Route Search Method for Railway Replacement Buses Adopting Ant Colony Optimization

In recent years, Japan, and especially rural areas have faced the growing problems of debt-ridden local railway lines along with the population decline and aging population. Therefore, it is best to consider the discontinuation of local railway lines and introduce replacement buses to secure the transportation methods of the local people especially in rural areas. Based on the above background, targeting local railway lines that may be discontinued in the near future, appropriate bus stops when provided with potential bus stops were selected, the present study proposed a method that introduces routes for railway replacement buses adopting ant colony optimization (ACO). The improved ACO was designed and developed based on the requirements set concerning the route length, number of turns, road width, accessibility of railway lines and zones without bus stops as well as the constraint conditions concerning the route length, number of turns and zones without bus stops. Original road network data were generated and processed adopting a geographic information systems (GIS), and these are used to search for the optimal route for railway replacement buses adopting the improved ACO concerning the 8 zones on the target railway line (JR Kakogawa line). By comparing the improved ACO with Dijkstra’s algorithm, its relevance was verified and areas needing further improvements were revealed.

A Bound Heuristic Technique for Solving DRAMA Spares Optimizations

This paper studies the solution technique to solve the DRAMA spares allocation optimization problem. DRAMA model is an analytic spare optimization model of a multi-item, multi-location, and two-echelon inventory system. The computation of its system spares availability is much complicated. The objective function and constraint functions of DRAMA model could be written as the separable forms. A new bound heuristic algorithm has been presented by improving the bound heuristic algorithm for solving the reliability redundancy optimization problem (BHA in short). With the results, the proposed algorithm has been found to be more economical and effective than BHA to obtain the solutions of large DRAMA model. The new algorithm could be used to solve reliability redundancy optimization problems with the separable forms.

Joint optimization of LORA and spares stocks considering corrective maintenance time

Level of repair analysis（LORA） is an important method of maintenance decision for establishing systems of operation and maintenance in the equipment development period. Currently, the research on equipment of repair level focuses on economic analysis models which are used to optimize costs and rarely considers the maintenance time required by the implementation of the maintenance program. In fact, as to the system requiring high mission complete success, the maintenance time is an important factor which has a great influence on the availability of equipment systems. Considering the relationship between the maintenance time and the spares stocks level, it is obvious that there are contradictions between the maintenance time and the cost. In order to balance these two factors, it is necessary to build an optimization LORA model. To this end, the maintenance time representing performance characteristic is introduced, and on the basis of spares stocks which is traditionally regarded as a decision variable, a decision variable of repair level is added, and a multi-echelon multiindenture（MEMI） optimization LORA model is built which takes the best cost-effectiveness ratio as the criterion, the expected number of backorder（EBO） as the objective function and the cost as the constraint. Besides, the paper designs a convex programming algorithm of multi-variable for the optimization model, provides solutions to the non-convex objective function and methods for improving the efficiency of the algorithm. The method provided in this paper is proved to be credible and effective according to the numerical example and the simulation result.

System optimization-oriented spare parts dynamic configuration model for multi-echelon multi-indenture system

In order to optimize the spares configuration project at different stages during the life cycle, the factor of time is considered to relax the assumption of the spares steady demand in multi-echelon technique for recoverable item control (METRIC) theory. According to the method of systems analysis, the dynamic palm theorem is introduced to establish the prediction model of the spares demand rate, and its main influence factors are analyzed, based on which, the spares support effectiveness evaluation index system is studied, and the system optimization-oriented spares dynamic configuration method for multi-echelon multi-indenture system is proposed. Through the analysis of the optimization algorithm, the layered marginal algorithm is designed to improve the model calculation efficiency. In a given example, the multi-stage spares configuration project during its life cycle is gotten, the research result conforms to the actual status, and it can provide a new way for the spares dynamic optimization.

Collaborative optimization of maintenance and spare ordering of continuously degrading systems

A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material degradation. The degrading decrement after the imperfect maintenance action is assumed as a random variable normal distribution. This model aims to ob- tain the optimal maintenance policy and spare ordering point with the expected cost rate within system lifecycle as the optimization objective. The rationality and feasibility of the model are proved through a numerical example.

Mission-oriented dynamic optimization model of carrying spares for warship redundant system

Redundancy is a common structure for warship system,and it is an effective way to improve the reliability of the system.In this paper, warship system is taken as the object of study,based on the system reliability equivalence principle, a spares demand rate calculation method for redundant system is proposed through structure transformation. According to the system analysis method, the general modeling data structure of spares support echelon and system indenture is established, and the mission success probability is taken as the optimization target to build the dynamic optimization model of carrying spares during the process of multi-phase. By introducing the Lagrange multiplier, the spares weight, volume and cost are transformed to the single target of the spares total scale, and the initial Lagrange factors and its dynamic adjustment policy is proposed. In a given example, the main influence factors of the carrying spares project are analyzed, and the study results are in accordance with the reality, which can provide a new approach to mission-oriented carrying spares optimization for the redundant system.

Joint optimization of maintenance inspection and spare provisioning for aircraft deteriorating parts

With the wide application of condition based maintenance(CBM) in aircraft maintenance practice, the joint optimization of maintenance and inventory management, which can take full advantage of CBM and reduce the aircraft operational cost, is receiving increasing attention. In order to optimize the inspection interval, maintenance decision and spare provisioning together for aircraft deteriorating parts, firstly, a joint inventory management strategy is presented, then, a joint optimization of maintenance inspection and spare provisioning for aircraft parts subject to the Wiener degradation process is proposed based on the strategy.Secondly, a combination of the genetic algorithm(GA) and the Monte Carol method is developed to minimize the total cost rate.Finally, a case study is conducted and the proposed joint optimization model is compared with the existing optimization model and the airline real case. The results demonstrate that the proposed model is more beneficial and effective. In addition, the sensitivity analysis of the proposed model shows that the lead time has higher influence on the optimal results than the urgent order cost and the corrective maintenance cost, which is consistent with the actual situation of aircraft maintenance practices and inventory management.

Mission-Oriented Configuration Model of Aircraft Carrying Spares and Dynamic Optimization Policy

Spare parts are critical to scheduled maintenance and fault repair, and can directly affect the readiness and combat capability of equipment. Equipmentrs capacity of carrying spares is influenced by its storage space and scales, so it is necessary to consider economic factors, e.g. spares cost, as well as non-economic ones, such as spares volume, mass and scale, when optimizing spares configuration. Aiming at this problem, the optimization model based on multi-constraints for carrying spares is built by METRIC theory and system analysis. Through the introduction of Lagrange factors, the spares cost is transformed to shadow price, and the optimization method for carrying spares and the dynamic adjustment policy of Lagrange factors are proposed. The result of a given example is analyzed, and demonstrates that the proposed model can be optimized with all constraints, and the research can provide a new way for carrying spares optimization.

An Optimal Replacement Policy for a System with Finite Spares

This paper develops a new replacement policy for a system with multiple spare units.An optimal replacement period is determined by maximizing the mean time to failure of the system.We show that there exists a finite and unique optimal replacement period T for units with strictly increasing failure rates. We also proof that the optimal repIacement period for a system with spares is decreasing in k. Furthermore, an algorithm is presented to get the optimal replacement period and a numerical example is given to illustrate the result. with strictly

Joint optimization of inspection-based and age-based preventive maintenance and spare ordering policies for single-unit systems

This paper presents a joint optimization policy of preventive maintenance(PM)and spare ordering for single-unit systems,which deteriorate subject to the delay-time concept with three deterioration stages.PM activities that combine a non-periodic inspection scheme with age-replacement are implemented.When the system is detected to be in the minor defective stage by an inspection for the first time,place an order and shorten the inspection interval.If the system has deteriorated to a severe defective stage,it is either repaired imperfectly or replaced by a new spare.However,an immediate replacement is required once the system fails,the maximal number of imperfect maintenance(IPM)is satisfied or its age reaches to a pre-specified threshold.In consideration of the spare’s availability as needed,there are three types of decisions,i.e.,an immediate or a delayed replacement by a regular ordered spare,an immediate replacement by an expedited ordered spare with a relative higher cost.Then,some mutually independent and exclusive renewal events at the end of a renewal cycle are discussed,and the optimization model of such a joint policy is further developed by minimizing the long-run expected cost rate to find the optimal inspection and age-replacement intervals,and the maximum number of IPM.A Monte-Carlo based integration method is also designed to solve the proposed model.Finally,a numerical example is given to illustrate the proposed joint optimization policy and the performance of the Monte-Carlo based integration method.

Research on Performance Optimization of Spark Distributed Computing Platform

Spark,a distributed computing platform,has rapidly developed in the field of big data.Its in-memory computing feature reduces disk read overhead and shortens data processing time,making it have broad application prospects in large-scale computing applications such as machine learning and image processing.However,the performance of the Spark platform still needs to be improved.When a large number of tasks are processed simultaneously,Spark’s cache replacementmechanismcannot identify high-value data partitions,resulting inmemory resources not being fully utilized and affecting the performance of the Spark platform.To address the problem that Spark’s default cache replacement algorithm cannot accurately evaluate high-value data partitions,firstly the weight influence factors of data partitions are modeled and evaluated.Then,based on this weighted model,a cache replacement algorithm based on dynamic weighted data value is proposed,which takes into account hit rate and data difference.Better integration and usage strategies are implemented based on LRU(LeastRecentlyUsed).Theweight update algorithm updates the weight value when the data partition information changes,accurately measuring the importance of the partition in the current job;the cache removal algorithm clears partitions without useful values in the cache to releasememory resources;the weight replacement algorithm combines partition weights and partition information to replace RDD partitions when memory remaining space is insufficient.Finally,by setting up a Spark cluster environment,the algorithm proposed in this paper is experimentally verified.Experiments have shown that this algorithmcan effectively improve cache hit rate,enhance the performance of the platform,and reduce job execution time by 7.61%compared to existing improved algorithms.

Confidence Level of System Spares OptimizationUsing Fuzzy Theory

To obtain a higher readiness for a complex system, the common method is to store some spares. Confidence level of system spares is an important parameter to control the probability of the spares required. By introducing fuzzy theory, this paper allocates the confidence level of system spares to its subsystems, thus to achieve a rational management of system spares,

An Adaptive Sequential Replacement Method for Variable Selection in Linear Regression Analysis

With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.

IOPS:computational graph optimization based on inter-operators parallel scheduling

To improve the inference efficiency of convolutional neural networks(CNN),the existing neural networks mainly adopt heuristic and dynamic programming algorithms to realize parallel scheduling among operators.Heuristic scheduling algorithms can generate local optima easily,while the dynamic programming algorithm has a long convergence time for complex structural models.This paper mainly studies the parallel scheduling between operators and proposes an inter-operator parallelism schedule(IOPS)scheduling algorithm that guarantees the minimum similar execution delay.Firstly,a graph partitioning algorithm based on the largest block is designed to split the neural network model into multiple subgraphs.Then,the operators that meet the conditions is replaced according to the defined operator replacement rules.Finally,the optimal scheduling method based on backtracking is used to schedule the computational graph.Network models such as Inception-v3,ResNet-50,and RandWire are selected for testing.The experimental results show that the algorithm designed in this paper can achieve a 1.6×speedup compared with the existing sequential execution methods.

A Dynamic Memory Allocation Optimization Mechanism Based on Spark

Spark is a distributed data processing framework based on memory.Memory allocation is a focus question of Spark research.A good memory allocation scheme can effectively improve the efficiency of task execution and memory resource utilization of the Spark.Aiming at the memory allocation problem in the Spark2.x version,this paper optimizes the memory allocation strategy by analyzing the Spark memory model,the existing cache replacement algorithms and the memory allocation methods,which is on the basis of minimizing the storage area and allocating the execution area according to the demand.It mainly including two parts:cache replacement optimization and memory allocation optimization.Firstly,in the storage area,the cache replacement algorithm is optimized according to the characteristics of RDD Partition,which is combined with PCA dimension.In this section,the four features of RDD Partition are selected.When the RDD cache is replaced,only two most important features are selected by PCA dimension reduction method each time,thereby ensuring the generalization of the cache replacement strategy.Secondly,the memory allocation strategy of the execution area is optimized according to the memory requirement of Task and the memory space of storage area.In this paper,a series of experiments in Spark on Yarn mode are carried out to verify the effectiveness of the optimization algorithm and improve the cluster performance.

A Bionic Approach for Topology Optimization for Tension-only or Compression-only Design

A new bionic approach is presented to find the optimal topologies of a structure with tension-only or compression-onlymaterial based on bone remodelling theory.By traditional methods,the computational cost of topology optimization of thestructure is high due to material nonlinearity.To improve the efficiency of optimization,the reference-interval with material-replacement method is presented.In the method,firstly,the optimization process of a structure is considered as bone remodellingprocess under the same loading conditions.A reference interval of Strain Energy Density (SED),corresponding to thedead zone or lazy zone in bone mechanics,is adopted to control the update of the design variables.Secondly,a material-replacement scheme is used to simplify the Finite Element Analysis (FEA) of structure in optimization.In the operation ofmaterial-replacement,the original tension-only or compression-only material in design domain is replaced with a new isotropicmaterial and the Effective Strain Energy Density (ESED) of each element can be obtained.Finally,the update of design variablesis determined by comparing the local ESED and the current reference interval of SED,e.g.,the increment of a relativedensity is nonzero if the local ESED is out of the current reference interval.Numerical results validate the method.

Repairable Configuration Optimization Model under Two-Echelon Inventory

Spares inventory configuration optimization is an effective way to improve readiness and reduce life cycle cost of equipment.Through analyzing two-echelon spares support system,the METRIC model basic theory was used.An inventory configuration optimization model of two-echelon spares support system was proposed which took the spares expected shortfall as the object and made the minimum repairable parts expected shortfall instead of the maximum spares supportability as the objective function.Marginal efficiency analysis algorithm was applied to optimizing the spares configuration and generating a rational spares inventory configuration.Finally,several examples are given to verify the model.

Warship Spare Parts Configuration Optimization for Stock Control:Investigating the Gap Between Qualitative and Quantitative Constraints

This paper investigates the gap between qualitative and quantitative constraints in spare parts stock control, with specific reference to warship spare parts support projects. A critical literature review of theoretical contributions about qualitative or quantitative factors for warship spare parts warehouse management is firstly provided, which allows to analyze the reasons for this qualitative-quantitative gap by addressing the limitations of spare parts models developed in the literature. Therefore a model including cloud model, marginal analysis and Lagrange multiplier method (CML) for study is proposed in this paper to bridge the gap. The model is used to solve the mix-constraints (both qualitative and quantitative constraints are considered) problem in a logic decision diagram particularly at the different decision nodes of the diagram. Finally, verifying test results show that the algorithm is feasible and its optimal support project meets the needs of engineering practices. © 2017, Shanghai Jiaotong University and Springer-Verlag GmbH Germany.

Optimal Replacement Time of Electrical Components Based on Constant-Interval Replacement Model: Equipment Inspection Method and Weibull Analysis

The operation of distribution system with the components in deteriorating condition makes the system reliability worsen. It is important to find the solution for balancing failure cost and maintenance benefits such as down-time and reliability. In this paper, time to replace the components in optimum condition based on constant-interval replacement mode is investigated. The optimal replacement time is mainly depended on component’s reliability and the cost ration of preventive replacement and failure replacement. In this paper, equipment inspection method and Weibull Analysis is applied to obtain the accurate reliability estimation. Weibull Analysis is applied with constant-interval replacement model to investigate the optimum replacement time for each component considering the different cost ratios. According to the quantitative results, the determination of the optimal replacement time (OPT) can minimize the total downtime and failure cost. Consequently, the reliability of the system is maximized and estimation also becomes more accurate due to sufficient approach.

Optimal Water Pipe Replacement Policy

Water scarcity is the major problem confronting both urban and rural dwellers in Enugu State. This scarcity emanated from indiscriminate pipe failure, lack of adequate maintenance, uncertainty on the time of repair or replacement of pipes etc. There is no systematic approach to determining replacement or repair time of the pipes. Hence, the rule of thumb is used in making such a vital decision. The population is increasing, houses are built but the network is not expanded and the existing ones that were installed for no less than two to three decades ago are not maintained. These compounded the problem of scarcity of water in the state. Replacement or repair of water pipes when they are seen spilling water cannot solve this lingering problem. The solution can be achieved by developing an adequate predictive model for water pipe replacement. Hence, this research is aimed at providing a solution to this problem of water scarcity by suggesting a policy that will be used for better planning. The interests in this paper were to obtain a water pipe failure model, the intensity function λ(t) [failure rate], the reliability R(t) and the optimal time of replacement and they were achieved. It was observed that the failure rate of the pipes increases with time while their reliability deteriorates with time. Hence, the Optimal replacement policy is that each pipe should be replaced after 4th break when the reliability = 0.0011.