Robot workstation failure recovery based on a layout optimization

2016 ◽  
Vol 40 (2) ◽  
pp. 375-388 ◽  
Author(s):  
Marko Filipović ◽  
Stjepan Bogdan ◽  
Tamara Petrović
Keyword(s):  
Objective Function ◽  
Flexible Manufacturing ◽  
Material Handling ◽  
Optimization Problems ◽  
Transportation Cost ◽  
Failure Recovery ◽  
Treatment Quality ◽  
Layout Problem ◽  
Material Handling Costs ◽  
Rate Of Failure

This article focuses on the robot workstation layout problem and briefly discusses a recovery control strategy. Since present industrial workstations utilize a flexible manufacturing cell served by a robot, researchers in this field try to find the best method determining the physical organization of resources in available space. As solving the facility layout problem (FLP) might reduce material handling expenses, the most common objective in these approaches is to minimize the material handling costs. Our work introduces a new approach in obtaining the optimal positions of resources in a robot workstation where considerable contribution to the final layout design comes from the failure recovery data. The optimization criteria include material flow and transportation cost as the standard FLP objectives. In our approach we also consider the resource rate of failure and treatment quality as a part of the failure recovery. The optimization problems were solved with the state of the art optimization algorithm for the nonlinear optimization problems. The computational results of the study are discussed and analysed on the basis of a real industrial application. The commonly used objective function is compared to the proposed objective function extended with the failure recovery. As an important part of the failure recovery strategy, making the proper recovery decision in the workstation control design is also discussed.

An Unequal-Sized Unidirectional Loop Layout Design Problem Considering Empty Vehicle Trip

2010 ◽  
Vol 37-38 ◽  
pp. 116-121
Author(s):  
Yu Lan Li ◽  
Bo Li ◽  
Su Jun Luo
Keyword(s):  
Material Handling ◽  
Design Principle ◽  
Facility Layout ◽  
Original Model ◽  
Layout Problem ◽  
Proposed Model ◽  
Loop Layout ◽  
Material Handling Costs ◽  
Loop Layout Problem ◽  
Better Than

In the facility layout decisions, the previous general design principle is to minimize material handling costs, and the objective of these old models only considers the costs of loaded trip, without regard to empty vehicle trip costs, which do not meet the actual demand. In this paper, the unequal-sized unidirectional loop layout problem is analyzed, and the model of facility layout is improved. The objective of the new model is to minimize the total loaded and empty vehicle trip costs. To solve this model, a heuristic algorithm based on partheno-genetic algorithms is designed. Finally, an unequal-sized unidirectional loop layout problem including 12 devices is simulated. Comparison shows that the result obtained using the proposed model is 20.4% better than that obtained using the original model.

A Pattern Recognition Approach for Manufacturing Facility Compaction by Machining Function Combination Using Flexible Manufacturing Modules

2003 ◽  
Vol 125 (4) ◽  
pp. 740-752
Author(s):  
Shahrukh Irani ◽  
Jin Zhou ◽  
Heng Huang
Keyword(s):  
Pattern Recognition ◽  
Flexible Manufacturing ◽  
Material Handling ◽  
Facility Layout ◽  
Integrated Approach ◽  
Machine Grouping ◽  
Function Combination ◽  
Machining System ◽  
Material Handling Costs ◽  
Space Requirements

Facility layout and flexible automation are two approaches for reduction of material handling costs and space requirements in a machining facility that have always been implemented independently of each other. This paper describes an integrated approach to compaction of existing machining facilities using machine grouping algorithms and multi-function machining centers, also referred to as Flexible Machining Modules (FMMs). First, we decompose a facility into a network of layout modules to reduce product travel distances and simplify material flow control. Then, subject to design feasibility, we identify those sets of machines in each module that could be replaced by multi-function FMMs that could be linked into a Flexible Machining System (FMS). The proposed approach uses a combination of pattern recognition and graph theory algorithms utilized for facility layout. The paper concludes with a description of a validation study conducted in an aerospace machining jobshop.

scholarly journals A new soft computing algorithm based on cloud theory for dynamic facility layout problem

2020 ◽  
Author(s):  
Mostafa Zandieh ◽  
Seyed Shamsodin Hosseini ◽  
parham azimi ◽  
Mani Sharifi
Keyword(s):  
Material Handling ◽  
Simulated Annealing Algorithm ◽  
Facility Layout ◽  
Planning Horizon ◽  
Test Problems ◽  
Layout Problem ◽  
Facility Layout Problem ◽  
Dynamic Facility Layout Problem ◽  
Material Handling Costs ◽  
Cloud Theory

This paper deals with dynamic facility layout problem (DFLP) in a plant which is concerned with determining the best position of machines in the plant during a multi-period planning horizon. The material handling costs and machines rearrangement costs are used to determine the best layout. In addition to positions of machines, the details of transportation such as type of transporters and sequence of transportation operations have a direct effect on MHC. Therefore, it is more realistic to consider the transportation details during DFLP optimization. This paper proposes a new mathematical model to simultaneously determine the best position of machines in each period and to plan the transportation operations. Minimizing sum of MHC and MRC is considered as the objective function. A new hybrid meta-heuristic approach has been developed by combining modified genetic algorithm and cloud-based simulated annealing algorithm to solve the model. Finally, the proposed methodology is compared with two meta-heuristics on a set of test problems.

Optimization of Linear Layout Problem in Flexible Manufacturing System Using Evolutionary Algorithms - Case Study

2015 ◽  
Vol 766-767 ◽  
pp. 896-901
Author(s):  
M. Saravanan ◽  
S. Ganesh Kumar ◽  
V. Srinivasa Raman
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Transportation Cost ◽  
Sheep Flock ◽  
Layout Problem ◽  
Proposed Model ◽  
Better Than

Linear layout is the commonly major preferred arrangement of the flexible manufacturing systems (FMS). The proposed enhanced sheep flock heredity algorithm to solving the unequal area linear layout problem through the real case study problem. The proposed model is to minimize the transportation cost with non-overlapping. Computational results show that proposed sheep flock heredity algorithm (SFHA) can obtain better than particle swarm optimization (PSO) and existing method.

Workshop Layout Optimization Based on Genetic Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 169-173 ◽  
Author(s):  
Long Qiao ◽  
Hong Bin Yu ◽  
Jian Jun Sun
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Objective Function ◽  
Material Handling ◽  
Job Shop ◽  
Job Shops ◽  
Transfer Time ◽  
Set Up ◽  
Material Handling Costs ◽  
First Time

To shorten the transfer time of workpiece in job shop, it is necessary to optimize the equipment arrangement of job shops based on the technological process of workpiece. The objective function only considers the material handling costs, but it ignores the geometry of the workshop area utilization and so on factors. We propose and take an objective function that considers material handling costs and utilization proposed at the same time. And we set up an optimization model of facility layout is proposed and genetic algorithms is used to solve this mode1. The author brings forward the concept of carry quadrature for the first time. It is good to use this concept for the workshop in which many kinds of workpiece are produced. The result of optimal design is consonant with the desire of actual manufacture.

PERBAIKAN TATA LETAK LANTAI PRODUKSI INDUSTRI MEBEL MENGGUNAKAN METODE GRAFIK DAN ALGORITMA CRAFT

2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Kuswanto Kuswanto ◽  
Juan Junius ◽  
Anita Christine Sembiring
Keyword(s):  
Operating System ◽  
Material Handling ◽  
Facility Layout ◽  
Integrated Planning ◽  
Furniture Manufacturing ◽  
Milling Machines ◽  
Material Handling Costs ◽  
The Cost ◽  
A Company ◽  
Research Show

Facility layout is integrated planning of the flow of a product in an operating system to obtain the most effective and efficient interrelation between workers, materials, machinery, and equipment as well as handling and transferring materials. A company engaged in furniture manufacturing has a problem in its production process, namely, the distance between machines is too far so that it affects the cost of handling materials. Distant workstations are found on profile machines, milling machines, measuring machines, cutting machines. Therefore, improvements must be made to the layout of facilities on the production floor so that facility layout is efficient and material handling costs are reduced. The problem-solving approach used is the Graph Method and CRAFT Algorithm. The results of the research show that material handling costs are reduced by 7.58% or Rp. 17,765 using the CRAFT algorithm.

Scalable Computing Architecture for Time-Dependent Transportation Optimization Problems Based on High-Performance Computing Techniques

2019 ◽  
Vol 2673 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Pengfei (Taylor) Li ◽  
Peirong (Slade) Wang ◽  
Farzana Chowdhury ◽  
Li Zhang
Keyword(s):  
Objective Function ◽  
High Performance Computing ◽  
High Performance ◽  
Optimization Problems ◽  
Dynamic Network ◽  
Alternative Formulation ◽  
High Fidelity ◽  
Objective Functions ◽  
Performance Computing ◽  
Transportation Optimization

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.

scholarly journals Global optimization based on active preference learning with radial basis functions

2020 ◽  
Author(s):  
Alberto Bemporad ◽  
Dario Piga
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Decision Maker ◽  
Optimization Problems ◽  
Global Optimum ◽  
Bayesian Optimization ◽  
Preference Learning ◽  
Global Optimizer ◽  
Radial Basis ◽  
Decision Vector

AbstractThis paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis.

scholarly journals Gumbel-softmax-based optimization: a simple general framework for optimization problems on graphs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yaoxin Li ◽  
Jing Liu ◽  
Guozheng Lin ◽  
Yueyuan Hou ◽  
Muyun Mou ◽  
...  
Keyword(s):  
Objective Function ◽  
Statistical Physics ◽  
Automatic Differentiation ◽  
Optimization Problems ◽  
Vertex Cover ◽  
Independent Set ◽  
Computational Social Science ◽  
Maximization Problem ◽  
Maximum Independent Set ◽  
Gradient Descent Algorithm

AbstractIn computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure, such that the designed objective function is optimized under some constraints. However, these problems are notorious for their hardness to solve, because most of them are NP-hard or NP-complete. Although traditional general methods such as simulated annealing (SA), genetic algorithms (GA), and so forth have been devised to these hard problems, their accuracy and time consumption are not satisfying in practice. In this work, we proposed a simple, fast, and general algorithm framework based on advanced automatic differentiation technique empowered by deep learning frameworks. By introducing Gumbel-softmax technique, we can optimize the objective function directly by gradient descent algorithm regardless of the discrete nature of variables. We also introduce evolution strategy to parallel version of our algorithm. We test our algorithm on four representative optimization problems on graph including modularity optimization from network science, Sherrington–Kirkpatrick (SK) model from statistical physics, maximum independent set (MIS) and minimum vertex cover (MVC) problem from combinatorial optimization on graph, and Influence Maximization problem from computational social science. High-quality solutions can be obtained with much less time-consuming compared to the traditional approaches.

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