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Facility location models for distribution system design
, 2004
"... The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamenta ..."
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Cited by 33 (0 self)
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The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational performance. This paper reviews some of the contributions to the current stateoftheart. In particular, continuous location models, network location models, mixedinteger programming models, and applications are summarized.
Greedy Randomized Adaptive Search Procedures
 Handbook of Applied Optimization
, 2001
"... . GRASP (greedy randomized adaptive search procedure) is a metaheuristic for combinatorial optimization. GRASP usually is implemented as a multistart procedure, where each iteration is made up of a construction phase, where a randomized greedy solution is constructed, and a local search phase wh ..."
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Cited by 29 (4 self)
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. GRASP (greedy randomized adaptive search procedure) is a metaheuristic for combinatorial optimization. GRASP usually is implemented as a multistart procedure, where each iteration is made up of a construction phase, where a randomized greedy solution is constructed, and a local search phase which starts at the constructed solution and applies iterative improvement until a locally optimal solution is found. This chapter gives an overview of GRASP. Besides describing the basic building blocks of a GRASP, the chapter covers enhancements to the basic procedure, including reactive GRASP, hybrid GRASP, and intensification strategies. 1. Introduction Consider a combinatorial optimization problem, where one is given a discrete set X of solutions and an objective function f(x) : x # X # to be minimized and seeks a solution x # # X such that f(x # ) # f(x), for all x # X . Problems of this type are sometimes easy to solve, i.e. they can be solved in polynomial time, but mor...
Reactive Search Optimization: Learning while Optimizing
"... The final purpose of Reactive Search Optimization (RSO) is to simplify the life for the final user of optimization. While researchers enjoy designing algorithms, testing alternatives, tuning parameters and choosing solution schemes — in fact this is part of their daily life — the final users ’ inter ..."
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Cited by 5 (2 self)
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The final purpose of Reactive Search Optimization (RSO) is to simplify the life for the final user of optimization. While researchers enjoy designing algorithms, testing alternatives, tuning parameters and choosing solution schemes — in fact this is part of their daily life — the final users ’ interests are different: solving a problem in the
An Iterated Local Search Heuristic for the Logistics Network Design Problem with Single Assignment
, 2006
"... In the logistics network design problem (LNDP), decisions must be made regarding the selection of suppliers, the location of plants and warehouses, the assignment of activities to these facilities, and the flows of raw materials and finished products in the network. This article introduces an iterat ..."
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Cited by 4 (0 self)
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In the logistics network design problem (LNDP), decisions must be made regarding the selection of suppliers, the location of plants and warehouses, the assignment of activities to these facilities, and the flows of raw materials and finished products in the network. This article introduces an iterated local search (ILS) heuristic for the LNDP variant arising when each raw material should be supplied by a unique supplier, and each finished product should be produced and distributed by a unique plant and a unique warehouse, respectively. The ILS heuristic exploits the combinatorial nature of the problem and relies on simple moves combined within a descent algorithm. Several perturbation operators are used to allow a broad exploration of the solution space. The performance of the algorithm is evaluated on randomly generated instances, and the solutions are compared with lower bounds computed by solving the LP relaxation of the problem.
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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Cited by 1 (1 self)
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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.
GRASP: BASIC COMPONENTS AND ENHANCEMENTS
"... Abstract. GRASP (Greedy Randomized Adaptive Search Procedures) is a multistart metaheuristic for producing goodquality solutions of combinatorial optimization problems. Each GRASP iteration is usually made up of a construction phase, where a feasible solution is constructed, and a local search phas ..."
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Abstract. GRASP (Greedy Randomized Adaptive Search Procedures) is a multistart metaheuristic for producing goodquality solutions of combinatorial optimization problems. Each GRASP iteration is usually made up of a construction phase, where a feasible solution is constructed, and a local search phase which starts at the constructed solution and applies iterative improvement until a locally optimal solution is found. While, in general, the construction phase of GRASP is a randomized greedy algorithm, other types of construction procedures have been proposed. Repeated applications of a construction procedure yields diverse starting solutions for the local search. This chapter gives an overview of GRASP describing its basic components and enhancements to the basic procedure, including reactive GRASP and intensification strategies. 1.
A Multicriteria Decision Making Environment for Engineering Design and Production DecisionMaking
"... A novel environment for optimization, analytics and decision support in general engineering design problems is introduced. The utilized methodology is based on reactive search optimization (RSO) procedure and its recently implemented visualization software packages. The new set of powerful integrate ..."
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A novel environment for optimization, analytics and decision support in general engineering design problems is introduced. The utilized methodology is based on reactive search optimization (RSO) procedure and its recently implemented visualization software packages. The new set of powerful integrated data mining, modeling, visualiztion and learning tools via a handy procedure stretches beyond a decisionmaking task and attempts to discover new optimal designs relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. In an optimal engineering design environment as such solving the multicriteria decisionmaking (MCDM) problem is considered as a combined task of optimization and decisionmaking. Yet in solving reallife MCDM problems often most of attention has been on finding the complete Paretooptimal set of the associated multiobjective optimization (MOO) problem and less on decisionmaking. In this paper, along with presenting two case studies, the proposed interactive procedure which involves the decisionmaker (DM) in the process addresses this issue effectively. Moreover the methodology delivers the capablity of handling the big data often associated with production decisionmaking as well as materials selection tasks in engineering design problems. Keywords: Opimal engineering design, interactive multicriteria decision making, reactive search optimization, multiobjective optimization 1.
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
Adaptive Selection of Heuristics within a GRASP for Exam Timetabling Problems
 MISTA
, 2009
"... In this paper, we describe the development of a Greedy Random Adaptive Search Procedure (GRASP) where two lowlevel graph heuristics, Saturation Degree (SD) and Largest Weighted Degree (LWD) are dynamically hybridised in the construction phase to construct solutions for exam timetabling problems. Th ..."
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In this paper, we describe the development of a Greedy Random Adaptive Search Procedure (GRASP) where two lowlevel graph heuristics, Saturation Degree (SD) and Largest Weighted Degree (LWD) are dynamically hybridised in the construction phase to construct solutions for exam timetabling problems. The problem is initially solved using an intelligent adaptive LWD and SD graph hyperheuristic which constructs the restricted candidate list (RCL) in the first phase of GRASP. It is observed that the size of the RCL used in each iteration affects the quality of the results obtained. In addition, the SD heuristic is essential to construct a RCL which leads to a feasible solution. However, SD does not perform well at the early stages of the construction. Therefore, LWD is used until a certain switching point is reached. The hyperheuristic adaptively determines the size of the RCL in each iteration and the best switching point after evaluating the quality of the solutions produced. In the improvement phase of GRASP, it is observed that tabu search slightly improves the constructed solutions when compared to steepest descent but it takes a longer time. The approach adapts to all the benchmark problems tested. The comparison of this approach with stateoftheart approaches indicates that it is a simple yet efficient technique. The results also indicate that the technique could adapt itself to construct good quality solutions for any timetabling problem with similar constraints.