Shu-Hsing Chung

A simulated annealing algorithm for manufacturing cell formation problems

The cell formation problem determines the decomposition of the manufacturing cells of a production system in which machines are assigned to these cells to process one or more part families so that each cell is operated independently and the intercellular movements are minimized or the number of parts flow processed within cells is maximized. In this study, a simple yet effective simulated annealing-based approach, SACF, is proposed to solve the cell formation problem. Considerable efforts are devoted to the design of parts and machine assignment procedures to direct SACF to converge to solutions with good values of grouping efficacy. A set of 25 test problems with various sizes drawn from the literature is used to test the performance of the proposed heuristic algorithm. The corresponding results are compared to several well-known algorithms published. The comparative study shows that the proposed SACF algorithm improves the grouping efficacy for 72% of the test problems. The proposed algorithm should thus be useful to both practitioners and researchers.

Hybrid simulated annealing algorithm with mutation operator to the cell formation problem with alternative process routings

In this study, a hybrid simulated annealing algorithm with mutation operator is proposed to solve the manufacturing cell formation problem considering multiple process routings for parts, so that either the intercellular movements are minimized or the grouping efficacy is maximized, depending on the definition of the decision objective. The proposed algorithm is designed mainly to explore solution regions efficiently and to expedite the solution search process. The performance of the proposed algorithm is tested by a range of test problems, some of which are from the literature and some of which are generated within this study. The comparative study shows that the proposed algorithm improves the best results found in the literature for 28.6% of the test problems and the percentages of improvement are even higher than 18% in several test instances.

Minimising makespan on parallel batch processing machines with non-identical ready time and arbitrary job sizes

This paper considers the parallel batch processing machine scheduling problem which involves the constraints of unequal ready times, non-identical job sizes, and batch dependent processing times in order to sequence batches on identical parallel batch processing machines with capacity restrictions. This scheduling problem is a practical generalisation of the classical parallel batch processing machine scheduling problem, which has many real-world applications, particularly, in the aging test operation of the module assembly stage in the manufacture of thin film transistor liquid crystal displays (TFT-LCD). The objective of this paper is to seek a schedule with a minimum total completion time for the parallel batch processing machine scheduling problem. A mixed integer linear programming (MILP) model is proposed to optimise the scheduling problem. In addition, to solve the MILP model more efficiently, an effective compound algorithm is proposed to determine the number of batches and to apply this number as one parameter in the MILP model in order to reduce the complexity of the problem. Finally, three efficient heuristic algorithms for solving the large-scale parallel batch processing machine scheduling problem are also provided.

An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations

Cell formation is the first step in the design of cellular manufacturing systems. In this study, an efficient tabu search algorithm based on a similarity coefficient is proposed to solve the cell formation problem with alternative process routings and machine reliability considerations. In the proposed algorithm, good initial solutions are first generated and later on improved by a tabu search algorithm combining the mutation operator and an effective neighborhood solution searching mechanism. Computational experiences from test problems show that the proposed approach is extremely effective and efficient. When compared with the mathematical programming approach which took three hours to solve problems, the proposed algorithm is able to produce optimal solutions in less than 2s.

A case study on the multistage IC final testing scheduling problem with reentry

Abstract The integrated-circuit final testing scheduling problem (ICFTSP) with reentry is a variation of the complex flow-shop scheduling problem, which is also a generalization of the classical reentrant flow batch process problem, and the identical parallel machine problem. In this paper, we present a case study on the ICFTSP with reentry, which is taken from a final testing shop floor in an integrated circuit manufacturing factory. For the case investigated, the jobs are clustered by their product types, which must be processed on groups of parallel machines at various process stages following the manufacturing sequence, which must be completed before the due dates. The job processing time depends on the product type, and the machine setup time is sequentially dependent on the orders of jobs processed. The objective is to schedule jobs without violating all constraints, while the total machine workload is minimized. Since the ICFTSP has reentry characteristic, and involves job processing precedence, serial-processing stage, batch-processing stage, job clusters, job-cluster dependent processing time, due dates, machine capacity, and sequence dependent setup time, it is more difficult to solve than the classical flow-shop scheduling problem. We present three fast network algorithms to efficiently solve the ICFTSP with reentry and provide a performance comparison between the three algorithms on eight test problems.

A water flow-like algorithm for manufacturing cell formation problems

Available research on the manufacturing cell formation problem shows that most solution approaches are either single- or multiple-solution-agent-based, with a fixed size of solution agents. Frequent problems encountered during the process of solving the cell formation problem include solutions being easily trapped in local optima and bad solution efficiency. Yang and Wang [Yang, F.-C., Wang, Y.-P., 2007. Water flow-like algorithm for object grouping problems. Journal of the Chinese Institute of Industrial Engineers, 24 (6), 475-488] proposed the water flow-like algorithm (WFA) to overcome the shortcomings of single- and multiple-solution -agent-based algorithms. WFA has the features of multiple and dynamic numbers of solution agents, and its mimicking of the natural behavior of water flowing from higher to lower levels coincides exactly with the process of searching for optimal solutions. This paper therefore adopts the WFA logic and designs a heuristic algorithm for solving the cell formation problem. Computational results obtained from running a set of 37 test instances from the literature and newly created have shown that the proposed algorithm has performed better than other benchmarking approaches both in solution effectiveness and efficiency, especially in large-sized problems. The superiority of the proposed WFACF over other approaches from the literature should be attributed to the collaboration of the WFA logic, the proposed prior estimation of the cell size, and the insertion-move. The WFA is a novel heuristic approach that deserves more attention. More attempts on adopting the WFA logic to solve many other combinatorial optimization problems are highly recommended.

A DEA window analysis on the product family mix selection for a semiconductor fabricator

In a competitive market, semiconductor fabricator must face an environment with multi-product types, multi-priority orders and demand changes in time. Since semiconductor fabrication has a very complicated production process, the above-stated characteristics make the production planning even more difficult. This paper applies data envelopment analysis (DEA) to find a set of product family mix that is efficient for the company to produce. To ensure long-term effectiveness in productivity and in profit gaining, window analysis is adopted to seek the most recommended set of product family mixes for manufacturing by measuring the performance changes over time. With this method, the performance of a mix in one period is compared not only with the performance of other mixes but also with its own performance in other periods. The proposed mechanism can provide guidance to the fabricator regarding strategies for aggregate planning so as to improve manufacturing efficiency.

Due-Date Assignment for Wafer Fabrication Under Demand Variate Environment

In the semiconductor industry, dynamic changes in demand force companies to change the product mix frequently and periodically. Assigning tight but attainable due dates is a great challenge under the circumstances that the product mix changes periodically. In this paper, we consider the due-date assignment problem for wafer fabrication and present a due-date assignment model to set manufacturing due dates satisfying the target on-time-delivery rate. The contamination model is applied to tackle the effect of that product mix varies periodically. We demonstrate the effectiveness and accuracy of the proposed model by solving a real-world example taken from a wafer fabrication shop floor in an IC manufacturing factory

Minimizing the total machine workload for the wafer probing scheduling problem

The Wafer Probing Scheduling Problem (WPSP) is a practical generalization of the parallel-machine scheduling problem, which has many real-world applications, particularly, in the Integrated Circuit (IC) manufacturing industry. In the wafer probing factories, the jobs are clustered by their product types, which must be processed on groups of identical parallel machines and be completed before the due dates. The job processing time depends on the product type, and the machine setup time is sequentially dependent on the orders of jobs processed. Since the wafer probing scheduling problem involves constraints on job clusters, job-cluster dependent processing time, due dates, machine capacity, and sequentially dependent setup time, it is more difficult to solve than the classical parallel-machine scheduling problem. In this paper, we consider the WPSP and formulate the WPSP as an integer programming problem to minimize the total machine workload. We demonstrate the applicability of the integer programming model by solving a real-world example taken from a wafer probing shop floor in an IC manufacturing factory.

A case study on the wafer probing scheduling problem

The wafer probing scheduling problem (WPSP) is a practical generalization of the classical parallel-machine scheduling problem, which has many real-world applications, particularly, in the integrated circuit (IC) manufacturing industry. In this paper, a case study on the WPSP is presented, which is taken from a wafer probing shop floor in an IC manufacturing factory. For the WPSP case investigated, the jobs are clustered by their product types, which are processed on groups of identical parallel machines and must be completed before the due dates. The job processing time depends on the product type, and the machine setup time is sequentially dependent on the orders of jobs processed. The objective in this case is to seek a probing job schedule with minimum total machine workload. Since the WPSP case investigated involves constraints on job clusters, job-cluster dependent processing time, due dates, machine capacity, and sequentially dependent setup time, it is more difficult to solve than the classical parallel-machine uling problem. The WPSP is formulated as an integer programming problem and the problem solved using the powerful CPLEX with effective implementation strategies. An efficient solution procedure to solve the WPSP near-optimally is proposed.