Sophie N. Parragh

Variable neighborhood search for the dial-a-ride problem

In dial-a-ride problems passengers have to be transported between pre-specified pickup and delivery locations under user inconvenience considerations. The problem variant considered in this paper aims at minimizing total routing costs while respecting maximum route duration limits, time windows, and maximum user ride time limits. We propose a competitive variable neighborhood search-based heuristic, using three classes of neighborhoods. The first neighborhood class uses simple swap operations tailored to the dial-a-ride problem; the second neighborhood class is based on the ejection chain idea; and the third neighborhood class exploits the existence of arcs where the vehicle load is zero, giving rise to natural sequences of requests. We report new best results for 16 out of 20 benchmark instances.

Adaptive large neighborhood search for service technician routing and scheduling problems

Motivated by the problem situation faced by infrastructure service and maintenance providers, we define the service technician routing and scheduling problem with and without team building: a given number of technicians have to complete a given number of service tasks. Each technician disposes of a number of skills at different levels and each task demands technicians that provide the appropriate skills of at least the demanded levels. Time windows at the different service sites have to be respected. In the case where a given task cannot be serviced by any of the technicians, outsourcing costs occur. In addition, in some companies technicians have to be grouped into teams at the beginning of the day since most of the tasks cannot be completed by a single technician. The objective is to minimize the sum of the total routing and outsourcing costs. We solve both problem versions by means of an adaptive large neighborhood search algorithm. It is tested on both artificial and real-world instances; high quality solutions are obtained within short computation times.

Hybrid column generation and large neighborhood search for the dial-a-ride problem

Demographic change towards an ever aging population entails an increasing demand for specialized transportation systems to complement the traditional public means of transportation. Typically, users place transportation requests, specifying a pickup and a drop off location and a fleet of minibuses or taxis is used to serve these requests. The underlying optimization problem can be modeled as a dial-a-ride problem. In the dial-a-ride problem considered in this paper, total routing costs are minimized while respecting time window, maximum user ride time, maximum route duration, and vehicle capacity restrictions. We propose a hybrid column generation and large neighborhood search algorithm and compare different hybridization strategies on a set of benchmark instances from the literature.

Models and algorithms for the heterogeneous dial-a-ride problem with driver-related constraints

This paper introduces models and algorithms for a static dial-a-ride problem arising in the transportation of patients by non-profit organizations such as the Austrian Red Cross. This problem is characterized by the presence of heterogeneous vehicles and patients. In our problem, two types of vehicles are used, each providing a different capacity for four different modes of transportation. Patients may request to be transported either seated, on a stretcher or in a wheelchair. In addition, some may require accompanying persons. The problem is to construct a minimum-cost routing plan satisfying service-related criteria, expressed in terms of time windows, as well as driver-related constraints expressed in terms of maximum route duration limits and mandatory lunch breaks. We introduce both a three-index and a set-partitioning formulation of the problem. The linear programming relaxation of the latter is solved by a column generation algorithm. We also propose a variable neighborhood search heuristic. Finally, we integrate the heuristic and the column generation approach into a collaborative framework. The column generation algorithm and the collaborative framework provide tight lower bounds on the optimal solution values for small-to-medium-sized instances. The variable neighborhood search algorithm yields high-quality solutions for realistic test instances.

A bi-objective home care scheduling problem: Analyzing the trade-off between costs and client inconvenience

Organizations providing home care services are inclined to optimize their activities in order to meet the constantly increasing demand for home care. In this context, home care providers are confronted with multiple, often conflicting, objectives such as minimizing their operating costs while maximizing the service level offered to their clients by taking into account their preferences. This paper is the first to shed some light on the trade-off relationship between these two objectives by modeling the home care routing and scheduling problem as a bi-objective problem. The proposed model accounts for qualifications, working regulations and overtime costs of the nurses, travel costs depending on the mode of transportation, hard time windows, and client preferences on visit times and nurses. A distinguishing characteristic of the problem is that the scheduling problem for a single route is a bi-objective problem in itself, thereby complicating the problem considerably. A metaheuristic algorithm, embedding a large neighborhood search heuristic in a multi-directional local search framework, is proposed to solve the problem. Computational experiments on a set of benchmark instances based on real-life data are presented. A comparison with exact solutions on small instances shows that the algorithm performs well. An analysis of the results reveals that service providers face a considerable trade-off between costs and client convenience. However, starting from a minimum cost solution, the average service level offered to the clients may already be improved drastically with limited additional costs.

The multi-objective generalized consistent vehicle routing problem

Abstract More and more companies in the routing industry are providing consistent service to gain competitive advantage. However, improved service consistency comes at the price of higher routing cost, i.e., routing cost and service consistency are conflicting objectives. In this paper, we extend the generalized consistent vehicle routing problem (GenConVRP) by considering several objective functions: improving driver consistency and arrival time consistency, and minimizing routing cost are independent objectives of the problem. We refer to the problem as the multi-objective generalized consistent vehicle routing problem (MOGenConVRP). A multi-objective optimization approach enables a thorough trade-off analysis between the conflicting objective functions. The results of this paper should help companies in finding adequate consistency goals to aim for. Results are generated for several test instances by two exact solution approaches and one heuristic. The exact approaches are based on the ϵ -constraint framework and are used to solve small test instances to optimality. Large instances with up to 199 customers and a planning horizon of 5 days are solved by multi directional large neighborhood search (MDLNS) that combines the multi directional local search framework and the LNS for the GenConVRP. The solution quality of the heuristic is evaluated by examining five multi-objective quality indicators. We find that MDLNS is an eligible solution approach for performing a meaningful trade-off analysis. Our analysis shows that a 70 percent better arrival time consistency is achieved by increasing travel cost by not more than 3.84 percent, on average; visiting each customer by the same driver each time is significantly more expensive than allowing at least two different drivers per customer; in many cases, arrival time consistency and driver consistency can be improved simultaneously.

Vehicle routing problems in which consistency considerations are important: A survey

An increasing number of companies focus on customer satisfaction to increase the lifetime value of each customer. In vehicle routing, customer satisfaction is often a result of consistent service. Customers appreciate service at regular times of the day provided by the same driver each time. Additionally, drivers become more familiar with their tasks if they visit the same customers and service regions repeatedly. In this article, we survey literature that addresses service consistency in vehicle routing. We present early solution approaches, starting from the 1970s, that focus on reducing the operational complexity resulting from planning and executing new routes each day. One side benefit of these approaches is service consistency; therefore, many recent solution approaches devised for improving customer satisfaction are based on previous achievements. We classify the literature according to three consistency features: arrival time consistency, person-oriented consistency, and delivery consistency. For each feature, we survey different modeling concepts and measurements, demonstrate solution approaches, and examine the increase in cost of improving service consistency. We close the article by presenting challenging ideas for future research.

A template-based adaptive large neighborhood search for the consistent vehicle routing problem

The importance of customer satisfaction was identified by many industries as a key factor of competitive advantage. So, for companies in the small package shipping industry, it can be reasonable to increase the service quality even at the expense of transportation cost to gain customer loyalty. These companies noticed that customer satisfaction can be increased by providing consistent service in the form of visiting customers with the same driver at approximately the same time of the day over a certain time period. Motivated by this real-world problem, the consistent vehicle routing problem ConVRP combines traditional vehicle routing constraints with the requirements for service consistency. This article presents a fast solution method called template-based adaptive large neighborhood search for the described problem. Compared to state-of-the-art heuristics, the developed algorithm is highly competitive on the available benchmark instances. Additionally, new test instances are provided. These seem to be more challenging due to the variation of different model parameters and consequently help to identify interesting effects. Finally, a relaxed variant of the original ConVRP is presented. In this variant, the departure times from the depot can be delayed to adjust the service times of the customers. Experiments show that allowing later departure times considerably improves the solution quality under tight consistency requirements.

The school bus routing and scheduling problem with transfers

In this article, we study the school bus routing and scheduling problem with transfers arising in the field of nonperiodic public transportation systems. It deals with the transportation of pupils from home to their school in the morning taking the possibility that pupils may change buses into account. Allowing transfers has several consequences. On the one hand, it allows more flexibility in the bus network structure and can, therefore, help to reduce operating costs. On the other hand, transfers have an impact on the service level: the perceived service quality is lower due to the existence of transfers; however, at the same time, user ride times may be reduced and, thus, transfers may also have a positive impact on service quality. The main objective is the minimization of the total operating costs. We develop a heuristic solution framework to solve this problem and compare it with two solution concepts that do not consider transfers. The impact of transfers on the service level in terms of time loss (or user ride time) and the number of transfers is analyzed. Our results show that allowing transfers reduces total operating costs significantly while average and maximum user ride times are comparable to solutions without transfers.

A multi-criteria large neighbourhood search for the transportation of disabled people

This paper addresses the problem of optimizing the transportation of disabled persons from home to specialized centres or schools. It is modelled as a Dial-a-ride problem (DARP), where several people share the same destination. Particular emphasis is placed on the objective function in order to consider several potentially conflicting interests. We propose a multi-criteria model from Multi-attribute Utility Theory based on the Choquet integral. The resulting multi-criteria DARP is then solved with a large neighbourhood search algorithm. This method includes classical destroy and repair heuristics as well as new operators exploiting the shared destination feature and criterion-specific operators. The algorithm is evaluated on a set of 14 real-world instances in the field of health care logistics, with up to 200 requests and 51 destination points.