Frédéric Semet

Ambulance location and relocation models

This article traces the evolution of ambulance location and relocation models proposed over the past 30years. The models are classified in two main categories. Deterministic models are used at the planning stage and ignore stochastic considerations regarding the availability of ambulances. Probabilistic models reflect the fact that ambulances operate as servers in a queueing system and cannot always answer a call. In addition, dynamic models have been developed to repeatedly relocate ambulances throughout the day. 2002 Elsevier Science B.V. All rights reserved.

A guide to vehicle routing heuristics

Several of the most important classical and modern heuristics for the vehicle routing problem are summarized and compared using four criteria: accuracy, speed, simplicity and flexibility. Computational results are reported.

A dynamic model and parallel tabu search heuristic for real-time ambulance relocation

Abstract This paper considers the redeployment problem for a fleet of ambulances. This problem is encountered in the real-time management of emergency medical services. A dynamic model is proposed and a dynamic ambulance management system is described. This system includes a parallel tabu search heuristic to precompute redeployment scenarios. Simulations based on real-data confirm the efficiency of the proposed approach.

Multi-objective vehicle routing problems

Routing problems, such as the traveling salesman problem and the vehicle routing problem, are widely studied both because of their classic academic appeal and their numerous real-life applications. Similarly, the field of multi-objective optimization is attracting more and more attention, notably because it offers new opportunities for defining problems. This article surveys the existing research related to multi-objective optimization in routing problems. It examines routing problems in terms of their definitions, their objectives, and the multi-objective algorithms proposed for solving them.

Solving an ambulance location model by tabu search

Abstract This paper considers a double coverage ambulance location problem. A model is proposed and a tabu search heuristic is developed for its solution. Computational results using both randomly generated data and real data confirm the efficiency of the proposed approach.

A generalized linear programming model for nurse scheduling

Abstract This paper presents a 0–1 column generation model with a resource constrained shortest path auxiliary problem for nurse scheduling. The master problem finds a configuration of individual schedules to satisfy the demand coverage constraints while minimizing salary costs and maximizing both employee preferences and team balance. A feasible solution of the auxiliary problem is an acceptable schedule for a given nurse, with respect to collective agreement requirements such as seniority, workload, rotations and days off. We define a new resource structure in the auxiliary problem in order to take into account the complex collective agreement rules specific to the nurse scheduling problem. This model generalizes further the previous formulations discussed in the literature and can be viewed as a general scheme for complex personnel scheduling problems, especially in the context of organizations which operate around the clock. Solution methods and preliminary test results are discussed.

Solving real-life vehicle routing problems efficiently using tabu search

This paper presents a tabu search based method for finding good solutions to a real-life vehicle routing problem. The problem considered deals with some new features beyond those normally associated with the classical problems of the literature: in addition to capacity constraints for vehicles and time windows for deliveries, it takes the heterogeneous character of the fleet into account, in the sense that utilization costs are vehicle-dependent and that some accessibility restrictions have to be fulfilled. It also deals with the use of trailers. In spite of the intricacy of the problem, the proposed tabu search approach is easy to implement and can be easily adapted to many other applications. An emphasis is placed on means that have to be used to speed up the search. In a few minutes of computation on a personal workstation, our approach obtains solutions that are significantly better than those previously developed and implemented in practice.

THE COVERING TOUR PROBLEM.

The Covering Tour Problem (CTP) is defined on a graph G = (V ∪ W, E), where W is a set of vertices that must be covered. The CTP consists of determining a minimum length Hamiltonian cycle on a subset of V such that every vertex of W is within a prespecified distance from the cycle. The problem is first formulated as an integer linear program, polyhedral properties of several classes of constraints are investigated, and an exact branch-and-cut algorithm is developed. A heuristic is also described. Extensive computational results are presented.

A tabu search heuristic for the undirected selective travelling salesman problem

The undirected Selective Travelling Salesman Problem (STSP) is defined on a graph G=(V, E) with positive profits associated with vertices, and distances associated with edges. The STSP consists of determining a maximal profit Hamiltonian cycle over a subset of V whose length does not exceed a preset limit L. We describe a tabu search (TS) heuristic for this problem. The algorithm iteratively inserts clusters of vertices in the current tour or removes a chain of vertices. Tests performed on randomly generated instances with up to 300 vertices show that the algorithm consistently yields near-optimal solutions.

The maximal expected coverage relocation problem for emergency vehicles

In the Maximal Expected Coverage Relocation Problem the aim is to provide a dynamic relocation strategy for emergency vehicle waiting sites in such a way that the expected covered demand is maximized and the number of waiting site relocations is controlled. The problem can be formulated as an integer linear program. When the number of vehicles is relatively small this program can be solved within reasonable computing time. Simulations conducted with real-life emergency medical services data from the Montreal area confirm the feasibility of the proposed approach.