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Solving reallife locomotive scheduling problems
 Transportation Science
, 2005
"... credit including © notice is given to the source." This paper also can be downloaded without charge from the ..."
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credit including © notice is given to the source." This paper also can be downloaded without charge from the
Very LargeScale Neighborhood Search Techniques In Timetabling Problems
 IN PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON THE PRACTICE AND THEORY OF AUTOMATED
, 2006
"... We describe the use of very largescale neighborhood search (VLSN) techniques in examination timetabling problems. We detail three applications of VLSN algorithms that illustrate the versatility and potential of such algorithms in timetabling. The first of these uses cyclic exchange neighborhood ..."
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We describe the use of very largescale neighborhood search (VLSN) techniques in examination timetabling problems. We detail three applications of VLSN algorithms that illustrate the versatility and potential of such algorithms in timetabling. The first of these uses cyclic exchange neighborhoods, in which an ordered subset of exams in disjoint time slots are swapped cyclically such that each exam moves to the time slot of the exam following it in the order. The neighborhood of all such cyclic exchanges may be searched e#ectively for an improving set of moves, making this technique computationally reasonable in practice. We next describe
METAHEURISTIC HYBRIDIZATION WITH GRASP
"... Abstract. GRASP, or greedy randomized adaptive search procedure, is a multistart metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this chapter we consider ways to hybridize GRASP to create new and more effective metaheuristi ..."
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Abstract. GRASP, or greedy randomized adaptive search procedure, is a multistart metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this chapter we consider ways to hybridize GRASP to create new and more effective metaheuristics. We consider several types of hybridizations: constructive procedures, enhanced local search, memory structures, and cost reformulations. 1.
Combining Lagrangian Decomposition with Very Large Scale Neighborhood Search for Capacitated Connected Facility Location
, 2009
"... We consider a generalized version of the rooted Connected Facility Location problem (ConFL) which occurs when extending existing communication networks in order to increase the available bandwidth for customers. In addition to choosing facilities to open and connecting them by a Steiner tree as in t ..."
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We consider a generalized version of the rooted Connected Facility Location problem (ConFL) which occurs when extending existing communication networks in order to increase the available bandwidth for customers. In addition to choosing facilities to open and connecting them by a Steiner tree as in the classic ConFL, we have to select a subset of all potential customers and assign them to open facilities respecting given capacity constraints in order to maximize profit. We present two exact mixed integer programming formulations and a Lagrangian decomposition (LD) based approach which uses the volume algorithm. Feasible solutions are derived using a Lagrangian heuristic. Furthermore, we present two hybrid variants combining LD with local search and a very large scale neighborhood search. By applying those improvement methods only to the most promising solutions, we are able to compute much better solutions without increasing the necessary runtime too much. As documented by our computational results, our hybrid approaches compute high quality solutions with tight optimality gaps in relatively short time.
A PathBased Local Search Heuristic for the Capacitated Minimum Spanning Tree Problem
, 2001
"... Let G =(V,E) be a connected undirected graph, where V = {0, 1,...,n} denotes the set of nodes and E is the set of edges. Nonnegative integers c e and b i are associated respectively with each edge e E and with each node i V . Given an integer Q and a special central node r V , the Cap ..."
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Let G =(V,E) be a connected undirected graph, where V = {0, 1,...,n} denotes the set of nodes and E is the set of edges. Nonnegative integers c e and b i are associated respectively with each edge e E and with each node i V . Given an integer Q and a special central node r V , the Capacitated Minimum Spanning Tree (CMST) problem consists of finding a minimum spanning tree T of G in terms of the edge costs, such that the sum of the node weights in each connected component of the graph {r} is less than or equal to Q. The CMST problem is NPhard [11] for 2 <Q<V 2 and has many applications in the design of communication networks, see e.g. [2, 4, 8]. Gouveia and Martins [7] reviewed exact and lower bounding schemes, including earlier works of Gavish [4, 5], the branchandbound algorithm of Malik and Yu [10], the Lagrangean relaxation approach of Gouveia [6], and the cutting plane method of Hall [9]. Gouveia and Martins [8] proposed an iterative method for c
HYBRID GRASP HEURISTICS
"... Abstract. Experience has shown that a crafted combination of concepts of different metaheuristics can result in robust combinatorial optimization schemes and produce higher solution quality than the individual metaheuristics themselves, especially when solving difficult realworld combinatorial opti ..."
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Abstract. Experience has shown that a crafted combination of concepts of different metaheuristics can result in robust combinatorial optimization schemes and produce higher solution quality than the individual metaheuristics themselves, especially when solving difficult realworld combinatorial optimization problems. This chapter gives an overview of different ways to hybridize GRASP (Greedy Randomized Adaptive Search Procedures) to create new and more effective metaheuristics. Several types of hybridizations are considered, involving different constructive procedures, enhanced local search algorithms, and memory structures. 1.
GRASP: GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES
"... Abstract. GRASP, or greedy randomized adaptive search procedure, is a multistart metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this chapter we review the basic building blocks of GRASP. We cover solution construction sche ..."
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Abstract. GRASP, or greedy randomized adaptive search procedure, is a multistart metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this chapter we review the basic building blocks of GRASP. We cover solution construction schemes, local search methods, and hybridization with pathrelinking. Combinatorial optimization can be defined by a finite ground set E = {1,...,n}, a set of feasible solutions F ⊆ 2E, and an objective function f: 2E → R, all three defined for each specific problem. In this chapter, we consider the minimization version of the problem, where we seek an optimal solution S ∗ ∈ F such that f(S ∗ ) ≤ f(S), ∀S ∈ F. Combinatorial optimization finds applications in many settings, including routing, scheduling, inventory and production planning, and facility location. While much progress has been made in finding provably optimal solutions to combinatorial optimization problems employing techniques such as branch and bound, cutting planes, and dynamic programming, as well as provably nearoptimal solutions