Advances in information technology and telecommunications, together with ever growing amounts of data, offer opportunities for transportation companies to improve the quality of the service that they provide to their customers. This paper compares two methods motivated by the opportunity that the availability of data and technology gives to improve on current practice. In particular, the two solution approaches are explored in the context of a dynamic and stochastic routing problem in which a single, uncapacitated vehicle serves a set of known customers locations. One approach, sample-scenario planning, offers the potential for higher quality solutions, but at the expensive of greater computational effort. On the other hand, anticipatory insertion offers reduced computation and increased managerial ease, but with the potential for reduced solution quality due to restrictions on solution structure. Our results show that anticipatory insertion can often match the quality of sample-scenario planning, particularly when the degree of dynamism is low.

Urban waste management is becoming an increasingly complex task, absorbing a huge amount of resources, and having a major environmental impact. The design of a waste management system consists in various activities, and one of these is related to the location of waste collection sites. In this paper, we propose an integer programming model that helps decision makers in choosing the sites where to locate the unsorted waste collection bins in a residential town, as well as the capacities of the bins to be located at each collection site. This model helps in assessing tactical decisions through constraints that force each collection area to be capacitated enough to fit the expected waste to be directed to that area, while taking into account Quality of Service constraints from the citizens’ point of view. Moreover, we propose an effective constructive heuristic approach whose aim is to provide a good solution quality in an extremely reduced computational time. Computational results on data related to the city of Nardò, in the south of Italy, show that both exact and heuristic approaches provide consistently better solutions than that currently implemented, resulting in a lower number of activated collection sites, and a lower number of bins to be used.

Nowadays, route planning algorithms are commonly used to generate detailed work schedules for solid waste collection vehicles. However, the reliability of such schedules relies heavily on the accuracy of a number of parameters, such as the actual service time at each collection location and the traversal times of the streets (which depend on the specific day of the week and the time of day). In this paper, we propose an automated classification and estimation algorithm that, based on Global Positioning System data collected by the fleet, estimates such parameters in a timely and accurate fashion. In particular, our approach is able to classify automatically events like stops due to traffic jams, stops at traffic lights and stops at collection sites. The system can also be used for automated fleet supervision and in order to notify on a web site whether certain services have been actually provided on a certain day, thus making waste management more accountable to citizens. An experimentation carried out in an Italian municipality shows the advantages of our approach.

In this paper, we study an assembly job shop scheduling problem with tree-structured precedence constraints and jobs characterized by specific bills of materials. We propose a mathematical model to deal with a simplified version of the problem, as well as a fast and efficient constructive heuristic that is able to easily face real-world-sized instances. The production schedule takes into account the actual availability of materials in stock as well as the supply times and the capacity constraints, with the goal to minimize the average delay with respect to the due dates associated to the customers' orders. Computational results on data related to real-life instances show that the mathematical model is able to solve (not always to optimality) small-sized instances only. On the other hand, our heuristic approach is able to solve efficiently very large problems. Moreover, the proposed heuristic turns out to be scalable as the instance size grows.

The definition of a suitable neighborhood structure on the solution space is a key step when designing a heuristic for Mixed Integer Programming (MIP). In this paper, we move on from a MIP compact formulation and show how to take advantage of its features to automatically design efficient neighborhoods, without any human analysis. In particular, we use unsupervised learning to automatically identify "good" regions of the search space "around" a given feasible solution. Computational results on compact formulations of three well-known combinatorial optimization problems show that, on large instances, the neighborhoods constructed by our procedure outperform state-of-the-art domain-independent neighborhoods.

Solid waste management (SWM) is an increasingly complex task, absorbing a huge amount of resources and having a major environmental impact. Computerized systems based on operations research techniques can help decision makers to achieve remarkable cost savings as well as to improve waste recovery. Nevertheless, the literature is quite scattered and disorganized. The objective of this paper is to present an updated survey of the most relevant operations research literature on SWM, mainly focusing on strategic and tactical issues. In addition to providing an extensive bibliographic coverage, we describe the relationships between the various problems, and outline future research.

For many NP-hard combinatorial optimization problems, the existence of constraints complicates the implementation of a heuristic search procedure. In this paper, we propose a general heuristic framework that extends the well known Variable Neighborhood Search algorithm to include dynamic constraint penalization. We specifically focus on what are known as scheduled penalty methods and call the new algorithm scheduled-penalty Variable Neighborhood Search. The proposed method is tested on two well known constrained combinatorial optimization problems, namely the Traveling Salesman Problem with Time Windows and the Orienteering Problem with Time Windows. Our results demonstrate the effectiveness of the proposed algorithm, which is capable of producing high-quality solutions to the well known benchmark problems chosen in this paper with only minimal problem-specific tailoring of the general algorithm. Moreover, we introduce new best known solutions for some instances from the orienteering problem with time windows literature.

This paper deals with the problem of minimizing the sta±ng cost of a same-day courier company subject to service-level requirements. The problem has been modeled as an integer program with non linear probabilistic constraints. We have devised a heuristic procedure able to explore the search space e±ciently through an approximated neighborhood evaluation (ANE) model, relying on the estimation (via simulation) of a reduced number of parameters. Computational results show that, compared to traditional neighborhood search procedures, the ANE approach provides signi¯cant cost reductions in a typical same-day courier setting.

Urban waste management is becoming an increasingly complex task, absorbing a huge amount of resources, and having a major environmental impact. The design of a waste management system consists in various activities, and one of these is related to the definition of shift schedules for both personnel and vehicles. This activity has a great incidence on the tactical and operational cost for companies. In this paper, we propose an integer programming model to find an optimal solution to the integrated problem. The aim is to determine optimal schedules at minimum cost. Moreover, we design a fast and effective heuristic to face large-size problems. Both approaches are tested on data from a real-world case in Southern Italy and compared to the current practice utilized by the company managing the service, showing that simultaneously solving these problems can lead to significant monetary savings.

In this paper, we study two decisional problems arising when planning the collection of solid waste, namely the location of collection sites (together with bin allocation) and the zoning of the service territory, and we assess the potential impact that an efficient location has on the subsequent zoning phase. We first propose both an exact and a heuristic approach to locate the unsorted waste collection bins in a residential town, and to decide the capacities and characteristics of the bins to be located at each collection site. A peculiar aspect we consider is that of taking into account the compatibility between the different types of bins when allocating them to collection areas. Moreover, we propose a fast and effective heuristic approach to identify homogeneous zones that can be served by a single collection vehicle. Computational results on data related to a real-life instance show that an efficient location is fundamental in achieving consistent monetary savings, as well as a reduced environmental impact. These reductions are the result of one vehicle less needed to perform the waste collection operations, and an overall traveled distance reduced by about 25% on the average.