Application of an Information-Based Design for Assembly Theory to Assembly Workstation Design

1993 ◽  
Author(s):  
David O. Hunt ◽  
Robert H. Sturges
Keyword(s):  
Problem Formulation ◽  
Mixed Integer ◽  
Computational Time ◽  
Design For Assembly ◽  
Assembly Time ◽  
Aspect Ratios ◽  
Workstation Design ◽  
Orientation Time ◽  
Np Hard Problem ◽  
Assembly Tasks

Abstract An effective presentation of components at the workstation can have a significant impact in reducing assembly time. Our goal is to reduce the assembly time by optimization of the component presentation. The assembly factors recognized in Design for Assembly theory as relevant to both parts acquisition and assembly workstation layout are recognition, orientation, weight, and handling distance. This study considers a single manual assembler at an assembly station, with the components in rectangular bins of differing sizes and aspect ratios. Ninety degree rotations are allowed for minimizing potential handling distance. The assembly task is modelled with multiple assembly points representing the final location of the components. Components can be preoriented or random in the bins, with preorientation removing the recognition and orientation time penalties. The problem formulation employs Mixed Integer Non-Linear Programming (MINLP), and numerical evidence suggests an np-hard problem. Heuristic methods control computational time to practical levels for realistic assembly tasks. Our results show that numerical optimization of assembly workstation layout can reduce the expected level of difficulty over random or manual workstation design methods.

scholarly journals A Sparse Optimization Approach for Energy-Efficient Timetabling in Metro Railway Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ziyan Luo ◽  
Xiaoyu Li ◽  
Naihua Xiu
Keyword(s):  
Optimization Model ◽  
Energy Efficient ◽  
Convex Relaxation ◽  
Sparse Optimization ◽  
Computational Time ◽  
Optimization Approach ◽  
Railway Systems ◽  
Alternating Direction ◽  
Np Hard Problem ◽  
Regenerative Energy

In this paper, we propose a sparse optimization approach to maximize the utilization of regenerative energy produced by braking trains for energy-efficient timetabling in metro railway systems. By introducing the cardinality function and the square of the Euclidean norm function as the objective function, the resulting sparse optimization model can characterize the utilization of the regenerative energy appropriately. A two-stage alternating direction method of multipliers is designed to efficiently solve the convex relaxation counterpart of the original NP-hard problem and then to produce an energy-efficient timetable of trains. The resulting approach is applied to Beijing Metro Yizhuang Line with different instances of service for case study. Comparison with the existing two-step linear program approach is also conducted which illustrates the effectiveness of our proposed sparse optimization model in terms of the energy saving rate and the efficiency of our numerical optimization algorithm in terms of computational time.

scholarly journals Peak load minimization of an e-bus depot: impacts of user-set conditions in optimization algorithms

2021 ◽  
Vol 4 (S3) ◽  
Author(s):  
Enrico Toniato ◽  
Prakhar Mehta ◽  
Stevan Marinkovic ◽  
Verena Tiefenbeck
Keyword(s):  
Optimization Algorithm ◽  
Peak Load ◽  
Mixed Integer ◽  
Computational Time ◽  
Transport Sector ◽  
Fleet Management ◽  
Peak Reduction ◽  
Management Options ◽  
Power Load ◽  
The Impact

AbstractThe transport sector is responsible for 25% of global CO2 emissions. To reduce emissions in the EU, a shift from the currently 745,000 operating public buses to electric buses (EBs) is expected in the coming years. Large-scale deployments of EBs and the electrification of bus depots will have a considerable impact on the local electric grid, potentially creating network congestion problems and spikes in the local energy load. In this work, we implement an exact, offline, modular multi-variable mixed-integer linear optimization algorithm to minimize the daily power load profile peak and optimally plan an electric bus depot. The algorithm accepts a bus depot schedule as input, and depending on the user input on optimization conditions, accounts for varying time granularity, preemption of the charging phase, vehicle-to-grid (V2G) charging capabilities and varying fleet size. The primary objective of this work is the analysis of the impact of each of these input conditions on the resulting minimized peak load. The results show that our optimization algorithm can reduce peak load by 83% on average. Time granularity and V2G have the greatest impact on peak reduction, whereas preemption and fleet splitting have the greatest impact on the computational time but an insignificant impact on peak reduction. The results bear relevance for mobility planners to account for innovative fleet management options. Depot infrastructure costs can be minimized by optimally sizing the infrastructure needs, by relying on split-fleet management or V2G options.

An Evaluation of Holograms as Training and Job Aids

1978 ◽  
Vol 20 (6) ◽  
pp. 661-670 ◽  
Author(s):  
Allan H. Frey ◽  
Edwin S. Eichert
Keyword(s):  
Visual Information ◽  
Visual Aids ◽  
Technical Documentation ◽  
Assembly Time ◽  
Line Drawings ◽  
Job Aids ◽  
The Subject ◽  
Assembly Tasks ◽  
And Storage

This study was concerned with an evaluation of holography in training and for job aids. Experimentation comparing holograms, line drawings, and photographs as methods of presenting visual information is reported. It appears that with the tasks used, holograms generally are as good as or better visual aids than either photographs or line drawings. The use of holograms tends to reduce errors rather than speed assembly time in assembly tasks. They also seem to enhance the discovery of errors when the subject is attempting to locate assembly errors in a construction. The results of this experimentation suggest that serious consideration should be given to the use of holography in the development of job aids and in training. Applications in technical documentation and storage relevant to the use of holograms as job aids are also considered.

scholarly journals Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

2019 ◽  
Vol 11 (11) ◽  
pp. 3127 ◽  
Author(s):  
Tarik Chargui ◽  
Abdelghani Bekrar ◽  
Mohamed Reghioui ◽  
Damien Trentesaux
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Sustainable Logistics ◽  
Cross Docking ◽  
Physical Internet

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

2019 ◽  
Vol 233 (10) ◽  
pp. 1282-1297 ◽  
Author(s):  
Binghai Zhou ◽  
Wenlong Liu
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Processing Time ◽  
Flow Shop ◽  
Scheduling Algorithm ◽  
Mixed Integer ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Multi Objective ◽  
Fuzzy Processing Time

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

PEDESTRIAN DETECTION SYSTEM USING CASCADED BOOSTING WITH INVARIANCE OF ORIENTED GRADIENTS

2009 ◽  
Vol 23 (04) ◽  
pp. 801-823 ◽  
Author(s):  
CHI-CHEN RAXLE WANG ◽  
JIN-YI WU ◽  
JENN-JIER JAMES LIEN
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Pedestrian Detection ◽  
Linear Interpolation ◽  
Detection Performance ◽  
Experimental Results ◽  
Computational Time ◽  
Aspect Ratios ◽  
Histograms Of Oriented Gradients ◽  
Interpolation Technique

This study presents a novel learning-based pedestrian detection system capable of automatically detecting individuals of different sizes and orientations against a wide variety of backgrounds, including crowds, even when the individual is partially occluded. To render the detection performance robust toward the effects of geometric and rotational variations in the original image, the feature extraction process is performed using both rectangular- and circular-type blocks of various sizes and aspect ratios. The extracted blocks are rotated in accordance with their dominant orientation(s) such that all the blocks extracted from the input images are rotationally invariant. The pixels within the cells in each block are then voted into rectangular- and circular-type 9-bin histograms of oriented gradients (HOGs) in accordance with their gradient magnitudes and corresponding multivariate Gaussian-weighted windows. Finally, four cell-based histograms are concatenated using a tri-linear interpolation technique to form one 36-dimensional normalized HOG feature vector for each block. The experimental results show that the use of the Gaussian-weighted window approach and tri-linear interpolation technique in constructing the HOG feature vectors improves the detection performance from 91% to 94.5%. In the proposed scheme, the detection process is performed using a cascaded detector structure in which the weak classifiers and corresponding weights of each stage are established using the AdaBoost self-learning algorithm. The experimental results reveal that the cascaded structure not only provides a better detection performance than many of the schemes presented in the literature, but also achieves a significant reduction in the computational time required to classify each input image.

The cycle shop scheduling: mathematical model, properties and solution algorithms

2019 ◽  
Vol 53 (1) ◽  
pp. 111-128
Author(s):  
Bahman Naderia ◽  
Sheida Goharib
Keyword(s):  
High Performance ◽  
Scatter Search ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Solution Algorithms ◽  
Proposed Model ◽  
Constructive Heuristics

Conventionally, in scheduling problems it is assumed that each job visits each machine once. This paper studies a novel shop scheduling called cycle shop problems where jobs might return to each machine more than once. The problem is first formulated by two mixed integer linear programming models. The characteristics of the problem are analyzed, and it is realized that the problem suffers from a shortcoming called redundancy, i.e., several sequences represents the same schedule. In this regard, some properties are introduced by which the redundant sequences can be recognized before scheduling. Three constructive heuristics are developed. They are based on the shortest processing time first, insertion neighborhood search and non-delay schedules. Then, a metaheuristic based on scatter search is proposed. The algorithms are equipped with the redundancy prevention properties that greatly reduce the computational time of the algorithms. Two sets of experiments are conducted. The proposed model and algorithms are evaluated. The results show the high performance of model and algorithms.

Optimisation of Design and Operation Parameters for Multicomponent Separation via Improved Lewis-Matheson Method

2017 ◽  
Vol 12 (4) ◽  
Author(s):  
Ching Ching Tiong ◽  
Jobrun Nandong
Keyword(s):  
Design Procedure ◽  
Problem Formulation ◽  
Computational Time ◽  
Separation And Purification ◽  
Efficient Design ◽  
Feed Composition ◽  
Design And Optimization ◽  
Purification Technique ◽  
Stage Matching ◽  
Matching Criteria

AbstractDistillation is the most commonly used separation and purification technique in the chemical and allied industries despite that it has been known as the most energy-consuming unit in process industry. The need to reduce this energy consumption has become one of the important focuses in the efficient design and optimization of distillation processes. In the present work, we propose an improved Lewis-Matheson stage-by-stage procedure by incorporating the Fenske equation to enhance the estimation of the non-key component distributions, and thus avoiding infeasible solutions to the stage-by-stage system of equations of mass and energy balances. A modified theta method is also included in the design procedure to satisfy the feed stage matching criteria which help reduces the computational time while increasing the accuracy of feed composition matching. By using the proposed modified Lewis-Matheson method, an optimization is conducted in Matlab environment where the problem formulation takes into account both sets of design and operating parameters with specified product purity as the constraint. The objective function of the optimization is to minimize the Total Annualized Cost (TAC), which includes both capital and operating costs. The effectiveness of the proposed design procedure is demonstrated using an industrial-scale natural gas liquids (NGLs) depropanizer fractionation unit.

A numerical study of the rheological properties of suspensions of rigid, non-Brownian fibres

1996 ◽  
Vol 329 ◽  
pp. 155-186 ◽  
Author(s):  
Michael B. Mackaplow ◽  
Eric S. G. Shaqfeh
Keyword(s):  
Integral Equations ◽  
Numerical Study ◽  
Problem Formulation ◽  
Viscosity Data ◽  
Slender Body Theory ◽  
Aspect Ratios ◽  
Wide Range ◽  
Orientation Distributions ◽  
Theological Properties ◽  
Body Theory

Using techniques developed in our previous publication (Mackaplow et al. 1994), we complete a comprehensive set of numerical simulations of the volume-averaged stress tensor in a suspension of rigid, non-Brownian slender fibres at zero Reynolds number. In our problem formulation, we use slender-body theory to develop a set of integral equations to describe the interfibre hydrodynamic interactions at all orders. These integral equations are solved for a large number of interacting fibres in a periodically extended box. The simulations thus developed are an accurate representation of the suspensions at concentrations up to and including the semidilute regime. Thus, large changes in the suspensions properties can be obtained. The Theological properties of suspensions with concentrations ranging from the dilute regime, through the dilute/semi-dilute transition, and into the semi-dilute regime, are surprisingly well predicted by a dilute theory that takes into account two-body interactions. The accuracy of our simulations is verified by their ability to reproduce published suspension extensional and shear viscosity data for a variety of fibre aspect ratios and orientation distributions, as well as a wide range of suspension concentrations.

Sign in / Sign up

Export Citation Format

Share Document