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 study a multivehicle inventory routing problem (MIRP) in which supplier delivers one type of product along a finite planning horizon, using a homogeneous fleet of vehicles. The main objective is to minimize the total cost of storage and transportation. In order to solve MIRP, we propose an algorithm based on iterated local search (ILS) metaheuristic, using a variable neighborhood descent wit...

This paper presents a parameter-less optimization framework that uses the extended compact genetic algorithm (ECGA) and iterated local search (ILS), but is not restricted to these algorithms. The presented optimization algorithm (ILS+ECGA) comes as an extension of the parameter-less genetic algorithm (GA), where the parameters of a selecto-recombinative GA are eliminated. The approach that we p...

The multiple depot vehicle scheduling problem (MDVSP) is a well-known and important problem arising in public transport. Although many solution approaches have been published in the literature, algorithms using metaheuristics appeared only very recently (Large Neighborhood Search and Tabu Search). In this paper, we introduce an Iterated Local Search algorithm for the MDVSP, incorporating a neig...

This contribution is devoted to the application of iterated local search to image registration, a very complex, real-world problem in the field of image processing. To do so, we first re-define this parameter estimation problem as a combinatorial optimization problem, then analyze the use of image-specific information to guide the search in the form of an heuristic function, and finally propose...

The genomic median problem is an optimization problem inspired by a biological issue: it aims to find the chromosome organization of the common ancestor to multiple living species. It is formulated as the search for a genome that minimizes a rearrangement distance measure among given genomes. Several attempts have been reported for solving this NP-hard problem. These range from simple heuristic...

This paper addresses a variation of the Traveling Salesman Problem with Pickup and Delivery in which loading and unloading operations have to be executed in a first-in-first-out fashion. It provides an integer programming formulation of the problem. It also describes five operators for improving a feasible solution, and two heuristics that utilize these operators: a probabilistic tabu search al...

This paper introduces ILS Miner, a novel method for discovering process models from event logs using iterated local search. A comparison between ILS Miner, GLS Miner and HeuristicMiner is presented in this paper. GLS Miner was chosen because applies Guided Local Search for discovering process models from event logs, HeuristicMiner[10] being a legacy method in business process discovery. It is s...

The importance of high performance algorithms for tackling difficult optimization problems cannot be understated, and in many cases the most effective methods are metaheuristics. When designing a metaheuristic, simplicity should be favored, both conceptually and in practice. Naturally, it must also lead to effective algorithms, and if possible, general purpose ones. If we think of a metaheurist...

The importance of high performance algorithms for tackling NP-hard optimization problems cannot be understated, and in many practical cases the most successful methods are metaheuristics. When designing a metaheuristic, it is preferable that it be simple, both conceptually and in practice. Naturally, it also must be effective, and if possible, general purpose. Iterated local search (ILS) is suc...