Multi Depot Research Articles

Multi-depot vehicle routing problem with roaming delivery locations considering hard time windows: Solved by a hybrid ELS-LNS algorithm

This paper investigates the multi-depot vehicle routing problem with roaming delivery locations considering hard time windows. In the mentioned problem, each customer can determine several locations with a distinct hard time windows and the service provider can choose between the proposed locations to visit the customer. Also, several depots are considered for the vehicles. A mathematical model is presented for the problem. The objective function of the model is minimizing the total travel time. This objective function is important in problems that include hard time windows; because in addition to the route navigation time, it considers the waiting time of the vehicles, which is one of the integral cost factors in hard time window problems. Considering the proposed problem is NP-hard, we proposed two heuristic algorithms. Also, a hybrid algorithm based on ELS is designed to solve the problem on large scales. The results show the appropriate efficiency of the algorithm for solving small and large instances. Overall, based on the results, the proposed algorithm improved the best-known solution of two instances in the multi depot vehicle routing problem with time windows benchmark datasets.

Optimization Model for Relief Distribution After Flood Disaster

Logistics planning is critical and a key component in meeting initial emergency needs in the aftermath of a disaster. The rapid and efficient distribution of logistical aid becomes critically important. In such situations, the construction of temporary depots in strategic locations and the determination of optimal distribution routes play an important role in ensuring that logistics aid can be distributed to the affected areas evenly. In this study, the Multi Depot Vehicle Routing Problem (MDVRP) is used which aims to minimize the total cost of distributing logistics aid which includes shipping costs, vehicle usage costs, temporary depot construction costs, and vehicle travel costs from distribution centers to temporary depots, while still meeting constraints such as logistics aid demand, vehicle capacity, area visits, maximum mileage, and depot construction. This model uses two types of vehicles where vehicle is tasked with carrying logistics aid from the distribution center to the temporary depot and vehicle is tasked with delivering logistics aid directly to the point of demand.

Mathematical Model for Multi Depot Simultaneously Pick Up and Delivery Vehicle Routing Problem with Stochastic Pick Up Demand

In classical vehicle routing problem (VRP), customer demands are known with certainty. On the other hand, in real life problems, customer demands may change over time. Therefore, in the classical VRP, the assumption that customer demands are stochastic should be taken into account. In order to meet the demands of the customers faster and to reduce the fuel consumption and carbon emissions, the companies meet the distribution and collection demands of the customers simultaneously. Customers' distribution requirements can be predicted, but it is impossible to predict in advance the product requirements they will send for recycling. Hence in this study, a mathematical programming model is developed for the multi-depot simultaneously pick up and delivery vehicle routing problem, under the assumption that customers' picking demands are stochastic. However, There are non-linear constraints in the developed model. Thereby firstly the stochastic model is linearized, and then the effectiveness of the model is analyzed. The efficiency of the linearized model is determined by creating test problems.

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.

A hybrid heuristic approach for the multi-objective multi depot vehicle routing problem

Efficiency in logistics is often affected by the fair distribution of the customers along the routes and the available depots for goods delivery. From this perspective, in this study, the Multi-depot Vehicle Routing Problem (MDVRP), by considering two objectives, is addressed. The two objectives in conflict for MDVRP are the distance traveled by vehicles and the standard deviation of the routes’ length. A significant standard deviation value provides a small distance traveled by vehicles, translated into unbalanced routes. We have used a weighted average objective function involving the two objectives. A Variable Neighborhood Search algorithm within a Chu-Beasley Genetic Algorithm has been proposed to solve the problem. For decision-making purposes, several values are chosen for the weight factors multiplying the terms at the objective function to build up a non-dominated front of solutions. The methodology is tested in large-size instances for the MDVRP, reporting noticeable results for managerial insights.

Depot Location for a Multi Depots Transport Problem with Electric Vehicles

Depot Location for a Multi Depots Transport Problem with Electric Vehicles

The model of goods delivery using multi depot vehicle routing problem at PT X

The model of goods delivery using multi depot vehicle routing problem at PT X

Decision Support System for Optimizing Rastra Distribution Routes Using Genetic Algorithm

Vehicle Routing Problem (VRP) is an optimal route design from a group of vehicles that deliver goods to a set of customers with a certain demand. VRP was widely studied as part of solving the distribution efficiency which minimizes the cost of traveled vehicle. Bulog Subdivre South Surabaya distribute Rastra to every village which has constraint of Multi depot and Split delivery (MDSDVRP). This study aims to minimize the traveled distance of MDSDVRP (Rastra distribution) using Genetic Algorithm (GA) and to find out the efficiency of the route solution. The research covers the steps to solve MDSDVRP using GA to generate feasible and efficient solution route. Then development of a Decision Support System (DSS) that applies the algorithm is implemented on web platform and the result of route solution is presented on the mobile platform. The system testing is carried out to test the user satisfaction (83.8%) which found that overall users were considered very agree, good, like for each component of user satisfaction. The traveled distance is compared between GA route and routes of the original data from 2013-2017. The efficiency of GA was evaluated and found that the traveled distance from the previous route is reduced by 3.7% (444 km) and in 2017 is reduced the distance traveled by 9.5 % (1,093 km). The GA can generate a better solution and optimize the distance than the original route.

A Robust Optimizing Reverse Logistics Model for Beef Products Using Multi Depot Vehicle Routing Problem

Beef is a perishable product and requires special handling. Demand for beef also fluctuates quite high and is heavily influenced by various religious events and traditions in Indonesia. Under these conditions, for various reasons, beef products are returned from customers to distributors. An increase in the number of products returned from customers leads to high costs and the risk of product damage. The research created an optimization model for product distribution and product recall from customers with minimal costs and risks. The research applied a clustering method using the Density-Based Spatial Clustering (DBSCAN) algorithm to determine the density of customers’ locations and the number of orders. Optimization of distance and distribution and withdrawal costs applied Multi Depot Vehicle Routing Problem (MDVRP) and Mixed Integer Linear Programming (MILP) mathematical modeling. The results indicate three customer clusters with one noise, with the most potential customers in cluster 1. From this condition, product delivery optimization is based on the distance and number of shipments from the two central warehouses. Optimization uses of MDVRP and MILP to model and make company-owned trucks more profitable at high rental truck replacement costs. The research produces a robust model for changes in the truck number and capacity based on sensitivity analysis.

Design and Optimization of an Eco-Efficient Vehicle Routing Model for Heterogeneous Fleets and Multi Depot Systems

In the dynamic landscape of vehicle routing problems (VRP), the traditional focus on cost minimization has significantly evolved due to the pressing need to address environmental concerns. With the logistics industry experiencing exponential growth and increased vehicle utilization, sustainability has become a central consideration both globally and in strategic planning. The Vehicle Routing Problem (VRP) remains a key challenge in logistics and transportation management, aiming to optimize routes and vehicle allocation to minimize transportation costs. This study introduces an innovative VRP model that intricately balances economic efficiency with ecological responsibility, addressing the urgent call for sustainable solutions amidst the ongoing environmental crisis. The proposed model features a diverse fleet of trucks, each designated for specific product types, creating a heterogeneous fleet. Originating from multiple depots, these trucks ensure comprehensive geographic coverage and enhance logistical efficiency. The core objective of the model is twofold: minimizing the financial transportation costs associated with supplying multiple products from multiple depots to customers and significantly reducing the carbon footprint, thereby addressing environmental sustainability. Two distinct solutions are proposed: one based on mixed-integer linear programming (MILP) and the other utilizing a heuristic approach. The choice of mixed-integer linear programming instead of linear programming is due to the need to make binary route decisions. Results are compared, focusing on outcome variation and computation time. Additionally, the solution demonstrates upto 100% variation in results when comparing the heuristic model with the optimal values derived from mixed-integer linear programming, though it requires less computation time. This research underscores the importance of seamlessly integrating economic and environmental considerations in the logistics industry. The proposed model embodies this principle, representing a substantial step towards a more sustainable future. By aligning objectives with global sustainability goals set by the United Nations in 2015, this research contributes to the discourse on sustainable development. It lays the foundation for future innovations in vehicle routing and logistics.

Solving the multi depot vehicle routing problem with limited supply capacity at the depots with a multi phase methodology

Solving the multi depot vehicle routing problem with limited supply capacity at the depots with a multi phase methodology

Multi Depot Vehicle Routing Problem and Geographical Information System Integration: Retail Stores Case Study

The improper delivery route planning of multi depot and delivery assignment model leads the logistic operation in high travel distance and high distribution cost. This paper aim to explain the integration of multi depot vehicle routing problem (MDVRP) and Geographical Information System (GIS) to obtain the optimum delivery route planning with minimum travel distance by using multi-objective mixed integer programming approach and visualize its delivery assignment with open-source public GIS integration. In this research, the coordinate location of the depots and the retail stores are obtained, process the raw data through mathematical programming, then integrate the delivery plan to GIS for vehicle routing visualization. To validate the proposed model, the primary data of a retail stores chain was used and the model was evaluated with sensitivity analysis, resulted optimum value of total travel distance in daily logistic operation.

A Multi Depot Multi Product Split Delivery Vehicle Routing Problem with Time Windows: A Real Cash in Transit Problem Application in Istanbul, Turkey

A Multi Depot Multi Product Split Delivery Vehicle Routing Problem with Time Windows: A Real Cash in Transit Problem Application in Istanbul, Turkey

Penentuan Rute Pengangkut Limbah Medis Optimal Menggunakan Vehicle Routing Problem with Time Window pada Kasus Multi Depot

The current COVID-19 pandemic has created new environmental problems. The increase of medical waste due to increased activity in public health facilities is a new threat to environmental health. Unfortunately, not all health facilities have the capacity to manage their own waste. This causes the medical waste need to be taken to a waste treatment center for proper treatment that meets environmental health standards. Reverse logistics is one of the activities that can help the process of transporting medical waste. This study uses a vehicle routing problem with a time window (VRPTW) for multi depot cases. The limitations used in this study are the operational time of each Puskesmas, vehicle capacity and operational working hours of waste transport officers. In this case, the optimization of the cost calculation for the transportation of waste from 42 health centers in the DKI Jakarta area is carried out to be taken to three central locations for waste treatment. The purpose of the model used is to minimize transportation costs, namely fixed costs and variable costs of the vehicle used. The routes generated from this research are six transportation routes with a total distance of 412,31 km, with a total cost of Rp 5.928.498,75.

An adaptive large neighborhood search for the larger-scale multi depot green vehicle routing problem with time windows

Global warming is a serious problem presently faced by human society, and CO2 is a major concern as a greenhouse gas (GHG), especially as the amount of atmospheric CO2 has been steadily increasing in recent years. Multi-depot model of vehicle routing has sparked widespread interest among researchers as an effective means of reducing cost comprising carbon emissions, fuel consumption, vehicles rental, and driver salaries. In this paper, we developed an improved adaptive large neighborhood search (ALNS) algorithm to efficiently solve large-scale instances of the multi-depot green vehicle routing problem with time windows (MDGVRPTW), based on the characteristics of the multi-depot model, and the formulation of carbon emissions from customers. For the destroy operation, three problem-specific destroy operators that took advantage of the structure of the multi-depot model and the calculation formula of carbon emissions, were deliberately tailored for the MDGVRPTW. The operators could rapidly remove customers causing significant carbon emissions, which enabled these customers to be better positioned in future repair operations. For the repair operation, a noise-greedy repair operator was proposed to enhance the diversification capabilities of the ALNS. Additionally, two methods for speeding up the repair process were proposed to improve computational time. Computational experiments revealed that the proposed ALNS algorithm exhibited a significant improvement in calculation speed and accuracy compared with existing algorithms.

Genetic-based algorithms for cash-in-transit multi depot vehicle routing problems: economic and environmental optimization

Genetic-based algorithms for cash-in-transit multi depot vehicle routing problems: economic and environmental optimization

Grey Wolf Optimizer algorithm for solving the multi depot vehicle routing problem and its implementation

Multi Depot Vehicle Routing Problem (MDVRP) is one of Vehicle Routing Problem (VRP) variants. MDVRP is a VRP that uses more than one depot for the distribution process. In this paper, the MDVRP will be solved using the Grey Wolf Optimizer (GWO). This algorithm is inspired by hunting technique and hierarchy of grey wolves. The completion of MDVRP consists of two stages, they are grouping and routing. In the grouping stage, customers are grouped to nearest depot. In the routing stage, the route is determined at each depot using “route-first, cluster-second”. In the route-first phase a giant tour is formed by forming population array. In the cluster-second phase, a split will be conducted on the giant tour, so that a number of routes are formed with the total demand for each route does not exceed the vehicle capacity. The implementation of GWO algorithm for MDVRP was designed in Borland Delphi 7.0 programming language. The algorithm was tested using 2 depots and 9 customers formed 2 routes at each depot with a total distance of 833 km. Another test was carried out by comparing the GWO with the ACO using the Cordeau dataset. GWO gives the best result (shortest distance) than ACO.

Logistic network optimization using balanced allocation multi depot vehicle routing problem

One of the organization’s logistic business models is to deliver the goods from warehouses (depots) to many retail stores in order to fulfil the customer demand and achieve the KPIs of logistic department. The purpose of the paper is to provide an optimum route of delivery routing operations. The problem was a part of Balanced Allocation Multi Depot Vehicle Routing Problem (BAMDVRP) as a multi-objective mixed integer programming, which solution could be determined to provide an optimum logistic delivery routing and the efficient resources to boost the logistic performance and uplift the overall supply chain sustainability.

Urban e-Grocery Distribution Design in Pamplona (Spain) Applying an Agent-Based Simulation Model with Horizontal Cooperation Scenarios

E-commerce has boosted in the last decades because of the achievements of the information and telecommunications technology along with the changes in the society life-style. More recently, the groceries online purchase (or e-grocery), has also prevailed as a way of making the weekly shopping, particularly, the one including fresh vegetables and fruit. Furthermore, this type of virtual shopping in supermarkets is gaining importance as the most efficient delivery system in cost and time. Thus, we have evaluated in this study the influence of the cooperation-based policies on costs and service quality among different supermarkets in Pamplona, Spain. Concerning methodology, first of all, we carried out a survey in Pamplona having the purpose of modelling the demand patterns about e-grocery. Second, we have developed an agent-based simulation model for generating scenarios in non-cooperative, limited cooperation, and full cooperation settings, considering the real data obtained from the survey analysis. At this manner, Vehicle Routing Problems (VRP) and Multi Depot VRPs (MDVRP) are dynamically generated and solved within the simulation framework using a biased-randomization algorithm. Finally, the results show significant reductions in distance driven and lead times when employing horizontal cooperation in e-grocery distribution.

A Hybrid Large Neighborhood Search Algorithm for Solving the Multi Depot UAV Swarm Routing Problem

This paper focuses on a modified Multi-Depot Unmanned Aerial Vehicle Routing Problem (MMDUAVRP). Comparing to classic multi-depot vehicle routing problem, our studied problem has no constraints to restrict the depot where the Unmanned Aerial Vehicle (UAV) departs and returns. This work aims to minimize the number of UAVs and total distance traveled by all UAVs. This problem is mathematically formulated in this paper and a heuristic-assignment based hybrid large neighborhood search(HLNS) is proposed to solve it. Extensive computational experiments are conducted to verify the performance of HLNS. The HLNS algorithm was first tested on Multi Traveling Salesman Problem which is a simplified version of the MMDUAVRP, and high quality solutions have been obtained. Experimental results by compared with CPLEX and other well-known algorithms suggest that our proposed algorithm provides better solutions within a comparatively shorter period of time. In addition, we also conduct sensitivity analysis on the location of depots and task points that may affect the total cost of solution.