Multi-Objective Topology Optimization for Curved Arm of Multifunctional Billet Tong Based on Characterization of Working Conditions

A windlass driven heavy duty multifunctional billet tong was designed for large-scale forging and casting to reduce the number of auxiliary material handling devices in manufacturing workshops. To improve its mechanical performance and safety, a novel multi-objective topology optimization method for its curved arm is proposed in this paper. Firstly, the influence of different open angles and working frequencies for the curved arm was simplified to a multi-objective optimization problem. A comprehensive evaluation function was constructed using the compromise programming method, and a mathematical model of multi-objective topology optimization was established. Meanwhile, a radar chart was employed to portray the comparative measures of working conditions, the weight coefficient for each working condition was determined based on the corresponding enclosed areas, combining the stress indices, the displacement indices and the frequency indices of all working conditions. The optimization results showed that the stiffness and strength of the curved arm can be improved while its weight can be reduced by 10.77%, which shows that it is feasible and promising to achieve a lightweight design of the curved arm of a billet tong. The proposed method can be extended to other equipment with complex working conditions

Multi-Objective Topology Optimization for Curved Arm of Multifunctional Billet Tong Based on Characterization of Working Conditions

A windlass driven heavy duty multifunctional billet tong was designed for large-scale forging and casting to reduce the number of auxiliary material handling devices in manufacturing workshops. To improve its mechanical performance and safety, a novel multi-objective topology optimization method for its curved arm is proposed in this paper. Firstly, the influence of different open angles and working frequencies for the curved arm was simplified to a multi-objective optimization problem. A comprehensive evaluation function was constructed using the compromise programming method, and a mathematical model of multi-objective topology optimization was established. Meanwhile, a radar chart was employed to portray the comparative measures of working conditions, the weight coefficient for each working condition was determined based on the corresponding enclosed areas, combining the stress indices, the displacement indices and the frequency indices of all working conditions. The optimization results showed that the stiffness and strength of the curved arm can be improved while its weight can be reduced by 10.77%, which shows that it is feasible and promising to achieve a lightweight design of the curved arm of a billet tong. The proposed method can be extended to other equipment with complex working conditions

SysML Modeling For Embedded Systems Design Optimization: A Case Study

Model-Based Systems Engineering (MBSE) with the SysML language allows the designer to include requirement capture and design representation in a single model. This paper proposes a methodology to obtain the best design alternative, from a SysML design, by using multi-objective optimization techniques. A SysML model is extended with stereotypes, objective functions, variability and constraints. Then an integer representation of the problem can be generated and solved as a constraint satisfaction problem (CSP). The paper illustrates our methodology using an Embedded Cognitive Safety System (ECSS) design. From a component repository and redundancy alternatives, the best design alternatives are generated, to minimize the total cost and maximize the estimated system reliability

Enriched multi objective optimization model based cloud disaster recovery

AbstractIn cloud computing massive data storage is one of the great challenging tasks in term of reliable storage of sensitive data and quality of storage service. Among various cloud safety issues, the data disaster recovery is the most significant issue which is required to be considered. Thus, in this paper, analysis of massive data storage process in the cloud environment is performed and the massive data storage cost is based on the data storage price, communication cost and data migration cost. The data storage reliability involves of data transmission, hardware dependability and reliability. Reliable massive storage is proposed by using Enriched Multi Objective Optimization Model (EMOOM). The main objective of this proposed optimization model is using Enriched Genetic Algorithm (EGA) for efficient Disaster Recovery in a cloud environment. Finally, the experimental results show that the proposed EMOOM model is effective and positive and reliable

Multi-objective integrated production planning model and simulation constrained doubly by resources and materials

Based on analysing the defects of ERP planning system and researching related literatures, a multi objective integrated production planning model is constructed which is constrained double by resources and materials. The model tekes delivery on-tome, reduces inventory , reduces overtime work, meintains safety inventory as its optimization objectives, and can achieve the integrated optimization method provided by the model has strong feasibility and effectness

SPEA2-based safety system multi-objective optimization

Safety systems are designed to prevent the occurrence of certain conditions and their future development into a hazardous situation. The consequence of the failure of a safety system of a potentially hazardous industrial system or process varies from minor inconvenience and cost to personal injury, significant economic loss and death. To minimise the likelihood of a hazardous situation, safety systems must be designed to maximise their availability. Therefore, the purpose of this thesis is to propose an effective safety system design optimization scheme. A multi-objective genetic algorithm has been adopted, where the criteria catered for includes unavailability, cost, spurious trip and maintenance down time. Analyses of individual system designs are carried out using the latest advantages of the fault tree analysis technique and the binary decision diagram approach (BDD). The improved strength Pareto evolutionary approach (SPEA2) is chosen to perform the system optimization resulting in the final design specifications. The practicality of the developed approach is demonstrated initially through application to a High Integrity Protection System (HIPS) and subsequently to test scalability using the more complex Firewater Deluge System (FDS). Computer code has been developed to carry out the analysis. The results for both systems are compared to those using a single objective optimization approach (GASSOP) and exhaustive search. The overall conclusions show a number of benefits of the SPEA2 based technique application to the safety system design optimization. It is common for safety systems to feature dependency relationships between its components. To enable the use of the fault tree analysis technique and the BDD approach for such systems, the Markov method is incorporated into the optimization process. The main types of dependency which can exist between the safety system component failures are identified. The Markov model generation algorithms are suggested for each type of dependency. The modified optimization tool is tested on the HIPS and FDS. Results comparison shows the benefit of using the modified technique for safety system optimization. Finally the effectiveness and application to general safety systems is discussed

An Advanced Pavement Management System based on a Genetic Algorithm for a Motorway Network

Maintenance and improvement, through the rehabilitation, of the road infrastructure is a strategic and priority objective for road agencies, nevertheless the economic resources required are often inadequate. Within road management, the pavement management system (PMS) plays an essential role because of both the money needed and the performance that should be provided in terms of safety, ride quality and transport cost. The PMS is based on searching for a balanced solution between the lowest cost and the increased level of performance (i.e. pavement condition). In this paper a PMS multi-objective optimization method, was proposed, using a genetic algorithm (GA) to identify the best solution considering different rehabilitation strategies. The multi-objective optimization GA permits a set of optimal solutions (the Pareto solution set) that takes into account all the considered constraints. Finally on the basis of a specific criteria the best solution was selected in relation to the ranking of the priorities of the agency. A detailed numerical study was conducted on the Italian A18 motorway and the results showed that the proposed model PMS-GA is a suitable support to the decision making process

A fuzzy multi-objective model and application for the discrete dynamic temporary facilities location planning problem

The location of temporary facilities on construction sites is essential to the enhancement of productivity and safety, but it is complex due to the unique issues associated with construction. To positively contribute to the dynamic construction site layout planning field, this paper proposes a new fuzzy multi-objective decision making model. The proposed hybrid optimization model utilizes fuzzy numbers and logic to represent the closeness relationship between temporary facilities. The two main phases presented represent a specific advance in knowledge in through: (1) an opti­mization model that considers both uncertainty and dynamic elements; (2) the application of this optimization to a spe­cial project. A multi-objective simulated annealing-based genetic algorithm (MOSA-based GA) is proposed to solve the model and the case of Jinping-II hydroelectric station is studied to evaluate the model’s performance. The computational study was carried out to demonstrate the practicality and efficiency of the proposed optimization method. The study is applicable and useful to the profession

Models and algorithms for trauma network design.

Trauma continues to be the leading cause of death and disability in the US for people aged 44 and under, making it a major public health problem. The geographical maldistribution of Trauma Centers (TCs), and the resulting higher access time to the nearest TC, has been shown to impact trauma patient safety and increase disability or mortality. State governments often design a trauma network to provide prompt and definitive care to their citizens. However, this process is mainly manual and experience-based and often leads to a suboptimal network in terms of patient safety and resource utilization. This dissertation fills important voids in this domain and adds much-needed realism to develop insights that trauma decision-makers can use to design their trauma network. In this dissertation, we develop multiple optimization-based trauma network design approaches focusing minimizing mistriages and, in some cases, ensuring equity in care among regions. To mimic trauma care in practice, several realistic features are considered in our approach, which include the consideration of: (i) both severely and non-severely injured trauma patients and associated mistriages, (ii) intermediate trauma centers (ITCs) along with major trauma centers (MTCs), (iii) three dominant criteria for destination determination, and (iv) mistriages in on-scene clinical assessment of injuries. Our first contribution (Chapter 2) proposes the Trauma Center Location Problem (TCLP) that determines the optimal number and location of major trauma centers (MTCs) to improve patient safety. The bi-objective optimization model for TCLP explicitly considers both types of patients (severe and non-severe) and associated mistriages (specifically, system-related under- and over-triages) as a surrogate for patient safety. These mistriages are estimated using our proposed notional tasking algorithm that attempts to mimic the EMS on-scene decision of destination hospital and transportation mode. We develop a heuristic based on Particle Swarm Optimization framework to efficiently solve realistic problem sizes. We illustrate our approach using 2012 data from the state of OH and show that an optimized network for the state could achieve 31.5% improvement in patient safety compared to the 2012 network with the addition of just one MTC; redistribution of the 21 MTCs in the 2012 network led to a 30.4% improvement. Our second contribution (Chapter 3) introduces a Nested Trauma Network Design Problem (NTNDP), which is a nested multi-level, multi-customer, multi-transportation, multi-criteria, capacitated model. The NTNDP model has a bi-objective of maximizing the weighted sum of equity and effectiveness in patient safety. The proposed model includes intermediate trauma centers (TCs) that have been established in many US states to serve as feeder centers to major TCs. The model also incorporates three criteria used by EMS for destination determination; i.e., patient/family choice, closest facility, and protocol. Our proposed ‘3-phase’ approach efficiently solves the resulting MIP model by first solving a relaxed version of the model, then a Constraint Satisfaction Problem, and a modified version of the original optimization problem (if needed). A comprehensive experimental study is conducted to determine the sensitivity of the solutions to various system parameters. A case study is presented using 2019 data from the state of OH that shows more than 30% improvement in the patient safety objective. In our third contribution (Chapter 4), we introduce Trauma Network Design Problem considering Assessment-related Mistriages (TNDP-AM), where we explicitly consider mistriages in on-scene assessment of patient injuries by the EMS. The TNDP-AM model determines the number and location of major trauma centers to maximize patient safety. We model assessment-related mistriages using the Bernoulli random variable and propose a Simheuristic approach that integrates Monte Carlo Simulation with a genetic algorithm (GA) to solve the problem efficiently. Our findings indicate that the trauma network is susceptible to assessment-related mistriages; specifically, higher mistriages in assessing severe patients may lead to a 799% decrease in patient safety and potential clustering of MTCs near high trauma incidence rates. There are several implications of our findings to practice. State trauma decision-makers can use our approaches to not only better manage limited financial resources, but also understand the impact of changes in operational parameters on network performance. The design of training programs for EMS providers to build standardization in decision-making is another advantage