Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care

International audienceThis paper addresses a vehicle scheduling problem encountered in home health care logistics. It concerns the delivery of drugs and medical devices from the home care company's pharmacy to patients' homes, delivery of special drugs from a hospital to patients, pickup of bio samples and unused drugs and medical devices from patients. The problem can be considered as a special vehicle routing problem with simultaneous delivery and pickup and time windows, with four types of demands: delivery from depot to patient, delivery from a hospital to patient, pickup from a patient to depot and pickup from a patient to a medical lab. Each patient is visited by one vehicle and each vehicle visits each node at most once. Patients are associated with time windows and vehicles with capacity. Two mixed-integer programming models are proposed. We then propose a Genetic Algorithm (GA) and a Tabu Search (TS) method. The GA is based on a permutation chromosome, a split procedure and local search. The TS is based on route assignment attributes of patients, an augmented cost function, route re-optimization, and attribute-based aspiration levels. These approaches are tested on test instances derived from existing VRPTW benchmarks

Survey on Ten Years of Multi-Depot Vehicle Routing Problems: Mathematical Models, Solution Methods and Real-Life Applications

A crucial practical issue encountered in logistics management is the circulation of final products from depots to end-user customers. When routing and scheduling systems are improved, they will not only improve customer satisfaction but also increase the capacity to serve a large number of customers minimizing time. On the assumption that there is only one depot, the key issue of distribution is generally identified and formulated as VRP standing for Vehicle Routing Problem. In case, a company having more than one depot, the suggested VRP is most unlikely to work out. In view of resolving this limitation and proposing alternatives, VRP with multiple depots and multi-depot MDVRP have been a focus of this paper. Carrying out a comprehensive analytical literature survey of past ten years on cost-effective Multi-Depot Vehicle Routing is the main aim of this research. Therefore, the current status of the MDVRP along with its future developments is reviewed at length in the paper

A metaheuristic for crew scheduling in a pickup-and-delivery problem with time windows

A vehicle routing and crew scheduling problem (VRCSP) consists of simultaneously planning the routes of a fleet of vehicles and scheduling the crews, where the vehicle-crew correspondence is not fixed through time. This allows a greater planning flexibility and a more efficient use of the fleet, but in counterpart, a high synchronisation is demanded. In this work, we present a VRCSP where pickup-and-delivery requests with time windows have to be fulfilled over a given planning horizon by using trucks and drivers. Crews can be composed of 1 or 2 drivers and any of them can be relieved in a given set of locations. Moreover, they are allowed to travel among locations with non-company shuttles, at an additional cost that is minimised. As our problem considers distinct routes for trucks and drivers, we have an additional flexibility not contemplated in other previous VRCSP given in the literature where a crew is handled as an indivisible unit. We tackle this problem with a two-stage sequential approach: a set of truck routes is computed in the first stage and a set of driver routes consistent with the truck routes is obtained in the second one. We design and evaluate the performance of a metaheuristic based algorithm for the latter stage. Our algorithm is mainly a GRASP with a perturbation procedure that allows reusing solutions already found in case the search for new solutions becomes difficult. This procedure together with other to repair infeasible solutions allow us to find high-quality solutions on instances of 100 requests spread across 15 cities with a fleet of 12-32 trucks (depending on the planning horizon) in less than an hour. We also conclude that the possibility of carrying an additional driver leads to a decrease of the cost of external shuttles by about 60% on average with respect to individual crews and, in some cases, to remove this cost completely

Pemecahan Masalah Rute Kendaraan Dengan Trip Majemuk, Jendela Waktu Dan Pengantaran-penjemputan Simultan Menggunakan Algortima Genetika

Vehicle routing problem (VRP) is one of decision problems having an important role in transportation and distribution activity in the logistic management. The VRP deals with determining vehicle routes that minimizes total distance by satisfying the following constraints: (1) each route starts and ends at the depot, (2) each vehicle serves only one route, (3) each costumer is served by one route, (4) all customers must be served, and (5) total load for each route does not exceed the vehicle capacity. In literature, this definition is the definition for the basic or classical VRP. This paper discusses an extension of the basic VRP including the following characteristics: (1)multiple trips (MT), (2) time windows (TW), and (3) simultaneous pickup-delivery (SPD). A solution method based on genetic algorithm (GA) is proposed to solve the VRP discussed in this papaer. The proposed GA is examined using some hypothetical instances

Revisión del estado del arte del problema de ruteo de vehículos con recogida y entrega (VRPPD)

This paper presents a literature review of the state of the art vehicle routing problem with deliveries and collections (VRPPD: Vehicle Routing Problem with pickups and deliveries). Is performed a classification of the different variants of the problem, and the work and conducted research on the subject according to its authors, according to the models and the solution methods used. Also are analyzed future trends in modeling and solution techniques. The VRPPD is a problem of type MILP (Mixed Integer Linear Programming) involving whole and continuous quantities, and that turns out to be NP-Hard problems with a medium or large number of customers. The research does emphasis on variants of the problem involving variables associated with the environment, and in particular reducing the impact of greenhouse gases. The review notes that published until 2016.En este trabajo se realiza una revisión bibliográfica del estado del arte del problema de ruteo de vehículos con entregas y recogidas (VRPPD: Vehicle routing problem with pickups and deliveries). Se presenta una clasificación de las diferentes variantes del problema, y de los trabajos e investigaciones realizados sobre el tema según sus autores, los modelos utilizados y los métodos de solución usados. También se analizan las tendencias futuras en modelamiento y técnicas de solución. El VRPPD es un problema del tipo MILP (programación lineal entera mixta) que involucra cantidades enteras y continuas, y que resulta ser NP-Hard en problemas con un número mediano o grande de clientes. En la búsqueda se hace énfasis en las variantes del problema que involucran variables asociadas al medio ambiente, y en particular con la reducción del impacto de gases de efecto invernadero. La revisión observa lo publicado hasta el año 2016

Exact Models, Heuristics, and Supervised Learning Approaches for Vehicle Routing Problems

This dissertation presents contributions to the field of vehicle routing problems by utilizing exact methods, heuristic approaches, and the integration of machine learning with traditional algorithms. The research is organized into three main chapters, each dedicated to a specific routing problem and a unique methodology. The first chapter addresses the Pickup and Delivery Problem with Transshipments and Time Windows, a variant that permits product transfers between vehicles to enhance logistics flexibility and reduce costs. To solve this problem, we propose an efficient mixed-integer linear programming model that has been shown to outperform existing ones. The second chapter discusses a practical workforce scheduling problem, formulated as a specific type of vehicle routing problem. The objective here is to efficiently assign consultants to various clients and plan their trips. This computational challenge is addressed by using a two-stage approach: the first stage employs a mathematical model, while the second stage refines the solution with a heuristic algorithm. In the final chapter, we explore methods that integrate machine learning with traditional approaches to address the Traveling Salesman Problem, a foundational routing challenge. Our goal is to utilize supervised learning to predict information that boosts the efficiency of existing algorithms. Taken together, these three chapters offer a comprehensive overview of methodologies for addressing vehicle routing problems

On the medication distribution system for home health care through convenience stores, lockers, and home delivery.

Medication distribution service can be delivered based on a combination of home delivery and customer pickup. That is, medications are delivered either to customers' homes directly or to the pickup facilities (e.g. lockers) close to customers' homes. In Taiwan, there are more than 11,000 convenience stores that provide a 24-h service for customers to pick up the ordered items from e-commerce, which is unique to the world. In the medication distribution system, convenience stores can provide a unique opportunity for customers to more conveniently collect medications at stores, and also can reduce the operating cost for a logistics company providing the medication delivery service. Therefore, this work proposes a medication distribution system through convenience stores, lockers, and home delivery. Under this system, this work investigates how to simultaneously determine employment of convenience store chains, the convenience store locations to be visited, locations of lockers, vehicle routes for convenience stores and lockers, and vehicle routes for customers' homes, so that the total operating cost is minimized. This work further proposes a genetic algorithm to solve the medication distribution problem. Through simulation, the experimental results show that the proposed algorithm is able to solve the problem efficiently