scholarly journals Automated Cable Road Layout and Harvesting Planning for Multiple Objectives in Steep Terrain

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 687 ◽  
Author(s):  
Bont ◽  
Maurer ◽  
Breschan
Keyword(s):  
Multiobjective Optimization ◽  
Cost Estimation ◽  
Planning Process ◽  
Pareto Frontier ◽  
Solution Process ◽  
Optimization Approach ◽  
Steep Terrain ◽  
And Performance ◽  
Harvesting Costs ◽  
Single Objective

Cable yarding is the most commonly used technique for harvesting timber from steep terrain in central Europe. During the planning process, one important task is to define the cable road layout. This means that the harvesting technology and cable road location must be specified for a given timber parcel. Although managers must minimize harvesting costs, it is even more important that such work on forests reduces the potential for damage to the residual stand and ensures that environmental conditions remain suitable for regeneration. However, current methods are geared only toward minimizing harvesting costs and are computationally demanding and difficult to handle for the end user. These limitations hinder broad application of such methods. Further, the underlying productivity models used for cost estimation do not cover all conditions of an area and they cannot be applied over a whole harvesting area. To overcome these shortcomings, we present: (1) a multiobjective optimization approach that leads to realistic, practicable results that consider multiple conflicting design objectives, and (2) a concept for an easy-to-use application. We compare the practical applicability and performance of the results achieved with multiobjective optimization with those achieved with single-objective (cost-minimal) optimization. Based on these points, we then present and discuss a concept for a user-friendly implementation. The model was tested on two sites in Switzerland. The study produced the following major findings: (1) Single-objective alternatives have no practical relevance, whereas multiobjective alternatives are preferable in real-world applications and lead to realistic solutions; (2) the solution process for a planning unit should include analysis of the Pareto frontier; and (3) results can only be made available within a useful period of time by parallelizing computing operations.

scholarly journals A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yindong Shen ◽  
Wenliang Xie ◽  
Jingpeng Li
Keyword(s):  
Multiobjective Optimization ◽  
Planning Process ◽  
Service Level ◽  
Vehicle Scheduling ◽  
Neighborhood Search ◽  
Optimization Approach ◽  
Transit Planning ◽  
Integrated Optimization ◽  
Link Cost ◽  
Integrated Approaches

The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization of TTP and VSP with uncertainty. In this paper, a novel multiobjective optimization approach with the objectives of minimizing the passenger travel cost, the vehicle scheduling cost, and the incompatible trip-link cost is proposed. Meanwhile, a multiobjective hybrid algorithm, which is a combination of the self-adjust genetic algorithm (SGA), large neighborhood search (LNS) algorithm, and Pareto separation operator (PSO), is applied to solve the integrated optimization problem. The experimental results show that the approach outperforms existing approaches in terms of service level and robustness.

Multiobjective Design Optimization of a Biconcave Mobile-Bearing Lumbar Total Artificial Disk Considering Spinal Kinematics, Facet Joint Loading, and Metal-on-Polyethylene Contact Mechanics

2019 ◽  
Vol 142 (4) ◽  
Author(s):  
Chaochao Zhou ◽  
Ryan Willing
Keyword(s):  
Multiobjective Optimization ◽  
Design Optimization ◽  
Performance Metrics ◽  
Facet Joint ◽  
Pareto Frontier ◽  
Mobile Bearing ◽  
Ultrahigh Molecular Weight Polyethylene ◽  
Joint Force ◽  
Multiobjective Design ◽  
Single Objective

Abstract Total disk arthroplasty (TDA) using an artificial disk (AD) is an attractive surgical technique for the treatment of spinal disorders, since it can maintain or restore spinal motion (unlike interbody fusion). However, adverse surgical outcomes of contemporary lumbar TDAs have been reported. We previously proposed a new mobile-bearing AD design concept featuring a biconcave ultrahigh-molecular-weight polyethylene (UHMWPE) mobile core. The objective of this study was to develop an artificial neural network (NN) based multiobjective optimization framework to refine the biconcave-core AD design considering multiple TDA performance metrics, simultaneously. We hypothesized that there is a tradeoff relationship between the performance metrics in terms of range of motion (ROM), facet joint force (FJF), and polyethylene contact pressure (PCP). By searching the resulting three-dimensional (3D) Pareto frontier after multiobjective optimization, it was found that there was a “best-tradeoff” AD design, which could balance all the three metrics, without excessively sacrificing each metric. However, for each single-objective optimum AD design, only one metric was optimal, and distinct sacrifices were observed in the other two metrics. For a commercially available biconvex-core AD design, the metrics were even worse than the poorest outcomes of the single-objective optimum AD designs. Therefore, multiobjective design optimization could be useful for achieving native lumbar segment biomechanics and minimal PCPs, as well as for improving the existing lumbar motion-preserving surgical treatments.

Design and planning of the balanced scorecard: A case study

2007 ◽  
Vol 26 (3) ◽  
pp. 217-227
Author(s):  
Ming-Hon Hwang ◽  
Hsin Rau
Keyword(s):  
Performance Evaluation ◽  
Balanced Scorecard ◽  
Process Management ◽  
Planning Process ◽  
Company Performance ◽  
Correct Process ◽  
The Balanced Scorecard ◽  
Strategy Planning ◽  
And Performance

In the industrial economy, evaluating company performance based on financial results was good enough. However, in the current globalized and highly competitive environment, maintaining long term competitiveness requires companies to engage in overall strategic planning and performance evaluation. The balanced scorecard is a tool or method for balancing an organization's performance and can react to situations where a company's direction becomes disoriented. This approach assists in strategy planning, process management, and performance evaluation from four perspectives, including financial, customer, internal process, and learning and growth. Good strategy planning provides companies with a correct management direction, correct process management ensures the efficient execution of plans, and correct performance evaluation illustrates the execution results. This study mainly focuses on how a large rubber company in Taiwan utilizes the balanced scorecard in its organization. As the technical perspective is important in the rubber keypad industry, besides the four above perspectives, this company has added the technical perspective. By introducing this company and its progress in implementing the balanced scorecard, this study hopes to provide other companies, especially rubber companies, with a planning direction and reference for the future implementation of the balanced scorecard.

An Evolutionary Multi-Objective Optimization Approach to the Topology Optimization of Auxetic Structures

2002 ◽  
Author(s):  
Ashraf O. Nassef
Keyword(s):  
Topology Optimization ◽  
Elastic Modulus ◽  
Finite Elements ◽  
Multiobjective Optimization ◽  
Poisson Ratio ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Finite Elements Analysis ◽  
Multi Objective ◽  
Auxetic Structures

Auxetic structures are ones, which exhibit an in-plane negative Poisson ratio behavior. Such structures can be obtained by specially designed honeycombs or by specially designed composites. The design of such honeycombs and composites has been tackled using a combination of optimization and finite elements analysis. Since, there is a tradeoff between the Poisson ratio of such structures and their elastic modulus, it might not be possible to attain a desired value for both properties simultaneously. The presented work approaches the problem using evolutionary multiobjective optimization to produce several designs rather than one. The algorithm provides the designs that lie on the tradeoff frontier between both properties.

Element-Level Optimization Module for Bridge Planning and Programming

2021 ◽  
pp. 036119812110413
Author(s):  
Karim Naji ◽  
Erin Santini-Bell ◽  
Kyle Kwiatkowski
Keyword(s):  
Multiobjective Optimization ◽  
Performance Management ◽  
Element Level ◽  
Screening Process ◽  
Predicting Performance ◽  
Asset Class ◽  
Decision Making Processes ◽  
Short And Long Term ◽  
Optimization Methodology ◽  
And Performance

The overall objective of this research is to support state departments of transportation with their decision-making processes and transitions to performance management and performance-based planning and programming mandated by the Moving Ahead for Progress in the 21st Century Act. Accomplishing this objective requires a systematic multiobjective optimization methodology. This research proposes such a methodology, referred to as an “element-based multiobjective optimization” (EB-MOO) methodology, which produces optimal or near-optimal sets of short- and long-term intervention strategies detailed at the bridge element level for planning and programming. The methodology currently focuses on the bridge asset class and consists of five modules: (1) data processing, (2) improvement, (3) element-level optimization (ELO), (4) bridge-level optimization (BLO), and (5) network-level optimization (NLO) modules. This paper details the ELO module, specifically: the basic framework of underlying processes and concepts, the alternative feasibility screening process, optimization problem types and mathematical formulations, and the heuristic algorithm used to solve the ELO problems. The paper also includes an illustrative example using a prototyping tool developed to implement EB-MOO methodology. The example presents several ELO problems under unconstrained scenarios. The implementation demonstrated the module’s capability in producing optimal or near-optimal ELO solutions, recommending element intervention actions, predicting performance, and determining funding requirements for the specified improvement type and program year. The broader EB-MOO methodology uses the ELO results as inputs for the BLO and NLO modules.

Design of Electrooptic Modulators Using a Multiobjective Optimization Approach

2008 ◽  
Vol 26 (16) ◽  
pp. 2969-2976 ◽  
Author(s):  
Ademar Muraro ◽  
Angelo Passaro ◽  
Nancy Mieko Abe ◽  
Airam Jonatas Preto ◽  
Stephan Stephany
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Approach ◽  
Electrooptic Modulators

scholarly journals Integrated civilian–military pre-positioning of emergency supplies: A multiobjective optimization approach

2017 ◽  
Vol 58 ◽  
pp. 732-741 ◽  
Author(s):  
Yu-Jun Zheng ◽  
Yue Wang ◽  
Hai-Feng Ling ◽  
Yu Xue ◽  
Sheng-Yong Chen
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Approach
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