This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.

The due-date bargainer is a useful tool to support negotiation on due dates between a manufacturer and its customers. To improve the computational performance of an earlier version of the due-date bargainer, we present a new soft computing approach. It uses a genetic algorithm to find the best priority sequence of customer orders for resource allocation and fuzzy logic operations to allocate the resources and determine the order-completion times, following the priority sequence of orders. To extend the due-date bargainer to accommodate bargaining with several customers at the same time, we propose a method to distribute the total penalty using marginal penalties for the individual bargainers. A demonstration software package implementing the improved due-date bargainer has been developed. It is targeted at apparel manufacturing enterprises. Experiments using realistic resource data and randomly generated orders have achieved satisfactory results.

For a make-to-order manufacturing system, the uncertainty and flexibility of the due dates required by customers and the production capacity owned by the manufacturer can be modeled in a fuzzy environment. In this paper, a combined due-date assignment and production planning methodology for the make-to-order manufacturing systems is developed. The fuzzy approach determines the optimal due dates for the manufacturer based upon a "rough-cut" resource balance, while a customer can request earlier due dates by paying a higher price to cover the extra manufacturing cost incurred. The resulting fuzzy due-date bargainer is exercised using manufacturing resource planning (MRPII) data from a furniture manufacturing company. Experimental results indicate its potential as a useful tool for real applications.

Due to the nondeterministic nature of the business environment of a manufacturing enterprise, it is more appropriate to describe the aggregated production planning by using a fuzzy mathematical programming model. In this paper, a genetics-based inexact approach is proposed to imitate the human decision procedure for production planning. Instead of locating one exact optimal solution, the proposed approach finds a family of inexact solutions within an acceptable level by adopting a mutation operator to move along a weighted gradient direction. Then, a decision maker can select a preferred solution by examining a convex combination of the solutions in the family via the human-computer interaction. Our computational experiments illustrate how the enterprise managers can be more satisfied by this new approach than others.

In any business process reengineering (BPR) project, a thorough understanding of various tasks and activities of the organization is required. Very often this idea is captured using a simple flow chart or static representation diagram. The weakness here is that the process design complexity is not adequately represented by the use of flow charts, and this allows for limited human-computer interaction during the process design and analysis. In this paper, we propose an enhanced flow chart approach; the concept of activity-section flow chart to support BPR, which is a combination of the existing activity flow chart and section flow chart. Using this approach, a human-computer interactive model for BPR is developed. This model can identify the unreasonable activity loops and excessive business rounds between sections by the adjacent and reachable matrices. Via the human-computer interaction, the process can be revised by human experience. This approach provides an e

In this paper, we present 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 nonanalytical 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 fire power station construction project. The results show us that the suggested approach is possible to quickly achieve optimal solution for large size problems.

An inexact approach is proposed to solve objective/resource type of fuzzy linear programming problems. Instead of finding an exact optimal solution, this approach uses a recommended genetic algorithm with mutation along the weighted gradient direction to find a family of inexact solutions with acceptable membership degree. Then, by means of the human-computer interaction, the solution preferred by the decision-maker can be achieved by a convex combination of the solutions selected from the family. The numerical analysis has shown that the more satisfactory results can be achieved by this new approach.

To closely describe the earliness/tardiness production planning problems in the JIT environment, a nonlinear semi-infinite programming model is proposed. Due to the issues of non-convexivity and having infinitely many constraints, instead of applying traditional optimization ap-proaches, a specially designed genetic algorithm with mutation along the negative gradient direction is developed. The proposed algorithm is a combination of the steepest descent method with the stochastic sampling algorithm. Some numerical results are included to show its potential for industrial applications.

Production scheduling of grouped jobs has been an active research area since GT (Group Technology) was widely applied in practical manufacturing systems. To minimize the total flowtime of grouped jobs on a single machine, we combine jobs into fundamental runs based upon the necessary condition of the optimal solution. It is proved that the optimal solution is a combination of fundamental runs. A genetic algorithm is designed based on studies on the combinatorial rules of fundamental runs. The numerical results show that the computational performance of the algorithm depends on the number of `fundamental' runs, not on the number of jobs. In general, the number of fundamental runs is far less than the number of jobs. Therefore, the algorithm has potential for practical application in large scale production systems.