This paper concerns the Split Delivery Vehicle Routing Problem (SDVRP). This problem is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) since the customers’ demands are allowed to be split. We deal with the cases where the fleet is unlimited (SDVRP-UF) and limited (SDVRP-LF). In order to solve them, we implemented a multi-start Iterated Local Search (ILS) based heuristic that inc...

In the unweighted set-cover problem we are given a set of elements E = {e1, e2, . . . , en} and a collection F of subsets of E. The problem is to compute a sub-collection SOL ⊆F such that ⋃ Sj∈SOL Sj = E and its size |SOL| is minimized. When |S| ≤ k for all S ∈ F we obtain the unweighted k-set cover problem. It is well known that the greedy algorithm is an Hk-approximation algorithm for the unw...

The multidimensional assignment problem (MAP) (abbreviated s-AP in the case of s dimensions) is an extension of the well-known assignment problem. The most studied case of MAP is 3-AP, though the problems with larger values of s have also a number of applications. In this paper we propose several local search heuristics for MAP and select the dominating ones. The results of computational experi...

We study the improvement of non-permutation over permutation schedules in flow shops when minimizing the total completion time. We solve both problems by a two-phase heuristic. The first phase uses an iterated local search to find a good permutation schedule. The second phase explores non-permutation schedules using an effective insertion neighborhood, that permits to anticipate or delay a job ...

We generalize the standard vehicle routing problem with time windows by allowing both traveling times and traveling costs to be time-dependent functions. In our algorithm, we use a local search to determine routes of the vehicles. When we evaluate a neighborhood solution, we must compute an optimal time schedule of each route. We show that this subproblem can be efficiently solved by dynamic pr...

Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve their behavior. This paper focuses on experimentally investigating the role of a recent approach, the binary-coded local genetic algorithm (BLGA), as context-independent local search o...

Local search techniques have widespread use for solving propositional satisfiability problems. We investigate the use of adaptive local search techniques for model generation problems for modal logics; we focus on the modal logic S5. A local search algorithm extended with an adaptive heuristic is presented and tested on an ensemble of randomly generated problem instances. We briefly discuss the...

The recent explosion of high dimensionality in datasets for several domains has posed a serious challenge to existing Bayesian network structure learning algorithms. Local search methods represent a solution in such spaces but suffer with small datasets. MMHC (MaxMin Hill-Climbing) is one of these local search algorithms where a first phase aims at identifying a possible skeleton by using some ...

The goal of assembly planning consists in generating feasible sequences to assemble a product and selecting an efficient assembly sequence from which related constraint factors such as geometric features, assembly time, tools, and machines are considered to arrange a feasible assembly sequence based on planner’s individual heuristics. Suchlike planning may implement genetic algorithms to go tow...

Local search algorithms have been very successful for solving constraint satisfaction problems (CSP). However, a major weakness has been that local search is unable to detect unsolvability and is thus not suitable for highly constrained or overconstrained problems. In this paper, we present a scheme where a local search algorithm, the breakout algorithm, is used to identify hard or unsolvable s...