We present a new genetic algorithm for playing the game of Mastermind. The algorithm requires low run-times and results in a low expected number of guesses. Its performance is comparable to that of other meta-heuristics for the standard setting with four positions and six colors, while it outperforms the existing algorithms when more colors and positions are examined. The central idea underlying the algorithm is the creation of a large set of eligible guesses collected throughout the different generations of the genetic algorithm, the quality of each of which is subsequently determined based on a comparison with a selection of elements of the set.
This paper presents a model for a dock assignment problem, where trailers need to be assigned to gates for a given period of time for loading or unloading activities. The parking lot is used as a buffer zone. Transportation between the parking lot and the gates is performed by additional resources called terminal tractors. The problem is modeled as a three-stage flexible flow shop, where the first and the third stage share the same identical parallel machines and the second stage consists of a different set of identical parallel machines. We examine multiple integer-programming formulations for the parallel-machine model in stage two and for the three-stage flow shop, we look into the issue of symmetry and we provide extensive computational results. Our goal is to explore the limits of the instance sizes that can be solved to guaranteed optimality within acceptable running times using integer programming.
Abstract:We study a distribution warehouse in which trailers need to be assigned to docks for loading or unloading. A parking lot is used as a buffer zone and transportation between the parking lot and the docks is performed by auxiliary resources called terminal tractors. Each incoming trailer has a known arrival time and each outgoing trailer a desired departure time. The primary objective is to produce a docking schedule such that the weighted sum of the number of late outgoing trailers and the tardiness of these trailers is minimized; the secondary objective is to minimize the weighted completion time of all trailers, both incoming and outgoing. The purpose of this paper is to produce high-quality solutions to large instances that are comparable to a real-life case. We implement several heuristic algorithms: truncated branch and bound, beam search and tabu search. Lagrangian relaxation is embedded in the algorithms for constructing an initial solution and for computing lower bounds. The different solution frameworks are compared via extensive computational experiments.
International audienceIn this paper we study the scheduling of the docking operations of trucks at a warehouse; each truck is either empty and needs to be loaded, or full and has to be unloaded (but not both). We focus on crossdocking, which is a recent warehouse concept that favors the transfers of as many incoming products as possible directly to outgoing trailers, without intermediate storage in the warehouse. We propose a time-indexed integer programming formulation for scheduling the loading and unloading of the trucks at the docks, and we distinguish between a so-called " mixed mode " , in which some or all of the docks can be used both for loading as well as unloading, and an " exclusive mode " , in which each dock is dedicated to only one of the two types of operations. Computational experiments are provided to compare the efficiency of the two modes
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