scholarly journals A Heuristic Algorithm for the Routing and Scheduling Problem with Time Windows: A Case Study of the Automotive Industry in Mexico

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 111 ◽  
Author(s):  
Marco Antonio Juárez Pérez ◽  
Rodolfo Eleazar Pérez Loaiza ◽  
Perfecto Malaquias Quintero Flores ◽  
Oscar Atriano Ponce ◽  
Carolina Flores Peralta
Keyword(s):  
Heuristic Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Second Phase ◽  
Two Phase ◽  
Routing And Scheduling ◽  
Logistics Company ◽  
Vehicle Production ◽  
Time Window Constraints

This paper investigates a real-world distribution problem arising in the vehicle production industry, particularly in a logistics company, in which cars and vans must be loaded on auto-carriers and then delivered to dealerships. A solution to the problem involves the loading and optimal routing, without violating the capacity and time window constraints for each auto-carrier. A two-phase heuristic algorithm was implemented to solve the problem. In the first phase the heuristic builds a route with an optimal insertion procedure, and in the second phase the determination of a feasible loading. The experimental results show that the purposed algorithm can be used to tackle the transportation problem in terms of minimizing total traveling distance, loading/unloading operations and transportation costs, facilitating a decision-making process for the logistics company.

scholarly journals Matheuristics and Column Generation for a Basic Technician Routing Problem

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 313
Author(s):  
Nicolas Dupin ◽  
Rémi Parize ◽  
El-Ghazali Talbi
Keyword(s):  
Machine Learning ◽  
Column Generation ◽  
Time Window ◽  
Time Windows ◽  
Machine Learning Techniques ◽  
Mixed Integer ◽  
Routing Problems ◽  
Routing Problem ◽  
Feasible Solutions ◽  
Time Window Constraints

This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.

scholarly journals VEHICLE ROUTING PROBLEM WITH TIME WINDOWS USING HYBRID ENCODING GENETIC ALGORITHM

2014 ◽  
Vol 12 (10) ◽  
pp. 3945-3951
Author(s):  
Dr P.K Chenniappan ◽  
Mrs.S.Aruna Devi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Time Windows ◽  
Minimal Cost ◽  
Total Size ◽  
Routing Problem ◽  
Time Window Constraints ◽  
Vehicle Capacity ◽  
Vehicle Routes

The vehicle routing problem is to determine K vehicle routes, where a route is a tour that begins at the depot, traverses a subset of the customers in a specified sequence and returns to the depot. Each customer must be assigned to exactly one of the K vehicle routes and total size of deliveries for customers assigned to each vehicle must not exceed the vehicle capacity. The routes should be chosen to minimize total travel cost. Thispapergivesasolutiontofindanoptimumrouteforvehicle routingproblem using Hybrid Encoding GeneticAlgorithm (HEGA)technique tested on c++ programming.The objective is to find routes for the vehicles to service all the customers at a minimal cost and time without violating the capacity, travel time constraints and time window constraints

scholarly journals A Bucket Graph–Based Labeling Algorithm with Application to Vehicle Routing

2020 ◽  
Author(s):  
Ruslan Sadykov ◽  
Eduardo Uchoa ◽  
Artur Pessoa
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Resource Constraints ◽  
Time Window ◽  
Time Windows ◽  
Routing Problem ◽  
Constrained Vehicle Routing ◽  
Time Window Constraints ◽  
Vehicle Capacity ◽  
First Time

We consider the shortest path problem with resource constraints arising as a subproblem in state-of-the-art branch-cut-and-price algorithms for vehicle routing problems. We propose a variant of the bidirectional label-correcting algorithm in which the labels are stored and extended according to the so-called bucket graph. This organization of labels helps to significantly decrease the number of dominance checks and the running time of the algorithm. We also show how the forward/backward route symmetry can be exploited and how to eliminate arcs from the bucket graph using reduced costs. The proposed algorithm can be especially beneficial for vehicle routing instances with large vehicle capacity and/or with time window constraints. Computational experiments were performed on instances from the distance-constrained vehicle routing problem, including multidepot and site-dependent variants, on the vehicle routing problem with time windows, and on the “nightmare” instances of the heterogeneous fleet vehicle routing problem. Significant improvements over the best algorithms in the literature were achieved, and many instances could be solved for the first time.

A Compressed Annealing Approach with Pre-Process for the Asymmetric Traveling Salesman Problem with Time Windows

2011 ◽  
Vol 5 (5) ◽  
pp. 669-678
Author(s):  
Tadanobu Mizogaki ◽  
◽  
Masao Sugi ◽  
Masashi Yamamoto ◽  
Hidetoshi Nagai ◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Feasible Solution ◽  
Time Window ◽  
Time Windows ◽  
Computing Time ◽  
Traveling Salesman ◽  
Asymmetric Traveling Salesman Problem ◽  
Insertion Method ◽  
Time Window Constraints ◽  
The Cost

This paper proposes a method of rapidly finding a feasible solution to the asymmetric traveling salesman problem with time windows (ATSP-TW). ATSP-TW is a problem that involves determining the route with the minimum travel cost for visiting n cities one time each with time window constraints (the period of time in which the city must be visited is constrained). “Asymmetrical” denotes a difference between the cost of outbound and return trips. For such a combinatorial optimization problem with constraints, we propose a method that combines a pre-process based on the insertion method with metaheuristics called “the compressed annealing approach.” In an experiment using a 3-GHz computer, our method derives a feasible solution that satisfies the time window constraints for all of up to about 300 cities at an average of about 1/7 the computing time of existing methods, an average computing time of 0.57 seconds, and a maximum computing time of 9.40 seconds.

scholarly journals A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry

2021 ◽  
Vol 11 (11) ◽  
pp. 4840
Author(s):  
Apichit Maneengam ◽  
Apinanthana Udomsakdigool
Keyword(s):  
Time Window ◽  
Set Covering ◽  
Scheduling Problem ◽  
Ship Routing ◽  
Routing And Scheduling ◽  
Weighted Sum Method ◽  
Ship Routing And Scheduling ◽  
Green Ship ◽  
Covering Model ◽  
Time Window Constraints

This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO2 equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.

Sign in / Sign up

Export Citation Format

Share Document