A genetic algorithm for vehicle routing in logistic networks with practical constraints

2021 ◽  
Vol 68 (3) ◽  
pp. 16-40
Author(s):  
Grzegorz Koloch ◽  
Michał Lewandowski ◽  
Marcin Zientara ◽  
Grzegorz Grodecki ◽  
Piotr Matuszak ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Real Life ◽  
Capacity Constraints ◽  
Problem Instance ◽  
Postal Service ◽  
Genetic Operators ◽  
Delivery Problem ◽  
Network Time ◽  
Problem Instances ◽  
Practical Constraints

We optimise a postal delivery problem with time and capacity constraints imposed on vehicles and nodes of the logistic network. Time constraints relate to the duration of routes, whereas capacity constraints concern technical characteristics of vehicles and postal operation outlets. We consider a method which can be applied to a brownfield scenario, in which capacities of outlets can be relaxed and prospective hubs identified. As a solution, we apply a genetic algorithm and test its properties both in small case studies and in a simulated problem instance of a larger (i.e. comparable with real-world instances) size. We show that the genetic operators we employ are capable of switching between solutions based on direct origin-to-destination routes and solutions based on transfer connections, depending on what is more beneficial in a given problem instance. Moreover, the algorithm correctly identifies cases in which volumes should be shipped directly, and those in which it is optimal to use transfer connections within a single problem instance, if an instance in question requires such a selection for optimality. The algorithm is thus suitable for determining hubs and satellite locations. All considerations presented in this paper are motivated by real-life problem instances experienced by the Polish Post, the largest postal service provider in Poland, in its daily plans of delivering postal packages, letters and pallets.

scholarly journals Simple Constructive, Insertion, and Improvement Heuristics Based on the Girding Polygon for the Euclidean Traveling Salesman Problem

Algorithms ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 5 ◽  
Author(s):  
Víctor Pacheco-Valencia ◽  
José Alberto Hernández ◽  
José María Sigarreta ◽  
Nodari Vakhania
Keyword(s):  
Traveling Salesman Problem ◽  
Real Life ◽  
Traveling Salesman ◽  
Computational Time ◽  
Problem Instance ◽  
Dimensional Euclidean Space ◽  
Benchmark Problem ◽  
Small Constant ◽  
Euclidean Traveling Salesman Problem ◽  
Problem Instances

The Traveling Salesman Problem (TSP) aims at finding the shortest trip for a salesman, who has to visit each of the locations from a given set exactly once, starting and ending at the same location. Here, we consider the Euclidean version of the problem, in which the locations are points in the two-dimensional Euclidean space and the distances are correspondingly Euclidean distances. We propose simple, fast, and easily implementable heuristics that work well, in practice, for large real-life problem instances. The algorithm works on three phases, the constructive, the insertion, and the improvement phases. The first two phases run in time O ( n 2 ) and the number of repetitions in the improvement phase, in practice, is bounded by a small constant. We have tested the practical behavior of our heuristics on the available benchmark problem instances. The approximation provided by our algorithm for the tested benchmark problem instances did not beat best known results. At the same time, comparing the CPU time used by our algorithm with that of the earlier known ones, in about 92% of the cases our algorithm has required less computational time. Our algorithm is also memory efficient: for the largest tested problem instance with 744,710 cities, it has used about 50 MiB, whereas the average memory usage for the remained 217 instances was 1.6 MiB.

scholarly journals An Instance Data Repository for the Round-robin Sports Timetabling Problem

2020 ◽  
Vol 45 (2) ◽  
pp. 184-200
Author(s):  
David Van Bulck ◽  
Dries Goossens ◽  
Jo¨rn Scho¨nberger ◽  
Mario Guajardo
Keyword(s):  
Optimization Problem ◽  
Real Life ◽  
Short Description ◽  
Problem Instance ◽  
Data Repository ◽  
Timetabling Problem ◽  
Classification Framework ◽  
Different Types ◽  
Problem Instances ◽  
Complex Matter

The sports timetabling problem is a combinatorial optimization problem that consists of creating a timetable that defines against whom, when and where teams play games. This is a complex matter, since real-life sports timetabling applications are typically highly constrained. The vast amount and variety of constraints and the lack of generally accepted benchmark problem instances make that timetable algorithms proposed in the literature are often tested on just one or two specific seasons of the competition under consideration. This is problematic since only a few algorithmic insights are gained. To mitigate this issue, this article provides a problem instance repository containing over 40 different types of instances covering artificial and real-life problem instances. The construction of such a repository is not trivial, since there are dozens of constraints that need to be expressed in a standardized format. For this, our repository relies on RobinX, an XML-supported classification framework. The resulting repository provides a (non-exhaustive) overview of most real-life sports timetabling applications published over the last five decades. For every problem, a short description highlights the most distinguishing characteristics of the problem. The repository is publicly available and will be continuously updated as new instances or better solutions become available.

scholarly journals Solving the Large-Scale TSP Problem in 1 h: Santa Claus Challenge 2020

2021 ◽  
Vol 8 ◽  
Author(s):  
Radu Mariescu-Istodor ◽  
Pasi Fränti
Keyword(s):  
Large Scale ◽  
Computing Time ◽  
Real Life ◽  
Problem Instance ◽  
Santa Claus ◽  
Design Choice ◽  
Long Time ◽  
Problem Instances ◽  
The Given ◽  
Important Design

The scalability of traveling salesperson problem (TSP) algorithms for handling large-scale problem instances has been an open problem for a long time. We arranged a so-called Santa Claus challenge and invited people to submit their algorithms to solve a TSP problem instance that is larger than 1 M nodes given only 1 h of computing time. In this article, we analyze the results and show which design choices are decisive in providing the best solution to the problem with the given constraints. There were three valid submissions, all based on local search, including k-opt up to k = 5. The most important design choice turned out to be the localization of the operator using a neighborhood graph. The divide-and-merge strategy suffers a 2% loss of quality. However, via parallelization, the result can be obtained within less than 2 min, which can make a key difference in real-life applications.

SOLVING THE UNCAPACITATED MULTIPLE ALLOCATION p-HUB CENTER PROBLEM BY GENETIC ALGORITHM

2006 ◽  
Vol 23 (04) ◽  
pp. 425-437 ◽  
Author(s):  
JOZEF KRATICA ◽  
ZORICA STANIMIROVIĆ
Keyword(s):  
Genetic Algorithm ◽  
Higher Dimensions ◽  
Computational Time ◽  
Computational Results ◽  
Center Problem ◽  
Genetic Operators ◽  
Binary Coding ◽  
All Solutions ◽  
Multiple Allocation ◽  
Problem Instances

In this paper we describe a genetic algorithm (GA) for the uncapacitated multiple allocation p-hub center problem (UMApHCP). Binary coding is used and genetic operators adapted to the problem are constructed and implemented in our GA. Computational results are presented for the standard hub instances from the literature. It can be seen that proposed GA approach reaches all solutions that are proved to be optimal so far. The solutions are obtained in a reasonable amount of computational time, even for problem instances of higher dimensions.

scholarly journals Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Classification Model ◽  
Maximum Clique Problem ◽  
Problem Instance ◽  
Np Hard ◽  
Machine Learning Classification ◽  
Hard Problems ◽  
Problem Instances ◽  
D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

AN EFFICIENT APPROACH FOR THE THREE-DIMENSIONAL CONTAINER PACKING PROBLEM WITH PRACTICAL CONSTRAINTS

2004 ◽  
Vol 21 (03) ◽  
pp. 279-295 ◽  
Author(s):  
ZHIHONG JIN ◽  
KATSUHISA OHNO ◽  
JIALI DU
Keyword(s):  
Three Dimensional ◽  
Real Life ◽  
Packing Problem ◽  
Utilization Rate ◽  
Benchmark Problems ◽  
Rectangular Container ◽  
Efficient Packing ◽  
High Degree ◽  
Practical Constraints ◽  
Inherent Stability

This paper deals with the three-dimensional container packing problem (3DCPP), which is to pack a number of items orthogonally onto a rectangular container so that the utilization rate of the container space or the total value of loaded items is maximized. Besides the above objectives, some other practical constraints, such as loading stability, the rotation of items around the height axis, and the fixed loading (unloading) orders, must be considered for the real-life 3DCPP. In this paper, a sub-volume based simulated annealing meta-heuristic algorithm is proposed, which aims at generating flexible and efficient packing patterns and providing a high degree of inherent stability at the same time. Computational experiments on benchmark problems show its efficiency.

Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems

2009 ◽  
Vol 419-420 ◽  
pp. 633-636 ◽  
Author(s):  
James C. Chen ◽  
Wun Hao Jaong ◽  
Cheng Ju Sun ◽  
Hung Yu Lee ◽  
Jenn Sheng Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Total Tardiness ◽  
Scheduling Problems ◽  
Test Bed ◽  
Resource Constrained ◽  
Multiple Resources ◽  
Practical Constraints

Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.

scholarly journals Research on Logistics Distribution Path Planning Based on Genetic Algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032013
Author(s):  
Shaokun Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulation Experiment ◽  
Time Window ◽  
Planning Model ◽  
Genetic Operators ◽  
Intelligent Algorithm ◽  
Total Cost ◽  
Logistics Distribution ◽  
Survey Results ◽  
Distribution Cost

Abstract In this paper, SF express company Jinzhou Guta District Pinganli business point as an example, to investigate its distribution, statistical analysis of the survey results, summed up the problems in logistics and distribution. Through the systematic study of the problem, a planning model with time window and with the objective of minimizing the total cost of distribution is established. At the same time, an intelligent algorithm for distribution path optimization - Genetic Algorithm (GA) is designed. Genetic algorithm is used to design chromosome coding methods and genetic operators for solving the planning model with the objective of minimizing the total cost of distribution. Finally, the simulation experiment is carried out. MATLAB software is used to solve the distribution route and the total driving distance of vehicles, and the distribution route with the goal of minimizing the total distribution cost is obtained.

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