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Journal articles on the topic 'Advanced planning and scheduling'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Katoh, Naoki, Hiroshi Kise, Takayoshi Tamura, et al. "Preface on Special Issue on Advanced Planning and Scheduling for Supply Chain Management(Advanced Planning and Scheduling for Supply Chain Management)." Journal of the Operations Research Society of Japan 49, no. 3 (2006): 151. http://dx.doi.org/10.15807/jorsj.49.151.

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2

Selvarajah, Esaignani, and George Steiner. "BATCH SCHEDULING IN CUSTOMER-CENTRIC SUPPLY CHAINS(Advanced Planning and Scheduling for Supply Chain Management)." Journal of the Operations Research Society of Japan 49, no. 3 (2006): 174–87. http://dx.doi.org/10.15807/jorsj.49.174.

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3

Steger-Jensen, Kenn, Hans-Henrik Hvolby, Peter Nielsen, and Izabela Nielsen. "Advanced planning and scheduling technology." Production Planning & Control 22, no. 8 (2011): 800–808. http://dx.doi.org/10.1080/09537287.2010.543563.

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4

Kuroda, M., H. Shin, and A. Zinnohara. "Robust scheduling in an advanced planning and scheduling environment." International Journal of Production Research 40, no. 15 (2002): 3655–68. http://dx.doi.org/10.1080/00207540210137567.

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5

Kung, Ling-Chieh, and Ching-Chin Chern. "Heuristic factory planning algorithm for advanced planning and scheduling." Computers & Operations Research 36, no. 9 (2009): 2513–30. http://dx.doi.org/10.1016/j.cor.2008.09.013.

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6

Tanizaki, Takashi, Takayoshi Tamura, Hideaki Sakai, Yutaka Takahashi, and Taichi Imai. "A HEURISTIC SCHEDULING ALGORITHM FOR STEEL MAKING PROCESS WITH CRANE HANDLING(Advanced Planning and Scheduling for Supply Chain Management)." Journal of the Operations Research Society of Japan 49, no. 3 (2006): 188–201. http://dx.doi.org/10.15807/jorsj.49.188.

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7

Hwang, Duk-Hyuong, Seung-Don Nam, Young-Wook Cho, Sun-Il Oh, Seoung-Hun La, and Kyong-Sik Kang. "A Study on the Process Design of Advanced Planning & Scheduling for Transformer Operation Improvement." Journal of the Korea Safety Management and Science 14, no. 2 (2012): 159–66. http://dx.doi.org/10.12812/ksms.2012.14.2.159.

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8

Zheng, Jianguo, Xiaomei Mi, and Huchang Liao. "Advanced Planning and Scheduling Based on the Constraint Theory and Improved Tabu Search Algorithm." Recent Patents on Engineering 14, no. 2 (2020): 221–28. http://dx.doi.org/10.2174/1872212113666191119103505.

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Background: Planning and scheduling of manufacturing enterprises are required to be rapid and accurate, which make is easy for the traditional enterprise resource planning system to meet the needs of enterprises. Objective: This paper introduces the theory of constraints and explains the control methods for production planning and scheduling. Methods: The main characteristics of the advanced planning and scheduling system are analyzed. Then, the modeling method based on the theory of constraints is used to study the scheduling path. Results: An example is given to validate the scheduling model and realize the optimization of production tasks on the bottleneck resources. Conclusion: The feasibility of the advanced planning and scheduling methods based on the theory of constraints is proved.

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9

Lin, Chao‐Hsien, Sheue‐Ling Hwang, and Eric Min‐Yang Wang. "A reappraisal on advanced planning and scheduling systems." Industrial Management & Data Systems 107, no. 8 (2007): 1212–26. http://dx.doi.org/10.1108/02635570710822822.

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10

Wang, Zhong, Gang Zhang, and Guo Wei Zhao. "The Advanced Production Planning of Automated Scheduling Problems in Irradiation Industry." Advanced Materials Research 139-141 (October 2010): 1688–91. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1688.

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Because of the continuity and discreteness during production, Irradiation industry becomes one of an especial industry. Various product type, uncertain lots and complex processes cycle increase the difficulty of production planning and scheduling. The implementation of CIMS/ERP systems during product processes in the enterprise can not fulfill the special requirements especially in product automation monitoring and quality information monitoring. Research and application of scheduling method and optimization technology effect crucially for enterprise to improve its production efficiency and reduce its production cost. So, more and more scholars pay their attention to this research field. For this reason, the paper presents a mathematical production scheduling model in irradiation workshop based on the research of irradiation industry development and shop scheduling in China and abroad. The author also design an application of the irradiation shop scheduling Optimization with this model in detailed, based on described the concept, principle of genetic algorithm and its method.

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11

McKay, Kenneth N., and Gary W. Black. "AVERSION DYNAMICS - ADAPTIVE PRODUCTION CONTROL HEURISTICS INCORPORATING RISK(Advanced Planning and Scheduling for Supply Chain Management)." Journal of the Operations Research Society of Japan 49, no. 3 (2006): 152–73. http://dx.doi.org/10.15807/jorsj.49.152.

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12

Lupeikiene, Audrone, Gintautas Dzemyda, Ferenc Kiss, and Albertas Caplinskas. "Advanced Planning and Scheduling Systems: Modeling and Implementation Challenges." Informatica 25, no. 4 (2014): 581–616. http://dx.doi.org/10.15388/informatica.2014.31.

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13

GUPTA, M. C., Y. P. GUPTA, and G. W. EVANS. "Operations planning and scheduling problems in advanced manufacturing systems." International Journal of Production Research 31, no. 4 (1993): 869–900. http://dx.doi.org/10.1080/00207549308956763.

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14

Chen, James C., Ling Huey Su, Gary C. Chao, et al. "Advanced Planning and Scheduling for Color Filter Fabrication Plants." Key Engineering Materials 450 (November 2010): 361–64. http://dx.doi.org/10.4028/www.scientific.net/kem.450.361.

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This research proposed Advanced Planning and Scheduling (APS) system to effectively and efficiently balance machine loading for color filter fabrication plants in Thin Film Transistor - Liquid Crystal Display industry. APS uses six modules to estimate future equipment loading and calculate order due dates according to capacity limits by taking into account the due date and size of orders, as well as the capacity, loading, and yield of fabrication plants. These six modules include: Order Priority Module, WIP-Pulling Module, Order Release Module, Material Management Module, Workload Accumulation Module, and Workload Balance Module. A lot’s start processing time at each production step is selected leading to the best workload balance. Simulation and experimental design are used to evaluate the performance of APS. Production manager can use APS to improve color filter fabrication plants’ productivity and competitiveness.

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15

David, F., H. Pierreval, and C. Caux. "Advanced planning and scheduling systems in aluminium conversion industry." International Journal of Computer Integrated Manufacturing 19, no. 7 (2006): 705–15. http://dx.doi.org/10.1080/09511920500504545.

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16

Chen, Kejia, Ping Ji, and Qing Wang. "A case study for advanced planning and scheduling (APS)." Journal of Systems Science and Systems Engineering 20, no. 4 (2011): 460–74. http://dx.doi.org/10.1007/s11518-011-5180-z.

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17

Patig, Susanne. "Zur Ausdrucksstärke der Stammdaten des Advanced Planning and Scheduling." Wirtschaftsinformatik 46, no. 2 (2004): 97–106. http://dx.doi.org/10.1007/bf03250929.

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18

Zhang, Chaoyang, Pingyu Jiang, Lei Zhang, and Peihua Gu. "Energy-aware integration of process planning and scheduling of advanced machining workshop." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 11 (2016): 2040–55. http://dx.doi.org/10.1177/0954405415616785.

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Increasing energy consumption of manufacturing industry demands novel approaches to achieve energy conservation and emission reduction. Most of the previous research efforts in this area focused more on analyzing manufacturing energy consumption of a process or that of a machine tool with less concern on the system level of advanced machining workshop, especially a flexible manufacturing system. In this article, a new energy-saving approach of flexible manufacturing system is put forward based on energy evaluation model for integration of process planning and scheduling problem in flexible manufacturing system (flexible manufacturing system-integration of process planning and scheduling). First, complying with feature precedence and other technological requirements, flexible manufacturing system -integration of process planning and scheduling is mapped as an asymmetric traveling salesman problem of which operations are provinces and candidate operations are cities belonging to different provinces. To evaluate the performance of each solution of the asymmetric traveling salesman problem, energy consumption evaluation criteria for flexible manufacturing system-integration of process planning and scheduling are established and three energy efficiency indicators are also provided to perform further analysis on manufacturing energy consumption, that is, part energy efficiency, machine tool energy efficiency and feasible solution energy efficiency. Then, a mutation-combined ant colony optimization algorithm is proposed to solve the flexible manufacturing system-integration of process planning and scheduling which combined roulette and mutation selection methods to pick out the next candidate operation. The pheromone trails associated with edges are released by the so-far-best ant or the iteration-best ant probabilistically to both keep the search directed and avoid converging to the local best. Finally, a case study of flexible manufacturing system in advanced machining workshop is employed to demonstrate the feasibility and applicability of this approach in three different scenarios and compared with the “process planning then scheduling” approach; energy consumption obtained by the proposed method drops 10.7%.

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19

Sanderson, Penelope M. "The Human Planning and Scheduling Role in Advanced Manufacturing Systems: An Emerging Human Factors Domain." Human Factors: The Journal of the Human Factors and Ergonomics Society 31, no. 6 (1989): 635–66. http://dx.doi.org/10.1177/001872088903100602.

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Advanced manufacturing technology is quickly displacing humans from the factory floor, but relatively little is known about how much responsibility humans should retain over plant operation. Scheduling operations are particularly critical to maintaining overall manufacturing system performance, and many researchers argue that humans should be retained in the control loop for this task. This paper critically reviews work done over the last 25 years on human scheduling abilities. The increasing amount of empirical work on human scheduling ability is widely dispersed, and many researchers are not aware of one another's work. Current work on manufacturing scheduling, field studies, and laboratory work on human scheduling abilities are reviewed. Methodological and conceptual problems are highlighted. The intention is to provide a sounder basis for future research on the human scheduling role by providing knowledge of what has been examined in the past and an awareness of the important issues for the future

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20

Koch, Ronny-Alexander, Thomas Rücker, Herfried M. Schneider, and Sören Stodt. "Auswahl von Manufacturing Execution Systems/Advanced Planning and Scheduling Systems." industrie 4.0 Management 2018, no. 4 (2018): 57–61. http://dx.doi.org/10.30844/i40m18-4_57-61.

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21

Lee, Young Hae, Chan Seok Jeong, and Chiung Moon. "Advanced planning and scheduling with outsourcing in manufacturing supply chain." Computers & Industrial Engineering 43, no. 1-2 (2002): 351–74. http://dx.doi.org/10.1016/s0360-8352(02)00079-7.

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22

Hsu, Tzu-Han, Li-Chih Wang, and Pei-Chun Chu. "Development of a Cloud-based Advanced Planning and Scheduling System." Procedia Manufacturing 17 (2018): 427–34. http://dx.doi.org/10.1016/j.promfg.2018.10.066.

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23

Hagazi, Hadgu Haftu, and Xiao Jun Guo. "Implementation of Advanced Planning and Scheduling System in Manufacturing Industries." Advanced Materials Research 718-720 (July 2013): 2485–90. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2485.

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Advanced planning and scheduling system's (APS) implementation and operation process for elaborating a production schedule in a production environment similar to what is found in industries. The main characteristics and restrictions of a real production environment were thus considered, such as multi-level structure products, production routes with sequential, parallel and alternative operations, more than one resource per operation and alternative resources. In terms of results, this study provides an overview of the main steps involved in the implementation and operational process of an APS system in an industry, providing an important indicator for choosing and using this sort of system. The expectations of APS systems have been high, both from academia and industry in the subject area of manufacturing planning and control. Relative the massive interest there has not been much written about how APS systems are used in practice and what effects an APS approach really results in.

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24

Öztürk, Cemalettin, and Arslan M. Ornek. "Operational extended model formulations for Advanced Planning and Scheduling systems." Applied Mathematical Modelling 38, no. 1 (2014): 181–95. http://dx.doi.org/10.1016/j.apm.2013.05.049.

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25

Montesco, Richard A. Estombelo, Marcosiris A. O. Pessoa, and Mauricio F. Blos. "Scheduling heuristic resourced-based on task time windows for APS (Advanced planning and scheduling) Systems." IFAC-PapersOnLine 48, no. 3 (2015): 2273–80. http://dx.doi.org/10.1016/j.ifacol.2015.06.426.

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26

Kjellsdotter Ivert, Linea, and Patrik Jonsson. "The potential benefits of advanced planning and scheduling systems in sales and operations planning." Industrial Management & Data Systems 110, no. 5 (2010): 659–81. http://dx.doi.org/10.1108/02635571011044713.

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27

Kjellsdotter Ivert, Linea, and Patrik Jonsson. "When should advanced planning and scheduling systems be used in sales and operations planning?" International Journal of Operations & Production Management 34, no. 10 (2014): 1338–62. http://dx.doi.org/10.1108/ijopm-03-2011-0088.

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Purpose – The purpose of this paper is to explore how the context affects successful use of advanced planning and scheduling (APS) systems in sales and operations planning (S&OP) processes, and how individual, technological, and organizational (ITO) dimensions affect this procedure. Design/methodology/approach – This is a qualitative case study of two APS system-supported S&OP processes. The work aims to generate propositions concerning the relationships among the use of APS system, the context, ITO dimensions, and fulfillment of S&OP aims. Findings – Use of APS systems was especially appropriate in support of S&OP processes in complex planning environments and when S&OP aims were ambitious. ITO dimensions were important influences on successful APS system use in most contexts. APS systems were not considered appropriate when having S&OP processes with ambitious aims and low individual and organizational maturities. Use of APS systems was also inappropriate when the extent of technological maturity was minimal. S&OP processes with ambitious aims, operating within a complex planning environment, are difficult if not impossible to implement without the support of APS systems. Practical implications – The suggestions on when APS systems should be used in different S&OP environments will be useful to companies implementing or about to implement APS systems. Originality/value – APS systems offer great potential if they are effectively used to support S&OP, still the use of APS system in S&OP is unexplored. The paper shows how the context and the ITO dimensions affect the successful use of APS systems in S&OP processes.

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28

Li, Juntao, Joe Kuwata, Mingzhe Lu, Hiroshi Kise, and Yoshiyuki Karuno. "ANALYSIS AND OPTIMIZATION FOR AUTOMATED VEHICLE ROUTING ON A SINGLE LOOP(Advanced Planning and Scheduling for Supply Chain Management)." Journal of the Operations Research Society of Japan 49, no. 3 (2006): 202–21. http://dx.doi.org/10.15807/jorsj.49.202.

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29

CHEN, Kejia. "Dynamic advanced planning and scheduling with frozen interval for new orders." Chinese Journal of Mechanical Engineering (English Edition) 20, no. 04 (2007): 117. http://dx.doi.org/10.3901/cjme.2007.04.117.

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30

Zhang, Haijun, Qiong Yan, and Xiying Peng. "Advanced planning and scheduling system with application in the tobacco industry." International Journal of Service and Computing Oriented Manufacturing 3, no. 1 (2017): 24. http://dx.doi.org/10.1504/ijscom.2017.087943.

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31

Peng, Xiying, Qiong Yan, and Haijun Zhang. "Advanced planning and scheduling system with application in the tobacco industry." International Journal of Service and Computing Oriented Manufacturing 3, no. 1 (2017): 24. http://dx.doi.org/10.1504/ijscom.2017.10008911.

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32

Ehrlich, Donna M., and Jeff A. Ehrlich. "Survey paper on adaptive agents applied to advanced planning and scheduling." International Journal of Engineering Management and Economics 1, no. 2/3 (2010): 200. http://dx.doi.org/10.1504/ijeme.2010.036950.

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33

Dayou, Liu, Yan Pu, and Yu Ji. "Development of a multiobjective GA for advanced planning and scheduling problem." International Journal of Advanced Manufacturing Technology 42, no. 9-10 (2008): 974–92. http://dx.doi.org/10.1007/s00170-008-1653-8.

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34

Hvolby, Hans-Henrik, and Kenn Steger-Jensen. "Technical and industrial issues of Advanced Planning and Scheduling (APS) systems." Computers in Industry 61, no. 9 (2010): 845–51. http://dx.doi.org/10.1016/j.compind.2010.07.009.

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35

Chen, Kejia, and Ping Ji. "A mixed integer programming model for advanced planning and scheduling (APS)." European Journal of Operational Research 181, no. 1 (2007): 515–22. http://dx.doi.org/10.1016/j.ejor.2006.06.018.

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36

Younus, Muhammad, Yong Yu, Hu Lu, and Yu Qing Fan. "Study and Execution of Advanced Batch Production Planning System for an Aerospace Industry." Applied Mechanics and Materials 16-19 (October 2009): 748–52. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.748.

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Modern manufacturing industries are increasingly faced with international competition and fluctuating market conditions in the age of globalization. In striving to remain competitive, manufacturing industries must deliver products to customer at the lowest cost, at the best quality and in the minimum lead time. As a result, it becomes mandatory to design and implement the advance production planning and scheduling system that supports shorter product cycles despite more complex and specialized manufacturing processes. The advance production planning and scheduling system provides the leaner production strategies and real time information throughout the industry. This paper presents an appropriate Advance Production Planning and Scheduling Software System for a batch production aerospace manufacturing industry. The Software system receive the customer‘s order and perform material requirement planning using software. The software system sends procurement requisition to the procurement department for the materials which is not available in the store for manufacturing of parts. It issues release order to the store section for the issuance of material to the manufacturing shops. It also issues weekly and daily plan with production schedule to the manufacturing shops. It also issues work orders to the production shops for the manufacturing and assembly of the parts. On the completion of the product assembly, proper closing of the work orders has to be done and product may deliver to the warehouse for further handing over to the customers. In case of modification of order by the customer, the Software System will automatically update the relevant data.

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37

Modares, A., and M. Sepehri. "Development of an integrated system for distribution planning in supply chain." South African Journal of Business Management 40, no. 4 (2009): 13–23. http://dx.doi.org/10.4102/sajbm.v40i4.547.

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Distribution planning, which includes Vehicle Routing and Scheduling Problem (VRSP), has become an important element in Supply Chain impacting its service level and efficiency. Computer Aided Routing and Scheduling (CARS) has been developed and implemented, which can handle complicated distribution models using advanced heuristic optimization algorithms. A classification scheme is introduced to classify various types of routing and scheduling problems in a structured manner, based on the main objects of VRSP. The integrated system described in this paper can manage the dynamic aspects of the Supply Chain in practice. The modelling and solution approach in the CARS optimization engine, its user interface, sample performance measurements, and planning and operational features of the system are described in detail.

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38

Shim, Sang Oh. "Line Level Scheduling by Integrating Area Level Scheduling in Manufacturing Systems." Applied Mechanics and Materials 267 (December 2012): 83–86. http://dx.doi.org/10.4028/www.scientific.net/amm.267.83.

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In current automated manufacturing systems, production managers try to implement more efficient and effective scheduling methods which play important roles in supply chain by maximizing utilization of the installed equipments and throughput as well as meeting customers' various demands. In relation to enhancing capabilities and responding demands, some raising issues are discussed, which are transportation of lots between manufacturing lines, management of urgent jobs and workload balancing among areas. In this research, by discussing these scheduling issues, a new concept and its design, which is called as line-level scheduling, are suggested for advanced planning and scheduling systems in the manufacturing systems.

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39

Sugimura, Nobuhiro, Koji Iwamura, and Tomohiko Maeda. "Special Issue on Production Planning and Scheduling." International Journal of Automation Technology 9, no. 3 (2015): 209. http://dx.doi.org/10.20965/ijat.2015.p0209.

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This issue focuses on production planning and scheduling for production system and the related problems that have arisen in these areas in the last half century as digital computer systems developed. These problems relate to production management, production planning, shop floor control, product design and process planning. In the first stage of production planning and scheduling systems R&D, optimization is a key issue that has been widely discussed and many theories and optimization algorithms proposed. Rule-based methods are discussed as potential solutions to these problems. With rapid advances in computer and information processing technologies and performance, tremendous progress has been made in the areas of production systems such as production planning, production scheduling, advances production systems (APS), enterprise resource planning (ERP), just-in time (JIT) processes, the theory of constraint (TOC), product data management (PDM) and computer-aided design / manufacturing / engineering (CAD/CAM/CAE). This special issue addresses the latest research advances, applications, and case studies in production planning and scheduling covering such as decentralized and autonomous production systems, distributed simulation models, robust capacity planning models, wireless sensor networks for production systems and applications to automotive component and steel production. We hope that learning about these advances will enable readers to share their own experience and knowledge in technology, new developments and the potential applications of production planning and scheduling methods and solutions.

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40

Sillapa-Archa, Tanaporn, Phiphlu Thaninthanadech, and Darin Smitasiri. "Advanced Cost-Efficient Production Scheduling in Hi-Tech Manufacturing Industry at Test Operation." Applied Mechanics and Materials 110-116 (October 2011): 3922–29. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.3922.

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Rapid change in customer demand and aggressive business competition in high technology industry has driven the need for greater performance with lower price product under short product life cycle. This stimulates semiconductor manufacturing companies to pay much more attention to their cost control and production built strategy. Bad performance in shop-floor planning results in huge inventory, poor equipment efficiency, and therefore it would jeopardize the company sustainability. Consequently, the main objectives of an effective production planning intelligence are to maximize company benefits, meet customer expectation and improve production efficiency. This paper presents an algorithm of production loading plan that balances cost, meet customer delivery request and optimize production efficiency by considering all important constrained factors in manufacturing environment, namely test cost per hour, throughput rate, machine capacity and utilization, hardware set up matrix and limitation, the number of change-over and customer delivery date. The analysis is done by comparing the performance outcome with the common scheduling rules. The result of this new planning logic solution is proved to increase the effectiveness of the production scheduling plan based on available resources on hand and complex of constraint. It also decreases human interfere causing error in planning procedure.

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41

Pessoa, Marcosiris A. O., Richard A. Estombelo Montesco, Fabricio Junqueira, Diolino Jose dos Santos Filho, and Paulo E. Miyagi. "Advanced Planning and Scheduling Systems based on Time Windows and Constraint Programming." IFAC Proceedings Volumes 46, no. 7 (2013): 192–97. http://dx.doi.org/10.3182/20130522-3-br-4036.00069.

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42

Ivert, Linea Kjellsdotter. "Shop floor characteristics influencing the use of advanced planning and scheduling systems." Production Planning & Control 23, no. 6 (2011): 452–67. http://dx.doi.org/10.1080/09537287.2011.564218.

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43

Moon, Chiung, and Yoonho Seo. "Evolutionary algorithm for advanced process planning and scheduling in a multi-plant." Computers & Industrial Engineering 48, no. 2 (2005): 311–25. http://dx.doi.org/10.1016/j.cie.2005.01.016.

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44

Giri, Bibhas Chandra, and Tadashi Dohi. "OPTIMAL INSPECTION SCHEDULE IN AN IMPERFECT EMQ MODEL WITH FREE REPAIR WARRANTY POLICY(Advanced Planning and Scheduling for Supply Chain Management)." Journal of the Operations Research Society of Japan 49, no. 3 (2006): 222–37. http://dx.doi.org/10.15807/jorsj.49.222.

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45

Sumita, Ushio, Jun-ya Gotoh, and Hui Jin. "NUMERICAL EXPLORATION OF DYNAMIC BEHAVIOR OF ORNSTEIN-UHLENBECK PROCESSES VIA EHRENFEST PROCESS APPROXIMATION(Advanced Planning and Scheduling for Supply Chain Management)." Journal of the Operations Research Society of Japan 49, no. 3 (2006): 256–78. http://dx.doi.org/10.15807/jorsj.49.256.

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46

Zhong, Ray Y., Z. Li, L. Y. Pang, Y. Pan, T. Qu, and George Q. Huang. "RFID-enabled real-time advanced planning and scheduling shell for production decision making." International Journal of Computer Integrated Manufacturing 26, no. 7 (2013): 649–62. http://dx.doi.org/10.1080/0951192x.2012.749532.

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47

Kristianto, Yohanes, Mian M. Ajmal, and Petri Helo. "Advanced planning and scheduling with collaboration processes in agile supply and demand networks." Business Process Management Journal 17, no. 1 (2011): 107–26. http://dx.doi.org/10.1108/14637151111105607.

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48

Kortenkamp, D. "A day in an astronaut's life: reflections on advanced planning and scheduling technology." IEEE Intelligent Systems 18, no. 2 (2003): 8–11. http://dx.doi.org/10.1109/mis.2003.1193650.

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49

Chen, James C., PoTsang B. Huang, Ji-Geng Wu, Ken Y. Lai, Chih-Cheng Chen, and Tzu-Wei Peng. "Advanced planning and scheduling for TFT-LCD color filter fab with multiple lines." International Journal of Advanced Manufacturing Technology 67, no. 1-4 (2013): 101–10. http://dx.doi.org/10.1007/s00170-013-4757-8.

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50

Al Nasseri, Hammad Abdullah, Kristian Widen, and Radhlinah Aulin. "A taxonomy of planning and scheduling methods to support their more efficient use in construction project management." Journal of Engineering, Design and Technology 14, no. 3 (2016): 580–601. http://dx.doi.org/10.1108/jedt-11-2013-0078.

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Purpose The implementation and control processes of project planning and scheduling involve a wide range of methods and tools. Despite the development and modification and integration of the project management theory with newer scheduling approaches in particular, practitioners’ views on the efficiency and effectiveness of these methods and tools differ. This situation can be attributed in part to a lack of understanding of the most appropriate basis for implementing these methods and tools. This study, therefore, aims to overcome this deficiency by conceptualizing and adopting a taxonomy of planning and scheduling methods. Design/methodology/approach The study is based on a review and discourse analysis of the literature covering a large number of theoretical and empirical studies. The underlying theories of various planning and scheduling methods were analyzed with respect to the taxonomy criteria adopted in the study. Findings Using the taxonomy, the key characteristics of planning and scheduling methods considered in this study were identified and interpreted. These included concepts and theories; key features; suitability and usability; and benefits and limitations. Overall, the findings suggest that project managers should consider taxonomy as a support tool for selecting and prioritizing the most appropriate method or combination of methods for managing their projects. Recommendations include the need for more advanced or multi-dimensional taxonomies to cope with the diversity of project type and size. Originality/value The results of the study allow project managers to improve their current practices by utilizing taxonomy when considering the implementation of planning and scheduling methods. Moreover, taxonomy can be considered as a tool to promote learning on the part of those less experienced in planning and scheduling. Taxonomy can be considered as an initial platform for further research in this area.

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