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Very LargeScale Neighborhood Search for the Quadratic Assignment Problem
 DISCRETE APPLIED MATHEMATICS
, 2002
"... The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NPhard, and can be solved to optimality only for fairly small size instances ..."
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Cited by 150 (13 self)
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The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NPhard, and can be solved to optimality only for fairly small size instances (typically, n < 25). Neighborhood search algorithms are the most popular heuristic algorithms to solve larger size instances of the QAP. The most extensively used neighborhood structure for the QAP is the 2exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities and thus has size O(n²). Previous efforts to explore larger size neighborhoods (such as 3exchange or 4exchange neighborhoods) were not very successful, as it took too long to evaluate the larger set of neighbors. In this paper, we propose very largescale neighborhood (VLSN) search algorithms where the size of the neighborhood is very large and we propose a novel search procedure to heuristically enumerate good neighbors. Our search procedure relies on the concept of improvement graph which allows us to evaluate neighbors much faster than the existing methods. We present extensive computational results of our algorithms on standard benchmark instances. These investigations reveal that very largescale neighborhood search algorithms give consistently better solutions compared the popular 2exchange neighborhood algorithms considering both the solution time and solution accuracy.
Task assignment algorithms for teams of UAVs in dynamic environments
, 2004
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Cooperative task assignment of unmanned aerial vehicles in adversarial environments
 In Proc. IEEE American Control Conference (ACC
, 2005
"... Abstract — This paper addresses the problem of risk in the environment and presents a new stochastic formulation of the UAV task assignment problem. This formulation explicitly accounts for the interaction between the UAVs – displaying cooperation between the vehicles rather than just coordination. ..."
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Cited by 8 (1 self)
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Abstract — This paper addresses the problem of risk in the environment and presents a new stochastic formulation of the UAV task assignment problem. This formulation explicitly accounts for the interaction between the UAVs – displaying cooperation between the vehicles rather than just coordination. As defined in the paper, cooperation entails coordinated task assignment with the additional knowledge of the future implications of a UAV’s actions on improving the expected performance of the other UAVs. The key point is that the actions of each UAV can reduce the risk in the environment for all other UAVs; and the new formulation takes advantage of this fact to generate cooperative assignments that achieve better performance. This change in the formulation is accomplished by coupling the failure probabilities for each UAV to the selected missions for all other UAVs. This results in coordinated plans that optimally exploit the coupling effects of cooperation to improve the survival probabilities and expected performance. This allocation is shown to recover realworld air operations planning strategies that provide significant improvements over approaches that do not correctly account for UAV attrition. The problem is formulated as a Dynamic Programming (DP) problem, which is shown to be more computationally tractable than previous MILP solution approaches. Two DP approximation methods (the onestep and twostep lookahead) are also developed for larger problems. Simulation results show that the onestep lookahead can generate cooperative solutions very quickly, but the performance degrades considerably. The twostep lookahead policy generates plans that are very close to (and in many cases, identical to) the optimal solution and the computation time is still significantly lower than the exact DP approach. I.
Random neural network for emergency management
 In The Workshop on Grand Challenges in Modeling, Simulation and Analysis for Homeland Security
, 2010
"... Abstract—We consider decision problems in emergency management, such as simultaneously dispatching emergency teams to locations where incidents have occurred, and propose an algorithmic solution using the Random Neural Newtwork. This is an NPhard optimisation problem, but the approach we suggest is ..."
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Abstract—We consider decision problems in emergency management, such as simultaneously dispatching emergency teams to locations where incidents have occurred, and propose an algorithmic solution using the Random Neural Newtwork. This is an NPhard optimisation problem, but the approach we suggest is solved in polynomial time, and is also distributed so that each of the teams can potentially decide where to go based on shared information about the location of the incidents and of the teams, without consulting the others concerning the decision. The proposed approach is evaluated on a large number of instances of the problem, and we observe that it comes within 10 % of the cost achieved by the optimal solution. I.
Efficient Decision Makings for Dynamic WeaponTarget Assignment by Virtual Permutation and Tabu Search Heuristics
 IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
, 2010
"... Abstract—The dynamic weapontarget assignment (DWTA) problem is a typical constrained combinatorial optimization problem with the objective of maximizing the total value of surviving assets threatened by hostile targets through all defense stages. A generic assetbased DWTA model is established, es ..."
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Abstract—The dynamic weapontarget assignment (DWTA) problem is a typical constrained combinatorial optimization problem with the objective of maximizing the total value of surviving assets threatened by hostile targets through all defense stages. A generic assetbased DWTA model is established, especially for the warfare scenario of force coordination, to formulate this problem. Four categories of constraints, involving capability constraints, strategy constraints, resource constraints (i.e., ammunition constraints), and engagement feasibility constraints, are taken into account in the DWTA model. The concept of virtual permutation (VP) is proposed to facilitate the generation of feasible decisions. A construction procedure (CP) converts VPs into feasible DWTA decisions. With constraint satisfaction guaranteed by the synergy of VPs and the CP, an elaborate local search (LS) operator, namely movetohead operator, is constructed to avoid repeatedly generating the same decisions. The operator is integrated into two tabu search (TS) algorithms to solve DWTA problems. Comparative experiments involving a random sampling method, an LS method, a hybrid genetic algorithm, a hybrid antcolony optimization algorithm, and our TS algorithms show that the proposed TS heuristics for DWTA outperform their competitors in most test cases and they are competent for highquality realtime DWTA decision makings. Index Terms—Combinatorial optimization, constraint handling, Dynamic weapontarget assignment (DWTA), metaheuristics, military decision making, tabu search (TS), virtual permutation (VP). I.
An Efficient RuleBased Constructive Heuristic to Solve Dynamic WeaponTarget Assignment Problem
"... Abstract—In this paper, we propose an efficient rulebased heuristic to solve assetbased dynamic weapontarget assignment (DWTA) problems. The main idea of the proposed heuristic is to utilize the domain knowledge of DWTA problems to directly achieve weapon assignment, without large number of funct ..."
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Abstract—In this paper, we propose an efficient rulebased heuristic to solve assetbased dynamic weapontarget assignment (DWTA) problems. The main idea of the proposed heuristic is to utilize the domain knowledge of DWTA problems to directly achieve weapon assignment, without large number of function evaluations. We update the saturation states of constraints in the assignment process to guarantee the feasibility of generated solutions. For the purpose of testing the performance of the proposed heuristic, we build a general Monte Carlo simulationbased DWTA framework. For comparison, we also employ a Monte Carlo method (MCM) to make DWTA decisions in different defense scenarios. From simulations with DWTA instances under different scales, the heuristic has obvious advantages over the MCM with regard to solution quality and computation time. The proposed method can solve largescale DWTA problems (e.g., those including 100 weapons, 100 targets, and four defense stages) within only a few seconds. Index Terms—Combinatorial optimization, constraint handling, decision making, dynamic weapontarget assignment (DWTA), heuristic, military operations. I.
Assettask assignment algorithms in the presence of execution uncertainty
 Future Internet 2013
"... We investigate the assignment of assets to tasks where each asset can potentially execute any of the tasks, but assets execute tasks with a probabilistic outcome of success. There is a cost associated with each possible assignment of an asset to a task, and if a task is not executed there is also a ..."
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We investigate the assignment of assets to tasks where each asset can potentially execute any of the tasks, but assets execute tasks with a probabilistic outcome of success. There is a cost associated with each possible assignment of an asset to a task, and if a task is not executed there is also a cost associated with the nonexecution of the task. As we proposed in [Gelenbe, E., Timotheou, S., and Nicholson, D. (2010). Fast distributed near optimum assignment of assets to tasks. Comput. J., doi:10.1093/comjnl/bxq010], we formulate the allocation of assets to tasks in order to minimize the overall expected cost, as a nonlinear combinatorial optimization problem. We propose the use of network flow algorithms which are based on solving a sequence of minimum cost flow problems on appropriately constructed networks with estimated arc costs. We introduce three different schemes for the estimation of the arc costs and we investigate their performance compared with a random neural network algorithm and a greedy algorithm. We also develop an approach for obtaining tight lower bounds to the optimal solution based on a piecewise linear approximation of the considered problem.