Nguyen Van Hop

A heuristic solution for fuzzy mixed-model line balancing problem

Abstract This paper addresses the mixed-model line balancing problem with fuzzy processing time. A fuzzy binary linear programming model is formulated for the problem. This fuzzy model is then transformed to a mixed zero–one program. Due to the complexity nature in handling fuzzy computation, new approximated fuzzy arithmetic operation is presented. A fuzzy heuristic is developed to solve this problem based on the aggregating fuzzy numbers and combined precedence constraints. The general idea of our approach is to arrange the jobs in a sequence by a varying-section exchange procedure. Then jobs are allocated into workstations based on these aggregated fuzzy times with the considerations of technological constraint and cycle time limit. Promising results are obtained by experiments.

Fuzzy stochastic goal programming problems

In this paper, we present a model to measure attainment value of fuzzy stochastic goals. Then, the new measure is used to de-randomize and de-fuzzify the fuzzy stochastic goal programming problem and obtain a standard linear program (LP). A numerical example is provided to illustrate the proposed method.

The scheduling problem of PCBs for multiple non-identical parallel machines

Abstract The scheduling problem of n printed circuit boards (PCBs) for m non-identical parallel machines is considered in this paper. The problem has to deal with three issues: (i) classifying the PCBs into m groups corresponding to m machines, (ii) sequencing the boards for each machine, and (iii) component switching (component unloading/loading) from the machine magazine. A general objective is to minimize the total makespan, which is shown here to be the same as minimizing the maximum of number of component switches. The complete problem is complex, and is usually dealt in stages, which may not yield a good solution. We model the problem in an integrated manner using weighted multiple objectives to deal with grouping of the boards, load balancing at each machine, board sequencing and component switching at a machine. A composite genetic algorithm is developed to solve this multi-objective problem. The integrated solution is encoded as a string of pair values for each group of boards. The first number indicates the board membership in a group, and the second one represents the sequencing position of a board in that group. A new population of solutions is generated by using both binary genetic operators for grouping and genetic operators for board sequencing. A fitness function evaluates workload balancing, board similarities and total setup time simultaneously. Experiments are designed and run to test the proposed methodology, and the results show that the solutions are efficient, and are obtained within a reasonable amount of time.

Approach to measure the mix response flexibility of manufacturing systems

The mix response flexibility of a manufacturing system is the ability to change between product types quickly and economically. This paper proposes a new approach to measure this flexibility in terms of both capability and capacity. While the processing capability is represented as the number of operations that the machines can perform, the manufacturing capacity is modelled as the efficiency of different machines. The proposed model is applied to measure the mix response flexibility for both single and multiple machine systems.The mix response flexibility of a manufacturing system is the ability to change between product types quickly and economically. This paper proposes a new approach to measure this flexibility in terms of both capability and capacity. While the processing capability is represented as the number of operations that the machines can perform, the manufacturing capacity is modelled as the efficiency of different machines. The proposed model is applied to measure the mix response flexibility for both single and multiple machine systems.

Multiple criteria approach for solving feeder assignment and assembly sequence problem in PCB assembly

The feeder assignment and assembly sequence problem in printed circuit board (PCB) assembly with the twin objectives of minimizing magazine travel time and minimizing board travel time is presented in this study. The problem uses Dynamic Pick-and-Place (DPP) model where robot arm, board and magazine move together with different speeds based on relative coordinates between consecutive assembled points. The difficulty of the problem is that the feeder assignment depends on assembly sequence and vice versa. A new approach is proposed to improve the existing approaches. The trade-off between two strategies, assembly by area and assembly by component types, can give better results. The numerical experiments proved the efficiency of the new algorithm.

Extended dynamic point specification approach to sequencing robot moves for PCB assembly

In PCB assembly planning, most of the current works solved the assembly sequence and feeder assignment problem directly. They have not decomposed the problem into subproblems. Consequently, the problem remains very complex and, therefore, it is hard to solve. Few research works considered about the detail on how to determine the optimal picking points (or placement points), but results are still limited. The extension of these works is necessary. This paper extends the point specification problem based on the Dynamic Pick and Place (DPP) model, namely, the Extended Dynamic Pick and Place (EDPP0 model. The EDPP determined the picking/placement point coordinates from global consideration of point relationship in the system. The whole system is analysed to establish the new model. From this guideline the system is modelled in a systematic scheme based on the objectives of each object in the systems (robot arm, feeder rack, board table). These objectives should be satisfied together to reach the global objective of the system. Numerical experiment shows that the EDPP model is better than the DPP model in terms of total assembly time.

Fuzzy multi-attribute decision-making for grouping of electronic components in process planning

The grouping of objects problem has a wide-range of applications in industry. In electronic assembly planning, grouping components play an important role as they can reduce drastically the number of possible sequences of operations. This paper considers the grouping problem of electronic component families based on their multiple attributes. The difficulty lies with the conflict of criteria when selecting a certain component into a group. In order to overcome this difficulty, the proposed approach introduces the concept of fuzziness, which expresses the degree of concordance on the decision. An example of classification electronic components is presented to illustrate the proposed method.

Board sequencing and component loading problem for a single machine in PCB assembly planning

Most current research sequentially solves the board sequencing and component-loading problem for a single machine in printed circuit board (PCB) assembly. The board sequence is first determined. The component-loading order is then established. In addition, other current approaches are used to solve the problem based on the upper bound on component changeovers between two jobs. However, consideration of current magazine status could be a source for total set-up time reduction. In this paper, a new approach is developed to establish board sequence and component-loading order simultaneously. Each board sequence position and component-loading order are obtained together from the process of current set-up determination. The obtained solution is then improved further by the combination of bias and random search processes. Some experiments are tested to confirm theoretical judgement.Most current research sequentially solves the board sequencing and component-loading problem for a single machine in printed circuit board (PCB) assembly. The board sequence is first determined. The component-loading order is then established. In addition, other current approaches are used to solve the problem based on the upper bound on component changeovers between two jobs. However, consideration of current magazine status could be a source for total set-up time reduction. In this paper, a new approach is developed to establish board sequence and component-loading order simultaneously. Each board sequence position and component-loading order are obtained together from the process of current set-up determination. The obtained solution is then improved further by the combination of bias and random search processes. Some experiments are tested to confirm theoretical judgement.

Improvement of Search Process in Genetic Algorithms: An Application of PCB Assembly Sequencing Problem

The challenge of modeling real-world problems, especially the class of NP-complete problems, is that the solution space is usually very large. It often requires very large time to examine all possible solutions to choose the best one. It is sometimes impossible to find the real optimum solution and we have to be satisfied with a certain pseudo-optimum one. There are many optimization and search methods to obtain the pseudo-optimum solutions. In general, these procedures start by checking some given number of variants, then change the search direction towards the more promising area, and finally pick up the best among the examined ones. In these methods, the key point is on how to guide the search process.