Lei Wu

Lei Wu
Zhongnan University of Economics and Law · School of Public Finance and Taxation

PhD

About

25
Publications
5,487
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
255
Citations
Citations since 2016
9 Research Items
209 Citations
2016201720182019202020212022010203040
2016201720182019202020212022010203040
2016201720182019202020212022010203040
2016201720182019202020212022010203040
Additional affiliations
September 2011 - present
Université de Picardie Jules Verne
Position
  • Maître de conférences

Publications

Publications (25)
Article
In this paper, we propose a data-driven robust optimization for establishing reliable itineraries through the use of GPS trajectories. The goal of the study is to provide a robust solution that is able to maximize the probability of achieving the expected travel time and minimize the delay. The designed framework can be viewed as an incremental app...
Article
In this paper we propose a robust optimization strategy for the vehicle routing problem with time windows, where the travel time is considered as uncertain. The objective is to minimize the risk of delay related to the time windows constraint. We first present a robust model in which the uncertain travel time is related to a discrete set of scenari...
Article
This article investigates a new robust criterion for the vehicle routing problem with uncertainty on the travel time. The objective of the proposed criterion is to find a robust solution which displays better behaviour on a majority of scenarios, where each scenario represents a potential state of an uncertain event. In order to highlight the robus...
Article
Full-text available
New information technology constantly improves the efficiency of social networks. Using optimization and decision models in the context of large data sets attracts extensive attention. This paper investigates a novel mathematical model for designing and optimizing environmental economic policies in a protection zone. The proposed model is referred...
Article
This article investigates the use of parallel computing for solving the disjunctively constrained knapsack problem. The proposed parallel computing model can be viewed as a cooperative algorithm based on a multi-neighbourhood search. The cooperation system is composed of a team manager and a crowd of team members. The team members aim at applying t...
Conference Paper
In this paper, we propose a parallel ant colony optimization based metaheuristic for solving the maximum-weight clique problem, which is a variation of the maximum clique problem. The advised parallel computing model is based on concept of cooperation among multiple ant colonies system. The cooperation system consists of a message center and a numb...
Article
We investigate the use of a parallel computing model for solving the disjunctively constrained knapsack problem. This parallel approach is based on a multi-neighborhood search. In this approach, search threads asynchronously exchange information about the best solutions and use the information to guide the search. The performance of the proposed me...
Article
This paper presents an exact algorithm for solving the knapsack sharing problem with common items. In literature, this problem is also denominated the Generalized Knapsack Sharing Problem (GKSP). The GKSP is NP-hard because it lays on the 0-1 knapsack problem and the knapsack sharing problem. The proposed exact method is based on a rigorous decompo...
Article
Full-text available
In this paper, we propose a heuristic based upon the large neighborhood search for the disjunctively constrained knapsack problem (DCKP). The proposed method combines a two-phase procedure and a large neighborhood search. First, the two-phase procedure is applied in order to provide a starting feasible solution for the large neighborhood search. Th...
Article
Full-text available
This article addresses a Lagrangian heuristic-based neighbourhood search for the multiple-choice multi-dimensional knapsack problem, an NP-hard combinatorial optimization problem. The problem is solved by using a cooperative approach that uses a local search for exploring a series of neighbourhoods induced from the Lagrangian relaxation. Each neigh...
Data
Full-text available
Conference Paper
Full-text available
In this paper we propose to solve the Vehicle Routing Problem with Time Windows (VRPTW) using a hybrid metaheuristic. The VRPTW is a bi-objective optimization problem where both the number of vehicles and the distance of the travel to use should be minimized. Because it is often difficult to optimize both objectives, we propose an approach that opt...
Conference Paper
Full-text available
In this paper we investigate the use of the large neighborhood search for solving the vehicle routing problem with two-dimensional loading constraints, an NP-hard combinatorial optimization problem. Such a problem may be viewed as the combination of two complementary well-known problems: two-dimensional bin-packing and capacitated vehicle routing....
Article
In this paper, we investigate the use of the large neighborhood search for solving the pickup and delivery problem with time windows. Such a problem may be viewed as a variant of the capacitated vehicle routing problem with time windows, where both precedence and coupling constraints are considered. The proposed method is based on the framework of...
Article
In this paper, we study the knapsack sharing problem (KSP), which is a variant of the well-known NP-Hard knapsack problem. We investigate the use of a dichotomous search-based exact method for solving the KSP. Such a method is based on decomposing the original problem into a series of minimizing and maximizing knapsack problems, where each of them...
Article
In this paper, we propose to solve the three-dimensional single bin-size bin packing problem (3D-SBSBPP) using a simple strategy based on integer linear programming (ILP) heuristics, without using any improvement based on metaheuristics. We first propose an ILP that is converted into a series of three-dimensional single knapsack problems (3D-SKP)....
Conference Paper
Full-text available
In this paper, we propose a heuristic based upon the large neighborhood search for the disjunctively constrained knapsack problem (DCKP). The proposed method combines a two-phase procedure and a large neighborhood search. First, the two-phase procedure is applied in order to provide a starting feasible solution for the large neighborhood search. Th...
Article
Full-text available
The multiple-choise multi-dimensional knapsack Problem (MMKP) is a problem wich can be encountered in real-world applications, such as service level agreement, model of allocation resources, or as a dynamic adaptation of system of resources for multimedia multi-sessions. In this paper, we investigate the use of a new model-based Lagrangian relaxati...
Article
In optimization, it is common to deal with uncertain and inaccurate factors which make it difficult to assign a single value to each parameter in the model. It may be more suitable to assign a set of values to each uncertain parameter. A scenario is defined as a realization of the uncertain parameters. In this context, a robust solution has to be a...
Article
Full-text available
In this paper we consider the three-dimensional bin packing problem (3D-BPP), when the bins are identical with the aim of minimizing the number of the used bins. We introduce a mixed-integer linear programming formulation (MILP1). Some special valid inequalities will be presented in order to improve the relaxed lower bound of MILP1. A large set of...
Conference Paper
In this paper we consider the three-dimensional bin packing problem (3D-SBSBPP) under the assumption that all the bins are of a single size. Such a problem is a well-known NP-hard problem and consists in packing a set of items in a minimal number of bins. First, we introduce a mixed-integer linear model to formulate the problem (MILP1). Some specia...
Article
Full-text available
En optimisation, les sources d’incertitude et d’imprécision sont nombreuses et rendent souvent difficiles l’affectation d’une unique valeur plausible à chacun des paramètres du modèle. Il peut alors être plus pertinent de retenir un ensemble de valeurs possibles pour chacun des paramètres. Un scénario est défini en choisissant une unique valeur dan...

Network

Cited By