糊需求量、随机加工能力约束的多阶段多品种集约生产计划问题进行了研究。在考虑生产任务跨阶段性的基础上,建立扩展型随机模糊混合优化模型,并运用随机模糊机会约束规划理论、不确定等价方法和Lingo软件对模型进行转化和求解。通过案例分析,证明了该模型和方法的有效性,为决策者在不确定环境下的生产计划决策提供了依据。     

模糊灰色智能优化算法在生产计划不确定规划中的应用

在模糊和灰色两类不确定因素并存条件下定义了模糊灰色变量,基于模糊灰色变量的可信性和机会测度建立了生产计划不确定规划模型.采用模糊灰色模拟技术对不确定目标函数和不确定约束产生输入输出数据,用产生的输人输出数据训练神经网络逼近不确定函数,通过基于遗传算法的混合智能优化算法得出最优解,并用实例说明了本模型和算法的有效性.     

A multi-objective genetic algorithm for solving cell formation problem using a fuzzy goal programming approach

A comprehensive multi-objective mathematical programming model is proposed in this paper to design a cellular manufacturing system. The model considers machine redundancy, production volume, processing time, cost of machines, sequence of operations, and alternative processing plans. A fuzzy goal programming approach is used to convert the proposed multi-objective model into a single-objective one. Due to NP-hard nature of the model, a genetic algorithm is developed for solving the proposed model. Performance of the proposed genetic algorithm is evaluated by adopting four problems from literature. The results indicate effectiveness and efficiency of the proposed algorithm in comparison with those obtained by Lingo and NSGA-II algorithm.     

A two-stage stochastic programming project scheduling approach to production planning

In engineering-to-order (ETO) or manufacturing-to-order (MTO) systems producing highly customized items, the high level of customization, together with long flow times, forces the production plan to be defined before information on items customization, and details on the manufacturing activities are completely disclosed. Due to the partial available information, the production plan must provide a robust schedule of the activities and of the resources utilization, trying to incorporate a certain degree of anticipation of uncertainty. This paper proposes a two-stage stochastic programming project scheduling approach to support production planning in ETO/MTO system. The approach provides a baseline production plan together with a set of revisions of the plan to react to the occurrence of uncertain events. A scenario-based approach is used to model the changes affecting the characteristics of the activities to be processed. The proposed approach is tested on random-generated instances and on a real manufacturing system producing machining centers.     

Dynamic production planning model: a dynamic programming approach

Production planning is one of the most important issues in manufacturing. The nature of this problem is complex and therefore researchers have studied it under several and different assumptions. In this paper, applied production planning problem is studied in a general manner and it is assumed that there exists an optimal control problem that its production planning strategy is a digital controller and must be optimized. Since this is a random problem because of stochastic values of sales in future, it is modeled as a stochastic dynamic programming and then it is transformed to a linear programming model using successive approximations. Then, it is proved that these two models are equivalent. The main objective of the proposed model is achieving optimal decisions using forecasting sales which can be applied in master production schedule, manufacturing resource planning, capacity requirements planning, and job shop/shop floor scheduling.     

A hybrid model of fuzzy goal programming and grey numbers in continuous project time,cost, and quality tradeoff

The purpose of this paper is to develop current mathematical models of cost, time, and quality tradeoffs in conditions that parameters of project activities are estimated uncertainly by grey numbers. In some projects like construction projects, activities can be done within a much shorter time by increasing in the resources, while project's cost may rise at the same time. In such situations, managers are usually required to determine the best combination of cost, time, and quality parameters of the activities, although their information regarding these parameters is limited and rather incomplete. The greyness of these parameters in the proposed method can aid managers to deal with these conditions. The most important aspect of the proposed model is that it considers uncertainty of the project planning data in the form of grey numbers. A combination of fuzzy goal programming and grey linear programming is also developed to solve the proposed model. Finally, this model will provide the managers with a stronger ability to face with uncertainty in project management and planning. The application of this model is examined in a numerical example. As its major finding, the model determines an optimal range in which the project managers can respond to intrinsic changes that may occur in the parameters during a project.     

Solving a multi-objective mixed-model assembly line sequencing problem by a fuzzy goal programming approach

This paper presents a fuzzy goal programming approach to solve a multi-objective mixed-model assembly line sequencing problem in a just-in-time production system. A mixed-model assembly line is a type of production line capable of diversified small-lot production and is able to promptly respond to sudden demand changes for a variety of models. Determining the sequence of introducing models to such an assembly line is of particular importance for the efficient implementation of just-in-time (JIT) systems. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. Because of conflicting objectives, we propose a fuzzy goal programming-based approach to solve the model. This approach is constructed based on the desirability of decision maker (DM) and tolerances considered on goal values. To illustrate the behavior of the proposed model, some of instances are solved optimally and computational results reported.     

A DEA approach for comparing solution efficiency in U-line balancing problem using goal programming

Line balancing problem plays an important role in the decision making process to increase efficiency and productivity. Recently, U-shaped layout in many production lines has replaced the traditional straight line layout using just-in-time concept. Here, we propose a model, using multi-objective decision making approach to the U-shaped line balancing problem, to offer enhanced decision maker flexibility, by allowing for conflicting goals. The assembly line operation efficiency is the most significant aim in our study, and this efficiency relates to management of resources and the solution of line balancing problem. First, the U-shaped line balancing problem is solved considering the model's goals. Then, the index function of assembly line balancing is determined and the efficiencies of the optimal solution outputs are evaluated using data envelopment analysis (DEA). Finally, the discrimination weakness and distribution of illogical weight in simple DEA models are resolved using a mixed method.     

Integer programming approach to process planning

In this paper an integer programming approach to the process planning problem is presented. Although the presented approach can be applied to any type of manufacturing system, some implications of the flexible manufacturing environment on process planning models are discussed. The relationship between the process planning models and the FMS software environment is shown. Algorithms for solving the developed models as well as computational results are discussed.     

Optimizing convexity defect in a tile industry using fuzzy goal programming

In most designed experiments, the main focus is to find the factor settings that optimize a quality response regardless of engineer’s preferences about factor settings. Further, in tiles industry convexity defects result in huge quality costs as well as production losses. This research, therefore, aims at optimizing convexity defect while considering process engineers’ preferences using fuzzy goal programming (FGP). Three two-level key process factors are considered, including below-rollers temperature, above-rollers temperature, direct blow air. Experiments are conducted with two repetitions; in each the convexity is measured on four tiles. Two optimization techniques are employed to determine the combination of optimal factor settings, including the Taguchi method and latter technique. The Taguchi approach and FGP approach provide relative improvements of 61.2% and 41.2%, respectively. Although the former technique reduces convexity larger than latter approach, it failed to satisfy the preferences on the settings of process factors. In contrast, the optimal factor settings obtained using FGP completely satisfy engineers’ preferences. In conclusion, FGP successfully optimizes process performance and completely satisfies process engineers’ preferences in tiles industry.     

A robot fuzzy motion planning approach in unknown environments

A fuzzy robot motion planning approach is proposed in unknown environments for three-degree industrial robots. The proposed planning system is composed of several separate fuzzy units, which control individually each manipulator joint. Each unit combines a repelling influence, which is related to the nearby obstacle, with the attracting influence produced by the final manipulator configuration, to generate actuating command for each link. Effectiveness of the proposed approach is verified through simulation. __________ Translated from Journal of Harbin Institute of Technology, 2005, 37(10) (in Chinese)     

A novel approach for remanufacturing process planning considering uncertain and fuzzy information

Frontiers of Mechanical Engineering - Remanufacturing, as one of the optimal disposals of end-of-life products, can bring tremendous economic and ecological benefits. Remanufacturing process...     

Aggregate production planning with multiple fuzzy goals

This work presents a novel interactive multiple fuzzy objective linear programming (MFOLP) model for solving the aggregate production planning (APP) decision problem in a fuzzy environment. The proposed model minimizes total production costs, carrying and backordering costs, and costs of changes in labor levels, while considering inventory levels, labor levels, machine capacity, warehouse space, and the time value of money. An industrial case demonstrates the feasibility of applying the proposed model to the APP problem. The proposed model yields an efficient compromise solution and the overall levels of DM satisfaction with the multiple fuzzy goal values. Furthermore, the proposed model provides a systematic framework for facilitating the decision-making process, enabling a decision maker interactively to modify the fuzzy data and related model parameters until a satisfactory solution is obtained. Several significant characteristics that differentiate the proposed model from other APP models are presented .     

模糊环境下基于可信性规划的生产计划方法

在客户需求、单位产品的价格、单位产品的生产成本和可用资源数量都是三角模糊变量的条件下,基于可信性规划建立了模糊生产计划模型。通过对模糊目标和约束的清晰化,将模糊生产计划模型转化为清晰等价形式,用非固定多段映射罚函数法将其转化为无约束两目标优化问题,并利用向量评价给出了求解此模型的改进粒子群算法。最后通过一个应用案例说明了所提方法的有效性。     

A weighted possibilistic programming approach for sustainable vendor selection and order allocation in fuzzy environment

This paper focused on the analysis of imprecise information in terms of many critical parameters for a multi-objective multi-item vendor selection-order allocation problem with price-breaks. We used both quantitative and qualitative criteria taking into account the economic, technological, social, environmental factors, and the price-breaks that were offered on order quantity following ‘all-unit discount schedule.’ We developed an optimization model that integrated fuzzy multi-objective integer linear programming and analytic hierarchy process techniques. A weighted possibilistic programming approach was presented to solve the optimization model, which simultaneously minimized (maximized) the best scenario, the likeliest scenario, and the worst scenario for the imprecise objective functions thereby avoiding situations that may force the solution process to divert. The integrated model and the solution approach were tested on the data set of an industrial case study. A detailed performance analysis and comparisons were done to show superiority of the proposed methodology over the existing related fuzzy programming approaches.     

A functional approach to feature recognition in production planning

One of the key elements for improving manufacturing productivity is the integration of CAD/CAM systems. An essential intermediate step is planning for production. This requires the automatic conversion of design data to an understandable format for computer-aided process planning (CAPP) systems in the preparation of process plans. The paper examines the functions of planning for production from the integration viewpoint and proposes a methodology for one of the functions — feature recognition.     

A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach

A supply chain (SC) distribution network design model is developed in this paper. The goal of the model is to select the optimum numbers, locations and capacity levels of plants and warehouses to deliver products to retailers at the least cost while satisfying desired service level to retailers. A maximal covering approach is used in statement of the service level. The model distinguishes itself from other models in this field in the modeling approach used. Because of somewhat imprecise nature of retailers’ demands and decision makers’ (DM) aspiration levels for the goals, a fuzzy modeling approach is used. Additionally, a novel and generic interactive fuzzy goal programming (IFGP)-based solution approach is proposed to determine the preferred compromise solution. To explore the viability of the proposed model and the solution approach, computational experiments are performed on realistic scale case problems.     

Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach

We propose a bi-objective cell formation problem with demand of products expressed in a number of probabilistic scenarios. To deal with the uncertain demand of products, a framework of two-stage stochastic programming model is presented. The proposed model considers minimizing the sum of the miscellaneous costs (machine constant cost, expected machine variable cost, cell fixed-charge cost, and expected intercell movement cost) and expected total cell loading variation. Because of conflicting objectives, we develop a two-phase fuzzy linear programming approach for solving bi-objective cell formation problem. To show the effectiveness of the proposed approach, numerical examples are solved and the results are compared with the two existing approaches in the literature. The computational results show that the proposed fuzzy method achieves lower objective functions as well as higher satisfaction degrees.     

Production planning of FMS under tool magazine constraints: A dynamic programming approach

Production planning is one of the most important activities for efficient operation of a flexible manufacturing system. This complex acivity is concerned with the decisions related to system set-up, involving solving the problems of selection of a set of part types for simultaneous processing, determining the production ratios, assigning the pallets and fixtures, and assigning operations and tools to machines. In this paper, a dynamic programming algorithm is developed to solve the above problems simultaneously by considering the flexibilities and constraints of the system in order to minimise the unbalanced workload of machines. In this context, various factors such as the tools required for operations, alternative routes available for operations, tool magazine capacity and the limited number of pallets and fixtures are considered. The method is validated with a case study.Notation B a large number - i part type,i=1,...,N - o operation,o=1,...,O _i - k machine,k=1,...,K - j stage,j=1,...,N (N is maximum number of stages required, i.e. equal to the number of part types) - n indicates the number of parts to be introduced into the system (represents the state in DP),n=j,...,nmax. In stagej at least one number of each part type should be introduced into the system. The maximum number of parts can be a user defined maximum,nmax. - P _iok processing time of part typei, operationo on machinek - OA _io set of operation alternatives available for part typei, operation - o an alternative consists of machine number, tool number along with its processing time - pw _ik average workload required by a part typei on machinek - a _i number of parts (ratio) of typei - f _i number of fixtures available to part typei - S(j) {S ₁(j),S ₂(j),...,S _p (j),...}, whereS _p (j) ispth set of selected part types with cardinalityj - |S(j)| ^N C _j - W _k a constant, indicating the average unbalanced workload on a machinek and is a user defined value - L _j,k (S _p (j),n,a _i ) load of machinek at stagej when selection isS _p (j), number of part types isn, and number of parts of type isa _i - L* _j,k (S _p (j),n) load of machinek in stagej at minimum unbalanced workload when selection isS _p (j) and number of parts in system isn - F _j (S _p )(j),n,a _i ) unbalanced workload of all machines at stagej, when selection isS _p (j), number of part types isn, and number of parts of type isa _i - F _j (S _p (j),n) minimum unbalanced workload of all machines, whenS _p (j) is the set of selected part types andn is the number of parts in the system     

机械产品设计方案的灰色模糊综合评价

对机械产品设计方案综合评价问题进行研究,建立评价指标体系,将模糊综合评判与灰色综合评估方法相结合,提出机械产品设计方案的灰色模糊综合评价模型,使模型适应性强,确保方案评价的可靠性。