Hyper-heuristics are aimed at providing a generalized solution to optimization problems rather than producing the best result for one or more problem instances. This paper examines the use of evolutionary algorithm (EA) selection hyper-heuristics to solve the offline one-dimensional bin-packing problem. Two EA hyper-heuristics are evaluated. The first (EA-HH1) searches a heuristic space of comb...

The bin packing problem is a well-studied problem in combinatorial optimization. In the classical bin packing problem, we are given a list of real numbers in (0, 1] and the goal is to place them in a minimum number of bins so that no bin holds numbers summing to more than 1. The problem is extremely important in practice and finds numerous applications in scheduling, routing and resource alloca...

We propose an iterated local search based on several classes of local and large neighborhoods for the bin packing problem with conflicts. This problem, which combines the characteristics of both bin packing and vertex coloring, arises in various application contexts such as logistics and transportation, timetabling, and resource allocation for cloud computing. We introduce O(1) evaluation proce...

line and oo-line approximation algorithms for vector covering problems. analysis of algorithms for dual bin packing problems. packing can be solved within 1 + in linear time.gorithms for the m-dimensional 0-1 knapsack problem: worst-case and probabilistic analyses.schatz. Resource scheduling in enhanced pay-per-view continuous media databases. An eecient approximation scheme for the one-dimensi...

Let G = (V ∪W,E) be an undirected graph, where V ∪W = {1, ..., n} is the vertex set and E = {(i, j) | i, j ∈ V ∪W, i < j} is the edge set. Vertex s = 1 is the source vertex, V is a set of vertices that might be visited, T ⊆ V is a set of vertices that must be visited (s ∈ T ), and W is a set of vertices that must be covered. A symmetric distance matrix C = (cij), defined on E, uses the Euclidea...

We present a 1 − 1 √ k -competitive algorithm for the online stochastic generalized assignment problem under the assumption that no item takes up more than 1 k fraction of the capacity of any bin. Items arrive online; each item has a value and a size; upon arrival, an item can be placed in a bin or discarded; the objective is to maximize the total value of the placement. Both value and size of ...

We consider a generalized one-dimensional bin-packing model where the cost of a bin is a nondecreasing concave function of the utilization of the bin. Four popular heuristics from the literature of the classical bin-packing problem are studied: First Fit (FF), Best Fit (BF), First Fit Decreasing (FFD), and Best Fit Decreasing (BFD). We analyze their worst-case performances when they are applied...

We address a problem of vehicle routing that arises in picking up and delivering full container load from/to an intermodal terminal. The substantial cost and time savings are expected by efficient linkage between pickup and delivery tasks, if the time of tasks and the suitability of containers for cargo allow. As this problem is NP-hard, we develop a subgradient heuristic based on a Lagrangian ...