Multiple-objective Model Research Articles

Installation and Dispatch of the Traffic Patrol Service Platform

In this paper, we construct three mathematical models to install and dispatch the traffic patrol service platform properly based on the real data of a certain city. Firstly, we build the shortest path model based on the Floyd algorithm to determine the jurisdictional of each platform. Then, we designed the dispatch model combined with 0-1 integer programming and the Hungarian algorithm to find the dispatching schemes when coming across large-scale emergencies. Lastly, we build the multiple-objective location model to optimize the present distribution situation of the traffic patrol platforms considering the workload differences among these existing platforms and overlong response time in some places.

Batch scheduling for minimal energy consumption and tardiness under uncertainties: A heat treatment application

A novel multiple-objective model for batch scheduling of an energy-intensive manufacturing process, e.g., heat treatment, is proposed. The model minimizes energy consumption and total weighted tardiness while considering the arrival times of each workpiece and the inherent uncertainties in gas heating values, processing times, and due dates. Fuzzy logic is adopted to characterize these uncertainties and to interpret objective dominance when finding a Pareto frontier. A non-dominated sorting genetic algorithm is employed. The approach is demonstrated using a pre-treatment (soaking) process prior to a hot rolling operation. Pareto optimal performance of the model under different parameter settings is discussed.

Optimization of the Waterbus Operation Plan Considering Carbon Emissions: The Case of Zhoushan City

Recently, as more people are concerned with the issues around environment protection, research about how to reduce carbon emissions has drawn increasing attention. Encouraging public transportation is an effective measure to reduce carbon emissions. However, overland public transportation does less to lower carbon because of the gradually increasing pressure of the urban road traffic. Therefore, the waterbus along the coast becomes a new direction of the urban public transport development. In order to optimize the operation plan of the waterbus, a bi-level model considering carbon emissions is proposed in this paper. In the upper-level model, a multiple objective model is established, which considers both the interests of the passengers and the operator while considering the carbon emissions. The lower-level model is a traffic model split by using a Nested Logit model. A NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm is proposed to solve the model. Finally, the city of Zhoushan is chosen as an example to prove the feasibility of the model and the algorithm. The result shows that the proposed model for waterbus operation optimization can efficiently reduce transportation carbon emissions and satisfy passenger demand at the same time.

Comparison Of Laser Scanning And Photogrammetry And Their Use For Digital Recording Of Cultural Monument Case Study: Byzantine Land Walls-Istanbul

Abstract. 3D modeling of cultural monuments is very crucial issue for preparing restoration projects. However, it has challenges such as data acquisition, preparation and processing. 3D modeling of objects can be time consuming and may include some difficulties due to the complexity of the structures. 3D terrestrial laser (TLS) scanning technique is one of the reliable and advantageous methods for 3D reconstruction of monuments. This technique is commonly acknowledged due to its accuracy, speed and flexibility. But the suitability and capability of this technique depends on proper usage, and good survey planning. Magnificent developments in highresolution digital sensor technologies leaded to manufacturing of new camera systems. Parallel to these innovations, development of computer systems and image processing techniques made enable to obtain multiple image-based 3D object models. In the presented study, TLS method has been compared to conventional photogrammetric and image-based dense matching methods. Automatic dense point creation has been realized by our developed algorithm and PIXEL-PHOTO software which generates 3D point clouds from stereo images. The reliability and encountered problems during point cloud measurement process have been discussed. The study area has been chosen as historical Byzantine Land Walls of Istanbul, which constitute a remarkable area defining the ancient city’s historical peninsula.

Order loading operation management systems for MRP: Mathematical programming versus a knowledge–based approach

This paper is concerned with a loading schedule of manufacturing orders in a material requirement planning (MRP) environment. In an environment in which production capacity and/or job due date is tight, ordinary loading work has become complicated in order to attain the management goals. So a multiple objective model, which is divided into two loading phases under a hierarchical procedure, is proposed to support the loading work. A prototype loading system is developed using MP (mathematical programming) logic. KB (knowledge–based) approaches are also discussed for practical problems and the features of the two systems are compared.

Geographic Video 3d Data Model And Retrieval

Abstract. Geographic video includes both spatial and temporal geographic features acquired through ground-based or non-ground-based cameras. With the popularity of video capture devices such as smartphones, the volume of user-generated geographic video clips has grown significantly and the trend of this growth is quickly accelerating. Such a massive and increasing volume poses a major challenge to efficient video management and query. Most of the today’s video management and query techniques are based on signal level content extraction. They are not able to fully utilize the geographic information of the videos. This paper aimed to introduce a geographic video 3D data model based on spatial information. The main idea of the model is to utilize the location, trajectory and azimuth information acquired by sensors such as GPS receivers and 3D electronic compasses in conjunction with video contents. The raw spatial information is synthesized to point, line, polygon and solid according to the camcorder parameters such as focal length and angle of view. With the video segment and video frame, we defined the three categories geometry object using the geometry model of OGC Simple Features Specification for SQL. We can query video through computing the spatial relation between query objects and three categories geometry object such as VFLocation, VSTrajectory, VSFOView and VFFovCone etc. We designed the query methods using the structured query language (SQL) in detail. The experiment indicate that the model is a multiple objective, integration, loosely coupled, flexible and extensible data model for the management of geographic stereo video.

Research on selection strategy of machining equipment in cloud manufacturing

Cloud manufacturing (CM) is a new type of networked manufacturing model, which is proposed in 2010. Optimization technology is one of the key techniques for CM operation, which are used for the efficient integration of manufacturing resources. In all kinds of manufacturing resources, the machining equipment is one of the most important resources. Using optimization techniques to achieve optimal selection of machining equipment is rarely studied in the CM. In order to handle the optimization selection of machining equipment in CM, comparing with the existing resources optimal configuration, an optimal selection strategy is introduced for the machining equipment in CM. In the selection strategy, first, a multiple objective and binary integer programming model is proposed to describe the optimal selection of machining equipment in CM. Second, after analyzing the mathematical model and the real-world problem of the machining equipment selection in CM, the priority method is adopted to convert the multiple-objective problem into a single-objective problem. Third, an improved particle swarm optimization (IPSO) algorithm based on a novel encoding scheme and fitness function is presented to solve the single-objective mathematical model. Finally, the simulation experiments verify the effectiveness of the IPSO algorithm and show that the selection strategy is more objective and effective to help the client select the machining equipment in the CM than current resources optimization model. This research provides a theoretical support for the development of CM.

Research on Operation Arrangement for Waiting Hall in Railway Passenger Station

The waiting hall is an important part for railway passenger station . To optimize and analyze the usage of waiting hall has great significance for improving operation efficiency of railway passenger station. Based on the research on the passenger volume and passenger flow construction, distribution regularities of passenger arrival time, passenger waiting time and the capacity of checking tickets windows can be obtained. By analyzing the distribution function of the above regularities, methods of calculating the efficient time of passengers occupying waiting halls can be achieved, so is the minimum departure time interval for trains in the same waiting area. On the basis of optimization and analysis of large scale railway passenger stations, algorithm for passenger stream lines of different platforms can be obtained and 0-1 planning model of the minimum waiting areas divided can be established. Based on the above work, constrains on whether there exist parallel operations for railway staffs are established , a nd phrases are divided into several time blocks , t he object function is the balance usage of waiting halls in large scale railway stations in one day and the shortest passenger stream lines in waiting halls , then a multiple-objective model on o peration a rrangement for waiting halls of large scale railway passenger stations is proposed , and its o ptimal algorithm is also present. Finally, a case study is carried out to verify the validity, objectivity and applicability of this model and its algorithm through calculated and analyzed practical data based on Lanzhou railway station. Optimal results show that the model on o peration a rrangement for waiting hall in large scale railway passenger stations can reach the usage balance of waiting halls and the shortest passenger stream lines , t he maximum difference between simulation passenger flow volume of each waiting hall and actual waiting passenger flow volume is 29 6 , and t he m in imum difference is 131 .To the railway stations which have mixed or sub-end arrangement and which have disorder passenger stream lines, more efficient results can be achieved with optimal model in the paper.

Study for Modeling and Solving Method of Water Resources Optimizing Allocation under Considering Uncertainties

Optimal allocation problem of water resources has some features that principal and subordinate hierarchical, multi-objective, multi-stage, risk decision-making, etc. Aimed to these characteristics, this paper constructs multiple objective risk decision model under principal and subordinate hierarchical risk decision-making, and by integrating a variety of optimization algorithms for solving idea ( such as fuzzy stochastic simulating technique, multi-objective satisfactory degree computation, principal and subordinate hierarchical game, and evolutionary game solving technology based on particle swarm algorithm), established an algorithm system of possessing logical relations. The result is convincible after a case study.

Real-time object detection and localization with SIFT-based clustering

This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based approaches are likely to be ineffective. The approach exploits SIFT keypoint extraction and mean shift clustering to partition the correspondences between the object model and the image onto different potential object instances with real-time performance. Then, the hypotheses of the object shape are validated by a projection with a fast Euclidean transform of some delimiting points onto the current image. Moreover, in order to improve the detection in the case of reflective or transparent objects, multiple object models (of both the same and different faces of the object) are used and fused together. Many measures of efficacy and efficiency are provided on random disposals of heavily-occluded objects, with a specific focus on real-time processing. Experimental results on different and challenging kinds of objects are reported.

Fuzzy Multiple Objective Models For Facility Location Problems Fuzzy Multiple Objective Models For Facility Location Problems

There are a variety of efficient approaches to solve crisp multiple objective decision making problems. However in the real life the input data may not be precisely determined because of the incomplete information. This paper deals with a multi objective facility location problem using the algorithm developed by Drezner and Wesolowski.

Modelling renewable supply chain for electricity generation with forest, fossil, and wood-waste fuels

In this paper, a multiple objective model to large-scale and long-term industrial energy supply chain scheduling problems is considered. The problems include the allocation of a number of fossil, peat, and wood-waste fuel procurement chains to an energy plant during different periods. This decision environment is further complicated by sequence-dependent procurement chains for forest fuels. A dynamic linear programming model can be efficiently used for modelling energy flows in fuel procurement planning. However, due to the complex nature of the problem, the resulting model cannot be directly used to solve the combined heat and electricity production problem in a manner that is relevant to the energy industry. Therefore, this approach was used with a multiple objective programming model to better describe the combinatorial complexity of the scheduling task. The properties of this methodology are discussed and four examples of how the model works based on real-world data and optional peat fuel tax, feed-in tariff of electricity and energy efficiency constraints are presented. The energy industry as a whole is subject to policy decisions regarding renewable energy production and energy efficiency regulation. These decisions should be made on the basis of comprehensive techno-economic analysis using local energy supply chain models.

A hierarchical feature fusion framework for adaptive visual tracking

A Hierarchical Model Fusion (HMF) framework for object tracking in video sequences is presented. The Bayesian tracking equations are extended to account for multiple object models. With these equations as a basis a particle filter algorithm is developed to efficiently cope with the multi-modal distributions emerging from cluttered scenes. The update of each object model takes place hierarchically so that the lower dimensional object models, which are updated first, guide the search in the parameter space of the subsequent object models to relevant regions thus reducing the computational complexity. A method for object model adaptation is also developed. We apply the proposed framework by fusing salient points, blobs, and edges as features and verify experimentally its effectiveness in challenging conditions.

The Study and Realization of Online Submission and Peer Review System Based on AOP

Aspect-Oriented Programming (AOP) is a new programming technology. It compensates the weakness of Object-Oriented Programming (OOP) at applying common behavior that spans multiple non-related object models. Interceptor adopts the logos of AOP and uses a way of hot swap solving these problems. At this issue, we research the AOP technology of Java Web called Struts interceptor. We show the advantage of this new programming method through an online submission and review system which using AOP method in the authentication and authorization.

Construction of a fuzzy QFD failure matrix using a fuzzy multiple-objective decision model

Global competition has forced companies to consider increasing quality and decreasing all costs, including quality costs. When constructing the quality plan for both the incoming raw materials and the manufacturing process, all quality characteristics (QCs) of products or processes cannot be measured or tested because of preventive costs. Companies should decide which QCs are significant and should be measured or tested. This paper presents a methodology for selecting the significant QCs of a product. A quality function deployment failure matrix is proposed, incorporating failure information from design failure mode and effect analysis, related QCs, vague information on technical difficulties, and imprecise cost and time measurements for QCs. In addition, this method uses a fuzzy multiple-objective decision model that addresses two objectives: maximising the priorities of QCs and minimising the technical difficulties within the constraints of limited cost and time. A two-phase approach is used for solving the fuzzy multiple-objective model. A real application to a backhoe loader wheel is presented as an illustration.

Decision Making with Prostate Cancer: A Multiple-Objective Model with Uncertainty

A patient who has been diagnosed with prostate cancer must make a difficult treatment decision. The available alternatives have varying cure rates and probabilities of side effects over a period of many years. This paper describes a well-informed, objective, and personalized model for comparing treatments to help a patient make the best treatment decision.

Combining market and traditional values in tribal forestry using interactive forest decision synthesis (INFODS)

Forestry joint production choices require clear relative values for multiple, and often conflicting, management objectives. Optimization is most difficult where values of forest attributes are: intangible, non-market, or sensitive. When most mathematical programming models are adapted for non-commensurable objectives, there is little guarantee that vague relative preference sets are met. We demonstrate how an iterative multiple objective programming approach finds preferred joint solutions in a Native American tribal forestry case where marketable outputs are managed in the context of traditional culturally based forest values. Without a priori specification of traditional tribal cultural values, conducting a series of feedback processes does appear to identify more preferable solutions than other types of multiple-objective models that do not use feedback.

Risk-Based Security and Economy Tradeoff Analysis for Real-Time Operation

This letter describes a new perspective on balancing system security level with cost for real-time operation. Security level is quantified using risk, which provides that security may be optimized. A risk-based multiple-objective (RBMO) model, considering security and economy together, is compared with the traditional security-constrained OPF (SCOPF) model. A six-bus test system is used to show the merits of RBMO.

Hybrid Generative-Discriminative Visual Categorization

Learning models for detecting and classifying object categories is a challenging problem in machine vision. While discriminative approaches to learning and classification have, in principle, superior performance, generative approaches provide many useful features, one of which is the ability to naturally establish explicit correspondence between model components and scene features--this, in turn, allows for the handling of missing data and unsupervised learning in clutter. We explore a hybrid generative/discriminative approach, using `Fisher Kernels' (Jaakola, T., et al. in Advances in neural information processing systems, Vol. 11, pp. 487---493, 1999), which retains most of the desirable properties of generative methods, while increasing the classification performance through a discriminative setting. Our experiments, conducted on a number of popular benchmarks, show strong performance improvements over the corresponding generative approach. In addition, we demonstrate how this hybrid learning paradigm can be extended to address several outstanding challenges within computer vision including how to combine multiple object models and learning with unlabeled data.

A multi‐model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images

Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model-based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user-provided training examples. Starting with an initial over-segmentation produced by a gradient-weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi-model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations.