234 research outputs found
Sequential and parallel large neighborhood search algorithms for the periodic location routing problem
We propose a large neighborhood search (LNS) algorithm to solve the periodic location routing problem (PLRP). The PLRP combines location and routing decisions over a planning horizon in which customers require visits according to a given frequency and the specific visit days can be chosen. We use parallelization strategies that can exploit the availability of multiple processors. The computational results show that the algorithms obtain better results than previous solution methods on a set of standard benchmark instances from the literature
Adaptive large neighborhood search algorithm – performance evaluation under parallel schemes & applications
Adaptive Large Neighborhood Search (ALNS) is a fairly recent yet popular single-solution heuristic for solving discrete optimization problems. Even though the heuristic has been a popular choice for researchers in recent times, the parallelization of this algorithm is not widely studied in the literature compared to the other classical metaheuristics. To extend the existing literature, this study proposes several different parallel schemes to parallelize the basic/sequential ALNS algorithm. More specifically, seven different parallel schemes are employed to target different characteristics of the ALNS algorithm and the capability of the local computers. The schemes of this study are implemented in a master-slave architecture to manage and assign loads in processors of the local computers. The overall goal is to simultaneously explore different areas of the search space in an attempt to escape the local minima, taking effective steps toward the optimal solution and, to the end, accelerating the convergence of the ALNS algorithm. The performance of the schemes is tested by solving a capacitated vehicle routing problem (CVRP) with available wellknown test instances. Our computational results indicate that all the parallel schemes are capable of providing a competitive optimality gap in solving CVRP within our investigated test instances. However, the parallel scheme (scheme 1), which runs the ALNS algorithm independently within different slave processors (e.g., without sharing any information with other slave processors) until the synchronization occurs only when one of the processors meets its predefined termination criteria and reports the solution to the master processor, provides the best running time with solving the instances approximately 10.5 times faster than the basic/sequential ALNS algorithm. These findings are applied in a real-life fulfillment process using mixed-mode delivery with trucks and drones. Complex but optimized routes are generated in a short time that is applicable to perform last-mile delivery to customers
Attention, Learn to Solve Routing Problems!
The recently presented idea to learn heuristics for combinatorial
optimization problems is promising as it can save costly development. However,
to push this idea towards practical implementation, we need better models and
better ways of training. We contribute in both directions: we propose a model
based on attention layers with benefits over the Pointer Network and we show
how to train this model using REINFORCE with a simple baseline based on a
deterministic greedy rollout, which we find is more efficient than using a
value function. We significantly improve over recent learned heuristics for the
Travelling Salesman Problem (TSP), getting close to optimal results for
problems up to 100 nodes. With the same hyperparameters, we learn strong
heuristics for two variants of the Vehicle Routing Problem (VRP), the
Orienteering Problem (OP) and (a stochastic variant of) the Prize Collecting
TSP (PCTSP), outperforming a wide range of baselines and getting results close
to highly optimized and specialized algorithms.Comment: Accepted at ICLR 2019. 25 pages, 7 figure
Routing Arena: A Benchmark Suite for Neural Routing Solvers
Neural Combinatorial Optimization has been researched actively in the last
eight years. Even though many of the proposed Machine Learning based approaches
are compared on the same datasets, the evaluation protocol exhibits essential
flaws and the selection of baselines often neglects State-of-the-Art Operations
Research approaches. To improve on both of these shortcomings, we propose the
Routing Arena, a benchmark suite for Routing Problems that provides a seamless
integration of consistent evaluation and the provision of baselines and
benchmarks prevalent in the Machine Learning- and Operations Research field.
The proposed evaluation protocol considers the two most important evaluation
cases for different applications: First, the solution quality for an a priori
fixed time budget and secondly the anytime performance of the respective
methods. By setting the solution trajectory in perspective to a Best Known
Solution and a Base Solver's solutions trajectory, we furthermore propose the
Weighted Relative Average Performance (WRAP), a novel evaluation metric that
quantifies the often claimed runtime efficiency of Neural Routing Solvers. A
comprehensive first experimental evaluation demonstrates that the most recent
Operations Research solvers generate state-of-the-art results in terms of
solution quality and runtime efficiency when it comes to the vehicle routing
problem. Nevertheless, some findings highlight the advantages of neural
approaches and motivate a shift in how neural solvers should be conceptualized
Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problem
The Capacitated Vehicle Routing Problem is a well-known NP-hard problem that
poses the challenge of finding the optimal route of a vehicle delivering
products to multiple locations. Recently, new efforts have emerged to create
constructive and perturbative heuristics to tackle this problem using Deep
Learning. In this paper, we join these efforts to develop the Combined Deep
Constructor and Perturbator, which combines two powerful constructive and
perturbative Deep Learning-based heuristics, using attention mechanisms at
their core. Furthermore, we improve the Attention Model-Dynamic for the
Capacitated Vehicle Routing Problem by proposing a memory-efficient algorithm
that reduces its memory complexity by a factor of the number of nodes. Our
method shows promising results. It demonstrates a cost improvement in common
datasets when compared against other multiple Deep Learning methods. It also
obtains close results to the state-of-the art heuristics from the Operations
Research field. Additionally, the proposed memory efficient algorithm for the
Attention Model-Dynamic model enables its use in problem instances with more
than 100 nodes
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