Matheuristics: Optimization, Simulation and Control

Boschetti, Marco Antonio; Maniezzo, Vittorio; Roffilli, Matteo; Bolufé-Röhler, Antonio

doi:10.1007/978-3-642-04918-7_13

Cited by 133 publications

(84 citation statements)

References 9 publications

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“…We also prove that our specific version of the cable routing problem is NP-hard. Section 3 introduces MILP-based heuristics in the so-called matheuristic framework (Boschetti et al 2009, Hansen et al 2009). We first propose a relaxation of the initial MILP model that allows one to quickly find an initial (possibly infeasible) solution, and then we introduce different ways of defining a restricted MILP to improve the current-best solution.…”

Section: Not To Be Used For Navigationmentioning

confidence: 99%

“…As its name suggests, a matheuristic (Boschetti et al 2009, Hansen et al 2009) is the hybridization of mathematical programming with metaheuristics. The hallmark of this approach is the possibility of designing sound heuristics on top of a black-box MILP solver, by just changing its input data in a way that favors finding a sequence of improved solutions.…”

Section: Matheuristicsmentioning

confidence: 99%

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Optimizing wind farm cable routing considering power losses

Fischetti

Pisinger

2018

European Journal of Operational Research

135

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Wind energy is the fastest growing source of renewable energy, but as wind farms are getting larger and more remotely located, installation and infrastructure costs are rising. It is estimated that the expenses for electrical infrastructure account for 15-30% of the overall initial costs, hence it is important to optimize offshore inter-array cable routing. The routing should connect all turbines to one (or more) offshore substation(s) while respecting cable capacities, no-cross restrictions, connection-limits at the substation, and obstacles at the site. The objective is to minimize both the capital that must be spent immediately in cable and installation costs, and the future reduced revenues due to power losses. The latter goal has not been addressed in previous work. We present a Mixed-Integer Linear Programming approach to optimize the routing using both exact and math-heuristic methods. In the power losses computation, wind scenarios are handled efficiently as part of the preprocessing, resulting in a MIP model of only slightly larger size. A library of real-life instances is introduced and made publicly available for benchmarking. Computational results on this testbed show the viability of our methods, proving that savings in the order of millions of Euro can be achieved.

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Section: Not To Be Used For Navigationmentioning

confidence: 99%

Section: Matheuristicsmentioning

confidence: 99%

Optimizing wind farm cable routing considering power losses

Fischetti

Pisinger

2018

European Journal of Operational Research

135

View full text Add to dashboard Cite

show abstract

“…As a matter of fact, for real-100 world cases even the LP relaxation at each node turns out to be very time consuming-even by using the barrier method. So we designed ad-hoc heuristic approaches to exploit the special structure of our MILP, following a so-called matheuristic paradigm [11].…”

Section: Matheuristicsmentioning

confidence: 99%

Mixed-integer linear programming heuristics for the prepack optimization problem

Fischetti¹,

Monaci²,

Salvagnin³

2016

Discrete Optimization

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In this paper we consider a packing problem arising in inventory allocation applications, where the operational cost for packing the bins is comparable, or even higher, than the cost of the bins (and of the items) themselves. This is the case, for example, of warehouses that have to manage a large number of different customers (e.g., stores), each requiring a given set of items. For this problem, we present Mixed-Integer Linear Programming heuristics based on problem substructures that lead to easy-to-solve and meaningful subproblems, and exploit them within an overall meta-heuristic framework. The new heuristics are evaluated on a standard set of instances, and benchmarked against known heuristics from the literature. Computational experiments show that very good (often proven optimal) solutions can consistently be computed in short computing times.

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“…This is because these techniques often allow to combine the strengths of different ways of solving optimization problems in a single algorithm. Especially the combination of heuristic search with exact techniques-a field of research often labelled as matheuristics [3]-has been quite fruitful. One of the most well known, and generally applicable, algorithms from this field is called Large Neighborhood Search (LNS) [4], which is based on the following general idea.…”

Section: Introductionmentioning

confidence: 99%

Construct, Merge, Solve and Adapt Versus Large Neighborhood Search for Solving the Multi-dimensional Knapsack Problem: Which One Works Better When?

Lizárraga

Blesa

Blum

2017

Evolutionary Computation in Combinatorial Optimization

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Abstract. Both, Construct, Merge Solve and Adapt (CMSA) and Large Neighborhood Search (LNS), are hybrid algorithms that are based on iteratively solving sub-instances of the original problem instances, if possible, to optimality. This is done by reducing the search space of the tackled problem instance in algorithm-specific ways which differ from one technique to the other. In this paper we provide first experimental evidence for the intuition that, conditioned by the way in which the search space is reduced, LNS should generally work better than CMSA in the context of problems in which solutions are rather large, and the opposite is the case for problems in which solutions are rather small. The size of a solution is hereby measured by the number of components of which the solution is composed, in comparison to the total number of solution components. Experiments are conducted in the context of the multi-dimensional knapsack problem.

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Matheuristics: Optimization, Simulation and Control

Cited by 133 publications

References 9 publications

Optimizing wind farm cable routing considering power losses

Optimizing wind farm cable routing considering power losses

Mixed-integer linear programming heuristics for the prepack optimization problem

Construct, Merge, Solve and Adapt Versus Large Neighborhood Search for Solving the Multi-dimensional Knapsack Problem: Which One Works Better When?

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