Metaheuristics for Logistics - download pdf or read online

By Laurent Deroussi

ISBN-10: 1848218087

ISBN-13: 9781848218086

This publication describes the most classical combinatorial difficulties that may be encountered whilst designing a logistics community or riding a offer chain. It indicates how those difficulties will be tackled by way of metaheuristics, either individually and utilizing an built-in process. a tremendous variety of strategies, from the best to the main complicated ones, are given for aiding the reader to enforce effective strategies that meet its needs.

A lot of books were written approximately metaheuristics (methods for fixing demanding optimization difficulties) and provide chain administration (the box during which we discover an incredible variety of combinatorial optimization difficulties) within the final a long time. So, the most cause of this publication is to explain how those tools may be applied for this type of problems.

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Metaheuristics for Logistics - download pdf or read online

This publication describes the most classical combinatorial difficulties that may be encountered while designing a logistics community or riding a offer chain. It exhibits how those difficulties may be tackled by way of metaheuristics, either individually and utilizing an built-in procedure. an important variety of suggestions, from the best to the main complicated ones, are given for assisting the reader to enforce effective strategies that meet its wishes.

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26] indicate that each part will be allocated to one position and that a position will receive one part. Data: I = {1,K, n} : set of parts J = {1,K, m} : set of machines pi , j , ∀ ( i, j ) ∈ I × J : production time of the part i in the machine j Variables: ⎧1 if the part i is located in the position k xi , k = ⎨ , ∀ ( i, k ) ∈ I × I ⎩0 otherwise I j , k ≥ 0, ∀ ( j, k ) ∈ J × I : idle time of the machine j between the k th and k + 1 th parts. Wk , j ≥ 0, ∀ ( k , j ) ∈ I × J : waiting time of the k th part in the stock located between the machines j and j + 1 .

10] shows that the capacity of the knapsack is respected. 4. Linear program of the knapsack problem The knapsack problem has numerous logistic applications such as the loading of cargo, stock reservation (operational decisions) or the realization of investment plans (strategic decisions). The “swing states” example shown in the previous chapter illustrates this last point. 2. The bin packing problem In its academic version (one-dimensional bin packing), the bin packing problem aims to determine the smallest number of bins, all thought to be identical, necessary to put away a set I of n objects.

These conditions are quite easy to satisfy in practice, unlike those of simulated annealing (accessibility properties – we must be able to link each pair of solutions with a chain of neighbors – and reversibility properties – x ∈ N ′ ( y ) ⇒ y ∈ N ′ ( x )). However, a wise programmer will define a stopping criterion while taking into account a maximum calculation time. In practice, the kangaroo algorithm will stop quite often before finding the Holy Grail! Anyway, even if we reach the optimum, we will probably not realize it since this kind of algorithm is not characterized by any process that proves optimality.

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Metaheuristics for Logistics by Laurent Deroussi


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