An interactive multi-objective algorithm for decentralized decision making in product design

To remain competitive and gain new shares of the market, industries must develop their products quickly while meeting the multiple customer requirements. To reduce product development time, the design step is often accomplished by several working groups working in parallel. These working groups are often decentralized and are supervised by a director. This paper focuses on solving a multi-objective problem in a setting that is called a “decentralized environment.” Collaborative optimization is a strategy used for solving problems in a decentralized environment. This strategy divides a problem into subproblems in order to give more autonomy to working groups, thus facilitating work in parallel. In this paper, collaborative optimization is paired with an interactive algorithm to solve multi-objective problems in a decentralized environment. It can be easily adjusted within the structure of a development process in a given industry and allows collaboration between the director and his/her working groups. The algorithm captures the director’s and the working groups’ preferences and generates several Pareto-optimal solutions. The algorithm was tested on a two-bar structure problem. The results obtained match those published in the literature.     

Fuzzy structural analysis using α-level optimization

In this paper new concepts and developments are presented for structural analysis involving uncertain parameters. Based on a classification of the uncertainties in structural analysis the uncertainty “fuzziness” is identified and its quantification is demonstrated. On the basis of fuzzy set theory a general method for fuzzy structural analysis is developed and formulated in terms of the α-level optimization with the application of a modified evolution strategy. Every known analysis algorithm for the realistic simulation of load-bearing behavior may be applied in the fuzzy structural analysis in the sense of a deterministic fundamental solution. By way of example, geometrically and physically nonlinear algorithms are adopted in the presented study as a deterministic fundamental solution for the analysis of steel and reinforced concrete structures. The paper also describes coupling between α-level optimization and the deterministic fundamental solution. Received 9 May 2000     

FASTSIM2: a second-order accurate frictional rolling contact algorithm

In this paper we consider the frictional (tangential) steady rolling contact problem. We confine ourselves to the simplified theory, instead of using full elastostatic theory, in order to be able to compute results fast, as needed for on-line application in vehicle system dynamics simulation packages. The FASTSIM algorithm is the leading technology in this field and is employed in all dominant railway vehicle system dynamics packages (VSD) in the world. The main contribution of this paper is a new version “FASTSIM2” of the FASTSIM algorithm, which is second-order accurate. This is relevant for VSD, because with the new algorithm 16 times less grid points are required for sufficiently accurate computations of the contact forces. The approach is based on new insights in the characteristics of the rolling contact problem when using the simplified theory, and on taking precise care of the contact conditions in the numerical integration scheme employed.     

基于混沌差分进化粒子群算法的模糊资源受限项目调度问题

本文研究了工期模糊情况下的资源受限项目调度问题,采用一种基于区间数距离的模糊取最大运算比较模糊工期的大小,解决了以往研究中忽略的工期模糊情况下,项目关键路径可能会发生改变,相应地各活动的模糊调度时间以及项目的模糊最短工期也可能随之发生改变的问题。引入一种基于混沌和差分进化的混合粒子群优化算法,并对算法的惯性权重进行改进来求解上述问题。通过一个算例验证了所建立模型及提出方法的有效性。     

云制造环境下板材余料资源的服务匹配方法

在云制造环境下,针对中小型企业加工后的板材余料造成极大资源浪费的问题,提出了一种基于语义相似度算法同时融入QoS（quality of service,服务质量）信息的分层次资源描述模型。首先,建立板材余料和板材加工设备资源的本体模型;然后,基于语义相似度算法对板材余料和板材加工设备的各个属性参数进行匹配,得到初选服务集合;最后,通过模糊QoS匹配算法将描述用户需求的自然语言转化成三角模糊数,得到服务设备的优选集合。实例仿真结果表明,所提出的方法将板材余料作为制造资源接入云制造平台,以服务的形式进行共享,增强了服务匹配过程的准确性和可靠性,实现了对板材余料和闲置的板材加工设备资源的合理调度和使用。研究结果可为云制造环境下余料和加工设备资源的匹配优选提供一种新思路。     

一种粗模糊神经分类器

介绍一种新的粗集编码模糊神经分类器。基于粗集理论的概念，讨论了知识编码、属性简化、分类系统简化的方法；并利用模糊隶属度函数将输入精确信息映射为模糊变量信息，解决分类中病态定义的数据问题和提高系统非线性映射的分类能力；提出了结合系统参数的重要性因子的网络的模糊推理方法和粗模糊神经分类器的网络结构以及有导师的最小平方误差学习训练算法。实现的粗集编码模糊神经分类器具有网络结构空间维数低、学习算法简单、网络训练时间短、非线性特性丰富等优点。     

A hybrid evolutionary algorithm for distribution feeder reconfiguration

Distribution feeder reconfiguration (DFR) is formulated as a multi-objective optimization problem which minimizes real power losses, deviation of the node voltages and the number of switching operations and also balances the loads on the feeders. In the proposed method, the distance (λ ₂ norm) between the vector-valued objective function and the worst-case vector-valued objective function in the feasible set is maximized. In the algorithm, the status of tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a non-differentiable optimization problem. Therefore, a new hybrid evolutionary algorithm based on combination of fuzzy adaptive particle swarm optimization (FAPSO) and ant colony optimization (ACO), called HFAPSO, is proposed to solve it. The performance of HFAPSO is evaluated and compared with other methods such as genetic algorithm (GA), ACO, the original PSO, Hybrid PSO and ACO (HPSO) considering different distribution test systems.     

Bayesian parametric inference in a nonparametric framework

This paper considers the problem of reporting a “posterior distribution” using a parametric family of distributions while working in a nonparametric framework. This “posterior” is obtained as the solution to a decision problem and can be found via a well-known optimization algorithm.      

Modelling and scheduling analysis of multi-cluster tools with residency constraints based on time constraint sets

This paper is dedicated to the scheduling problem of multi-cluster tools with process module residency constraints and multiple wafer product types. The problem is formulated as a non-linear programming model based on a set of time constraint sets. An effective algorithm called the time constraint sets based (TCSB) algorithm is presented as a new method to schedule the transport modules to minimise the makespan of a number of wafers. In approach, time constraint sets are maintained for all the resources and necessary operations to exploit the remaining production capacities during the scheduling process. To validate the proposed algorithm on a broader basis, a series of simulation experiments are designed to compare our TCSB algorithm with the benchmark with regard to cluster factor, configuration flexibilities and the variation of the processing times and residency constraint times. The results indicate that the proposed TCSB algorithm gives optimal or near optimal scheduling solutions in most cases.     

Fuzzy bi-objective formulation for a parallel machine scheduling problem with machine eligibility restrictions and sequence-dependent setup times

In this paper, a fuzzy bi-objective mixed-integer linear programming (FBOMILP) model is presented. FBOMILP encompasses the minimisation workload imbalance and total tardiness simultaneously as a bi-objective formulation for an unrelated parallel machine scheduling problem. To make the proposed model more practical, sequence-dependent setup times, machine eligibility restrictions and release dates are also considered. Moreover, the inherent uncertainty of processing times, release dates, setup times and due dates are taken into account and modelled by fuzzy numbers. In order to solve the model for small-scale problems, a two-stage fuzzy approach is proposed. Nevertheless, since the problem belongs to the class of NP-hard problems, the proposed model is solved by two meta-heuristic algorithms, namely fuzzy multi-objective particle swarm optimisation (FMOPSO) and fuzzy non-dominated sorting genetic algorithm (FNSGA-II) for solving large-scale instances. Subsequently, through setting up various numerical examples, the performances of the two mentioned algorithms are compared. When α?=?0.5 (α is a level of risk-taking and when it increases the decision-maker’s risk-taking decreases), FNSGA-II is fairly more effective than FMOPSO and has better performance especially in solving large-sized problems. However, when α rises, it can be stated that FMOPSO moderately becomes more appropriate. Finally, directions for future studies are suggested and conclusion remarks are drawn.     

A novel enthalpy formulation for multidimensional solidification and melting of a pure substance

This paper presents a new finite-difference formulation of the multidimensional phase change problems involving unique phase change temperature. The solutions obtained with this formulation show that the problem of “waviness” of the temperature histories encountered with the conventional enthalpy formulation is now removed. The formulation derived provides a simple method for “local” tracking of the interface using the enthalpy variable in a novel way. During the solution of the finite-difference equations, the present formulation obviates the need for “book-keeping” of the phase-change nodes, and hence allows solution of the equations by tridiagonal matrix algorithm. It is argued that the benefits of enthalpy formulation can be extended to phase-change problems involving convection by solving the equations of motion on non-staggered grid.     

An optimization-based algorithm for job shop scheduling

Scheduling is a key factor for manufacturing productivity. Effective scheduling can improve on-time delivery, reduce inventory, cut lead times, and improve the utilization of bottleneck resources. Because of the combinatorial nature of scheduling problems, it is often difficult to find optimal schedules, especially within a limited amount of computation time. Production schedules therefore are usually generated by using heuristics in practice. However, it is very difficult to evaluate the quality of these schedules, and the consistency of performance may also be an issue. In this paper, near-optimal solution methodologies for job shop scheduling are examined. The problem is formulated as integer optimization with a “separable” structure. The requirement of on-time delivery and low work-in-process inventory is modelled as a goal to minimize a weighted part tardiness and earliness penalty function. Lagrangian relaxation is used to decompose the problem into individual part subproblems with intuitive appeal. By iteratively solving these subproblems and updating the Lagrangian multipliers at the high level, near-optimal schedules are obtained with a lower bound provided as a byproduct. This paper reviews a few selected methods for solving subproblems and for updating multipliers. Based on the insights obtained, a new algorithm is presented that combines backward dynamic programming for solving low level subproblems and interleaved conjugate gradient method for solving the high level problem. The new method significantly improves algorithm convergence and solution quality. Numerical testing shows that the method is practical for job shop scheduling in industries. This work was supported in part by the National Science Foundation under DMI-9500037, and the Advanced Technology Center for Precision Manufacturing, University of Connecticut.     

Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects

This paper investigates a multi-objective parallel machine scheduling problem under fully fuzzy environment with fuzzy job deterioration effect, fuzzy learning effect and fuzzy processing times. Due dates are decision variables for the problem and objective functions are to minimise total tardiness penalty cost, to minimise earliness penalty cost and to minimise cost of setting due dates. Due date assignment problems are significant for Just-in-Time (JIT) thought. A JIT company may want to have optimum schedule by minimising cost combination of earliness, tardiness and setting due dates. In this paper, we compare different approaches for modelling fuzzy mathematical programming models with a local search algorithm based on expected values of fuzzy parameters such as job deterioration effect, learning effect and processing times.     

Evolutionary modelling and optimization of grinding processes

The objective of this study is to develop a framework of modelling the complex grinding processes and finding optimal process conditions to meet the general class of process requirements. In order to achieve the above goal, novel modelling schemes and optimization methods based on evolutionary algorithms (EA) are developed. The optimization problem of grinding processes can be formulated as a constrained non-linear programming problem with mixed-discrete variables. The adaptive least-squares (ALS) algorithm proposed by Lee and Shin's 1998 study is extended for modelling multi-input-multi-output (MIMO) complex grinding processes using fuzzy basis function networks (FBFN), while the modified evolution strategies (ES) is proposed for successful optimization of grinding processes. Two grinding optimization problems demonstrate the superior performance of the proposed scheme.     

Heuristic approaches for mixed-model sequencing problem with stochastic processing times

Despite many pioneering efforts and works over the past decades, stochastic events have not been studied extensively in mixed-model assembly lines thus far. For a mixed-model sequencing problem with stochastic processing times, this paper aims to minimise expected total work overload. It also focuses on the most critical workstation of the line. In practice, this assumption is useful when the whole or a big portion of the assembly line is considered as a single station. In order to tackle the problem, a dynamic programming (DP) algorithm as well as two greedy heuristics from the literature is employed. However, it is realised that the DP cannot guarantee the optimal sequence neither for stochastic nor deterministic problems. It is because the calculation of work overload is involved in a recursive procedure that affects the states’ value functions. Therefore, by the use of network representation, the problem is modelled as a shortest path problem and a new heuristic, inspired by Dijkstra’s algorithm is developed to deal with it. Numerical results show that the proposed method outperforms other algorithms strongly. Finally, some discussion is provided about why one should consider stochastic parameters and why the proposed heuristic performs well in this regard.     

研发型企业多项目人力资源调度研究——基于蚁群优化的超启发式算法

现阶段,研发型企业的项目处于多项目环境下,为了解决多项目并行时人力资源争夺问题,本文针对该类企业多项目管理中人力资源调度进行优化研究,以考虑项目延期惩罚成本的最小总成本为目标函数,将现实问题抽象建模。基于国内外的研究提出了一种超启发式算法进行求解,该算法将人力资源调度问题分为项目活动分配和人员选择项目活动两个部分,采用蚁群优化作为高层启发式策略搜索低层启发式规则,再进一步根据规则解构造出可行解。最后本研究设计多组仿真实验与启发式规则进行对比,结果表明该算法有较好的搜索性能,为人力资源的调度问题提供了新的解决方案。     

Fuzzy computer-aided design of process manufacturing facilities

Preliminary design of process manufacturing facilities involves, among other things, the synthesis of detailed layout designs. In current practice, this spatial design process is very labor-intensive and expensive. This paper describes a prototype CAD system which models design decision-making, providing a computable framework for automation. The CAD system performs auto-elicitation of an expert's judgment in the form of fuzzy sets using interactive computer graphics. These fuzzy sets are then used in a heuristic search process employing multi-objective, non-linear optimization. Designs synthesized by this fuzzy CAD system are comparable to those generated by hand, and in some cases exceed a practitioner's design in quality. The CAD system, as presently constructed, provides multiple solutions. Conclusions and recommendations regarding processing speed and unrepresented heuristic content are made.     

Distributed parameter thermal controllability: a numerical method for solving the inverse heat conduction problem

This paper addresses the inverse heat conduction problem encountered in thermal manufacturing processes. A numerical control algorithm is developed for distributed parameter conduction systems, based on Galerkin optimization of an energy index employing Green's functions. Various temperature profiles of variable complexity are studied, using the proposed technique, in order to determine the surface heat input distribution necessary to generate the desired temperature field inside a solid body. Furthermore, the effect of altering the iterative time step and duration of processing time, on the convergence of the solution generated by the aforementioned method is investigated. It is proved that despite the variations in numerical processing, the iterative technique is able to solve the problem of inverse heat conduction in the thermal processing of materials. Copyright © 2004 John Wiley & Sons, Ltd.     

Cause and effect analysis by fuzzy relational equations and a genetic algorithm

This paper proposes using a genetic algorithm as a tool to solve the fault diagnosis problem. The fault diagnosis problem is based on a cause and effect analysis which is formally described by fuzzy relations. Fuzzy relations are formed on the basis of expert assessments. Application of expert fuzzy relations to restore and identify the causes through the observed effects requires the solution to a system of fuzzy relational equations. In this study this search for a solution amounts to solving a corresponding optimization problem. An optimization algorithm is based on the application of genetic operations of crossover, mutation and selection. The genetic algorithm suggested here represents an application in expert systems of fault diagnosis and quality control.