Abstract. GRASP is a multi-start metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discuss in detail implementation strategies of memory-based intensification and post-optimization techniques using path-relinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.
GRASP is a multi-start metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discuss in detail implementation strategies of memory-based intensification and post-optimization techniques using path-relinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.
In an effort to control costs, airlines have begun to concentrate on their maintenance operations as a potential source for savings. Nevertheless, federal regulations and internal safety policies effectively limit cost savings to improvements in productivity and scheduling. The purpose of this paper is to present a model that can be used by planners to both locate maintenance stations and to develop flight schedules that better meet the cyclical demand for maintenance. The problem is formulated as a min-cost, multicommodity flow network with integral constraints, and solved using a two-phase heuristic. The procedure is demonstrated with data supplied by American Airlines for their Boeing 727 fleet. The results show a significant improvement over current techniques, and indicate that substantial cost reductions can be achieved by eliminating up to 5 of the 22 maintenance bases now in operation. Similar results were obtained for American's Super 80 and DC-10 fleets. Perturbation analysis confirms the robustness of these findings, and suggests that loss in flexibility due to interruptions in the flight schedule will be negligible.airline scheduling, maintenance base planning, facilities location, set covering, multicommodity network
Abstract. Populating printed circuit .boards is one of the most costly and time-consuming steps in electronics assembly. At the beginning of each work order, three decisions are required: (1) a sequence must be specified for placing the individual components on the board; (2) tape reels must be assigned to positions on the magazine rack; and (3) a retrieval plan must be determined should the same component type be assigned to more than one magazine slot. Collectively, these problems can be modeled as a nonlinear integer program. In this paper, we develop a series of algorithms for solving each using an iterative two step approach.Initially, a placement sequence is generated with a weighted, nearest neighbor lxaveling salesman problem (TSP) heuristic; the two remaining problems are then formulated as a quadratic integer program and solved with a Lagrangian relaxation scheme. As a final step, the current magazine assignments are used to update the placement sequence, and the entire process is repeated.Our ability to deal, at least in part, with simultaneous machine operations represents the major contribution of this work. The methodology was simulated for a set of boards obtained from Texas Instruments and theoretically compared with a heuristic currently in use.
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