Operation sequencing with a single box position

The optimal scheduling of crude-oil operation in refineries has been studied by various groups during the past decade leading to different mixed integer linear programming or mixed nonlinear programming formulations. This paper presents a new formulation with oil residency time constraint based on single-operation sequencing (SOS). It is different from previous formulations as it considers oil residency time constraint and pipeline transfer and does not require to postulate the number of priority-slots in which operations take place. This model is also based on the representation of a crude-oil scheduling by a single sequence of transfer operations. A simple MILP procedure has been used to solve this model leading to an satisfactory optimal result.

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A Hybrid Approach to Computer-Aided Process Planning for Prismatic Parts

ASME 1994 International Computers in Engineering Conference and Exhibition ◽

10.1115/cie1994-0429 ◽

1994 ◽

Author(s):

Y. F. Zhang ◽

A. Y. C. Nee ◽

J. Y. H. Fuh

Keyword(s):

Process Planning ◽

Hybrid Approach ◽

Optimization Method ◽

Optimal Operation ◽

Operation Sequencing ◽

Computer Aided Process Planning ◽

Prismatic Parts ◽

Minimum Number ◽

Set Up

Abstract One of the most difficult tasks in automated process planning is the determination of operation sequencing. This paper describes a hybrid approach for identifying the optimal operation sequence of machining prismatic parts on a three-axis milling machining centre. In the proposed methodology, the operation sequencing is carried out in two levels of planning: set-up planning and operation planning. Various constraints on the precedence relationships between features are identified and rules and heuristics are created. Based on the precedence relationships between features, an optimization method is developed to find the optimal plan(s) with minimum number of set-ups in which the conflict between the feature precedence relationships and set-up sequence is avoided. For each set-up, an optimal feature machining sequence with minimum number of tool changes is also determined using a developed algorithm. The proposed system is still under development and the hybrid approach is partially implemented. An example is provided to demonstrate this approach.

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Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

10.26686/wgtn.13158314.v1 ◽

2020 ◽

Author(s):

Fangfang Zhang ◽

Yi Mei ◽

S Nguyen ◽

Mengjie Zhang

Keyword(s):

Genetic Programming ◽

Job Shop ◽

Job Shop Scheduling ◽

Computational Time ◽

Flexible Job Shop ◽

Flexible Job Shop Scheduling ◽

Genetic Operators ◽

Operation Sequencing ◽

Shop Scheduling ◽

Selection For

© 2020, Springer Nature Switzerland AG. Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time.

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Feature Recognition and Datum Extraction for Setup Planning and Operation Sequencing for Prismatic Parts

Dynamic Methods and Process Advancements in Mechanical, Manufacturing, and Materials Engineering ◽

10.4018/978-1-4666-1867-1.ch006 ◽

2012 ◽

pp. 102-129

Author(s):

T. Srikanth Reddy ◽

M. S. Shunmugam

Keyword(s):

Feature Recognition ◽

Geometric Dimension ◽

Connectivity Matrix ◽

Setup Planning ◽

Priority Rules ◽

Operation Sequencing ◽

Prismatic Parts ◽

Manufacturing Activity ◽

Edge Features ◽

Planning And Operation

An automated planning system extracts data from design models and processes it efficiently for transfer to manufacturing activity. Researchers have used face adjacency graphs and volume decomposition approaches which make the feature recognition complex and give rise to multiple interpretations. The present work recognizes the features in prismatic parts considering Attributed Adjacency Matrix (AAM) for the faces of delta volume that lie on rawstock faces. Conceptually, intermediate shape of the workpiece is treated as rawstock for the next stage and tool approach direction is used to recognize minimum, yet practically feasible, set of feature interpretations. Edge-features like fillets/undercuts and rounded/chamfer edges are also recognized using a new concept of Attributed Connectivity Matrix (ACM). In the first module, STEP AP-203 format of a model is taken as the geometric data input. Datum information is extracted from Geometric Dimension and Tolerance (GD&T) data. The second module uses features and datum information to arrive at setup planning and operation sequencing on the basis of different criteria and priority rules.

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