Variable-depth adaptive large meighbourhood search algorithm for Open Periodic Vehicle Routing Problem with time windows

The Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is a practical transportation routing and scheduling problem arising from real-world scenarios. It shares some common features with some classic VRP variants. The problem has a tightly constrained large-scale solution space and requires well balanced diversification and intensification in search. In Variable Depth Neighbourhood Search, large neighbourhood depth prevents the search from trapping into local optima prematurely, while small depth provides thorough exploitation in local areas. Considering the multi-dimensional solution structure and tight constraints in OPVRPTW, a Variable-Depth Adaptive Large Neighbourhood Search (VD-ALNS) algorithm is proposed in this paper. Contributions of four tailored destroy operators and three repair operators at variable depths are investigated. Comparing to existing methods, VD-ALNS makes a good trade-off between exploration and exploitation, and produces promising results on both small and large size benchmark instances

Freight Distribution Problems in Congested Urban Areas: Fast and Effective Solution Procedures to Time-Dependent Vehicle Routing Problems

Congestion is a common phenomenon in all medium to large cities of the world. Reliability of freight movement in urban areas is an important issue to manufacturing or service companies whose operation is based in just-in-time approaches. These companies tend to provide high value or time sensitive products/services. As congestion increases, carriers face increasing challenges to satisfy their time sensitive customers in an economical way. Route designs or schedules which require long computation times or ignore travel time variations will result in inefficient and suboptimal solutions. Poorly designed routes that lead freight vehicles into congested arteries and streets not only increases supply chain and logistics costs but also exacerbate externalities associated with freight traffic in urban areas such as greenhouse gases, air pollution, noise, and accidents. Whilst it is rarely possible to entirely avoid the impacts of congestion, it is feasible to schedule operations so that the effects of congestion are minimized. Better scheduling can be effectively supported by the advent of inexpensive and ubiquitous Information and Communication Technologies (ICT). The use of mobile phone technology and on-board routing devices allows fluid communication between truck drivers and fleet operators in real-time. In such a real time operation it becomes possible to dynamically reassign vehicles, including modifying the order in which customers are served and diverting a vehicle already en-route to service another customer. However, without fast routing methods that can take advantage of real time congestion information carriers cannot reap the benefits of real-time information. From the operational point of view, congestion creates a substantial variation in travel times during peak morning and evening hours. This is problematic for all vehicle routing models which rely on a constant value to represent vehicle speeds. And while the ubiquitous availability of real time traffic information allows drivers to reactively alter routes and customer service sequences to better cope with congestion, static routing models are unable to take advantage of these advances in real-time information provision in order to proactively find adequate routing solutions. In addition, changes in travel time caused by congestion cannot be accurately represented in static models. Research in time-dependent vehicle routing problem is comparatively meager and current solution methods are inadequate for practical carrier operations which need to provide fast solutions for medium to large instances. Even faster solution methods are essential to take advantage of real time information. The major aim of this proposal is to develop and evaluate new methods for vehicle routing in congested urban areas. The emphasis will be placed on improving the running time of the existing methods

Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows

The vehicle routing problem with time windows (VRPTW) is a non-deterministictime hard (NP-hard) with combinatorial optimization problem (COP). The Artificial Bee Colony (ABC) is a popular swarm intelligence algorithm for COP. In this study, existing Modified ABC (MABC) algorithm is revised to solve the VRPTW. While MABC has been reported to be successful, it does have some drawbacks, including a lack of neighbourhood structure selection during the intensification process, a lack of knowledge in population initialization, and occasional stops proceeding the global optimum. This study proposes an enhanced Modified ABC (E-MABC) algorithm which includes (i) N-MABC that overcomes the shortage of neighborhood selection by exchanging the neighborhood structure between two different routes in the solution; (ii) MABC-ACS that solves the issues of knowledge absence in MABC population initialization by incorporating ant colony system heuristics, and (iii) PMABC which addresses the occasional stops proceeding to the global optimum by introducing perturbation that accepts an abandoned solution and jumps out of a local optimum. The proposed algorithm was evaluated using benchmark datasets comprising 56 VRPTW instances and 56 Pickup and Delivery Problems with Time Windows (PDPTW). The performance has been measured using the travelled distance (TD) and the number of deployed vehicles (NV). The results showed that the proposed E-MABC has lower TD and NV than the benchmarked MABC and other algorithms. The E-MABC algorithm is better than the MABC by 96.62%, MOLNS by 87.5%, GAPSO by 53.57%, MODLEM by 76.78%, and RRGA by 42.85% in terms of TD. Additionally, the E-MABC algorithm is better than the MABC by 42.85%, MOLNS by 17.85%, GA-PSO and RRGA by 28.57%, and MODLEN by 46.42% in terms of NV. This indicates that the proposed E-MABC algorithm is promising and effective for the VRPTW and PDPTW, and thus can compete in other routing problems and COPs

A Metaheuristic Based Approach for the Customer-Centric Perishable Food Distribution Problem

The CNRST has awarded H. El Raoui an excellence scholarship. D. Pelta acknowledges support from projects TIN2017-86647-P (Spanish Ministry of Economy, Industry, and Competitiveness. Including FEDER funds) and PID2020-112754GB-I00 (Spanish Ministry of Science and Innovation).High transportation costs and poor quality of service are common vulnerabilities in various logistics networks, especially in food distribution. Here we propose a many-objective Customercentric Perishable Food Distribution Problem that focuses on the cost, the quality of the product, and the service level improvement by considering not only time windows but also the customers’ target time and their priority. Recognizing the difficulty of solving such model, we propose a General Variable Neighborhood Search (GVNS) metaheuristic based approach that allows to efficiently solve a subproblem while allowing us to obtain a set of solutions. These solutions are evaluated over some non-optimized criteria and then ranked using an a posteriori approach that requires minimal information about decision maker preferences. The computational results show (a) GVNS achieved same quality solutions as an exact solver (CPLEX) in the subproblem; (b) GVNS can generate a wide number of candidate solutions, and (c) the use of the a posteriori approach makes easy to generate different decision maker profiles which in turn allows to obtain different rankings of the solutions.CNRSTSpanish Ministry of Economy, Industry, and Competitiveness TIN2017-86647-PEuropean Commission TIN2017-86647-PSpanish Government PID2020-112754GB-I0