scholarly journals Research on Collaborative Planning and Symmetric Scheduling for Parallel Shipbuilding Projects in the Open Distributed Manufacturing Environment

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 161
Author(s):  
Xuezhang Mao ◽  
Jinghua Li ◽  
Hui Guo ◽  
Xiaoyuan Wu
Keyword(s):  
Decision Making ◽  
Project Scheduling ◽  
Collaborative Planning ◽  
Project Planning ◽  
Decision Making Process ◽  
Distributed Manufacturing ◽  
Manufacturing Environment ◽  
Distributed Resources ◽  
Planning And Scheduling ◽  
Shipbuilding Industry

In the current distributed manufacturing environment, more extensive enterprise cooperation is an effective means for shipbuilding companies to increase the competitiveness. However, considering the project scale and the uneven production capacity between the collaborative enterprises, a key issue for shipbuilding companies is to effectively combine the product-oriented project tasks and the specialized production-oriented plants. Due to information privatization, the decision-making process of project planning and scheduling is distributed and symmetric. Existing project scheduling methods and collaboration mechanisms in the shipbuilding industry are somehow inefficient. The aim of the research is to provide an assistant decision-making method to support effective task dispatching and multi-party cooperation for better utilization of the distributed resources and to help project managers control the shipbuilding process. The article initially establishes an agent-based complex shipbuilding project collaborative planning and symmetric scheduling framework, simulating the distributed collaborative decision-making process and bridging the multi-project planning with the individual project scheduling in much detail, which fills the research gap. A negotiation method based on iterative combination auction (ICA) is further proposed to solve the integration problem of project planning and task scheduling, and an illustrative example is conducted to demonstrate the effectiveness and rationality of the methods. Finally, an application case using a prototype system on shipbuilding projects collaborative planning and scheduling will be reported as a result.

Managing the Decision-Making Process during Project Planning

1999 ◽  
Vol 15 (2) ◽  
pp. 79-84 ◽  
Author(s):  
Alexander Laufer ◽  
Hugh Woodward ◽  
Gregory A. Howell
Keyword(s):  
Decision Making ◽  
Project Planning ◽  
Decision Making Process

Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment

2012 ◽  
pp. 242-261 ◽  
Author(s):  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Timothy Ganesan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Control ◽  
Future Research ◽  
Manufacturing Environment ◽  
Planning And Scheduling ◽  
Future Research Directions ◽  
Effective Decision ◽  
Effective Decision Making

Production control, planning, and scheduling are forms of decision making, which play a crucial role in manufacturing industries. In the current competitive environment, effective decision-making has become a necessity for survival in the marketplace. This chapter provides insight into the issues relating to integration of fuzzy logic techniques into decision support systems for profitability quantification in a manufacturing environment. The chapter is divided into five sections with a general introduction of the topic, followed by a thorough literature review on the existing techniques. Thereafter, fuzzy logic algorithms using logistic membership functions and resource variables for decision making aiming at quality improvement are discussed. A case study involving a textile firm is then described with the computational results and findings, and finally, future research directions are presented.

Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment

2012 ◽  
pp. 171-192 ◽  
Author(s):  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Timothy Ganesan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Control ◽  
Future Research ◽  
Manufacturing Environment ◽  
Planning And Scheduling ◽  
Future Research Directions ◽  
Effective Decision ◽  
Effective Decision Making

Production control, planning, and scheduling are forms of decision making, which play a crucial role in manufacturing industries. In the current competitive environment, effective decision-making has become a necessity for survival in the marketplace. This chapter provides insight into the issues relating to integration of fuzzy logic techniques into decision support systems for profitability quantification in a manufacturing environment. The chapter is divided into five sections with a general introduction of the topic, followed by a thorough literature review on the existing techniques. Thereafter, fuzzy logic algorithms using logistic membership functions and resource variables for decision making aiming at quality improvement are discussed. A case study involving a textile firm is then described with the computational results and findings, and finally, future research directions are presented.

Weld Quality Prediction Using Artificial Intelligence Technique

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Gaurav Kumar Ojha ◽  
Gyanendra Kumar Yadav ◽  
Pankaj Kumar Yadav
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Welding Parameters ◽  
Decision Making Process ◽  
Regression Equations ◽  
Weld Quality ◽  
Manufacturing Environment ◽  
Inference System ◽  
Artificial Intelligent ◽  
Input Variables

Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design and demand of quality products. To make decision making process online, effective and efficient artificial intelligent tools like neural networks are being attempted. Usually, the desired welding parameters are determined using traditional methods like welder's experiences, charts and handbooks (preferred values) which are simple and inexpensive. But this does not ensure that the selected welding parameters result in satisfactory welding and this method is not applicable to new welding process. To overcome this problem, various methods of obtaining the desired output variables through models to correlate input variables with output variables have been developed. Fractional factorial techniques, Mathematical modeling, curvilinear regression equations, linear regression equations, response surface methodology, finite element modeling, grey-based Taguchi method and sensitivity analysis were used to model SAW process. These methods are limited in application due to difficulties in modeling, time consuming and cumbersome. Due to the inadequacy and inefficiency of the mathematical models to explain the nonlinear properties existing between the input and output parameters of welding lead to the development of intelligent modeling techniques. Precise simulation and analysis of the process needs attention which helps to predict the wide variety of process parameters to set the factory floor in real time. The type of artificial intelligence capable of responding to changes in the automated manufacturing environment, and having the ability to capture vast manufacturing knowledge is Adaptive Neuro Fuzzy Inference System (ANFIS). It is becoming widely used in all aspects of manufacturing process to assist humans. Realizing that matter, ANFIS a state of the art artificial intelligent method, has the possibility to enhance the prediction of weld quality to find the best combination of independent variables which is welding current (I), speed (S) and welding voltage (V) as the input variables in order to achieve desired weld quality. Thus, the main objectives of this project is to develop ANFIS model to predict weld quality.

scholarly journals 4D modelling using virtual collaborative planning and scheduling

2021 ◽  
Vol 26 ◽  
pp. 763-782 ◽  
Author(s):  
Mikael Viklund Tallgren ◽  
Mattias Roupé ◽  
Mikael Johansson
Keyword(s):  
Construction Projects ◽  
Collaborative Planning ◽  
Stakeholder Involvement ◽  
Project Planning ◽  
Planning System ◽  
Steady Increase ◽  
Full Potential ◽  
Planning And Scheduling ◽  
Information Models ◽  
Scheduling Method

During the last decades, the introduction of Building Information Models (BIM) has opened new possibilities to ensure better communication and a shared understanding between stakeholders in construction projects. Similar benefits have been found in 4D simulations of the schedule. While BIM and 4D use has seen a steady increase, the potential benefits have not fully been realised, primarily due to tools not supporting and enabling the full potential of collaborative stakeholder involvement in scheduling. The benefits of 4D simulations come from improving communication between stakeholders. While traditional 4D modelling connects an existing BIM model with an existing schedule, a move from this type of passive 4D visualisation toward social co-creation enables all stakeholders to be involved in the scheduling process. This connection is further enhanced with the use of Virtual Reality (VR). Recent research has shown VR to enhance understanding and perception of the space and details and, thus, a better understanding of the project. This paper maps collaborative planning and scheduling method and traditional 4D scheduling using process modelling. This mapping is followed by implementing the 4D collaborative planning and scheduling method in the virtual project planning system with support for a multi-user interactive VR environment. The system enables social co-creation both during the planning and scheduling and during 4D modelling; it improves understanding of the project and the planning and scheduling.

scholarly journals Social impact assessment process for industry 4.0 to achieve sustainable artificial intelligence systems

2020 ◽  
Vol 10 (2) ◽  
pp. 27-47
Author(s):  
Vijayan Gurumurthy Iyer
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Impact Assessment ◽  
Intelligent Systems ◽  
Social Impact ◽  
Project Planning ◽  
Assessment Process ◽  
Decision Making Process ◽  
Business Economics ◽  
Artificial Intelligence Systems

The strategic environmental assessment (SEA) process can be broadly defined as a study of the social impacts of a proposed project, plan, policy or legislative action of intelligence systems on the society, environment and sustainability. The SEA process for sustainable intelligent systems has been aimed to incorporate society, environment and sustainability factors into the project planning and decision-making process for sustainable intelligent systems. Artificial intelligence systems (AIS) should consider the titled ‘environmental impact assessment (EIA)’ process that can be defined as the systematic identification and evaluation of the potential impacts (effects) of proposed projects, plans, programmes, policies or legislative actions relative to the biological physical, physico-chemical, biological, cultural, socio-economic and anthropological components of the total environment. The SEA process protocol is important as it has been proposed for studying and checking the productivity and quality of AIS. This treaty and official government procedures of SEA were helpful in the decision-making process much earlier than the EIA process.   Keywords: Artificial intelligence, business, economics, environment, industry.

LOGISTICS PROJECT PLANNING UNDER CONDITIONS OF RISK AND UNCERTAINTY

2017 ◽  
Vol 68 (1) ◽  
pp. 89-102 ◽  
Author(s):  
Iwona Pisz ◽  
Iwona Łapuńka
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Incomplete Information ◽  
Practical Knowledge ◽  
Complete Information ◽  
Project Planning ◽  
Decision Making Process ◽  
Risk And Uncertainty ◽  
New Knowledge

The paper discusses highly important issues related to the practice of logistics project planning under conditions of certainty, risk, and uncertainty. This process requires the use of a variety of planning information (complete or incomplete, express or implied) as well as the generation of new knowledge. The decision-making process takes place in a deterministic or an nondeterministic situation; the former involves complete information (i.e., certainty) and the latter incomplete information (i.e., risk and uncertainty). Becoming acquainted with the discussed issues provides valuable practical knowledge that may be helpful in making important decisions when formulating concepts and models of logistics projects planning undertaken by a single enterprise or a supply chain.

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