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 A Synchronous Optimization Model for Multiship Shuttle Tanker Fleet Design and Scheduling Considering Hard Time Window Constraint

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Zhenfeng Jiang ◽  
Dongxu Chen ◽  
Zhongzhen Yang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Transportation Cost ◽  
Exact Algorithm ◽  
Mixed Integer ◽  
Routing Problem ◽  
Hard Time ◽  
Proposed Model ◽  
Total Transportation Cost ◽  
Time Window Constraints

A Synchronous Optimization for Multiship Shuttle Tanker Fleet Design and Scheduling is solved in the context of development of floating production storage and offloading device (FPSO). In this paper, the shuttle tanker fleet scheduling problem is considered as a vehicle routing problem with hard time window constraints. A mixed integer programming model aiming at minimizing total transportation cost is proposed to model this problem. To solve this model, we propose an exact algorithm based on the column generation and perform numerical experiments. The experiment results show that the proposed model and algorithm can effectively solve the problem.

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.

Variable Fixing for Two-Arc Sequences in Branch-Price-and-Cut Algorithms on Path-Based Models

2020 ◽  
Vol 54 (5) ◽  
pp. 1170-1188 ◽  
Author(s):  
Guy Desaulniers ◽  
Timo Gschwind ◽  
Stefan Irnich
Keyword(s):  
Vehicle Routing ◽  
Time Windows ◽  
Solution Process ◽  
Mixed Integer ◽  
Linear Programs ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Benchmark Instances ◽  
Variable Fixing

Variable fixing by reduced costs is a popular technique for accelerating the solution process of mixed-integer linear programs. For vehicle-routing problems solved by branch-price-and-cut algorithms, it is possible to fix to zero the variables associated with all routes containing at least one arc from a subset of arcs determined according to the dual solution of a linear relaxation. This is equivalent to removing these arcs from the network used to generate the routes. In this paper, we extend this technique to routes containing sequences of two arcs. Such sequences or their arcs cannot be removed directly from the network because routes traversing only one arc of a sequence might still be allowed. For some of the most common vehicle-routing problems, we show how this issue can be overcome by modifying the route-generation labeling algorithm in order to remove indirectly these sequences from the network. The proposed acceleration strategy is tested on benchmark instances of the vehicle-routing problem with time windows (VRPTW) and four variants of the electric VRPTW. The computational results show that it yields a significant speedup, especially for the most difficult instances.

scholarly journals A Comprehensive Study of Vehicle Routing Problem With Time Windows Using Ant Colony Optimization Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 80 ◽  
Author(s):  
Avirup Guha Neogi ◽  
Singamreddy Mounika ◽  
Salagrama Kalyani ◽  
S A. Yogananda Sai
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Routing Problem ◽  
Time Window Constraints

Ant Colony Optimization (ACO) is a nature-inspired swarm intelligence technique and a metaheuristic approach which is inspired by the foraging behavior of the real ants, where ants release pheromones to find the best and shortest route from their nest to the food source. ACO is being applied to various optimization problems till date and has been giving good quality results in the field. One such popular problem is known as Vehicle Routing Problem(VRP). Among many variants of VRP, this paper presents a comprehensive survey on VRP with Time Window constraints(VRPTW). The survey is presented in a chronological order discussing which of the variants of ACO is used in each paper followed by the advantages and limitations of the same.  

scholarly journals Model of Flexible Periodic Vehicle Routing Problem-Service Choice Considering Inventory Status

2021 ◽  
Vol 22 (1) ◽  
pp. 125-137
Author(s):  
Muhammad Alde Rizal ◽  
Ifa Saidatuningtyas
Keyword(s):  
Mathematical Model ◽  
Vehicle Routing ◽  
Time Window ◽  
Programming Model ◽  
Mixed Integer ◽  
Routing Problem ◽  
Inventory Costs ◽  
Delivery Times ◽  
Periodic Vehicle Routing Problem ◽  
Time Window Constraints

Vehicle routing problems and inventory problems need to be integrated in order to improve performance. This research discusses the determination of vehicle routes for product delivery with periodic delivery times that are released at any time depending on the inventory status. A mixed-integer linear programming model in determining periodic flexible visiting vehicles' route considering inventory is proposed to solve this problem. This model also accommodates time window constraints, retailer warehouse capacity. The search for solutions was carried out using the branch-and-bound method with the help of Lingo 18.0. The mathematical model testing result saves shipping costs and inventory costs. In addition, the developing mathematical model offers the flexibility of visiting depending on the inventory status of the consumer. The sensitivity analysis of the model results in the vehicle capacity influence the total cost and routes formed.

scholarly journals Simultaneous Optimization of the Liner Shipping Route and Ship Schedule Designs with Time Windows

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xi Jiang ◽  
Haijun Mao ◽  
Hao Zhang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Time Windows ◽  
Operating Cost ◽  
Optimal Solution ◽  
Liner Shipping ◽  
Simultaneous Optimization ◽  
Mixed Integer ◽  
Time Window Constraints ◽  
Call Sequence

This paper proposes to address the problem of the simultaneous optimization of the liner shipping route and ship schedule designs by incorporating port time windows. A mathematical programming model was developed to minimize the carrier’s total operating cost by simultaneously optimizing the port call sequence, ship arrival time per port of call, and sailing speed per shipping leg under port time window constraints. In view of its structure, the nonlinear nonconvex optimization model is further transformed into a mixed-integer linear programming model that can be efficiently solved by extant solvers to provide a global optimal solution. The results of the numerical experiments performed using a real-world case study indicated that the proposed model performs significantly better than the models that handle the design problems separately. The results also showed that different time windows will affect the optimal port call sequence. Moreover, port time windows, bunker price, and port efficiency all affect the total operating cost of the designed shipping route.

Sweep Algorithm and Mixed Integer Linear Program for Vehicle Routing Problem with Time Windows

2018 ◽  
Vol 17 (04) ◽  
pp. 505-513 ◽  
Author(s):  
H. Savitri ◽  
D. A. Kurniawati
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Time Windows ◽  
Heuristic Method ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Routing Problem ◽  
Sweep Algorithm ◽  
A Company

CV. Jogja Transport is a company that distribute cakes “Sari Roti” in Yogyakarta, Indonesia. It has responsibility to distribute the cakes for every customer during the customers’ time windows. The distribution problem of CV. Jogja Transport belongs to Vehicle Routing Problem with Time Window (VRPTW). This paper tries to solve the problem of CV. Jogja Transport by proposing “cluster first route second” algorithm of simple heuristic method. Then the algorithm is combined with sweep algorithm for clustering the customers and Mixed Integer Linear Programming (MILP) to select the best route so that it can minimize the distance of each cluster. The result indicate that implementation of sweep algorithm and MILP can reduce the distances and the fuel up to 10.95% and the travel distance up to 2.60%.

Multi Depot Probabilistic Vehicle Routing Problems with a Time Window

2013 ◽  
pp. 857-879
Author(s):  
Sutapa Samanta ◽  
Manoj K. Jha
Keyword(s):  
Vehicle Routing ◽  
Time Window ◽  
Vehicle Routing Problems ◽  
Problem Size ◽  
Routing Problems ◽  
Routing Problem ◽  
Logistics Systems ◽  
Time Window Constraints ◽  
Limiting Case ◽  
Crossover And Mutation

Vehicle Routing Problems (VRPs) are prevalent in all large pick up and delivery logistics systems and are critical to city logistics operations. Of notable significance are three key extensions to classical VRPs: (1) multi-depot scenario; (2) probabilistic demand; and (3) time-window constraints, which are considered simultaneously with VRPs in this paper. The issue then becomes a Multi Depot Probabilistic Vehicle Routing Problem with a Time Window (MDPVRPTW). The underlying complexities of MDPVRPTW are analyzed and a heuristic approach is presented to solve the problem. Genetic algorithms (GAs) are found to be capable of providing an efficient solution to the so-called MDPVRPTW. Within the GAs, two modification operators namely, crossover and mutation, are designed specially to solve the MDPVRPTW. Three numerical examples with 14, 25, and 51 nodes are presented to test the efficiency of the algorithm as the problem size grows. The proposed algorithms perform satisfactorily and the limiting case solutions are in agreement with the constraints. Additional work is needed to test the robustness and efficiency of the algorithms as the problem size grows.

Multi Depot Probabilistic Vehicle Routing Problems with a Time Window

2013 ◽  
pp. 251-273
Author(s):  
Sutapa Samanta ◽  
Manoj K. Jha
Keyword(s):  
Vehicle Routing ◽  
Time Window ◽  
Vehicle Routing Problems ◽  
Problem Size ◽  
Routing Problems ◽  
Routing Problem ◽  
Logistics Systems ◽  
Time Window Constraints ◽  
Limiting Case ◽  
Crossover And Mutation

Vehicle Routing Problems (VRPs) are prevalent in all large pick up and delivery logistics systems and are critical to city logistics operations. Of notable significance are three key extensions to classical VRPs: (1) multi-depot scenario; (2) probabilistic demand; and (3) time-window constraints, which are considered simultaneously with VRPs in this paper. The issue then becomes a Multi Depot Probabilistic Vehicle Routing Problem with a Time Window (MDPVRPTW). The underlying complexities of MDPVRPTW are analyzed and a heuristic approach is presented to solve the problem. Genetic algorithms (GAs) are found to be capable of providing an efficient solution to the so-called MDPVRPTW. Within the GAs, two modification operators namely, crossover and mutation, are designed specially to solve the MDPVRPTW. Three numerical examples with 14, 25, and 51 nodes are presented to test the efficiency of the algorithm as the problem size grows. The proposed algorithms perform satisfactorily and the limiting case solutions are in agreement with the constraints. Additional work is needed to test the robustness and efficiency of the algorithms as the problem size grows.

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