scholarly journals Solving a Large-Scale Multi-Depot Vehicle Scheduling Problem in Urban Bus Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaomei Xu ◽  
Zhirui Ye ◽  
Jin Li ◽  
Chao Wang
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Set Partitioning ◽  
Vehicle Scheduling ◽  
Computational Time ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Solution Quality ◽  
Time Space ◽  
Improved Model

This study proposes an improved model and algorithm for the large-scale multi-depot vehicle scheduling problem (MDVSP) with departure-duration restrictions. In this study, the time-space network is applied to model the large-scale MDVSP. Considering that crews usually change shifts in the depot, departure-duration restrictions are added to the classic set-partitioning model to ensure that buses return to the depot when crews reach their working time limits. By embedding a preliminary exploring tactic to the shortest path faster algorithm (SPFA), researchers developed an improved large neighborhood search (LNS) algorithm to solve large-scale instances of MDVSP with departure-duration restrictions. The proposed methodology is applied to a real-life case in China and several test instances. The results show that the improved LNS algorithm can achieve very good performance in computational efficiency without deteriorating solution quality, which is important for large-scale systems. More specifically, the total cost of the improved LNS algorithm is approximately equal to branch-and-price, but the computational time is much shorter in the case study. For test instances with different number of timetabled trips (500, 1000, 1500, and 2000), the Quality Gap (QG) is very small, approximately 0.35%, 0.38%, 0.63%, and 0.93%, while the Efficiency Ratio (ER) reaches up to 2.89, 2.98, 3.65, and 3.79, respectively.

scholarly journals A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes

2020 ◽  
Vol 54 (5) ◽  
pp. 1467-1494
Author(s):  
Binhui Chen ◽  
Rong Qu ◽  
Ruibin Bai ◽  
Wasakorn Laesanklang
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Search Algorithm ◽  
Real Life ◽  
Planning Horizon ◽  
Neighborhood Search ◽  
Variable Neighbourhood Search ◽  
Container Transportation ◽  
Solution Quality ◽  
Routing Problem

This paper studies a real-life container transportation problem with a wide planning horizon divided into multiple shifts. The trucks in this problem do not return to depot after every single shift but at the end of every two shifts. The mathematical model of the problem is first established, but it is unrealistic to solve this large scale problem with exact search methods. Thus, a Variable Neighbourhood Search algorithm with Reinforcement Learning (VNS-RLS) is thus developed. An urgency level-based insertion heuristic is proposed to construct the initial solution. Reinforcement learning is then used to guide the search in the local search improvement phase. Our study shows that the Sampling scheme in single solution-based algorithms does not significantly improve the solution quality but can greatly reduce the rate of infeasible solutions explored during the search. Compared to the exact search and the state-of-the-art algorithms, the proposed VNS-RLS produces promising results.

scholarly journals Solving the Short-Term Scheduling Problem of Hydrothermal Systems via Lagrangian Relaxation and Augmented Lagrangian

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Rafael N. Rodrigues ◽  
Edson L. da Silva ◽  
Erlon C. Finardi ◽  
Fabricio Y. K. Takigawa
Keyword(s):  
Lagrangian Relaxation ◽  
Large Scale ◽  
Augmented Lagrangian ◽  
Augmented Lagrangian Method ◽  
Real Life ◽  
Hydrothermal Systems ◽  
Bundle Method ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Short Term

This paper addresses the short-term scheduling problem of hydrothermal power systems, which results in a large-scale mixed-integer nonlinear programming problem. The objective consists in minimizing the operation cost over a two-day horizon with a one-hour time resolution. To solve this difficult problem, a Lagrangian Relaxation (LR) based on variable splitting is designed where the resulting dual problem is solved by a Bundle method. Given that the LR usually fails to find a feasible solution, we use an inexact Augmented Lagrangian method to improve the quality of the solution supplied by the LR. We assess our approach by using a real-life hydrothermal configuration extracted from the Brazilian power system, proving the conceptual and practical feasibility of the proposed algorithm. In summary, the main contributions of this paper are (i) a detailed and compatible modelling for this problem is presented; (ii) in order to solve efficiently the entire problem, a suitable decomposition strategy is presented. As a result of these contributions, the proposed model is able to find practical solutions with moderate computational burden, which is absolutely necessary in the modern power industry.

scholarly journals Variable Neighborhood Search for Parallel Machines Scheduling Problem with Step Deteriorating Jobs

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Wenming Cheng ◽  
Peng Guo ◽  
Zeqiang Zhang ◽  
Ming Zeng ◽  
Jian Liang
Keyword(s):  
Variable Neighborhood Search ◽  
Parallel Machines ◽  
Programming Model ◽  
Search Algorithm ◽  
Optimal Schedule ◽  
Deteriorating Jobs ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Scheduling Problem

In many real scheduling environments, a job processed later needs longer time than the same job when it starts earlier. This phenomenon is known as scheduling with deteriorating jobs to many industrial applications. In this paper, we study a scheduling problem of minimizing the total completion time on identical parallel machines where the processing time of a job is a step function of its starting time and a deteriorating date that is individual to all jobs. Firstly, a mixed integer programming model is presented for the problem. And then, a modified weight-combination search algorithm and a variable neighborhood search are employed to yield optimal or near-optimal schedule. To evaluate the performance of the proposed algorithms, computational experiments are performed on randomly generated test instances. Finally, computational results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time even for large-sized problems.

scholarly journals Solving balanced multi-weighted attribute set partitioning problem with variable neighborhood search

Filomat ◽  
2019 ◽  
Vol 33 (9) ◽  
pp. 2875-2891
Author(s):  
Dusan Dzamic ◽  
Bojana Cendic ◽  
Miroslav Maric ◽  
Aleksandar Djenic
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Variable Neighborhood Search ◽  
Heuristic Method ◽  
Set Partitioning ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Computationally Efficient ◽  
Local Search Procedure ◽  
Problem Instances

This paper considers the Balanced Multi-Weighted Attribute Set Partitioning (BMWASP) problem which requires finding a partition of a given set of objects with multiple weighted attributes into a certain number of groups so that each attribute is evenly distributed amongst the groups. Our approach is to define an appropriate criterion allowing to compare the degree of deviation from the ?perfect balance? for different partitions and then produce the partition that minimizes this criterion. We have proposed a mathematical model for the BMWASP and its mixed-integer linear reformulation. We evaluated its efficiency through a set of computational experiments. To solve instances of larger problem dimensions, we have developed a heuristic method based on a Variable Neighborhood Search (VNS). A local search procedure with efficient fast swap-based local search is implemented in the proposed VNS-based approach. Presented computational results show that the proposed VNS is computationally efficient and quickly reaches all optimal solutions for smaller dimension instances obtained by exact solver and provide high-quality solutions on large-scale problem instances in short CPU times.

General lot-sizing and scheduling for perishable food products

2020 ◽  
Vol 54 (3) ◽  
pp. 913-931 ◽  
Author(s):  
Zohreh Alipour ◽  
Fariborz Jolai ◽  
Ehsan Monabbati ◽  
Nima Zaerpour
Keyword(s):  
Lot Sizing ◽  
Food Products ◽  
Production Costs ◽  
Mixed Integer ◽  
Computational Time ◽  
Scheduling Problem ◽  
Solution Quality ◽  
Perishable Product ◽  
Tactical Decision ◽  
Lot Sizing And Scheduling

General lot-sizing and scheduling is a well-studied problem in the literature, but for perishable or time-sensitive products is less investigated. Also, most of studies on perishable product supply chains focus on strategic and tactical decision levels rather than operational decision level and integrated operational and tactical decision levels. We focus on a general lot-sizing and scheduling problem faced by perishable food products. The lifespan and shelf life are two important key features of perishable products that are considered in the problem. This problem can be described as a multi-product, multi-parallel line, multi-period general lot-sizing and scheduling problem with sequence dependent change over time. The objective function is sum of production costs, inventory holding costs, waste costs, and lifespan related cost function. We apply two mixed-integer programming based heuristics to solve generated instances. The heuristics are compared in terms of solution quality and computational time. Also, the sensitivity analysis is presented to analyze the effects of parameters’ changes.

scholarly journals Uso da técnica de busca em vizinhança de grande porte para a programação da escala de motoristas de ônibus urbano

TRANSPORTES ◽  
2010 ◽  
Vol 18 (2) ◽  
Author(s):  
Gustavo Peixoto Silva ◽  
Claudio Barbieri da Cunha
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Crew Scheduling ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Search Technique ◽  
New Approach ◽  
Proposed Model ◽  
Belo Horizonte ◽  
The City

<p><strong>Resumo:</strong> Este artigo apresenta uma nova abordagem para a resolução do Problema de Programação de Tripulações no Sistema de Transporte Público (PPT). O modelo se baseia na metaheurística GRASP cuja busca local é realizada pelo método da Busca em Vizinhança de Grande Porte, conhecida na literatura como Very Large-Scale Neighborhood Search. O grande diferencial da aplicação desta técnica de busca para o PPT é que, além de incorporar os movimentos de realocação e troca de tarefas, realizados tradicionalmente, ela também permite considerar trocas do tipo 3-optimal, 4-optimal, até o limite de n-optimal, para uma solução com n tripulações. A implementação da heurística proposta foi testada com dados de problemas reais de uma empresa que opera em Belo Horizonte, e os resultados foram comparados com as soluções adotadas pela empresa. Desta forma foi possível observar que o modelo apresentado neste trabalho produziu soluções mais econômicas do que aquelas praticadas pela empresa.</p><strong>Abstract:</strong> This paper presents a new approach to solve the Crew Scheduling Problem (CSP) for public mass transport system. The proposed model is based on the GRASP metaheuristic framework, where the local search is performed by the Very Large-Scale Neighborhood (VLSN) search technique. The great differential of this search technique applied to the CSP is that, in addition to task reassigning and swapping movements, adopted in previous work, it also allows considering 3-optimal, 4-optimal, up to n-optimal task movements, for a solution with n crews, yielding to improved solutions. The proposed heuristic was tested with data from real problems of a bus company operating in the city of Belo Horizonte, and the results compared to the manual solution adopted by the company. Thus it was observed that the model presented in this work have produced more economical solutions than those used by the company.

Heuristic Model for Dynamic Single Machine Group Scheduling in Laser Cutting Job Shop to Minimize the Makespan

2011 ◽  
Vol 383-390 ◽  
pp. 6236-6241
Author(s):  
Nyeoh Cheng Ying ◽  
Mohzani Bin Mokhtar
Keyword(s):  
Single Machine ◽  
Laser Cutting ◽  
Group Technology ◽  
Dynamic Scheduling ◽  
Real Life ◽  
Setup Times ◽  
Computational Time ◽  
Scheduling Problem ◽  
Scheduling System ◽  
Dependent Setup Times

In today’s highly competitive market, laser cutting which has a characteristic of “make to order” and high product variety is under pressure to reduce costs, to increase productivity and to respond to the rapidly changing demands from customers. To maintain the competitive advantage, companies need to have a real-time dynamic scheduling system, which can handle large combinations of jobs, allowing sequencing of jobs to achieve multi-objective goals. Motivated by a real-life scheduling problem in a sheet metal processing company in Malaysia, this research addressed single machine scheduling problem with sequence-dependent setup times and group technology assumption to minimize makespan and with the secondary objective of minimizing setup times. The focus of this paper is on developing a simple heuristic algorithm based dynamic scheduling system. This algorithm has been coded in vb.net and is integrated with a database system. The scheduling system developed is verified and validated by comparing to the actual production run. Results show that the algorithm model can find good solutions within short computational time.

Sign in / Sign up

Export Citation Format

Share Document