Yin-Yann Chen

A genetic algorithm for the multi-stage and parallel-machine scheduling problem with job splitting – A case study for the solar cell industry

This paper studies a multi-stage and parallel-machine scheduling problem with job splitting which is similar to the traditional hybrid flow shop scheduling (HFS) in the solar cell industry. The HFS has one common hypothesis, one job on one machine, among the research. Under the hypothesis, one order cannot be executed by numerous machines simultaneously. Therefore, multiprocessor task scheduling has been advocated by scholars. The machine allocation of each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage is determined. However, machine allocation and production sequence decisions are highly interactive. As a result, this study, motivated from the solar cell industry, is going to explore these issues. The multi-stage and parallel-machine scheduling problem with job splitting simultaneously determines the optimal production sequence, multiprocessor task scheduling and machine configurations through dynamically splitting a job into several sublots to be processed on multiple machines. We formulate this problem as a mixed integer linear programming model considering practical characteristics and constraints. A hybrid-coded genetic algorithm is developed to find a near-optimal solution. A preliminary computational study indicates that the developed algorithm not only provides good quality solutions but outperforms the classic branch and bound method and the current heuristic in practice.

A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry

This paper studies a solar cell industry scheduling problem, which is similar to traditional hybrid flowshop scheduling (HFS). In a typical HFS problem, the allocation of machine resources for each order should be scheduled in advance. However, the challenge in solar cell manufacturing is the number of machines that can be adjusted dynamically to complete the job. An optimal production scheduling model is developed to explore these issues, considering the practical characteristics, such as hybrid flowshop, parallel machine system, dedicated machines, sequence independent job setup times and sequence dependent job setup times. The objective of this model is to minimise the makespan and to decide the processing sequence of the orders/lots in each stage, lot-splitting decisions for the orders and the number of machines used to satisfy the demands in each stage. From the experimental results, lot-splitting has significant effect on shortening the makespan, and the improvement effect is influenced by the processing time and the setup time of orders. Therefore, the threshold point to improve the makespan can be identified. In addition, the model also indicates that more lot-splitting approaches, that is, the flexibility of allocating orders/lots to machines is larger, will result in a better scheduling performance.

Transferring cognitive apprenticeship to manufacturing process knowledge management system

Purpose – This study aims to develop a manufacturing process management system which aims to benefit the excavation, collection and search of the mentors’ experience and knowledge. The coating painting industry is a typical small and medium-sized traditional industry and usually depends on masters experience to solve the manufacturing problem. Design/methodology/approach – Based on the characteristics and manufacturing process of the architectural coating industry, this study develops a practical knowledge management system (KMS) with two stages. The first stage collects and analyzes manufacturing process data, and the second stage constructs the KMS of the manufacturing process. Findings – This manufacturing process KMS can accumulate and share the operators’ experience and knowledge systematically; this KMS not only improves the apprentices’ skill and problem-solving abilities but also enhances the enterprise’s overall product quality, undoubtedly. Research limitations/implications – This manufacturing...

Genetic Algorithm Approach for Multi-Objective Optimization of Closed-Loop Supply Chain Network

This paper applies multi-objective genetic algorithm (MOGA) to solve a closed-loop supply chain network design problem with multi-objective sustainable concerns. First of all, a multi-objective mixed integer programming model capturing the tradeoffs between the total cost and the carbon dioxide (CO2) emission is developed to tackle the multi-stage closed-loop supply chain design problem from both economic and environmental perspectives. The multi-objective optimization problem raised by the model is then solved using MOGA. Finally, some experiments are made to measure the performance.

Multi-stage Parallel Machines and Lot-Streaming Scheduling Problems – A Case Study for Solar Cell Industry

This research focuses on a parallel machines scheduling problem considering lot streaming which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be determined in advance. In addition, the size of each sublot is splited by parallel machines configuration. However, allocation of machine resources, sublot size and lot sequence are highly mutual influence. If allocation of machine resources has been determined, adjustment on production sequence is unable to reduce production makespan. Without splitting a given job into sublots, the production scheduling cannot have overlapping of successive operations in multi-stage parallel machines environment thereby contributing to the best production scheduling. Therefore, this research motivated from a solar cell industry is going to explore these issues. The multi-stage and parallel-machines scheduling problem in the solar cell industry simultaneously considers the optimal sublot size, sublot sequence, parallel machines sublot scheduling and machine configurations through dynamically allocating all sublot to parallel machines. We formulate this problem as a mixed integer linear programming (MILP) model considering the practical characteristics including parallel machines, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. A hybrid-coded particle swarm optimization (HCPSO) is developed to find a near-optimal solution. At the end of this study, the result of this research will compare with the optimization method of mixed integer linear programming and case study.

Closed-Loop Sustainable Supply Chain Design Under Uncertainties

This paper studies an integrated forward and reverse (closed-loop) supply chain network design problem with sustainable concerns under the uncertain environment. We are interested in the logistics flow, capacity expansion, and technology investments of existing and potential facilities in the multi-stage closed-loop supply chain. First, a deterministic multi-objective mixed integer programming model capturing the tradeoffs between the total cost and the carbon dioxide (CO2) emission is developed to tackle the multi-stage closed-loop supply chain design problem from both economic and environmental perspectives. Then, due to the uncertainty in supply side, customer demand and return quantities, the robust counterpart of the proposed multi-objective supply chain design model is presented using the robust optimization theory. Both deterministic and robust multi-objective supply chain design models are transformed into single-objective models to obtain non-dominated compromise solutions using LP-metrics-based compromise programming method. In the numerical evaluation and results, we analyzed the relationship between the total cost and carbon emission in integrated supply chain network and verified robustness of the proposed robust multi-objective supply chain design model by the generated non-dominated compromise supply chain design solutions.

A Multi-stage and Parallel-Machine Scheduling Problem for Solar Cell Industry

This paper studies a multi-stage and parallel-machines scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS) in the solar cell industry. The multi-stage and parallel-machines scheduling problem in the solar cell industry simultaneously determines the optimal production sequence, multiprocessor task scheduling and machine configurations through dynamically allocating all jobs to multiple machines. We formulate this problem as a mixed integer linear programming model considering the practical characteristics and constraints. A hybrid-coded genetic algorithm is developed to find a near-optimal solution. Preliminary computational study indicates that the developed algorithm not only provides good quality solutions.