Interactive online gaming is a widely successful application that is becoming more and more popular even on mobile devices. In this context, an emerging scenario is represented by having players' mobile devices di...
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Indoor localization systems are usually based on distributed sensors. The sensor output is thereby converted into a distance or angle measure in order to estimate a target's position via triangulation or trilatera...
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ISBN:
(纸本)9781479940431
Indoor localization systems are usually based on distributed sensors. The sensor output is thereby converted into a distance or angle measure in order to estimate a target's position via triangulation or trilateration. For such systems, the localization error on the one hand depends on the quality of the sensor measurements and on the other hand on the sensor placement In this paper, the problem of optimally placing a minimum number of distributed sensors to fulfill and optimize task requirements is addressed. It is shown how adequate task requirements can be stated for different types of sensor systems by exploiting the Geometric Dilution of Precision (GDOP). Using these requirements, the problem can be formulated in a discrete and continuous search space. The discrete formulations are presented and evaluated using Binary integerprogramming (BIP) and mixedintegerprogramming (MIP) solver. In contrast, the continuous formulation is briefly described and evaluated using Nonlinearprogramming (NLP) methods. Both approaches are evaluated with respect to their solvability, runtime and quality. To find solutions for the NP-hard placement problem in reasonable time even for large problem sizes, a new approximation heuristic is introduced. Its worst case solution quality is derived and its solutions are compared to the optimal placements. All evaluations are done using the properties of a visual sensor system that exploits the thermal infrared radiation of humans for indoor localization.
We study on the modeling and solution of practical airline crew scheduling problems. The problem determines a feasible assignment of flight duties to the set of crews. Two types of integerprogramming formulations wit...
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ISBN:
(纸本)9781479909865
We study on the modeling and solution of practical airline crew scheduling problems. The problem determines a feasible assignment of flight duties to the set of crews. Two types of integerprogramming formulations with cell model and graph models are developed. An efficient solution algorithm based on labeling algorithm is proposed. The validity of the proposed models is compared by using a general-purpose solver. The computational results show that cell model is better than those of the graph model with less decision variables.
We address a special TSP in which the set of cities is partitioned into two subsets: mandatory cities and selective cities. All mandatory cities should be visited once within one of the corresponding predefined multip...
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ISBN:
(纸本)9783642410192;9783642410185
We address a special TSP in which the set of cities is partitioned into two subsets: mandatory cities and selective cities. All mandatory cities should be visited once within one of the corresponding predefined multiple time windows. A subset of the selective cities, whose cardinality depends on the tour completion time, should be visited within one of the associated multiple time windows. The objective is to plan a tour, not exceeding a predefined number of days, that minimizes a linear combination of the total traveled distance as well as the total waiting time. We present a mixed integer linear programming (MILP) model for the problem and propose a heuristic approach to solve it. Computational experiments address two real world problems that arise in different practical contexts.
In this paper, the value of thermal inertia in demand response to benefit customers is determined through a mixed integer linear programming (MILP) algorithm. Thermal models with different sophistications for a smart ...
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ISBN:
(纸本)9781862959132
In this paper, the value of thermal inertia in demand response to benefit customers is determined through a mixed integer linear programming (MILP) algorithm. Thermal models with different sophistications for a smart house are investigated. The energy consumption for cooling a smart house is optimized to minimize the expenditure of cooling load. One parameter and two-parameter thermal models are integrated into the optimization. The optimization of thermal load for maintaining the smart house within thermal comfort level is formulated as a MILP algorithm under the dynamic pricing policy. It is observed that the utilization of thermal inertia could potentially benefit both smart house owners and grid operators in the context of smart grid.
In this work we introduce mixed integer linear programming (MILP) formulations for the Order Batching Problem considering traversal and return routing policies. The proposed MILP formulations have been tested on rando...
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In this work we introduce mixed integer linear programming (MILP) formulations for the Order Batching Problem considering traversal and return routing policies. The proposed MILP formulations have been tested on randomly generated instances and compared with the well-known savings algorithm. According to our extensive computational experiments we can say that the proposed MILP formulations are quite useful to yield good upper bounds and hence, they can be used for benchmarking purposes.
In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a mixed integer linear programming (MILP) approach. Co-operation among team members not only helps reduce...
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In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a mixed integer linear programming (MILP) approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments. However, the problem becomes more challenging as it requires optimal resource allocation and is NP-hard. Since UAVs may be lost or may suffer significant damage during the course of the mission, plans may need to be modified in real-time as the mission proceeds. Therefore, multiple UAVs have a better chance of completing a mission in the face of failures. Such military operations can be treated as a variant of the Multiple Depot Vehicle Routing Problem (MDVRP). The proposed solution must be such that m UAVs start from multiple source locations to visit n targets and return to a set of destination locations such that (1) each target is visited exactly by one of the chosen UAVs (2) the total distance travelled by the group is minimized and (3) the number of targets that each UAV visits may not be less than K or greater than L.
This research investigates a mixed integer linear programming (MILP) framework that solves the virtual topology problem under the communication delay constraint. An arbitrary optical network has been considered with d...
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This research investigates a mixed integer linear programming (MILP) framework that solves the virtual topology problem under the communication delay constraint. An arbitrary optical network has been considered with different distances between the nodes and different link capacities. A traffic matrix is assumed for the optical network at an instance and the corresponding virtual topology design is solved by using the mixedintegerlinear formulation that includes lightpath routing, wavelength assignment, wavelength continuity, flow routing, and traffic losses. The numerical optimization results illustrate the efficiency of the proposed MILP formulation and superior network performances, validating the proposed framework for optimal virtual topology designs. Moreover, the proposed framework may alleviate computational time issues related to the network learning process of some heuristic approaches.
This paper considers a flow-shop scheduling problem which is characterized by reentrants and aims on the minimization of makespan. Reentrants are events of a repeated processing of a specific job on a machine, which c...
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This paper considers a flow-shop scheduling problem which is characterized by reentrants and aims on the minimization of makespan. Reentrants are events of a repeated processing of a specific job on a machine, which can occur due to, e.g., quality reasons. In the following, we focus on missing operations which did not receive adequate attention in the literature so far. After describing different types of reentrants, a specific level formulation is given in order to cope with processing times equal to zero in an appropriate way. Finally, we perform first computational tests with different problem sizes and compare our results with an already existing formulation with respect to solution quality and computation times. Our main results are as follows: (i) it turns out that our formulation is well suitable when zero processing times occur, as we derive better objective values for makespan; (ii) however, longer computation times have to be accepted compared to traditional formulations; (iii) the computation times are mainly influenced by the total number of jobs.
We will develop a mathematical model for the integration of lot sizing and flow shop scheduling with lot streaming. We will develop a mixed-integerlinear model for multiple products lot sizing and lot streaming probl...
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We will develop a mathematical model for the integration of lot sizing and flow shop scheduling with lot streaming. We will develop a mixed-integerlinear model for multiple products lot sizing and lot streaming problems. mixed-integerprogramming formulation is presented which will enable the user to find optimal production quantities, optimal inventory levels, optimal sublot sizes, and optimal sequence simultaneously. We will use numerical example to show practicality of the proposed model. We test eight different lot streaming problems: (1) consistent sublots with intermingling, (2) consistent sublots and no intermingling between sublots of the products (without intermingling), (3) equal sublots with intermingling, (4) equal sublots without intermingling, (5) no-wait consistent sublots with intermingling, (6) no-wait equal sublots with intermingling, (7) no-wait consistent sublots without intermingling, and (8) no-wait equal sublots without intermingling. We showed that the best makespan can be achieved through the consistent sublots with intermingling case.
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