Dingwei Wang

Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise

Dynamic alliance and virtual enterprise (VE) are essential components of global manufacturing. Minimizing risk in partner selection and ensuring the due date of a project are the key problems to overcome in VE, in order to ensure success. In this paper, a risk-based partner selection problem is described and modeled. Based on the concept of inefficient candidate, the solution space of the problem is reduced effectively. By using the characteristics of the problem considered and the knowledge of project scheduling, a rule-based genetic algorithm (R-GA) with embedded project scheduling is developed to solve the problem. The performance of this algorithm is demonstrated by a problem encountered in the construction of a stadium station and the experimental problems of different sizes. The results of this trial demonstrate the real life capability of the algorithm.

Path selection model and algorithm for emergency logistics management

Path selection is one of the fundamental problems in emergency logistics management. Two mathematical models for path selection in emergency logistics management are presented considering more actual factors in time of disaster. First a single-objective path selection model is presented taking into account that the travel speed on each arc will be affected by disaster extension. The objective of the model is to minimize total travel time along a path. The travel speed on each arc is modeled as a continuous decrease function with respect to time. A modified Dijkstra algorithm is designed to solve the model. Based on the first model, we further consider the chaos, panic and congestions in time of disaster. A multi-objective path selection model is presented to minimize the total travel time along a path and to minimize the path complexity. The complexity of the path is modeled as the total number of arcs included in the path. An ant colony optimization algorithm is proposed to solve the model. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper.

A new approach to quality function deployment planning with financial consideration

Quality function deployment (QFD) is becoming a widely used customer-oriented approach and tool in product design. Taking into account the financial factors and uncertainties in the product design process, this paper deals with a fuzzy formulation combined with a genetic-based interactive approach to QFD planning. By introducing new concepts of planned degree, actual achieved degree, actual primary costs required and actual planned costs, two types of fuzzy optimisation models are discussed in this paper. These models consider not only the overall customer satisfaction, but also the enterprise satisfaction with the costs committed to the product. With the interactive approach, the best balance between enterprise satisfaction and overall customer satisfaction can be obtained, and the preferred solutions under different business criteria can be achieved through human-computer interaction.

Particle Swarm Optimization With Composite Particles in Dynamic Environments

In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a “worst first” principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.

Product design resources optimization using a non-linear fuzzy quality function deployment model

Quality Function Deployment (QFD) is a well-known customer-oriented methodology that is widely used to assist decision-making in product design and development in various types of production including highly customized One-of-a-Kind Production (OKP), batch production as well as continuous/ mass production. Determining how and to what extent (degree) certain characteristics/technical attributes (TA) of products are to be met with a view to gaining a higher level of overall customer satisfaction is a key to successful product design and development. Most of the existing approaches and models for QFD planning seldom consider the resource constraints in product design, nor do they normally take into account the impacts of the correlation among various TA. In other words, most of the existing QFD applications assume that the resources committed fully to attaining the design target for one TA have no impacts on those for other TA. Hence, the costs/resources required are usually worked out individually by linear formulation. In practice, design resource requirements should be expressed in fuzzy terms to accommodate the imprecision and uncertainties innate in the design process, such as ill-defined or incomplete understanding of the relationship between a given set of customer requirements (CR) and TA, the complexity of interdependence among TA, etc. A non-linear fuzzy model is proposed here to offer a more practical and effective means of incorporating the resource factors in QFD planning. The impacts of the correlation among TA are also considered. In the model, the resources for achieving the design target for a certain TA are expressed in a non-linear formulation of its relationship, correlation as well as interdependence with other customer requirements or TA. The concepts of the achieved attainments and planned attainments for TA, and the corresponding primary costs, planned costs and actual costs are introduced. Solutions to the non-linear fuzzy model can be obtained using a parametric optimization method or a hybrid genetic algorithm. A case study is also given to illustrate how the proposed fuzzy model and the optimization routine can be applied to help decision-makers in a company deploy their design resources towards gaining better overall customer satisfaction.

A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment

Presents an investigation of how partner selection problems may be optimized by the use of a precedence network of subprojects. At the start, the problem is described by a model with the subscript type of variables and a non-analytical objective function. It cannot be solved by general mathematical programming methods. By using the fuzzy rule quantification method, a fuzzy logic-based decision-making approach for the project scheduling is proposed. We then develop a fuzzy decision embedded heuristic genetic algorithm (GA/FD) to find the solution for partner selection. The approach was demonstrated by the use of an experimental example drawn from a coal-fired power station construction project. The results show us that the suggested approach can quickly achieve the optimal solution for large-sized problems.

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.

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 ones 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.

A branch and bound algorithm for sub-contractor selection in agile manufacturing environment

Abstract Partnership and partner selection play a key role for “Opportunity Driven” project contractors in agile manufacturing environment. In this paper, we present an investigation on the partner selection problems with engineering projects. Firstly, the problem is described by a 0–1 integer programming with non-analytical objective function. It is proven that the partner selection problem is a type of earliness and tardiness production planning problems. By introducing the concepts of inefficient and ideal candidates, we propose the theory of solution space reduction which can efficiently reduce the complexity of the problem. Then, a branch and bound algorithm embedded project scheduling is developed to obtain the solution. The approach was demonstrated by the use of an experimental example drawn from a construction project of coal fire power station. The computational results have been found to be satisfactory.

Resilience and Friability of Transportation Networks: Evaluation, Analysis and Optimization

To analyze the resilience of transportation networks, it is proposed to use a quantifiable resilience evaluation approach. First, we represent transportation networks by an undirected graph with the nodes as cities and edges as traffic roads. Because the survivability of transportation of a pair of cities depends on the number of passageways between them, the resilience of a city node can be evaluated by the weighted average number of reliable passageways with all other city nodes in the network. The network resilience can then be calculated by the weighted sum of the resilience of all nodes. To identify critical road lines or hub cities in networks, the concept of friability is proposed. This is defined as the reduction in total resilience upon removing an edge or hub city. Following the resilience and friability evaluation, a structure optimization model with a computational algorithm for transportation network design is recommended. Based on the recommended approaches, the resilience and friability of the railway network within the Chinese mainland is evaluated and analyzed. Several interesting conclusions are drawn from the computational results. The friability value of the railway lines in the Sichuan Basin which was damaged by the recent earthquake in China was also calculated.