Nadia Lahrichi

A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems

We propose an algorithmic framework that successfully addresses three vehicle routing problems: the multidepot VRP, the periodic VRP, and the multidepot periodic VRP with capacitated vehicles and constrained route duration. The metaheuristic combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and advanced population-diversity management schemes. Extensive computational experiments show that the method performs impressively in terms of computational efficiency and solution quality, identifying either the best known solutions, including the optimal ones, or new best solutions for all currently available benchmark instances for the three problem classes. The proposed method also proves extremely competitive for the capacitated VRP.

A patient assignment algorithm for home care services

We consider the problem of assigning patients to nurses for home care services. The aim is to balance the workload of the nurses while avoiding long travels to visit the patients. We analyse the case of the CSSS Côte-des-Neiges, Métro and Parc Extension for which a previous analysis has shown that demand fluctuations may create work overload for the nursing staff. We propose a mixed integer programming model with some non-linear constraints and a non-linear objective which we solve using a Tabu Search algorithm. A simplification of the workload measure leads to a linear mixed integer program which we optimize using CPLEX.

Analysis of a territorial approach to the delivery of nursing home care services based on historical data

We analyze a territorial approach to deliver nursing homecare services to a territory public health. We present the case of the CSSS assigned to Côte-des-Neiges, Mètro center and Parc Extension, specifically the case of the Côte-des-Neiges site (CLSC CDN), where a territorial approach is used since 1980. We first give an historical comparison of patient visits delivered in 1998–1999 and in 2002–2003. We follow with an in-depth analysis of the home services delivered in 2002–2003 to determine whether or not the territorial approach can well support the changing needs of the population. We conclude that the territorial approach to deliver homecare nursing services does not sufficiently support fluctuations in population needs for services. Not only is it difficult to predict these fluctuations, but it is difficult to accurately quantify the true needs for services since the availability of nursing services tends to determine the services actually delivered. In sectors of the territory where resources are more scarce (based on previous population needs analyses) or demand for services is greater, the result is work overload for the nursing staff. In addition, this results in service delivery inequities across the entire territory. Therefore, a more dynamic assignment of clients to the nurses based on each nurses work load and case load rather than based on the geographic location of clients is worth the extra administrative time in case assignment to ensure a more equitable case load attribution between nurses as well as less inequities between clients in terms of service delivery considering their needs.

A flexible MILP model for multiple-shift workforce planning under annualized hours

Flexibility in workforce planning is one of the best ways to respond to fluctuations of the demand. This paper proposes a flexible mixed integer linear programming (MILP) model to solve a multiple-shift workforce planning problem under annualized working hours. The model takes into account laws and collective agreements that impose constraints on overtime and holidays. We consider possible gradual hiring of full time and partial time workers. Several objectives are pursued such as balancing the workload of the employees or minimizing the workforce size. Computational experiments on a real life problem demonstrate the effectiveness of the model.

Strategic Analysis of the Dairy Transportation Problem

The dairy transportation problem (DTP) consists of determining the best routes to be performed for collecting milk from farms and delivering it to processing plants. We study the particular case of the province of Quebec, where the Fédération des producteurs de lait du Québec is responsible for negotiating the transportation costs on behalf of producers. Several issues are highlighted in the actual process of designing contracts such as using historical data. We propose an approach based on scenario analysis that consists of revising both the steps and the information used to construct the routes. We develop a generalized tabu search algorithm that integrates the different characteristics of the DTP.

Online stochastic optimization of radiotherapy patient scheduling

The effective management of a cancer treatment facility for radiation therapy depends mainly on optimizing the use of the linear accelerators. In this project, we schedule patients on these machines taking into account their priority for treatment, the maximum waiting time before the first treatment, and the treatment duration. We collaborate with the Centre Intégré de Cancérologie de Laval to determine the best scheduling policy. Furthermore, we integrate the uncertainty related to the arrival of patients at the center. We develop a hybrid method combining stochastic optimization and online optimization to better meet the needs of central planning. We use information on the future arrivals of patients to provide an accurate picture of the expected utilization of resources. Results based on real data show that our method outperforms the policies typically used in treatment centers.

A concurrent evolutionary approach for rich combinatorial optimization

In this paper, we propose a meta-heuristic method based on the concurrent evolution of heterogeneous populations, decomposition/recomposition principles and specialized operators to address multi-attribute, rich, combinatorial optimization problems. We illustrate the method through an application to a rich Vehicle Routing Problem that considers duration and capacity constraints as well as time windows, multiple periods and multiple depots.

An integrative cooperative search framework for multi-decision-attribute combinatorial optimization: Application to the MDPVRP

We introduce the integrative cooperative search method (ICS), a multi-thread cooperative search method for multi-attribute combinatorial optimization problems. ICS musters the combined capabilities of a number of independent exact or meta-heuristic solution methods. A number of these methods work on sub-problems defined by suitably selected subsets of decision-set attributes of the problem, while others combine the resulting partial solutions into complete ones and, eventually, improve them. All these methods cooperate through an adaptive search-guidance mechanism, using the central-memory cooperative search paradigm. Extensive numerical experiments explore the behavior of ICS and its interest through an application to the multi-depot, periodic vehicle routing problem, for which ICS improves the results of the current state-of-the-art methods.

A two-stage solution method for the annual dairy transportation problem

The annual dairy transportation problem involves designing the routes that collect milk from farms and deliver it to processing plants. The demands of these plants can change from one week to the next, but the collection is fixed by contract and must remain the same throughout the year. While the routes are currently designed using the historical average demand from the plants, we show that including the information about plants demands leads to significant savings. We propose a two-stage method based on an adaptive large neighborhood search (ALNS). The first phase solves the transportation problem and the second phase ensures that the optimization of plant assignment is performed. An additional analysis based on period clustering is conducted to speed up the resolution.

The Nurse Scheduling Problem in Real-Life

The aim of this paper is to study the scheduling process for two types of nursing teams, regular teams from care units and the float team that covers for shortages in the hospital. When managers address this problem, they either use a manual approach or have to invest in expensive commercial tool. We propose a simple heuristic approach, flexible and easy enough to be implemented on spreadsheets, and requiring almost no investment. The approach leads to streamlined process and higher-quality schedules for nurses. The multi-objective model and heuristics are presented, and additional analysis is performed to compare the performance of the approach. We show that our approach compares very well with an optimization software (CPLEX solver) and may be implemented at no cost. It addresses the lack of choice between either manual solution method or a commercial package at a high cost.