Chen-Yang Cheng

Distributed feedback control algorithm in an auction-based manufacturing planning and control system

In todays manufacturing enterprise, customer service performance is highly dependent on the effectiveness of the companys manufacturing planning and control system (MPCS). Most of the current MPCSs that employ the centralised planning approach can have drawbacks, such as structural rigidity, difficulty in designing a control system, and lack of flexibility. An auction-based manufacturing planning and control system (AMPCS) allows negotiation-based decision making, however, the distributed scheduling algorithm usually attempts to achieve only its objective without considering the global objective, and a contradiction problem might occur between the local objective and the overall system performance. Therefore, the objective of this research is to develop a distributed scheduling algorithm called a closed-loop feedback simulation (CLFS) approach for an AMPCS which includes adaptive control of the auction-based bidding sequence to prevent the first bid first serve rule and may dynamically allocate production resources to operations. CLFS iteratively adjusts the bidding sequence using the deviation between the predicted completion time and the due date to improve the scheduling performance. The results obtained from the computational experiments show that the proposed CLFS algorithm can obviously improve production performance compared to previous studies.

Demand-pull replenishment model for hospital inventory management: a dynamic buffer-adjustment approach

Hospital inventory management faces a continually increasing challenge to ensure the availability of medical and surgical supplies at the lowest inventory cost. For overcoming the drawbacks of existing re-order point approaches commonly applied in hospital materials replenishment management, this research presents an innovative demand-pull replenishment approach named the dynamic drum-buffer-rope (DDBR) replenishment model. The DDBR model is implemented using a system dynamics approach in which two essential mechanisms – the demand-pull characteristics and dynamic buffer-adjustment activities – are simulated and experimented on. To determine appropriate buffer sizes and replenishment quantities, this research adopted Powell search algorithm to achieve the objective of no stock-out occurrence and low inventory cost. The evaluation of the proposed DDBR model in a real hospital case through a series of comparisons shows that the DDBR model can determine optimal replenishment timing and quantity for total inventory cost with no stock-out occurrence.

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.

Application of fault tree analysis to assess inventory risk: a practical case from aerospace manufacturing

In this case study, an aerospace manufacturer seeks to decrease inventory and improve its turnover rate using fault tree analysis (FTA). Historically, the company controlled its inventory using material capacity planning, material requirement planning modules provided as part of Enterprise Resource Planning and a self-developed inventory management system. Historical data indicated a rising trend in the inventory turnover rate, and the total inventory cost is substantial, up to US

Transferring cognitive apprenticeship to manufacturing process knowledge management system

260 million. This study uses a FTA approach to prioritise improvement options on the basis of risk reduction. Study results showed that risk-based decision-making, supported by FTA, is useful in handling the inventory problem. Inventory turnover rate indicates that inventory level has been improved 30% using the FTA.

Flexible supply network planning for hybrid shipment: a case study of memory module industry

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...

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

The modern supply chain network has geographically spread out across the globe. The performance of a customer service level is highly dependent on the effectiveness of its supply chain planning. To improve the service provided to downstream customers, planners must not only decide order allocation among multiple distribution centres but also consider reducing the order-to-delivery time. Directed shipment delivery from manufacturing sites provides the flexibility of direct shipment; however, it also makes order allocation more difficult. In this study, a flexible supply network planning (FSNP) model based on integer linear programming is developed for the memory module industry. In addition to multisite order allocation planning, the FSNP model explicitly considers directed shipment from manufacturing sites for reducing the order-to-delivery time. Furthermore, the combination of characteristics of the memory module industry, such as multilevel and multisite production environments, multiple-to-multiple product structures, transportation and production lead times and capacity constraints, makes FSNP highly complicated. The results of the experiments reveal that the FSNP model improves supply chain planning regarding order due date and inventory and transportation costs.

A Multi-stage and Parallel-Machine Scheduling Problem 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.

A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study 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.