Product development in modern enterprises usually involves collaboration among designers, suppliers, contract manufacturers to achieve efficiency and rapid response to changing markets. Product-development cost, lead time, and reliability are very critical elements in addition to functional features. However, it becomes increasingly challenging to obtain an optimal decision with respect to these multiple criteria as the number of involved entities increases in modern product developments. There is a clear need for planning tools to support effective decision making in this domain. The availability of efficient and accurate multiobjective optimization (MOO) algorithms becomes critical in such a decision support tool. This paper poses the product development as a multiobjective assignment problem in the context of printed circuit board assembly (PCBA) industry. We describe a new class of MOO algorithm based on the principles of differential evolution (DE). The multiobjective DE (MODE) algorithm is shown to approach Pareto-optimal solutions in a wide class of problems with better performance than the nondominated sorting genetic algorithm II from the literature, providing a practical tool for product-development decision support. A decision support system based on the object-oriented design methodology is described in this paper with the MODE as the core search engine. Experimental study of this decision support system is conducted using two real-world PCBA designs. We demonstrate the effectiveness of this proposed MODE algorithm and some use cases of such decision support system on facilitating decision makers' tradeoff analysis.

The development of theoretical-based methods for the assessment of multi-agent systems properties is of critical importance. This work is a preliminary investigation on methodologies for modeling, analysis and design of multi-agent systems. Multi-agent systems are regarded as discrete-event dynamic systems and Petri nets are used as a modeling tool to assess the structural properties of the multi-agent system. Our methodology consists of defining a simple multi-agent system based on the abstract architecture for intelligent agents. The abstract architecture is modeled as a discrete-event system using Petri nets and structural analysis of the net provides an assessment of the communication and coordination properties of the multi-agent system. Deadlock avoidance in the multi-agent system is considered as an initial key property, and it is evaluated using liveness and boundedness properties of the Petri net model.

This paper reports a mathematical modeling and convergence analysis of a continuous multi-objective differential evolution (C-MODE) algorithm that is proposed very recently. This C-MODE is studied in the context of global random search. The convergence of the population to the Pareto optimal solutions with probability one is developed. In order to facilitate the understanding of the C-MODE operators in a continuous space, a mathematical analysis of the operators is conducted based upon a Gaussian distributed initial population. A set of guidelines is derived for the parameter setting of the C-MODE based on the theoretical results from the mathematical analysis. A simulation analysis on a specific numerical example is conducted to validate the mathematical analytical results and parameter-setting guidelines. The performance comparison based on a suite of complex benchmark functions also demonstrates the merits of such parameter-setting guidelines

The revival of multi-objective optimization (MOO) is mostly due to the recent development of evolutionary multi-objective optimization that allows the generation of the whole Pareto optimal front. Several evolutionary algorithms have been developed for this purpose. This paper focuses on the recent development of differential evolution (DE) algorithms for the multi-objective optimization purposes. Although there are a few other papers on the extension of DE concept to the MOO domain, this paper is intended to provide an overall picture of one specific multi-objective differential evolution (MODE) algorithm. In the MODE, the DE concept for the continuous single-objective optimization is extended to MOO for both continuous and discrete problems (C-MODE and D-MODE, respectively). The MODE is modeled in the context of Markov framework and global random search. Convergence properties are developed for both C-MODE and D-MODE. In particular, a set of parameter-setting guidelines for the C-MODE is derived based on the mathematical analysis. An application of the D-MODE to the planning of design, supply, and manufacturing resources in product development is also reported in this paper

In recent years, multi-objective evolutionary algorithms (MOEA) have generated a large research interest. MOEA's attraction stems from their ability to find a set of Pareto solutions rather than any single, aggregated optimal solution for a multi-objective problem. As for single-objective evolutionary algorithms (SOEA), multi-objective evolutionary algorithms also require parameter tuning to achieve desirable performance. In the literature we can find fuzzy logic controllers (FLC's) applied to online parameter control for SOEA. In this paper, we propose to use a FLC to dynamically adjust the parameters of a particular multi-objective differential evolution (MODE) algorithm. The fuzzy logic controlled multi-objective differential evolution (FLC-MODE) is applied to a suite of benchmark functions. Its results are compared to those obtained by using MODE with constant parameter settings. We show that the FLC-MODE obtains better results in 80% of the testing examples. Given that the benchmarks were synthetic test functions, we designed the FLC using only our understanding of the working mechanism of the MODE, without incorporating any additional problem-specific knowledge. When addressing real-world applications, we expect the FLC to be an excellent way for representing and leveraging their associated heuristic knowledge

Evolutionary multiobjective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multiobjective problem. The purpose is to describe a newly developed evolutionary approach-Pareto-based multiobjective differential evolution (MODE). The concept of differential evolution, which is well-known in the continuous single-objective domain for its fast convergence and adaptive parameter setting, is extended to the multiobjective problem domain. A Pareto-based approach is proposed to implement the differential vectors. A set of benchmark test functions is used to validate this new approach. We compare the computational results with those obtained in the literature, specifically by strength Pareto evolutionary algorithm (SPEA). It is shown that this new approach tends to be more effective in finding the Pareto front in the sense of accuracy and approximate representation of the real Pareto front with comparable efficiency.

Agility is important to modern enterprises. The effective coordination of large numbers of potential suppliers and manufacturer, demands a scientific methodology rather than just practical experience to make decisions on supply manufacturing planning problems. Particularly in cases where multiple decision objectives are important to process planning, empirical decisions are insufficient. This paper introduces formal methods to solve such multi-objective decision problems involved in general supply manufacturing planning, and specifically describes the extension of differential evolution methods to discrete problem domains. An enterprise planning problem with two objectives-cycle time and cost is used as a principal example. Such multi-objective optimization problems usually are very large and nonlinear. In this paper, the concept of differential evolution, which is well-known in single-objective continuous domain for its fast convergence and adaptive parameter setting, is extended to the discrete domain by introducing greedy probability, mutation probability, and crossover probability. Moreover, this concept is extended to discrete multi-objective optimization problem. The proposed discrete multi-objective differential evolution, or D-MODE algorithm is applied to obtain Pareto solutions of this general planning problem. A practical example in the electronics industry is used as an illustrative example to demonstrate the effectiveness of the proposed D-MODE.

Dynamic network-based decision systems search the information in the distributed databases and provide an appropriate solution for the design-supplier-manufacturing planning problem using evolutionary algorithms. The paper focuses on the development of a performance model to support such enterprise-level decision-making in network based scalable systems. Generalized Stochastic Petri Nets (GSPNs) are introduced to characterize network traffic and evolutionary algorithms. The network traffic model is based on the hyperexponential transition for analytical tractability. The algorithm model transforms the execution of the program into a stochastic activity net. The performance evaluation of the system can be explored in two directions: first, analyze and reconfigure the network connection for a specific algorithm, and second, given the network configuration, predict the performance of the algorithm. The results show that transient analysis is more important than steady-state analysis in the heavy-tailed network traffic. The paper also compares performance of the algorithms under different network configurations.

Agile electronics manufacturing requires integrated design, supply and manufacturing planning for modular products where suppliers and manufacturing resources is network distributed. This research is concerned with the modeling of such a distributed manufacturing network. The performance of network-based distributed decision systems is dominated by the network configuration and related access delays. Traffic measurement studies observed heavy-tails in the network traffic. Our basic approach is based on the quantile match to fit hyperexponential distributions to heavy-tailed distribution. We adopt the ON/OFF network traffic model and compare the fitted hyperexponential model with the heavy-tailed model. The results validate our fitting hyperexponentials. Generalized stochastic Petri nets (GSPNs) are introduced to analyze both the steady state and the transient behaviors of the distributed system. The results indicate that the transient analysis is more important than the steady-state analysis in network traffic modeling, since in most cases the distributed network does not reach the steady state.

In recent years, the hybrid control framework has received attention from the research community. Variations of this control framework are available in the literature. In this paper, a hybrid intelligent agent-based scheduling and control system architecture is presented for an actual industrial warehouse order-picking problem, where goods are stored at multiple locations and the pick location of goods can be selected dynamically in near-real time. The presented architecture includes a higher level optimizer, a middle-level guide agent, and lower level agents. The need for a higher level optimizer and communication between higher and lower level controllers is demonstrated. A mathematical model and a genetic algorithm for the resource assignment problem are presented. Simulation results demonstrating efficiency of the new approach are also presented.

Describes a framework to support enterprise-level decision-making in network-based scalable systems. The fundamental principle of this approach asserts that decision tasks over a set of multiple, distributed, logically interrelated databases may be cast in the semantics of the underlying distributed database management system (DBMS). In this form, the augmented relational decision framework takes advantage of the principles of normalization and decomposition that are inherent to the relational DBMS model. The decision process is viewed as an instantiation of the relational schema, and the resulting representation of normal form hierarchies and data dependencies structures the process. Definition of a value function (or rule set) over the augmented relational decision space guides the search for desirable instances of the decision relations that constitute the suggested outcomes.

Organizations in today's business environment often participate in short term alliances to manufacture sophisticated products. There are often a large number of potential designs for such products and no elementary technique for creating an optimal design. As a result, it is often necessary to utilize some form of computer algorithm to obtain a nearly optimal design. A variety of such algorithms exist including generic ones such as random search and genetic algorithms as well as application-specific algorithms. Any optimization algorithm will require information from a number of databases maintained by the individual companies in the alliance. Hence, the optimization is distributed over a wide area network of computers. The paper examines optimization algorithms which may be used to design printed circuit boards in this distributed manufacturing environment.

Existing modeling frameworks for manufacturing system control can be classified into hierarchical, heterarchical, and hybrid control frameworks. The main drawbacks of existing frameworks are discussed in this paper. A new hybrid modeling framework is also described. It is a hybrid of the two: hierarchical and heterarchical frameworks. In this proposed framework, entities (e.g., parts) and resources (e.g., material handling devices, machines, cells, departments) are modeled as holonic structures that use intelligent agents to function in a cooperative manner so as to accomplish individual, as well as cell-wide and system-wide objectives. To overcome the structural rigidity and lack of flexibility, negotiation mechanisms for real-time task allocation are used. Lower-level holons may autonomously make their negotiations within the boundary conditions that the higher-level holons set. Horizontal, as well as vertical decisions, are made between various levels of controllers, and these are explicitly captured in the model.

The scheduling and dispatching problem for flexible assembly systems operating in a MRP environment is described. These flexible assembly systems (FAS) can perform the required operations on multiple product types which are assembled simultaneously. A non-exact procedure is presented as the basis for SCHEDULER and DISPATCHER functionality in a hierarchical scheduling system design.