With the recent development of container transportation, the imbalance of empty containers among ports has become more serious. We consider the problem of positioning empty containers. The goal of this study is to pro...
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With the recent development of container transportation, the imbalance of empty containers among ports has become more serious. We consider the problem of positioning empty containers. The goal of this study is to propose a plan for transporting empty containers between container ports (terminals) to reduce the imbalance. There is currently a demand at each port and any backlog of containers is not permitted. The objective is to minimize the total relevant costs such as transportation cost, handling cost, and holding cost, etc. In this study, we develop a model with respect to the leasing and purchasing of containers. mixed integer programming and genetic algorithms are used to solve the model. A hybrid GA is also proposed to reduce the computation time while still obtaining an acceptable result.
Currently, there is a national push for a smarter electric grid, one that is more controllable and flexible. Only limited control and flexibility of electric assets is currently built into electric network optimizatio...
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Currently, there is a national push for a smarter electric grid, one that is more controllable and flexible. Only limited control and flexibility of electric assets is currently built into electric network optimization models. Optimal transmission switching is a low cost way to leverage grid controllability: to make better use of the existing system and meet growing demand with existing infrastructure. Such control and flexibility can be categorized as a "smart grid application" where there is a cooptimization of both generators or loads and transmission topology. In this paper we form the dual problem and examine the multi-period N-1 reliable unit commitment and transmission switching problem with integer variables fixed to their optimal values. Results including LMPs and marginal cost distributions are presented for the IEEE RTS 96 test problem. The applications of this analysis in improving the efficiency of ISO and RTO markets are discussed.
We consider the generalized minimum edge-biconnected network problem where the nodes of a graph are partitioned into clusters and exactly one node from each cluster is required to be connected in an edge-biconnected w...
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We consider the generalized minimum edge-biconnected network problem where the nodes of a graph are partitioned into clusters and exactly one node from each cluster is required to be connected in an edge-biconnected way. Instances of this problem appear, for example, in the design of survivable backbone networks. We present different variants of a variable neighborhood search approach that utilize different types of neighborhood structures, each of them addressing particular properties as spanned nodes and/or the edges between them. For the more complex neighborhood structures, we apply efficient techniques such as a graph reduction to essentially speed up the search process. For comparison purposes, we use a mixedinteger linear programming formulation based on multi-commodity flows to solve smaller instances of this problem to proven optimality. Experiments on such instances indicate that the variable neighborhood search is also able to identify optimal solutions in the majority of test runs, but within substantially less time. Tests on larger Euclidean and random instances with up to 1,280 nodes, which could not be solved to optimality by mixed integer programming, further document the efficiency of the variable neighborhood search. In particular, all proposed neighborhood structures are shown to contribute significantly to the search process. (C) 2010 Wiley Periodicals, Inc. NETWORKS, Vol. 55(3), 256-275 2010
A cost-efficient use of harvesting resources is important in the forest industry. The main planning is carried out in an annual resource plan that is continuously revised. The harvesting operations are divided into ha...
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A cost-efficient use of harvesting resources is important in the forest industry. The main planning is carried out in an annual resource plan that is continuously revised. The harvesting operations are divided into harvesting and forwarding. The harvesting operation fells trees and puts them in piles in the harvest areas. The forwarding operation collects piles and moves them to storage locations adjacent to forest roads. These operations are conducted by machines (harvesters, forwarders and harwarders), and these are operated by crews living in cities/villages that are within some maximum distance from the harvest areas. Machines, harvest teams and harvest areas have different characteristics and properties and it is difficult to find the best possible match throughout the year. The aim of the planning is to find an annual plan with the lowest possible cost. The total cost is based on three parts: production cost, traveling cost and moving cost. The production cost is the cost for the harvesting and forwarding. The traveling cost is the cost for driving back and forwards (daily) from the home base to the harvest area and the moving cost is associated with moving the machines and equipment between harvest areas. The Forest Research Institute of Sweden (Skogforsk), together with a number of Swedish forest companies, has developed a decision support platform for the planning. One important element of this platform is that it should find high-quality plans within short computational times. One central element is an optimization model that integrates the assignment of machines to harvest areas and schedules the harvest areas during the year for each machine. The problem is complex and we propose a two-phase solution method where, first, we solve the assignment problem and, second, the scheduling. In order to be able to control the scheduling in phase 1 as well, we have introduced an extra cost component that controls the geographical distribution of harvest areas for ea
Selection and establishment of reserves was often done unplanned and uncoordinated between regions. Systematic conservation planning provides tools to identify optimally located priority areas for conservation. Planni...
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Selection and establishment of reserves was often done unplanned and uncoordinated between regions. Systematic conservation planning provides tools to identify optimally located priority areas for conservation. Planning for multiple species promises adequate provision for the needs of a range of threatened species simultaneously. Several studies apply the set-covering problem by minimizing resources for given conservation targets of multiple species. We extend this method by also considering different degrees of coordination in multiple-species conservation planning and representing reserve sizes endogenously. A deterministic, spatially explicit programming model solved with mixed integer programming is used to represent minimum habitat area thresholds for all included biodiversity features. The empirical model application to European wetland species addresses five different scenarios of coordination in conservation planning, including taxonomic, political, and biogeographical coordination of planning. Our approach illustrates and quantifies the efficiency of multi-species conservation activities. We show that maximum coordination in conservation planning enhances area efficiency by 30% compared to no coordination. Furthermore, strong coordination in conservation planning does not only reduce the area requirement, but synergy effects even enable the conservation features to achieve higher conservation objectives. Spatial subdivision of planning, however, leads to highest area requirements and less conservation target achievement. (C) 2010 Elsevier Ltd. All rights reserved.
In this paper, we present an integrated framework for the optimization of Internet banner advertising. The framework consists of three parts: statistical predictive modeling on web data, optimization through mixed int...
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In this paper, we present an integrated framework for the optimization of Internet banner advertising. The framework consists of three parts: statistical predictive modeling on web data, optimization through mixed integer programming, and the use of information repository technology. The integrated, quantitative approach allows for the automatic improvement of banner advertising strategies and nonintrusive personalized advertising at a variety of banner display levels.
Wise resource allocation is necessary to increase the quality of service (QoS), the system capacity, the network performance, and also decrease the power consumption and connection cost. This paper, first, investigate...
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ISBN:
(纸本)9781424437092
Wise resource allocation is necessary to increase the quality of service (QoS), the system capacity, the network performance, and also decrease the power consumption and connection cost. This paper, first, investigates the located global system for mobile (GSM) network in a region of Tehran province in Iran as a case of study and then proposes a novel approach to minimize the connection cost among different parts and components of the GSM network while considering our network practical constraints. The approach of this paper can also be used for the other network planning problems such as the universal mobile telecommunication system (UMTS) in the third generation of the mobile systems. In order to find an optimal solution for the total network connections cost, we use mathematical programming based on mixed integer programming (MIP) algorithm to minimize the connection cost. At the end, simulation and practical results show us that our proposed algorithm decreases the connection cost and improve the QoS of the GSM network.
This paper proposes a new mechanism to give added incentive to invest in new capacities in deregulated electricity markets. An optimization problem to maximize long term social welfare includes binary variables for th...
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ISBN:
(纸本)9781424483570
This paper proposes a new mechanism to give added incentive to invest in new capacities in deregulated electricity markets. An optimization problem to maximize long term social welfare includes binary variables for the building of new facilities, and continuous variables for generation, i.e. the model is a mixedinteger nonlinear program. The new mechanism also includes a new approach to calculate capacity prices in addition to the commodity prices: an auxiliary mathematical program calculates the minimum capacity price that is necessary to ensure that all firms investing in new capacities are satisfied with their profit levels.
The electricity generation sector is currently under strong pressure to curb emissions of greenhouse gases. To achieve this, targets on the amount of energy that needs to come from âgreenâ?power sources a...
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ISBN:
(纸本)9781424483570
The electricity generation sector is currently under strong pressure to curb emissions of greenhouse gases. To achieve this, targets on the amount of energy that needs to come from âgreenâ?power sources are being imposed around the world. In response to these targets, large amounts of wind power generation are being integrated and are likely to represent soon a significant share of the overall generation mix in power systems. The presence of wind generation brings new challenges to the system operators (SO), since these power sources cannot be scheduled and dispatched in the conventional manner due to their inherent dependence on stochastic factors. Accommodating this stochastic production source does not simply reduce the overall production from conventional sources, but also increases the uncertainty on the system's residual demand. Operating the system under such uncertain residual demand has consequences on reserve deployment and thus on the overall system's security margins. The paper discusses the cost impact that the integration of wind power generation imposes on the daily operation of the power system. It also reports on recent results on techniques for determining the optimal amount of reserve required to securely operate a power system with a significant proportion of wind generation. Finally, it discusses the need to re-assess the security margins and the provision of operating reserve for systems with a significant wind power production.
Resource allocation problem in cognitive radio networks (CRN) is one of the key issues to improve the efficiency of spectrum utilization. Most of previous work on resource allocation mainly concentrates on the seconda...
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ISBN:
(纸本)9781424456383
Resource allocation problem in cognitive radio networks (CRN) is one of the key issues to improve the efficiency of spectrum utilization. Most of previous work on resource allocation mainly concentrates on the secondary users (SUs) with only one type of service requirement, without considering the scenario with heterogenous services requirement. In this paper, we study the dynamic channel and power allocation for SUs supporting heterogenous services in CRN. Firstly we classify the SUs by service requirement, i.e., SUs with minimum rate guarantee and SUs with best-effort services. Then we introduce the minimum rate constraints and proportional fairness constraints for SUs respectively. Under this setup, we formulate the problem of dynamic channel and power allocation for SUs as a mixed integer programming problem. And the heuristic optimal algorithm and suboptimal algorithm are proposed to realize the dynamic channel and power allocation. Extensive simulation results are presented to demonstrate the performance of the proposed scheme.
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