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Agents for logistics: A provisional agreement approach
, 2006
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A Dynamic Systems Framework for MultiAgent Experiments
, 1999
"... The construction of a MultiAgent System (MAS) is difficult because the design process takes place in a space with many dimensions. That is, a MAS must control a system taking into account a wide variety of constraints, it must establish cooperation between its constituent agents each having thei ..."
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The construction of a MultiAgent System (MAS) is difficult because the design process takes place in a space with many dimensions. That is, a MAS must control a system taking into account a wide variety of constraints, it must establish cooperation between its constituent agents each having their own goals and different views on the world, and it must be working in an environment in which the agent society itself changes over time. To some extent, it is possible to solve each of these problems in isolation; in combination, however, no satisfactory solution is known. What we would like is a design formalism in which it is possible to tackle the problems in an integral way. Our approach is to view a MAS as a collection of decision makers operating on a dynamic system. This allows us to specify agents that take multiple criteria into account and that can operate in a changing environment. To coordinate agents, we need a formalism in which it is possible to reason over the beh...
A Genetic Algorithm for Constrained Statistical Matching
, 2007
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Package Routing in Transportation Networks with Fixed Vehicle Schedules: Formulation, Complexity Results and Approximation Algorithms
, 1992
"... We consider a special case of a general problem involving the deployment of vehicles to transport packages in transportation networks. In this special case, the schedules of the vehicles are fixed and packages are routed by transferring them between vehicles as these vehicles make stops according ..."
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We consider a special case of a general problem involving the deployment of vehicles to transport packages in transportation networks. In this special case, the schedules of the vehicles are fixed and packages are routed by transferring them between vehicles as these vehicles make stops according to their fixed schedules. We show that this problem is hard and explore approximation algorithms for its solution. In particular, we cast this problem as a multicommodity flow problem with a mixed integer/linear program formulation. We then apply combinatorial optimization techniques based on solving the relaxed linear programming formulation of the problem to obtain provable feasibility and expected performance guarantees, where performance is measured in terms of the sum of the time in transit over all packages. We investigate the sensitivity of the performance guarantees to certain scaling factors and other limitations of this technique.
Transport Planning and Scheduling
, 1999
"... frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4. The General Transportation Problem (GTP) . . . . . . . . . . . . . . . . . . . 18 2.4.1. Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.4.2. Related problems . . . . . . . . . . . ..."
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frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4. The General Transportation Problem (GTP) . . . . . . . . . . . . . . . . . . . 18 2.4.1. Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.4.2. Related problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4.3. Dynamic GTP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3. Complexity analysis 25 3.1. A model for transport scheduling problems . . . . . . . . . . . . . . . . . . . . . 25 3.2. The optimization problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3. Disjoint connecting paths problem (DCPP) . . . . . . . . . . . . . . . . . . . . 29 3.3.1. Solving large instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4. Overview of current technology 32 4.1. Planning tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.1. Smodels . . . . . . . . . . . . . . . ....
Optimising Intensive Interprocess Communication in a Parallelised Telecommunication Traffic Simulator
"... This paper focuses on an efficient userlevel handling of intensive interprocess communication in distributed parallel applications that are characterised by a high rate of data exchange. A common feature of such systems is that any parallelisation strategy focusing on the largest parallelisable fra ..."
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This paper focuses on an efficient userlevel handling of intensive interprocess communication in distributed parallel applications that are characterised by a high rate of data exchange. A common feature of such systems is that any parallelisation strategy focusing on the largest parallelisable fraction results in the highest possible rate of interprocess communication, compared to other parallelisation strategies. An example of such applications is the class of telecommunication traffic simulators, where the partitioncommunication phenomenon reveals itself due to the strong data interdependencies among the major parallelisable tasks, namely encoding of messages, decoding of messages, and interpretation of messages. 1