Hybrid Genetic Algorithm for Multi-Period Vehicle Routing Problem with Mixed Pickup and Delivery with Time Window, Heterogeneous Fleet, Duration Time and Rest Area

Most logistics industries are improving their technology and innovation in competitive markets in order to serve the various needs of customers more efficiently. However, logistics management costs are one of the factors that entrepreneurs inevitably need to reduce, so that goods and services are distributed to a number of customers in different locations effectively and efficiently. In this research, we consider the multi-period vehicle routing problem with mixed pickup and delivery with time windows, heterogeneous fleet, duration time and rest area (MVRPMPDDR). In the special case that occurs in this research, it is the rest area for resting the vehicle after working long hours of the day during transportation over multiple periods, for which with confidence no research has studied previously. We present a mixed integer linear programming model to give an optimal solution, and a meta-heuristic approach using a hybrid genetic algorithm with variable neighborhood search algorithm (GAVNS) has been developed to solve large-sized problems. The objective is to maximize profits obtained from revenue after deducting fuel cost, the cost of using a vehicle, driver wage cost, penalty cost and overtime cost. We prepared two algorithms, including a genetic algorithm (GA) and variable neighborhood search algorithm (VNS), to compare the performance of our proposed algorithm. The VNS is specially applied instead of the mutation operator in GA, because it can reduce duplicate solutions of the algorithms that increase the difficulty and are time-consuming. The numerical results show the hybrid genetic algorithm with variable neighborhood search algorithm outperforms all other proposed algorithms. This demonstrates that the proposed meta-heuristic is efficient, with reasonable computational time, and is useful not only for increasing profits, but also for efficient management of the outbound transportation logistics system

Optimal paths in multi-stage stochastic decision networks

This paper deals with the search of optimal paths in a multi-stage stochastic decision network as a first application of the deterministic approximation approach proposed by Tadei et al. (2019). In the network, the involved utilities are stage-dependent and contain random oscillations with an unknown probability distribution. The problem is modeled as a sequential choice of nodes in a graph layered into stages, in order to find the optimal path value in a recursive fashion. It is also shown that an optimal path solution can be derived by using a Nested Multinomial Logit model, which represents the choice probability at the different stages. The accuracy and efficiency of the proposed method are experimentally proved on a large set of randomly generated instances. Moreover, insights on the calibration of a critical parameter of the deterministic approximation are also provided

Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning

Large-scale and complex mission environments require unmanned aerial vehicles (UAVs) to deal with various types of missions while considering their operational and dynamic constraints. This article proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV. We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP). Then, we solve this by using an attention-based deep reinforcement learning approach. Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP. For the input to the attention-based neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors. In addition, a masking strategy is introduced to be able to consider the resource constraint (e.g., flight time) of the UAV. Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance

Integrated zone picking and vehicle routing operations with restricted intermediate storage

The competitiveness of a retailer is highly dependent on an efficient distribution system. This is especially true for the supply of stores from distribution centers. Stores ask for high flexibility when it comes to their supply. This means that fast order processing is essential. Order processing affects different subsystems at the distribution center: Orders are picked in multiple picking zones, transferred to intermediate storage, and delivered via dedicated tours. These processing steps are highly interdependent. The schedule for picking needs to be synchronized with the routing decisions to ensure availability of the delivery orders at the DC’s loading docks when their associated tours are scheduled. Concurrently, intermediate storage represents a bottleneck as capacities for order storage are limited. The simultaneous planning of picking and routing operations with restricted intermediate storage is therefore relevant for retail practice but has not so far been considered within an integrated planning approach. Our work addresses this task and discusses an integrated zone picking and vehicle routing problem with restricted intermediate storage. We present a comprehensive model formulation and introduce a general variable neighborhood search for simultaneous consideration of the given planning stages. We also present two alternative sequential approaches that are motivated by the prevailing planning situation in industry. Numerical experiments that we have conducted show the need for an integrated planning approach to obtain practicable results. Further, we identify the impact of the main problem characteristics on the overall planning problem and provide valuable insights for the application of this approach in industry

Vehicle Routing with Compartments Under Product Incompatibility Constraints

This study focuses on a distribution problem involving incompatible products which cannot be stored in a compartment of a vehicle. To satisfy different types of customer demand at minimum logistics cost, the products are stored in different compartments of fleet vehicles, which requires the problem to be modeled as a multiple-compartment vehicle routing problem (MCVRP). While there is an extensive literature on the vehicle routing problem (VRP) and its numerous variants, there are fewer research papers on the MCVRP. Firstly, a novel taxonomic framework for the VRP literature is proposed in this study. Secondly, new mathematical models are proposed for the basic MCVRP, together with its multiple-trip and split-delivery extensions, for obtaining exact solutions for small-size instances. Finally, heuristic algorithms are developed for larger instances of the three problem variants. To test the performance of our heuristics against optimum solutions for larger instances, a lower bounding scheme is also proposed. The results of the computational experiments are reported, indicating validity and a promising performance of an approach

A multiperiod drayage problem with customer-dependent service periods

We investigate a routing problem arising in the domain of drayage operations. to determine mimimum-cost vehicle routes in several periods. We adapt a set-covering model, which is solved either with all feasible routes by an off-the-shelf MIP solver, or by and a Price-and-Branch algorithm in which the pricing problem is a formulated as a collection of shortest path problems in tailor-made auxiliary acyclic networks. We propose a new arc-flow formulation based on the previous auxiliary networks and show that solving it by a MIP solver is usually preferable. Finally, we characterize how possible changes in flexibility levels affect routing costs

Exact and hyper?heuristic solutions for the distribution?installation problem from the VeRoLog 2019 challenge

This work tackles a rich vehicle routing problem (VRP) problem integrating a capacitated vehicle routing problem with time windows (CVRPTW), and a service technician routing and scheduling problem (STRSP) for delivering various equipment based on customers' requests, and the subsequent installation by a number of technicians. The main objective is to reduce the overall costs of hired resources, and the total transportation costs of trucks/technicians. The problem was the topic of the fourth edition of the VeRoLog Solver Challenge in cooperation with the ORTEC company. Our contribution to research is the development of a mathematical model for this problem and a novel hyper?heuristic algorithm to solve the problem based on a population of solutions. Experimental results on two datasets of small and real?world size revealed the success of the hyper?heuristic approach in finding optimal solutions in a shorter computational time, when compared to our exact model. The results of the large size dataset were also compared to the results of the eight finalists in the competition and were found to be competitive, proving the potential of our developed hyper?heuristic framework

The flexible periodic vehicle routing problem: modeling alternatives and solution techniques

In this thesis the Flexible Periodic Vehicle Routing Problem is introduced and studied. In this problem a carrier must establish a distribution plan to serve a given set of customers over a planning horizon using a fleet of homogeneous capacitated vehicles. The total demand of each customer is known for the time horizon and it can be satisfied by visiting the customer in several time periods. There is, however, a limit on the maximum quantity that can be delivered at each visit. The aim is to minimize the total routing cost. This problem can be seen as a generalization of the Periodic Vehicle Routing Problem which, instead, has fixed service schedules and fixed delivered quantities per visit. On the other hand, the Flexible Periodic Routing Problem shares some characteristics with the Inventory Routing Problem in which inventory levels are considered at each time period, the delivery of product is a decision of the problem and, typically, an inventory cost is involved in the objective function. The relation among these periodic routing problems is discussed and a worst-case analysis, which shows the advantages of the studied problem with respect to the problems with periodicity mentioned above, is presented. Furthermore, alternative mixed-integer programming formulations are described and computationally tested. Given the difficulty to optimally solve the studied problem for small size instances, a matheuristic is developed, which is able to solve large size instances efficiently. Extensive computational experiments illustrate the characteristics of the solutions of the problem and show that, also in practice, allowing flexible policies may produce substantial savings in the routing costs in comparison with both the Periodic Vehicle Routing Problem and the Inventory Routing Problem.: En esta tesis se presenta y estudia el Problema de Ruteo de Vehículos Periódico Flexible. En este problema, un transportista debe establecer un plan de distribución para atender a un conjunto determinado de clientes durante un horizonte de planificación utilizando una flota de vehículos con capacidad homogénea. La demanda total de cada cliente es conocida por el horizonte temporal y se puede satisfacer visitando al cliente en varios períodos de tiempo. Sin embargo, hay un límite en la cantidad máxima que se puede entregar en cada visita. El objetivo es minimizar el costo total de ruteo. Este problema puede verse como una generalización del Problema clásico de Ruteo de Vehículos Periódico que, en cambio, tiene programas de servicio fijos y cantidades de entrega fijas por visita. Por otro lado, el Problema de Ruteo de Vehículos Periódico Flexible comparte algunas características con el Problema de Ruteo de Inventarios en el cual los niveles de inventario se consideran en cada período de tiempo, la entrega del producto es una variable de decisión y, típicamente, un costo de inventario está involucrado en la función objetivo. Se discute la relación entre estos problemas periódicos de rutas y se presenta un análisis del peor de los casos, que muestra las ventajas del problema estudiado con respecto a los problemas periódicos mencionados anteriormente. Además, las formulaciones alternativas de programación entera mixta se describen y se prueban computacionalmente. Dada la dificultad de resolver a optimalidad el problema estudiado para instancias de tamaño pequeño , se desarrolla una matheurística que puede resolver instancias de gran tamaño de manera eficiente. Una extensa experiencia computacional ilustra las características de las soluciones del problema y muestra que, también en la práctica, permitir políticas flexibles puede producir ahorros sustanciales en los costos de ruteo en comparación con el Problema de Ruteo de Vehículos Periódico y el Problema de Rutas de Inventario.Postprint (published version