Back to results list
Please use this identifier to cite or link to this item:
|Title:||Intelligent production scheduling for mould making|
|Authors:||Tang, Lap Ying|
Production scheduling -- China -- Hong Kong -- Case studies.
Plastics industry and trade -- Management.
Injection molding of plastics -- Management.
Hong Kong Polytechnic University -- Dissertations
|Publisher:||The Hong Kong Polytechnic University|
|Abstract:||Scheduling is an important decision-making process in manufacturing industries. Scheduling problems are usually modeled and solved in a mathematically feasible way. As a result, the solutions generated from these greatly simplified problems are infeasible for real-life cases. The complexity and instability of production systems are still underestimated in many scheduling techniques in academic literature. Furthermore, most of the scheduling techniques are problem-specific; and the flexible production model in mould shop has been rarely studied. It is important to develop an appropriate scheduling algorithm to meet the industry's need. Asahi (H.K.) Ltd. is a plastic product and plastic injection mould manufacturer. Their products are diversified, including electronic product dummies and accessories. These products are mainly produced by thermoplastic injection moulding, and specific mould has to be prepared before injection moulding of any plastic part. The tooling department of Asahi (H.K.) Ltd. is responsible for the mould design and manufacture. Due to the uniqueness of its injection moulds, the components in each mould are different. Different operations and processing routes have to be taken. This high flexibility component variety however causes difficulty in today’s quick response production planning and scheduling. In order to efficiently find an optimal schedule for real life mould shop, this research thus proposed to tackle the Asahi’s mould shop scheduling problem with new heuristic and meta-heuristic algorithms. They are hybrid Nawaz-Enscore-Ham (NEH), Random Keys Harmony Search (RKHS), and Random Keys Genetic Algorithm (RKGA). For the meta-heuristic algorithms, different parameter values are selected for parameter tuning and choice of parameters is provided. These constraint-handling-free algorithms are implemented with MATLAB. The random keys representation can avoid the existence of duplicated position value in sequencing after re-sequencing. The computational results demonstrate that RKGA performs the best among the proposed algorithms. In average, the best value RKGA generated is 3.84% better than the best value of the proposed heuristic algorithms. Data adapted from Asahi’s production were tested. All the algorithms can finish computation within 10 seconds. It is suggested that the proposed algorithms can be applied in different scheduling problems in future study.|
|Description:||xvii, 144 leaves : illustrations (some color) ; 30 cm|
PolyU Library Call No.: [THS] LG51 .H577M ISE 2014 Tang
|Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
Show full item record
Files in This Item:
|b2780561x_link.htm||For PolyU Users||203 B||HTML||View/Open|
|b2780561x_ir.pdf||For All Users (Non-printable)||3.62 MB||Adobe PDF||View/Open|
Checked on May 28, 2017
Checked on May 28, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.