Search Results (10)

The rapid growth of e-commerce has intensified operational and sustainability challenges in urban last-mile delivery, necessitating routing methods that perform reliably under realistic traffic and spatial conditions. This study evaluates three routing algorithms, Nearest Neighbor (NN), Clarke–WrightSavings (CWS), and Ant Colony Optimization (ACO), using 1764 real-world Amazon delivery stops grouped into ten operational clusters in the Nashville metropolitan area. Travel distances and times were obtained through the Google Maps Distance Matrix API in driving mode to reflect actual road network structure and typical traffic conditions. Substantial performance differences were observed across algorithms and cluster configurations. NN achieved a strong performance in compact clusters (18.43 miles and 58.48 min in Cluster 4) but performed poorly in dispersed clusters (82.44 miles and 196.48 min in Cluster 9), reflecting high sensitivity to spatial dispersion. In contrast, CWS consistently reduced travel distance and time across clusters, achieving the shortest observed route (18.50 miles and 47.82 min in Cluster 10). Relative to ACO, CWS reduced travel distance by up to 42% (Cluster 9) and reduced travel time by over 45% in high-dispersion clusters. ACO exhibited the highest variability, with distances reaching 98.77 miles and travel times exceeding 218 min. Multi-criteria evaluation using efficiency ratios, distributional analysis, performance quadrant visualization, and a Composite Performance Index (CPI) confirmed the dominance of CWS. CPI scores of 1.00 (CWS), 0.78 (NN), and 0.00 (ACO) reflected balanced spatial and temporal efficiency under identical traffic-informed inputs. The results demonstrate that deterministic savings-based routing provides superior stability, efficiency, and scalability in semi-static urban delivery systems. However, the present study did not benchmark the evaluated algorithms against state-of-the-art exact TSP solvers (e.g., Concorde, LKH) or more recent metaheuristics such as Genetic Algorithms or Variable Neighborhood Search. The objective was to provide a controlled empirical comparison under consistent traffic-informed cost matrices rather than to establish global optimality bounds. Consequently, while the findings strongly support the relative superiority of the Clarke–Wright Savings approach within the evaluated framework, future research incorporating advanced exact and hybrid optimization methods would further contextualize algorithmic performance. Full article

The safe and sustainable management of healthcare waste (HCW) is essential for minimizing environmental impacts and protecting public health, particularly in developing countries with limited logistical infrastructure. Despite the growing adoption of routing optimization in HCW logistics, few studies integrate waste generator segmentation with algorithmic planning. This study proposes an optimization approach based on the Periodic Vehicle Routing Problem (PVRP), incorporating a segmentation of waste generators by volume. Two solution methods, the Clarke and Wright (CW) heuristic and Particle Swarm Optimization (PSO), are applied and compared through a real-world case study in Paraná, Brazil. Results show that PSO significantly outperforms CW in reducing travel distance and CO₂ emissions. For small generators, PSO achieves reductions of up to 41% in distance and 41.37% in emissions, compared to CW’s 35.42%. For large generators, PSO was reduced by 22% and 21.81%, respectively. The proposed method demonstrates the potential for scalable, data-efficient waste management strategies. This research contributes to sustainable urban logistics by bridging segmentation and routing optimization in resource-constrained settings, offering actionable insights for policymakers and planners. Full article

To duly and correctly deliver parcels, both the capacity and the delivery route of a delivery vehicle need to be considered. Thus, the delivery process of a delivery vehicle can be characterized as a capacitated vehicle routing problem with three-dimensional loading constraints (3L-CVRP), which is an NP-hard problem. To solve the problem, a mathematical model is established in this paper to minimize the total delivery distance and maximize the loading rate, simultaneously. Additionally, a hybrid algorithm that combines a three-dimensional (3D) packing algorithm based on the residual space optimized (RSO) strategy and an improved genetic algorithm (IGA) is proposed. Initially, the proposed hybrid algorithm employs a modified Clarke–Wright savings algorithm to generate a feasible set of route solutions. Furthermore, building upon the traditional genetic algorithm, an elite retention strategy is introduced, and an enhanced order crossover method is utilized to improve the stability of the hybrid algorithm and its global search capability for optimal solutions. Finally, during each iteration of the algorithm, the RSO algorithm is integrated to verify the feasibility of 3D packing scheme. Two comparative experiments are conducted on 22 modified benchmark instances and actual logistics data of a university against two other algorithms, demonstrating that the proposed RSO-IGA algorithm achieves superior solutions in delivery efficiency. Full article

The incessant consumption of goods and materials underscores the need to address the growing problem of waste generation and its profound impact on environmental sustainability. The problem of waste removal can be approached in different ways, whether it is the routing of vehicles, the work of drivers, the optimal distribution of waste bins, or other matters in the entire waste process. The aim of this study is to investigate the possibilities of optimizing waste collection processes in the region using a slightly modified Clark–Wright method. Optimal waste collection routes are defined with a focus on cost reduction and overall optimization of logistic chain processes. The established mathematical model for the capacitated vehicle routing problem includes the principles of sustainability and environmental friendliness. The results indicate that the largest messenger of all the newly proposed routes are the routes containing the surrounding settlements. Newly designed routes lead to significant reductions in fuel consumption and vehicle maintenance, which has a positive impact on financial and environmental resources. The conclusion indicates that by applying the Clark–Wright method, we have achieved a reduction in the number of routes of twenty fewer routes. This study provides regions with a detailed plan to improve waste management practices, contributing to a future of increased sustainability and environmental awareness. Full article

This paper addresses the Capacitated Vehicle Routing Problem (CVRP), which is a widely studied optimization problem due to its relevance to the field of transportation, distribution, and logistics. We present a matheuristic method for CVRP that adopts the main idea of the Kernel Search algorithm (KS) based on decomposing the original problem into sub-problems that are easier to solve. Unlike the original scheme of KS, our approach uses the Clarke–Wright savings algorithm to construct a sequence of smaller sub-problems, which are subsequently solved using mathematical programming strategies. The computational experiments performed on a set of benchmark instances showed that the proposed matheuristics achieves good results in acceptable computational time. Full article

Background: Improper disposal of urban medical waste is likely to cause a series of neglective impacts. Therefore, we have to consider how to improve the efficiency of urban medical waste recycling and lowering carbon emissions when facing disposal. Methods: This paper considers the multi-cycle medical waste recycling vehicle routing problem with time windows for preventing and reducing the risk of medical waste transportation. First, a mixed-integer linear programming model is formulated to minimize the total cost consisting of the vehicle dispatch cost and the transportation costs. In addition, an improved neighborhood search algorithm is designed for handling large-sized problems. In the algorithm, the initial solution is constructed using the Clarke–Wright algorithm in the first stage, and the variable neighborhood search algorithm with a simulated annealing strategy is introduced for exploring a better solution in the second stage. Results: The computational results demonstrate the performance of the suggested algorithm. In addition, the total cost of recycling in the periodic strategy is lower than with the single-cycle strategy. Conclusions: The proposed model and algorithm have the management improvement value of the studied medical waste recycling vehicle routing problem. Full article

This paper addresses the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery (EVRP-SPD), in which electric vehicles (EVs) simultaneously deliver goods to and pick up goods from customers. Due to the limited battery capacity of EVs, their range is shorter than that of internal combustion vehicles. In the EVRP, in addition to the depot and the customers, there are also charging stations (CS) because EVs need to be charged when their battery is empty. The problem is formulated as an integer linear model, and an efficient solution is proposed to minimize the total distance traveled. To create a feasible initial solution, Clarke and Wright’s savings algorithm is used. Several variants of variable neighborhood search are tested, and the reduced-variable neighborhood search algorithm is used to find the best solution in a reasonable time. Computer experiments are performed with benchmark instances to evaluate the effectiveness of our approach in terms of solution quality and time. The obtained results show that the proposed method can achieve efficient solutions in terms of solution quality and time in all benchmark instances. Full article

In the event of a disaster, the road network is often compromised in terms of its capacity and usability conditions. This is a challenge for humanitarian operations in the context of delivering critical medical supplies. To optimise vehicle routing for such a problem, a Multi-Depot Dynamic Vehicle-Routing Problem with Stochastic Road Capacity (MDDVRPSRC) is formulated as a Markov Decision Processes (MDP) model. An Approximate Dynamic Programming (ADP) solution method is adopted where the Post-Decision State Rollout Algorithm (PDS-RA) is applied as the lookahead approach. To perform the rollout effectively for the problem, the PDS-RA is executed for all vehicles assigned for the problem. Then, at the end, a decision is made by the agent. Five types of constructive base heuristics are proposed for the PDS-RA. First, the Teach Base Insertion Heuristic (TBIH-1) is proposed to study the partial random construction approach for the non-obvious decision. The heuristic is extended by proposing TBIH-2 and TBIH-3 to show how Sequential Insertion Heuristic (SIH) (I1) as well as Clarke and Wright (CW) could be executed, respectively, in a dynamic setting as a modification to the TBIH-1. Additionally, another two heuristics: TBIH-4 and TBIH-5 (TBIH-1 with the addition of Dynamic Lookahead SIH (DLASIH) and Dynamic Lookahead CW (DLACW) respectively) are proposed to improve the on-the-go constructed decision rule (dynamic policy on the go) in the lookahead simulations. The results obtained are compared with the matheuristic approach from previous work based on PDS-RA. Full article

Before the COVID-19 pandemic there had already been an increase in individual shipment transportation including inner-city areas. During the pandemic and implementation of adopted preventive measures, it has increased by more than 100% in some cities. This presents an unsustainable development, particularly in terms of urban environment. The above-mentioned development has accelerated the research related to optimal allocation of logistics centres considering the last-mile distribution. Unfortunately, the theoretical mathematical model that finds an optimal urban logistics centre location based on the matrix of distance, number, and weight of shipments is not applicable in most cities. Therefore, the following research methodology was chosen in accordance with the approved territorial plan. The authors considered those locations in Bratislava—the capital of Slovak Republic—which are designated, or suitable for building up of an urban logistics centre. These localities were afterwards evaluated in a real-world case study employing methods of mathematical programming (linear programming), the nearest neighbour method, and the Clarke-Wright method. The presented methodology can be applied not only when deciding on the appropriate location of the city logistics centre, but also at optimizing the vehicle routing problem. Taking into account the urban logistics sustainability and the e-commerce growth, it was analysed whether the suggested location of urban logistics centre is feasible to provision examined facilities using electric vehicles. The range of considered electric vehicles of N2 category present in the market tends to be at the limits of distribution routes length for the given case study. Therefore, the article also deals with the fast-charging possibilities of vehicles during handling operations and the use of hybrid freight vehicles in city logistics. Full article

The formation of a cooperative alliance is an effective means of approaching the vehicle routing optimization in two-echelon reverse logistics networks. Cooperative mechanisms can contribute to avoiding the inefficient assignment of resources for the recycling logistics operations and reducing long distance transportation. With regard to the relatively low performance of waste collection, this paper proposes a three-phase methodology to properly address the corresponding vehicle routing problem on two echelons. First, a bi-objective programming model is established to minimize the total cost and the number of vehicles considering semitrailers and vehicles sharing. Furthermore, the Clarke–Wright (CW) savings method and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) are combined to design a hybrid routing optimization heuristic, which is denoted CW_NSGA-II. Routes on the first and second echelons are obtained on the basis of sub-optimal solutions provided by CW algorithm. Compared to other intelligent algorithms, CW_NSGA-II reduces the complexity of the multi-objective solutions search and mostly converges to optimality. The profit generated by cooperation among retail stores and the recycling hub in the reverse logistics network is fairly and reasonably distributed to the participants by applying the Minimum Costs-Remaining Savings (MCRS) method. Finally, an empirical study in Chengdu City, China, reveals the superiority of CW_NSGA over the multi-objective particle swarm optimization and the multi objective genetic algorithms in terms of solutions quality and convergence. Meanwhile, the comparison of MCRS method with the Shapley value model, equal profit method and cost gap allocation proves that MCRS method is more conducive to the stability of the cooperative alliance. In general, the implementation of cooperation in the optimization of the reverse logistics network effectively leads to the sustainable development of urban and sub-urban areas. Through the reasonable reorganization of the entire network, recycling companies can provide more reliable services, contribute to the reduction of environmental pollution, and guarantee significant profits. Thus, this paper provides manufacturing companies, logistics operators and local governments with tools to protect the environment, while still making profits. Full article