Habib Chabchoub

Genetic algorithm with iterated local search for solving a location-routing problem

Highlights? Hybrid genetic algorithm and iterated local search outperforms tabu search. ? Local and routing decisions are solved simultaneously. ? Iterated local search refines the genetic algorithm through successive iterations. This paper deals with a location routing problem with multiple capacitated depots and one uncapacitated vehicle per depot. We seek for new methods to make location and routing decisions simultaneously and efficiently. For that purpose, we describe a genetic algorithm (GA) combined with an iterative local search (ILS). The main idea behind our hybridization is to improve the solutions generated by the GA using a ILS to intensify the search space. Numerical experiments show that our hybrid algorithm improves, for all instances, the best known solutions previously obtained by the tabu search heuristic.

A Hybrid Particle Swarm Optimization Algorithm for the Redundancy Allocation Problem

Abstract The Redundancy Allocation Problem generally involves the selection of components with multiple choices and redundancy levels that produce maximum system reliability given various system level constraints as cost and weight. In this paper we investigate the series–parallel redundant reliability problems, when a mixing of components was considered. In this type of problem both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximise the reliability of system. A hybrid algorithm is based on particle swarm optimization and local search algorithm. In addition, we propose an adaptive penalty function which encourages our algorithm to explore within the feasible region and near feasible region, and discourage search beyond that threshold. The effectiveness of our proposed hybrid PSO algorithm is proved on numerous variations of three different problems and compared to Tabu Search and Multiple Weighted Objectives solutions.

A ranking model in uncertain, imprecise and multi-experts contexts: The application of evidence theory

We consider ranking problems where the actions are evaluated on a set of ordinal criteria. The evaluation of each alternative with respect to each criterion may be imperfect and is provided by one or several experts. We model each imperfect evaluation as a basic belief assignment (BBA). In order to rank the BBAs characterizing the performances of the actions according to each criterion, a new concept called RBBD and based on the comparison of these BBAs to ideal or nadir BBAs is proposed. This is performed using belief distances that measure the dissimilarity of each BBA to the ideal or nadir BBAs. A model inspired by Xu et al.s method is also proposed and illustrated by a pedagogical example.

The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach

Abstract The increased awareness in just-in-time supply systems with the apparition of the new advances in communication and information technologies, have recently led researchers to focus on dynamic vehicle routing problem (DVRP). In DVRP, client requests are not known and are exposed after making some decisions. An alternative variant dynamic pickup and delivery strategy of the vehicle routing problem arises when new customers appear in the tours after the starting visit. The DVRP are among the more important and more challenging extensions of VRP. The purpose of this paper is to propose an Artificial Ant Colony based on 2_Opt local search (AAC_2_Opt) to solve the DVRP. We demonstrate the effectiveness of our approach by comparing its results with those of existing methods in the literature on the same tests problems. The AAC_2_Opt algorithm can efficiently optimize the routing problem and provide highly competitive solution.

A hybrid tabu search to solve the heterogeneous fixed fleet vehicle routing problem

One of the most significant problems of supply chain management is the distribution of products between locations. The delivery of goods from a warehouse to local customers is a critical aspect of material logistics. The Heterogeneous Fixed Fleet Vehicle Routing Problem (HFFVRP) is a variant of the Vehicle Routing Problem (VRP) that aims to provide service to a specific customer group with minimum cost using a limited number of vehicles. We assume that the number of vehicles is fixed. We must decide how to make the best use of the fixed fleet of vehicles. In this paper we describe a Tabu Search algorithm embedded in the Adaptive Memory (TSAM) procedure to solve the HFFVRP. Computational experiments indicating the performance of the algorithm concerning quality of solution and processing time are reported.

An Iterated Local Search for Solving A Location-Routing Problem

Abstract This paper focuses on a location routing problem with multiple capacitated depots. Our aim is to deal with this problem by considering the decisions of location and routing simultaneously. We develop an iterative local search to deal with this problem. The fundamental feature is to improve the solution successively by intensifying the solution space with the local search. A computational study is provided to compare our approach with the tabu search heuristic.

Building a binary outranking relation in uncertain, imprecise and multi-experts contexts: The application of evidence theory

We consider multicriteria decision problems where the actions are evaluated on a set of ordinal criteria. The evaluation of each alternative with respect to each criterion may be uncertain and/or imprecise and is provided by one or several experts. We model this evaluation as a basic belief assignment (BBA). In order to compare the different pairs of alternatives according to each criterion, the concept of first belief dominance is proposed. Additionally, criteria weights are also expressed by means of a BBA. A model inspired by ELECTRE I is developed and illustrated by a pedagogical example.

A combined approach for supply chain risk management: description and application to a real hospital pharmaceutical case study

Managing risks in supply chains has emerged as an important issue in supply chain management. This research area has become familiar as supply chain risk management (SCRM). There are numerous approaches and techniques that are proposed in SCRM literature, but little in concrete and systematic approach for SCRM. In this paper, a SCRM framework comprising of different techniques and specialized procedures is proposed that can assist supply chain decision makers to risk identification, assessment and management. The combined approach consists of including the following. (1) Failure mode, effects, and criticality analysis to identify risk. (2) design of experiment to design risks mitigation and action scenarios. (3) Discrete event simulation to assess risks mitigation action scenario. (4) analytic hierarchy process to evaluate risk management scenarios. (5) desirability function approach to minimize the risk. The proposed approach is illustrated through a real hospital pharmaceutical supply chain case study.

A variable neighborhood search for the capacitated location-routing problem

In this work, we present a variable neighborhood search for solving a capacitated location routing problem with capacitated depots and a capacitated homogenous vehicle fleet in order to minimize the location and routing cost. We present the basic elements for this purpose and implement the variable neighborhood search algorithm using the variable neighborhood descent as the local search. We tested our heuristic approach on a large number of instances and the results were compared with the literature.

Adaptive record-to-record travel method to solve lexicographic goal programming models

In this paper, a record-to-record travel (RRT) algorithm with an adaptive memory named taboo central memory (TCM) is adapted to solve the lexicographic goal programming problem. The proposed method can be applied to non-linear, linear, integer and combinatorial goal programmes. Because that the RRT has no memory, the adaptive memory TCM is inserted to diversify research. Computational experiments in several types of problems with different variable types (integer, continuous, zero-one and discrete) collected from the literature demonstrate that the proposed metaheuristic reaches high-quality solutions in short computational times. Furthermore, it requires very few user-defined parameters.