The investment portfolio with stochastic returns can be represented as a maximum flow generalized network with sto chastic multipliers. Modern portfolio theory (MPT) [1] provides a myopic short horizon solution to thi...
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The investment portfolio with stochastic returns can be represented as a maximum flow generalized network with sto chastic multipliers. Modern portfolio theory (MPT) [1] provides a myopic short horizon solution to this network by adding a parametric variance constraint to the maximize flow objective function. MPT does not allow the number of securities in solution portfolios to be specified. integer constraints to control portfolio size in MPT results in a nonlinear mixedinteger problem and is not practical for large universes. Digital portfolio theory (DPT) [2] finds a non-myopic long-term solution to the nonparametric variance constrained portfolio network. This paper discusses the long horizon nature of DPT and adds zero-one (0-1) variables to control portfolio size. We find optimal size constrained allocations from a universe of US sector indexes. The feasible size of optimal portfolios depends on risk. Large optimal portfolios are infeasible for low risk investors. High risk investors can increase portfolio size and diversification with little effect on return.
We consider the stochastic economic lot sizing problem with remanufacturing under customer service level constraints. The problem is a stochastic extension of the classical lot sizing problem where demand can be met v...
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We consider the stochastic economic lot sizing problem with remanufacturing under customer service level constraints. The problem is a stochastic extension of the classical lot sizing problem where demand can be met via two alternative sources: manufacturing new products and remanufacturing returned products. It is known that even the deterministic version of this problem is NP-hard. We propose a mixed integer programming based heuristic for the problem building on a static-dynamic uncertainty strategy.
This paper considers a collision avoidance problem of the vehicle and the moving obstacle. The prohibited region is defined for the vehicle and the obstacle considering a specified size of them. The problem is formula...
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This paper considers a collision avoidance problem of the vehicle and the moving obstacle. The prohibited region is defined for the vehicle and the obstacle considering a specified size of them. The problem is formulated as a mixed integer programming problem. In the problem the obstacles and environments around the automobile can be represent as inequality conditions. Then the driver assistance algorithm for the collision avoidance using the feasibility of the optimization is proposed. Computer simulation shows that the proposed algorithm can provide appropriate control input for collision avoidance.
This paper considers an optimal path generation problem that generates a path of an automobile without any collision with obstacles. The problem is formulated as a mixed integer programming problem. In the problem the...
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This paper considers an optimal path generation problem that generates a path of an automobile without any collision with obstacles. The problem is formulated as a mixed integer programming problem. In the problem the obstacles and environments around the automobile can be represent as inequality conditions. The dynamics of the obstacle is described as variation of a prohibited region. According to model predictive control we solve the optimal path generation problem at each time step then apply the first element of the optimal input. The method proposed in this paper can deal with an explicit representation of the dynamics of the obstacle and provides no collision between the automobile and the obstacle.
We investigate the Steiner tree problem with revenues, budget and hop constraints (STPRBH) on graph, which is a generalization of the well-known Steiner tree problem. Given a root node, edge costs, nodes revenues, as ...
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ISBN:
(纸本)9781467358125
We investigate the Steiner tree problem with revenues, budget and hop constraints (STPRBH) on graph, which is a generalization of the well-known Steiner tree problem. Given a root node, edge costs, nodes revenues, as well as a preset budget and hop, the STPRBH seeks to find a subtree that includes the root node and maximizes the sum of the total edge revenues respecting the budget and hop constraints. These constraints impose limits on the total cost of the network and the number of edges between any vertex and the root. Not surprisingly, the STPRBH is NP-hard. For this challenging network design problem that arises in telecommunication settings and multicast routing, we present several polynomial size formulations. We propose an enhanced formulation based on the classical work of Miller, Tucker, and Zemlin by using additional set of variables representing the rank-order of visiting the nodes. Also, we investigate a new formulation for the STPRBH by tailoring a partial rank-1 of the Reformulation-Linearization Technique. Extensive results are exhibited using a set of benchmark instances to compare the proposed formulations by using a general purpose MIP solver.
Because of the scarcity and diversity of outliers, it is very difficult to design a robust outlier detector. In this paper, we first propose to use the maximum margin criterion to sift unknown outliers, which demonstr...
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Because of the scarcity and diversity of outliers, it is very difficult to design a robust outlier detector. In this paper, we first propose to use the maximum margin criterion to sift unknown outliers, which demonstrates superior performance. However, the resultant learning task is formulated as a mixed integer programming (MIP) problem, which is computationally hard. Therefore, we alter the recently developed label generating technique, which efficiently solves a convex relaxation of the MIP problem of outlier detection. Specifically, we propose an effective procedure to find a largely violated labeling vector for identifying rare outliers from abundant normal patterns, and its convergence is also presented. Then, a set of largely violated labeling vectors are combined by multiple kernel learning methods to robustly detect outliers. Besides these, in order to further enhance the efficacy of our outlier detector, we also explore the use of maximum volume criterion to measure the quality of separation between outliers and normal patterns. This criterion can be easily incorporated into our proposed framework by introducing an additional regularization. Comprehensive experiments on toy and real-world data sets verify that the outlier detectors using the two proposed criteria outperform existing outlier detection methods. Furthermore, our models are employed to detect corporate credit risk and demonstrate excellent performance.
This paper presents a new model to identify the topology status of power system. In the proposed model, measurement errors are unknown but belong to given bounded sets and the objective is to find a topology status wh...
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This paper presents a new model to identify the topology status of power system. In the proposed model, measurement errors are unknown but belong to given bounded sets and the objective is to find a topology status which is closest to the initial topology status and consistent with the sets of measurement uncertainties. Measurement equations are linearized by redefining state variables. mixedinteger nonlinear constraints are transformed to linear inequalities. Consequently, the problem is formulated as a mixedinteger quadratic constrained programming(MIQCP). Numerical tests on various systems show that the proposed formulation is accurate and computationally efficient.
In today's highly competitive global environment, companies are forced to compete on price and delivery speed. Global logistics transportation presents some special challenges and issues for business organizations...
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In today's highly competitive global environment, companies are forced to compete on price and delivery speed. Global logistics transportation presents some special challenges and issues for business organizations, and these issues differ from those posed by domestic logistics transportation. This study considers road transportation problems between two countries. A mixed-integerprogramming model is formulated to determine the optimal fleet components, route plans, and warehouse control in two countries. A series of experiments is designed to test the effectiveness of the proposed model. To enhance the practical implications of the model, different logistics plans are evaluated according to future changes.
Purpose - Global manufacturers have faced unprecedented cost pressures in China because of Chinese currency appreciation, rising labour costs, higher oil prices and reduced value-added tax rebates. This paper aims to ...
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Purpose - Global manufacturers have faced unprecedented cost pressures in China because of Chinese currency appreciation, rising labour costs, higher oil prices and reduced value-added tax rebates. This paper aims to reassess the decision of operating global manufacturing facilities in China. Design/methodology/approach - A mixed integer programming model is developed for a typical global manufacturing supply chain that includes production in the Pearl River Delta region and trade in Hong Kong. A case study with a footwear product is used to illustrate model application and present detailed analyses. Findings - The modelling results affirm the need of relocating labour-intensive production that mainly competes on low costs. Nevertheless, coastal China offers considerable benefits from industrial clustering and a logistics advantage in comparison with inland China and Asian countries where labour costs are still relatively low. Hong Kong remains a robust location choice for trade operations because of its favourable tax policies. Practical implications - Retaining production in China faces high risks from Chinese currency appreciation, while relocation to lower-cost Asian countries is more vulnerable to risks from high oil prices. An intermediate trade operation in Hong Kong can be used to hedge against risks from unfavourable tax policy changes at manufacturing locations. Originality/value - China has risen to an important position in global manufacturing because of its cost advantages. This paper analyzes the new phenomenon of dramatically increasing cost pressures in China. It develops a first-of-its-kind supply chain configuration model for the popular front-shop-back-factory business model in China.
The quadratic assignment problem (QAP) is a challenging combinatorial problem. The problem is NP-hard and in addition, it is considered practically intractable to solve large QAP instances, to proven optimality, withi...
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The quadratic assignment problem (QAP) is a challenging combinatorial problem. The problem is NP-hard and in addition, it is considered practically intractable to solve large QAP instances, to proven optimality, within reasonable time limits. In this paper we present an attractive mixedinteger linear programming (MILP) formulation of the QAP. We first introduce a useful non-linear formulation of the problem and then a method of how to reformulate it to a new exact, compact discrete linear model. This reformulation is efficient for QAP instances with few unique elements in the flow or distance matrices. Finally, we present optimal results, obtained with the discrete linear reformulation, for some previously unsolved instances (with the size n = 32 and 64), from the quadratic assignment problem library, QAPLIB. (C) 2012 Elsevier B.V. All rights reserved.
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