http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.7230&rep=rep1&type=pdf

A simple and effective evolutionary algorithm for the vehicle routing problem

Radial measures of efficiency estimated using linear programming (LP) methods can be biased since slack in the constraints defining the technology suggests that at least one input can be reduced, or one output can be expanded, even though a firm is deemed to be “technically efficient.” In this paper, we propose a directional slacks-based measure of technical inefficiency to account for the potential of slack in technological constraints. When no such slacks exist, directional slacks-based inefficiency collapses to the directional technology distance function. Our proposed measure helps to generalize some of the existing slacks-based measures of inefficiency. We examine the financial services provided by Japanese cooperative Shinkin banks, and estimate their inefficiency during the period 2002–2005. This inefficiency declined slightly during the period. We thus propose that slack is an important source of inefficiency which is often not captured by the directional technology distance function.

A directional slacks-based measure of technical inefficiency

We propose an algorithmic framework that successfully addresses three vehicle routing problems: the multidepot VRP, the periodic VRP, and the multidepot periodic VRP with capacitated vehicles and constrained route duration. The metaheuristic combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and advanced population-diversity management schemes. Extensive computational experiments show that the method performs impressively in terms of computational efficiency and solution quality, identifying either the best known solutions, including the optimal ones, or new best solutions for all currently available benchmark instances for the three problem classes. The proposed method also proves extremely competitive for the capacitated VRP.

/pdf/a-hybrid-genetic-algorithm-for-multidepot-and-periodic-58backiyrn.pdf

A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems

Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials explains the most important results achieved in this area.The presenters show how runtime behavior can be analyzed in a rigorous way, in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems.The tutorial is based on a book written by the authors with the same title. Further information about the book can be found at www.bioinspiredcomputation.com.

/pdf/bioinspired-computation-in-combinatorial-optimization-35uq8b5p53.pdf

Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity

https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2013-22.pdf

A unified solution framework for multi-attribute vehicle routing problems ☆

The Capacitated Arc Routing Problem or CARP arises in applications like waste collection or winter gritting Metaheuristics are tools of choice for solving large instances of this NP-hard problem The paper presents basic components that can be combined into powerful memetic algorithms (MAs) for solving an extended version of the CARP (ECARP) The best resulting MA outperforms all known heuristics on three sets of benchmark files containing in total 81 instances with up to 140 nodes and 190 edges In particular, one open instance is broken by reaching a tight lower bound designed by Belenguer and Benavent, 26 best-known solutions are improved, and all other best-known solutions are retrieved

/pdf/competitive-memetic-algorithms-for-arc-routing-problems-2bkcwcbvyz.pdf

Competitive Memetic Algorithms for Arc Routing Problems

The NP-hard Capacitated Arc Routing Problem (CARP) allows to model urban waste collection or road gritting, for instance. Exact algorithms are still limited to small problems and metaheuristics are required for large scale instances. The paper presents the first genetic algorithm (GA) published for the CARP. This hybrid GA tackles realistic extensions like mixed graphs or prohibited turns. It displays excellent results and outperforms the best metaheuristics published when applied to two standard sets of benchmarks: the average deviations to lower bounds are 0.24 % and 0.69 % respectively, a majority of instances are solved to optimality, and eight best known solutions are improved.

A Genetic Algorithm for the Capacitated Arc Routing Problem and Its Extensions

Evolutionary algorithms for periodic arc routing problems

This paper considers the stochastic capacitated arc routing problem (SCARP), obtained by taking random demands in the CARP. For real-world problems, it is important to create solutions that are insensitive to changes in demand, because these quantities are not deterministic but randomly distributed. This paper provides the basic concept of a new technique to compute such solutions, based upon the best method published for CARP: a hybrid genetic algorithm (HGA). The simulation analysis was achieved with the well-known DeArmon's, Eglese's and Belenguer's instances. This intensive evaluation process was carried out with 1000 replications providing high-quality statistical data. The results obtained prove that there is a great interest to optimize not only the solution cost but also the robustness of solutions. This work is a step forward to treat more realistic problems including industrial goals and constraints linked to demand variations.

Improving robustness of solutions to arc routing problems

https://hal.inria.fr/inria-00523287/document

Wahiba Ramdane-Cherif

Papers

Competitive Memetic Algorithms for Arc Routing Problems

A Genetic Algorithm for the Capacitated Arc Routing Problem and Its Extensions

Evolutionary algorithms for periodic arc routing problems

Improving robustness of solutions to arc routing problems

Scheduling supply chain node with fixed components arrivals and two partially flexible deliveries