Multi-depot Pickup Research Articles

A Bayesian Optimization Approach for Tuning a Grouping Genetic Algorithm for Solving Practically Oriented Pickup and Delivery Problems

Background: The Multi Depot Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets (MDPDPTWHV) is a strongly practically oriented routing problem with many real-world constraints. Due to its complexity, solution approaches with sufficiently good quality ideally contain several operators with certain probabilities.Thus, automatically selecting the best parameter configurations enhances the overall solution quality. Methods: To solve the MDPDPTWHV, we present a Grouping Genetic Algorithm (GGA) framework with several operators and population management variants. A Bayesian Optimization (BO) approach is introduced to optimize the GGA’s parameter configuration. The parameter tuning is evaluated on five data sets which differ in several structural characteristics and contain 1200 problem instances. The outcomes of the parameter-tuned GGA are compared to both the initial GGA parameter configuration and a state-of-the-art Adaptive Large Neighborhood Search (ALNS). Results: The presented GGA framework achieves a better solution quality than the ALNS, even for the initial parameter configuration used. The mean value of the relative error is less than 0.9% and its standard deviation is less than 1.31% for every problem class. For the ALNS, these values are up to three times higher and the GGA is up to 38% faster than the ALNS. Conclusions: It is shown that the BO, as a parameter tuning approach, is a good choice in improving the performance of the considered meta-heuristic over all instances in each data set. In addition, the best parameter configuration per problem class with the same characteristics is able to improve both the frequency of finding the best solution, as well as the relative error to this solution, significantly.

The pickup and delivery problem with multiple depots and dynamic occasional drivers in crowdshipping delivery

In recent years, crowdshipping has gained much popularity because it not only has economic and environmental advantages, but also is regarded as an effective way to increase the resilience of supply chains and logistics. In this work, we focus on a last-mile delivery system that supports its O2O e-commerce partner for processing and fulfilling online orders in an urban area. Besides the full-time regular drivers, the system also relies on occasional drivers from society who dynamically announce their availability over the time horizon. We model the studied problem as a multi-depot pickup and delivery problem with dynamic occasional drivers. An event-based rolling horizon solution approach is proposed to solve the problem through iteratively calling for a re-optimization procedure to update the delivery plan at each decision epoch. Besides, we investigate the scenarios of disruptions to the delivery system. Extensive numerical experiments have proven the effectiveness of our proposed approach and have also demonstrated the studied crowdshipping system can help achieve the supply chain and logistics resilience. Moreover, sensitivity analysis on several important parameters have been carried out and managerial insights are drawn.

The multi-depot pickup and delivery problem with capacitated electric vehicles, transfers, and time windows

The Pickup and Delivery Problem has received increasing attention as a result of the recent growth of third-party delivery companies, and electric vehicles (EVs) are becoming a preferable choice for such large delivery systems due to their environmental benefits. The EVs, however, have limited energy capacity; thus, intra-route facilities are required to recharge them. These facilities can also be visited to transfer requests to other vehicles. In this study, we introduce a novel pickup and delivery problem with electric vehicles and transfers. The traditional constraint that each request should be handled by a single vehicle, is relaxed in this problem with transfers, and additionally, we incorporate other practical considerations such as multi-depots, time-windows, and EVs’ battery and carrying capacity constraints. We develop a mixed-integer linear programming model encompassing all these constraints. To address the computational difficulty of the problem, we propose a hybrid heuristic combining Simulated Annealing (SA) and Large Neighborhood Search (LNS). Experimental results reveal that for small instances where CPLEX can find optimal solutions, our heuristic finds them about 90% faster.

Two-Echelon Multidepot Logistics Network Design with Resource Sharing

Resource sharing within a logistics network offers an effective way to solve problems resulting from inefficient and costly operations of individual logistics facilities. However, the existing analysis of resource sharing and profit allocation is still limited. Therefore, this study aims to model resource sharing in two-echelon delivery and pickup logistics networks to improve the overall efficiency and decrease the total network operating cost. A bi-objective integer programming model is first proposed for two-echelon collaborative multidepot pickup and delivery problems with time windows (2E-CMDPDTW) to seek the minimization of operating costs and number of vehicles. Integrating a customer clustering algorithm, a greedy algorithm, and an improved nondominated sorting genetic algorithm-II (Im-NSGA-II), a hybrid method is then designed to handle the 2E-CMDPDTW model. The customer clustering and the greedy algorithms are employed to generate locally optimized initial solutions to accelerate the calculating velocity and guarantee the diversity of feasible solutions. The Im-NSGA-II combines the order crossover operation and the polynomial mutation process to find the optimal solution of the 2E-CMDPDTW. The comparative results show that the proposed hybrid method outperforms the NSGA-II and the multiobjective genetic algorithm. Furthermore, a Shapley value method is used for allocating total profits of established alliances and finding an optimal coalition sequence of the logistics facilities joining alliances based on the strictly monotonic path strategy. Finally, a case study of 2E-CMDPDTW in Chongqing China is conducted to validate the feasibility. Results indicate that this study contributes to long-term partnerships between logistics facilities within multi-echelon logistics networks in practice and contributes to the long-term sustainability of urban logistics pickup and delivery networks’ development.

Collaborative multi-depot pickup and delivery vehicle routing problem with split loads and time windows

Optimization of collaborative multi-depot pickup and delivery logistics networks (CMDPDLN) with split loads and time windows involves a customer demand splitting strategy and a multi-depot pickup and delivery vehicle routing problem under time window constraints. In the collaborative network, the customer demand splitting scheme based on customer clustering aims to achieve the balance of demands’ spatial distribution and improve the efficiency of logistics transportation. The multi-depot pickup and delivery vehicle routing problem focuses on establishing a collaborative network optimization model to coordinate the pickup and delivery services among multiple depots and determine the optimal routes with reduced operating cost through logistics resource sharing. A 3D customer clustering algorithm with split load strategies is developed to reassign each customer to its favorable service provider considering multiple customer service characteristics. A hybrid genetic algorithm with tabu search is designed to optimize the pickup and delivery routes and maximize the logistics resource utilization. A realistic logistics network in Chongqing, China is used to test the performance of the proposed solution methods for the CMDPDLN optimization. Computational results show the effectiveness of customer clustering and demand splitting in simplifying and improving the large-scale collaborative network, and the adaptability of the hybrid algorithm in finding the minimal-cost vehicle routes. Therefore, the collaboration and demand split strategy adopted in network optimization can provide a reference for logistics operational management and facilitate sustainable pickup and delivery networks.

Multi-Depot Pickup and Delivery Problem with Resource Sharing

Resource sharing (RS) integrated into the optimization of multi-depot pickup and delivery problem (MDPDP) can greatly reduce the logistics operating cost and required transportation resources by reconfiguring the logistics network. This study formulates and solves an MDPDP with RS (MDPDPRS). First, a bi-objective mathematical programming model that minimizes the logistics cost and the number of vehicles is constructed, in which vehicles are allowed to be used multiple times by one or multiple logistics facilities. Second, a two-stage hybrid algorithm composed of a k-means clustering algorithm, a Clark-Wright (CW) algorithm, and a nondominated sorting genetic algorithm II (NSGA-II) is designed. The k-means algorithm is adopted in the first stage to reallocate customers to logistics facilities according to the Manhattan distance between them, by which the computational complexity of solving the MDPDPRS is reduced. In the second stage, CW and NSGA-II are adopted jointly to optimize the vehicle routes and find the Pareto optimal solutions. CW algorithm is used to select the initial solution, which can increase the speed of finding the optimal solution during NSGA-II. Fast nondominated sorting operator and elite strategy selection operator are utilized to maintain the diversity of solutions in NSGA-II. Third, benchmark tests are conducted to verify the performance and effectiveness of the proposed two-stage hybrid algorithm, and numerical results prove that the proposed methodology outperforms the standard NSGA-II and multi-objective particle swarm optimization algorithm. Finally, optimization results of a real-world logistics network from Chongqing confirm the applicability of the mathematical model and the designed solution algorithm. Solving the MDPDPRS provides a management tool for logistics enterprises to improve resource configuration and optimize logistics operation efficiency.

An Adaptive Genetic Algorithm for a New Variant of the Gas Cylinders Open Split Delivery and Pickup with Two-dimensional Loading Constraints

This paper studies a combination of two well-known problems in distribution logistics, which are the truck loading problem and the vehicle routing problem. In our context, a customer daily demand exceeds the truck capacity. As a result, the demand has to be split into several routes. In addition, it is required to assign customers to depots, which means that each customer is visited just once by any truck in the fleet. Moreover, we take into consideration a customer time windows. The studied problem can be defined as a Multi-depots open split delivery and pickup vehicle routing problem with two-dimensional loading constraints and time windows (2L-MD-OSPDTW). A mathemat-ical formulation of the problem is proposed as a mixed-integer linear programming model. Then, a set of four class instances is used in a way that reflects the real-life case study. Furthermore, a genetic algorithm is proposed to solve a large scale dataset. Finally, preliminary results are reported and show that the MILP performs very well for small test instances while the genetic algorithm can be efficiently used to solve the problem for a wide-reaching test instances.

Delivery systems with crowd‐sourced drivers: A pickup and delivery problem with transfers

AbstractRapid urban growth, the increasing importance of e‐commerce and high consumer service expectations have given rise to new and innovative models for freight delivery within urban environments. Crowdsourced solutions—where drivers are not employed by a carrier but occasionally offer their services through on‐line platforms and are contracted as required by carriers—are receiving growing attention from industry. We consider a crowdsourced system where drivers express their availability to perform delivery tasks for a given period of time and the platform communicates a schedule with requests to serve. We investigate the potential benefits of introducing transfers to support driver activities. At transfer locations, drivers can drop off packages for pick up by other drivers at a later time. We frame the problem as a multidepot pickup and delivery problem with time windows and transfers, and propose an adaptive large neighborhood search algorithm that effectively identifies beneficial transfer opportunities and synchronizes driver operations. Computational experiments indicate that introducing transfer options can significantly reduce system‐wide travel distance as well as the number of drivers required to serve a given set of requests, especially when drivers have short availability and requests have high service requirements.

Optimisation of the multi-depots pick-up and delivery problems with time windows and multi-vehicles using PSO algorithm

Many sectors in the transport industry are concerned about the vehicle routing problem (VRP), hence the growing interest of researchers for this type of problem and its variants. This is due essentially to its many real applications in logistics for the transport of goods. The originality and contribution of our work is that we have dealt a problem that combines several variants: multiple vehicles (m), multiple depots (MD), pickup and delivery problem (PDP) with time windows (TW). Hence the notation of our problem: m-MDPDPTW. In this paper, we present the m-MDPDPTW, which is an optimisation problem belonging to the category of NP Hard problems. This problem must meet requests for transport between customers and suppliers satisfying precedence, capacity and time constraints. The goal is to find the best solution, which is the best route minimising the total travelled distance. To solve and optimise our m-MDPDPTW, we have developed a new algorithm based on the particle swarm optimisation (PSO) method. The performance of this new approach is tested on data set instances of Li and Lim's benchmark problems in which we have added multiple depot locations. Comparing with prior works, our proposed approach gave better results by decreasing the distance for several studied instances.

Total distance approximations for routing solutions

In order to make strategic, tactical and operational decisions, carriers and logistic companies need to evaluate scenarios with high levels of accuracy by solving a large number of routing problems. This might require relatively high computational efforts and time. In this paper, we present regression-based estimation models that provide fast predictions for the travel distance in the traveling salesman problem (TSP), the capacitated vehicle routing problem with Time Windows (CVRP-TW), and the multi-region multi-depot pickup and delivery problem (MR-MDPDP). The use of general characteristics such as distances, time windows, capacities and demands, allows us to extend the models and adjust them to different problems and also to different solution methods. The resulting regression models in most cases achieve good approximations of total travel distances except in cases where strong random noise is present, and outperform previous models.

The two-region multi-depot pickup and delivery problem

Logistics networks are constantly evolving such that new and more varied structures arise and need to be studied. Carriers are aiming for opportunities to save costs by efficient planning. Motivated by this, we define the two-region multi-depot pickup and delivery problem. A region in this setting refers to an area where customers and depots are located. We differentiate two kinds of requests depending on whether their customers are located in the same region or not. Due to geographical characteristics, direct transportation between different regions is considered inefficient and a long-distance transportation mode needs to be used to connect them. Hence, we face a complex problem where interrelated decisions are to be made. We propose a decomposition into three subproblems, which relate to well-known problems in the literature. For solving the global problem, an adaptive large neighborhood search (ALNS) algorithm is developed. The algorithm mixes operators tailored to each of the different decisions of each subproblem. We demonstrate that these operators are efficient when applied to problems of their primal nature. In an extensive computational study, we show that the proposed ALNS dominates alternative ALNS schemes, where subproblems are treated sequentially. A detailed analysis of the solution convergence is provided. The proposed approach is a powerful tool to tackle complex decision problems in large distribution networks.

A Comparative Study of the PSO and GA for the m-MDPDPTW

The m-MDPDPTW is the multi-vehicles, multi-depots pick-up and delivery problem with time windows. It is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers for the purpose of satisfying precedence, capacity and time constraints. This problem is a very important class of operational research, which is part of the category of NP-hard problems. Its resolution therefore requires the use of evolutionary algorithms such as Genetic Algorithms (GA) or Particle Swarm Optimization (PSO). We present, in this sense, a comparative study between two approaches based respectively on the GA and the PSO for the optimization of m-MDPDPTW. We propose, in this paper, a literature review of the Vehicle Routing Problem (VRP) and the Pick-up and Delivery Problem with Time Windows (PDPTW), present our approaches, whose objective is to give a satisfying solution to the m-MDPDPTW minimizing the total distance travelled. The performance of both approaches is evaluated using various sets instances from [10] PDPTW benchmark data problems. From our study, in the case of m-MDPDPTW problem, the proposed GA reached to better results compared with the PSO algorithm and can be considered the most appropriate model to solve our m-MDPDPTW problem.

Multidepot pickup and delivery problems in multiple regions: a typology and integrated model

AbstractThe rapid development experienced by the transportation industry in the past decades has led to many configurations of networks and therefore to an explosion of variants in transportation problems, motivating researchers to look at broader logistic problems, beyond the basic vehicle routing problems. This work introduces a new type of problem scenario combining various attributes: a pickup and delivery problem with multiple regions, multiple depots, and multiple transportation modes. We provide definitions, a literature review, and a step‐by‐step construction of the mathematical models from a simple and well‐known scenario to the multiregion multidepot pickup and delivery problem (MR‐MDPDP). For each step the relevant literature is examined. Furthermore, we suggest possible extensions for prospective research.

Genetic Algorithm for Multi-Criteria Optimization of Multi-Depots Pick-up and Delivery Problems with Time Windows and Multi-Vehicles

In this paper, multi-vehicles, multi-depots pick-up and delivery problems with time windows (m-MDPDPTW) is presented as a multi-criteria optimization problem. The m-MDPDPTW is a variant of pick-up and delivery problem (PDP) and a challenging problem in the field of vehicle routing problem (VRP). The aim is to discover a set of satisfying solutions (routes) minimizing total travel distance, total tardiness time and the total number of vehicles. These routes satisfy transportation requests without contravening any of the instance specific constraints (precedence, capacity and time window constraints). In our problem each request is transported by one of the vehicles between paired pick-up and delivery locations. Such that, the depot does not retain the goods. In this paper, we assume that all vehicles have the same capacity and depart from and return to the same depot. The new encoding and structure algorithm on which this contribution is based uses a genetic algorithm, a selection process using ranking with several Pareto fronts and an elitist selection strategy for replacement. An improved encoding chromosome path representation is given to simulate the process of evolution using several types of populations in different sizes. The performance of the new algorithm is tested on data sets instances of Li & Lim's PDPTW benchmark problems. The results indicate that the proposed algorithm gives good results.

A nested decomposition approach for solving the paratransit vehicle scheduling problem

The paratransit vehicle scheduling problem involves scheduling a fleet of specially equipped vehicles for serving transportation needs of disabled and elderly people. It is also referred to as a dial-a-ride problem, and it can be classified as a multi-depot pickup and delivery problem with time windows and side constraints. The column generation approach constitutes one of the effective methodologies to solve this problem. However, in order to cope with the complexity of the problem, it is commonly applied in two consecutive stages: clustering and routing. First, clusters of customers are formed who will receive service together; next, vehicle routing decisions are made subject to the clustering decisions. This paper develops a nested column generation method, which integrates clustering and routing decisions, thus extending the applicability of the column generation approach in the context of the subject problem. The proposed method is implemented and applied to solve a problem faced by the transit authority of a mid size US city. A detailed account of implementation experience is provided. The computational results, based on actual data, indicate that problem instances with up to 680 requests and 48 vehicles can be solved within 2% of optimality under mild assumptions, and a 12% performance improvement over a well-established manual planning system can be achieved.

Vehicle routing concepts in the closed-loop container network of ARN—a case study

In this paper we discuss a real-life case study to optimize the logistics network for the collection of containers from end-of-life vehicle dismantlers in the Netherlands. Advanced planning concepts, such as dynamic assignment of dismantlers to logistic service providers, are analyzed using a simulation model. Based on this model, we periodically solve a vehicle routing problem to gain insight into the long-term performance of the system. The vehicle routing problem considered is a multi-depot pickup and delivery problem with alternative delivery locations. A special characteristic of the problem is the limited vehicle capacity of two containers. We solve this problem with a heuristic based on route generation and set partitioning.

Advanced Planning Concepts in the Closed-Loop Container Network of ARN

In this paper we discuss a real-life case study in the optimization of the logistics network for the collection of containers from end-of-life vehicle dismantlers in the Netherlands.Advanced planning concepts like dynamic assignment of dismantlers to logistic service providers are analyzed by a simulation model.In this model, we periodically solve a vehicle routing problem to gain insight in the long-term performance of the system.The vehicle routing problem considered is a multi depot pickup and delivery problem with alternative delivery locations.We solve this problem with a heuristic based on route generation and set partitioning.

A multi-depot pickup and delivery problem with a single hub and heterogeneous vehicles

This paper introduces a special kind of multi-depot pickup and delivery problem. In contrast to the general pickup and delivery problem (GPDP, see, e.g., Y. Dumas, J. Desrosiers, F. Soumis, European Journal of Operational Research 54 (1991) 7–22; M.W.P. Savelsbergh, M. Sol, Transportation Science 29 (1995) 17–29) all requests have to be picked up at or delivered to one central location which has the function of a hub or consolidation point. In hub transportation networks routes between customers and the hub are often short, i.e., involve only one or very few customers. The reason for this can been seen in narrow time windows as well as in high quantities which make it possible to fully load a vehicle at one customer. Thus, the focus here is on problems where all possible routes can easily be enumerated, i.e., the problem primarily considers the assignment of transportation requests to routes. We assume that many problems in transportation logistics can be modeled and solved similarly whenever routes can be enumerated and the temporal aspects of transportation requests are important.