Nacima Labadie

The Team Orienteering Problem with Time Windows: An LP-based Granular Variable Neighborhood Search

The Team Orienteering Problem (TOP) is a known NP-hard problem that typically arises in vehicle routing and production scheduling contexts. In this paper we introduce a new solution method to solve the TOP with hard Time Window constraints (TOPTW). We propose a Variable Neighborhood Search (VNS) procedure based on the idea of exploring, most of the time, granular instead of complete neighborhoods in order to improve the algorithm’s efficiency without loosing effectiveness. The method provides a general way to deal with granularity for those routing problems based on profits and complicated by time constraints. Extensive computational results are reported on standard benchmark instances. Performance of the proposed algorithm is compared to optimal solution values, when available, or to best known solution values obtained by state-of-the-art algorithms. The method comes out to be, on average, quite effective allowing to improve the best know values for 25 test instances.

Hybridized evolutionary local search algorithm for the team orienteering problem with time windows

The orienteering problem (OP) consists in finding an elementary path over a subset of vertices. Each vertex is associated with a profit that is collected on the visitor’s first visit. The objective is to maximize the collected profit with respect to a limit on the path’s length. The team orienteering problem (TOP) is an extension of the OP where a fixed number m of paths must be determined. This paper presents an effective hybrid metaheuristic to solve both the OP and the TOP with time windows. The method combines the greedy randomized adaptive search procedure (GRASP) with the evolutionary local search (ELS). ELS generates multiple distinct child solutions using a mutation mechanism. Each child solution is further improved by a local search procedure. GRASP provides multiple starting solutions to the ELS. The method is able to improve several best known results on available benchmark instances.

Handling uncertainty in agricultural supply chain management: A state of the art

Given the evolution in the agricultural sector and the new challenges it faces, managing agricultural supply chains efficiently has become an attractive topic for researchers and practitioners. Against this background, the integration of uncertain aspects has continuously gained importance for managerial decision making since it can lead to an increase in efficiency, responsiveness, business integration, and ultimately in market competitiveness. In order to capture appropriately the uncertain conjuncture of most agricultural real-life applications, an increasing amount of research effort is especially dedicated to treating uncertainty. In particular, quantitative modeling approaches have found extensive use in agricultural supply chain management. This paper provides an overview of the latest advances and developments in the application of operations research methodologies to handling uncertainty occurring in the agricultural supply chain management problems. It seeks to: (i) offer a representative overview of the predominant research topics, (ii) highlight the most pertinent and widely used frameworks, and (iii) discuss the emergence of new operations research advances in the agricultural sector. The broad spectrum of reviewed contributions is classified and presented with respect to three most relevant discerned features: uncertainty modeling types, programming approaches, and functional application areas. Ultimately, main review findings are pointed out and future research directions which emerge are suggested.

A GRASP × ILS for the vehicle routing problem with time windows, synchronization and precedence constraints

This article addresses a new routing problem encountered in the field of Home Health Care. Researches in this emerging area share the goal of establishing fine coordination to optimize the planning of human and material resources to provide support and quality monitoring while controlling costs. Home Health Care services allow keeping patients who are not entirely dependent at home, or to facilitate the return to normal life for people who have suffered serious illnesses by permitting them to leave traditional health institutions. They often need specific cares provided by caregivers. Usually, these services must be performed at specific times, and require the intervention of several caregivers, sometimes linked by precedence constraints. By associating the patients to customers and caregivers to vehicles, the problem introduced in this study can be seen as a particular vehicle routing problem with time windows and side timing constraints, where some patients require more than one visit simultaneously or in a given priority order. To solve this problem, a mixed integer programming model is presented and a meta-heuristic method based on a Greedy Randomized Adaptive Procedure (GRASP) is proposed. Numerical results are shown on a new benchmark derived from the literature.

Modelling and optimisation of biomass supply chains: a review

Abstract A growing number of researchers are attracted by the domain of bioenergies, due to the problems induced by greenhouse gas emissions and increasing energy demand. One possible way of producing biofuels in a renewable way is to use biomass, however the economic viability of a biorefinery system depends critically on the cost of its supplies. As biomass is not very expensive, logistics is responsible of an important fraction of this cost. Models and methods to optimise biomass supply chains are surveyed in this article, after introducing some technical terms and describing the main activities in these chains. This review aims to give a comprehensive overview of the research in this field with a focus on optimisation modelling issues and solution approaches. Recent advances in the current research and possible new directions are sketched.

A quality risk management problem: case of annual crop harvest scheduling

This paper presents a stochastic optimisation model for the annual harvest scheduling problem of the farmers’ entire cereal crop production at optimum maturity. Gathering the harvest represents an important stage for both agricultural cooperatives and individual farmers due to its high cost and considerable impact on seed quality and yield. The meteorological conditions represent the deciding factor that affects the harvest scheduling and progress. Using chance-constrained programming, a mixed-integer probabilistically constrained model is proposed, with a view to minimising the risk of crop quality degradation under climate uncertainty with a safe confidence level. The chance-constrained optimisation problem is tackled and solved via an equivalent linear mixed-integer reformulation jointly with scenario-based approaches. Moreover, a new concept of -scenario pertinence is introduced in order to defy efficiently the probabilistically constrained problem complexity and time limitations. From the practical standpoint, this study is aimed at helping an agricultural cooperative in decision-making on crop quality risk management and harvest scheduling over a medium time horizon (10–15 time periods).

The bi-objective two-dimensional loading vehicle routing problem with partial conflicts

The two-dimensional loading vehicle routing problem with partial conflicts combines two NP-hard problems: the classical vehicle routing problem and the two-dimensional bin-packing problem with partial conflicts. This new variant of transportation problems is inspired from hazardous materials classification and compatibilities, where some materials can be partially conflicting. In this case, they can be loaded together but an additional constraint on the distance separating them must be respected. We propose in this paper an NSGA-II algorithm to perform a bi-objective study in which the minimization of the total cost of transportation as well as the load balancing between different routes in terms of used area of vehicles are the considered objectives. The first results for this new problem are presented, using benchmarks available in the literature that have been adapted to deal with the problem. Moreover, the adding value of path relinking is proved with different statistical measurements.

On the combined maintenance and routing optimization problem

This work focuses on the problem of planning and scheduling maintenance operations for a set of geographically distributed machines, subject to non-deterministic failures with a set of technicians that perform preventive maintenance and repair operations on the machines at the customer sites within a specific time window. This study presents a two-step iterative approach. In the first step, a maintenance model determines the optimal time until the next preventive maintenance operation, its frequency, and the time window for each customer, while minimizing the total expected maintenance costs. In the second step, a routing model assigns and schedules maintenance operations to each technician over the planning horizon within the workday. This two-step iterative process balances the maintenance cost, the failure probabilities, and waiting times at each customer. The novelty of this work lies in the integration of maintenance scheduling and a routing model that considers several machines.

Iterated Local Search for a Vehicle Routing Problem with Synchronization Constraints

This paper deals with vehicle routing problem (VRP) with synchronization constraints. This problem consists in determining a least-cost set of routes to serve customers who may require several synchronized visits. The main contribution of the paper is: 1) it presents a definition and a classification of different types of synchronization constraints considered in the VRP literature; 2) it describes a variant of vehicle routing problem with synchronization constraints which is formulated as a mixed integer programming model; 3) finally, it provides a constructive heuristics and an iterated local search metaheuristic to solve the considered problem. The performance of the proposed approaches is evaluated on small and medium sized instances.

Vehicle Routing Nowadays: Compact Review and Emerging Problems

Due to their importance in logistics, vehicle routing problems have been intensively investigated by the operations research community during the last five decades. The two first goals of this chapter are to provide key references about the two core problems in node routing, the capacitated vehicle routing problem (CVRP) and the capacitated arc routing problem (CARP), and to present the more complex extensions handled in the last decade. The other contributions are an introduction to rich vehicle routing problems, a description of emerging problems in the service industry and a presentation of a few applications in developing countries.