Introduction to stochastic programming

https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2011-62.pdf

A review of dynamic vehicle routing problems

The vehicle routing problem

Green Logistics has emerged as the new agenda item in supply chain management. The traditional objective of distribution management has been upgraded to minimizing system-wide costs related to economic and environmental issues. Reflecting the environmental sensitivity of vehicle routing problems (VRP), an extensive literature review of Green Vehicle Routing Problems (GVRP) is presented. We provide a classification of GVRP that categorizes GVRP into Green-VRP, Pollution Routing Problem, VRP in Reverse Logistics, and suggest research gaps between its state and richer models describing the complexity in real-world cases. The purpose is to review the most up-to-date state-of-the-art of GVRP, discuss how the traditional VRP variants can interact with GVRP and offer an insight into the next wave of research into GVRP. It is hoped that OR/MS researchers together with logistics practitioners can be inspired and cooperate to contribute to a sustainable industry.

Survey of Green Vehicle Routing Problem: Past and future trends

Variable neighbourhood search (VNS) is a metaheuristic, or a framework for building heuristics, based upon systematic changes of neighbourhoods both in descent phase, to find a local minimum, and in perturbation phase to emerge from the corresponding valley It was first proposed in 1997 and has since then rapidly developed both in its methods and its applications In the present paper, these two aspects are thoroughly reviewed and an extensive bibliography is provided Moreover, one section is devoted to newcomers It consists of steps for developing a heuristic for any particular problem Those steps are common to the implementation of other metaheuristics

Variable neighbourhood search: methods and applications

When a vehicle routing model is cast and solved, it is normally assumed that the values of all input parameters are known with certainty. However, this is hardly the case in reallife applications where parameters such as customer demands, travel and service times, or even the information of whether a particular customer will require service or not are often incomplete, uncertain, or unknown during the route design phase (see Gounaris et al. [60]). As pointed out by Psaraftis [123], there are two important dimensions of input data, namely evolution and quality of information. The former implies that the available information is subject to change even after the routing plan is realized, while the latter reflects the possible uncertainties in the available data. What is common in both cases is that the partially known, uncertain, or unknown input parameters are revealed or updated concurrently with the execution of the routing process. Depending on the availability and quality of a priori information and other characteristics of the problem under consideration, two alternatives emerge for solving the routing problem. Assuming that sufficient information is available (e.g., all input data are known in advance with a predefined degree of uncertainty), the first option is to treat the problem as static and solve it once during the design phase. The goal in this case is to obtain a robust routing plan that will possibly be subject to relatively small changes during the actual execution. This option is named a priori optimization, for which anticipating uncertainty is crucial in order to find realizable routing plans, and to avoid hefty penalties, both economic and reputational, when one fails to provide the required level of service (see Gounaris et al. [60]). The second option is to address the problem in an ongoing and dynamic fashion as new input data arrive or are revealed in real time. While information evolves and decisions must be continuously made in a changing environment, the goal is to react to the new events as well as to anticipate future events, particularly if exploitable stochastic

Dynamic Vehicle Routing Problems.

A hybrid evolution strategy for the open vehicle routing problem

This paper presents a solution methodology for the heterogeneous fleet vehicle routing problem with time windows. The objective is to minimize the total distribution costs, or similarly to determine the optimal fleet size and mix that minimizes both the total distance travelled by vehicles and the fixed vehicle costs, such that all problem's constraints are satisfied. The problem is solved using a two-phase solution framework based upon a hybridized Tabu Search, within a new Reactive Variable Neighborhood Search metaheuristic algorithm. Computational experiments on benchmark data sets yield high quality solutions, illustrating the effectiveness of the approach and its applicability to realistic routing problems.

A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows

In this paper, we consider the open vehicle routing problem with time windows (OVRPTW). The OVRPTW seeks to find a set of non-depot returning vehicle routes, for a fleet of capacitated vehicles, to satisfy customers’ requirements, within fixed time intervals that represent the earliest and latest times during the day that customers’ service can take place. We formulate a comprehensive mathematical model to capture all aspects of the problem, and incorporate in the model all critical practical concerns. The model is solved using a greedy look-ahead route construction heuristic algorithm, which utilizes time windows related information via composite customer selection and route-insertion criteria. These criteria exploit the interrelationships between customers, introduced by time windows, that dictate the sequence in which vehicles must visit customers. Computational results on a set of benchmark problems from the literature provide very good results and indicate the applicability of the methodology in real-life routing applications.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2602143.pdf

Panagiotis P. Repoussis

Papers

Dynamic Vehicle Routing Problems.

A hybrid evolution strategy for the open vehicle routing problem

A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows

The open vehicle routing problem with time windows

Personalized multi-period tour recommendations