A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows

Research on cross-regional emergency materials intelligent dispatching model in major natural disasters

The increasingly frequent occurrence of major natural disasters can pose a serious threat to national stability and the safety of people's lives, and cause serious economic losses. How to quickly and accurately dispatch emergency materials to all disaster areas across regions in post-disaster has attracted wide attention from the government and academia. In response to the characteristic of high uncertainty in emergency rescue for major natural disasters, and considering differentiated disaster severity levels in different disaster areas, the entropy weight method is used to determine the urgency coefficient of emergency material demand for disaster areas. This study aims to minimize the emergency materials dispatching time and cost, also maximize the dispatching fairness for disaster areas. The triangular fuzzy number method is used to represent the uncertain variables mentioned above, so that a cross-regional emergency materials intelligent dispatching model in major natural disasters (CREMIDM-MND) is constructed. The extremely heavy rainstorm disaster in Henan Province of China in 2021 is selected as a typical case. Based on objective disaster data obtained from official websites, this study applies the constructed model to real disaster case and calculates the results by MATLAB. The ant colony algorithm is further used to optimize the transportation route based on the calculation results of the emergency material dispatching for disaster areas, and finally forms the intelligent emergency materials dispatching scheme that meets the multiple objectives. The research results indicate that compared to the actual situation, CREMIDM-MND can help decision-maker to develop a cross-regional emergency materials intelligent dispatching scheme in time, thereby effectively improving the government's emergency rescue performance in major natural disasters. Moreover, some managerial insights related to cross-regional emergency materials dispatching practice problem in major natural disasters are presented.

Scheduling optimization of electric energy meter distribution vehicles for intelligent batch rotation

As industrial technology continues to advance through integration, society's demand for electricity is rapidly increasing. To meet the requirements of refined grid management and address the elevated challenges arising from the increased electrical load, this paper delves into the investigation of distribution vehicle scheduling for the practical scenario of batch rotation of smart meters. Initially, based on the practical distribution task requirements of a provincial metrology verification center, a multi-level optimization model is constructed for the batch rotation and distribution vehicle scheduling of smart meters. The primary objective is to maximize the enhancement of smart meter distribution efficiency while minimizing the overall distribution cost. Moreover, this paper introduces a refined Grey Wolf Optimization algorithm (OLC-GWO) based on Opposition-based Learning, Levy flight strategy, and Cauchy mutation to solve the model. By generating an opposite population to improve the quality of initial feasible solutions and further harnessing the global search capabilities of Levy flight and Cauchy mutation operators, the algorithm's effectiveness is enhanced. The algorithm is subjected to testing using multiple benchmark functions and its performance is compared with variants of GWO, as well as several cutting-edge intelligent optimization algorithms including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Honey Bee Algorithm (HBA). The results indicate that OLC-GWO exhibits excellent performance in terms of convergence speed and optimization capability. Finally, the improved algorithm is subjected to simulation experiments by incorporating order data from the practical distribution operations of a provincial metrology verification center. The outcomes verify the efficiency of the proposed algorithm, reinforcing the practical significance of the established model in addressing the real-world challenge of batch rotation and distribution vehicle scheduling for smart meters.

A hybrid adaptive large neighborhood search for time-dependent open electric vehicle routing problem with hybrid energy replenishment strategies

As competition intensifies, an increasing number of companies opt to outsource their package distribution operations to professional Third-Party Logistics (3PL) fleets. In response to the growing concern over urban pollution, 3PL fleets have begun to deploy Electric Vehicles (EVs) to perform transportation tasks. This paper aims to address the Time-Dependent Open Electric Vehicle Routing Problem with Hybrid Energy Replenishment Strategies (TDOEVRP-HERS) in the context of urban distribution. The study considers the effect of dynamic urban transport networks on EV energy drain and develops an approach for estimating energy consumption. Meanwhile, the research further empowers 3PL fleets to judiciously oscillate between an array of energy replenishment techniques, encompassing both charging and battery swapping. Based on these insights, a Mixed-Integer Programming (MIP) model with the objective of minimizing total distribution costs incurred by the 3PL fleet is formulated. Given the characteristics of the model, a Hybrid Adaptive Large Neighborhood Search (HALNS) is designed, synergistically integrating the explorative prowess of Ant Colony Optimization (ACO) with the localized search potency of Adaptive Large Neighborhood Search (ALNS). The strategic blend leverages the broad-based solution initiation of ACO as a foundational layer for ALNS's deeper, nuanced refinements. Numerical experiments on a spectrum of test sets corroborate the efficacy of the HALNS: it proficiently designs vehicular itineraries, trims down EV energy requisites, astutely chooses appropriate energy replenishment avenues, and slashes logistics-related outlays. Therefore, this work not only introduces a new hybrid heuristic technique within the EVRP field, providing high-quality solutions but also accentuates its pivotal role in fostering a sustainable trajectory for urban logistics transportation.

Improved whale swarm algorithm for solving material emergency dispatching problem with changing road conditions

To overcome the problem of easily falling into local extreme values of the whale swarm algorithm to solve the material emergency dispatching problem with changing road conditions, an improved whale swarm algorithm is proposed. First, an improved scan and Clarke-Wright algorithm is used to obtain the optimal vehicle path at the initial time. Then, the group movement strategy is designed to generate offspring individuals with an improved quality for refining the updating ability of individuals in the population. Finally, in order to maintain population diversity, a different weights strategy is used to expand individual search spaces, which can prevent individuals from prematurely gathering in a certain area. The experimental results show that the performance of the improved whale swarm algorithm is better than that of the ant colony system and the adaptive chaotic genetic algorithm, which can minimize the cost of material distribution and effectively eliminate the adverse effects caused by the change of road conditions.

Modified Ant Colony Optimization with Route Elimination and Pheromone Reset for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window

The vehicle routing problem (VRP) has many applications in goods distribution and goods transportation. Today, many companies have requirements for VRP with multiple pickup and multiple delivery within due time. This problem is called multiple pickup and multiple delivery vehicle routing problem with time window (PDPTW). PDPTW has many constraints and ant colony optimization (ACO) has been used to solve it although ACO creates too many infeasible routes. Moreover, it often gets trapped in local optimum. To solve these problems, this paper proposed an improved ACO by using the route elimination technique and the pheromone reset technique. The ACO with route elimination technique, it has proven to solve the PDPTW problem with increased performance. The proposed technique was tested on datasets from the Li & Lim’s PDPTW benchmark problems and provided more satisfactory results compared to other ACO techniques.

Adaptive Coordination Ant Colony Optimization for Multipoint Dynamic Aggregation

Multipoint dynamic aggregation is a meaningful optimization problem due to its important real-world applications, such as post-disaster relief, medical resource scheduling, and bushfire elimination. The problem aims to design the optimal plan for a set of robots to execute geographically distributed tasks. Unlike the majority of scheduling and routing problems, the tasks in this problem can be executed by multiple robots collaboratively. Meanwhile, the demand of each task changes over time at an incremental rate and is affected by the abilities of the robots executing it. This poses extra challenges to the problem, as it has to consider complex coupled relationships among robots and tasks. To effectively solve the problem, this article develops a new metaheuristic algorithm, called adaptive coordination ant colony optimization (ACO). We develop a novel coordinated solution construction process using multiple ants and pheromone matrices (each robot/ant forages a path according to its own pheromone matrix) to effectively handle the collaborations between robots. We also propose adaptive heuristic information based on domain knowledge to promote efficiency, a pheromone-based repair mechanism to tackle the tight constraints of the problem, and an elaborate local search to enhance the exploitation ability of the algorithm. The experimental results show that the proposed adaptive coordination ACO significantly outperforms the state-of-the-art methods in terms of both effectiveness and efficiency.

Deep reinforcement learning algorithm for solving material emergency dispatching problem

In order to solve the problem that the scheduling scheme cannot be updated in real time due to the dynamic change of node demand in material emergency dispatching, this article proposes a dynamic attention model based on improved gated recurrent unit. The dynamic codec framework is used to track the change of node demand to update the node information. The improved gated recurrent unit is embedded between codecs to improve the representation ability of the model. By weighted combination of the node information of the previous time, the current time and the initial time, a more representative node embedding is obtained. The results show that compared with the elitism-based immigrants ant colony optimization algorithm, the solution quality of the proposed model was improved by 27.89, 27.94, 28.09 and 28.12% when the problem scale is 10, 20, 50 and 100, respectively, which can effectively deal with the instability caused by the change of node demand, so as to minimize the cost of material distribution.

Green Vehicle-Routing Problem of Fresh Agricultural Products Considering Carbon Emission

The need to reduce carbon emission to cope with climate change has gradually become a global consensus, which also poses a great challenge to cold-chain logistics companies. It forces them to implement green distribution strategies. To help the distribution companies reduce carbon emission, this paper studies two aspects—carbon tax value and investing in the freshness-keeping cost—and proposes corresponding solutions. A new green vehicle-routing model for fresh agricultural products with the goal of minimizing the total cost is proposed. To solve the model proposed, an improved ant-colony optimization (IACO) is designed specifically. On one hand, the experimental results show that the increase in carbon tax will restrict the carbon emission behaviors of the distribution companies, but it will also reduce their economic benefits to a certain extent, at the same time. On the other hand, investing in the freshness-keeping cost can help actively achieve the carbon emission reduction target, reduce the loss of fresh agricultural products in the distribution process, improve the company’s economic benefits and satisfy customers. The comparison results of different algorithms prove that the IACO proposed in this paper is more effective in solving the model, which can help increase the economic benefits of the companies and reduce carbon emission. This study provides a new solution for cold-chain logistics distribution companies to reduce carbon emission in the distribution process, and also provides a reference for government departments to formulate carbon tax policies.

Customer-Centric, Two-Product Split Delivery Vehicle Routing Problem under Consideration of Weighted Customer Waiting Time in Power Industry

This paper introduces a new model of the customer-centric, two-product split delivery vehicle routing problem (CTSDVRP) in the context of a mixed-flow manufacturing system that occurs in the power industry. Different from the general VRP model, the unique characteristics of our model are: (1) two types of products are delivered, and the demand for them is interdependent and based on a bill of materials (BOM); (2) the paper considers a new aspect in customer satisfaction, i.e., the consideration of the production efficiency on the customer side. In our model, customer satisfaction is not measured by the actual customer waiting time, but by the weighted customer waiting time, which is based on the targeted service rate of the end products. We define the targeted service rate as the ratio of the quantity of the end product produced by the corresponding delivery quantities of the two products to the demand of the end product. We propose a hybrid ant colony-genetic optimization algorithm to solve this model with actual data from a case study of the State Grid Corporation of China. Finally, a case study is explored to assess the effectiveness of the CTSDVRP model and highlight some insights. The results show that the CTSDVRP model can improve customer satisfaction and increase the average targeted service rate of the end products effectively.

Optimization and Machine Learning Applied to Last-Mile Logistics: A Review

The growth in e-commerce that our society has faced in recent years is changing the view companies have on last-mile logistics, due to its increasing impact on the whole supply chain. New technologies are raising users’ expectations with the need to develop customized delivery experiences; moreover, increasing pressure on supply chains has also created additional challenges for suppliers. At the same time, this phenomenon generates an increase in the impact on the liveability of our cities, due to traffic congestion, the occupation of public spaces, and the environmental and acoustic pollution linked to urban logistics. In this context, the optimization of last-mile deliveries is an imperative not only for companies with parcels that need to be delivered in the urban areas, but also for public administrations that want to guarantee a good quality of life for citizens. In recent years, many scholars have focused on the study of logistics optimization techniques and, in particular, the last mile. In addition to traditional optimization techniques, linked to the disciplines of operations research, the recent advances in the use of sensors and IoT, and the consequent large amount of data that derives from it, are pushing towards a greater use of big data and analytics techniques—such as machine learning and artificial intelligence—which are also in this sector. Based on this premise, the aim of this work is to provide an overview of the most recent literature advances related to last-mile delivery optimization techniques; this is to be used as a baseline for scholars who intend to explore new approaches and techniques in the study of last-mile logistics optimization. A bibliometric analysis and a critical review were conducted in order to highlight the main studied problems, the algorithms used, and the case studies. The results from the analysis allow the studies to be clustered into traditional optimization models, machine learning approaches, and mixed methods. The main research gaps and limitations of the current literature are assessed in order to identify unaddressed challenges and provide research suggestions for future approaches.

Parallel m-dimensional relative ant colony optimization (mDRACO) for the Costas-array problem

The Costas-array problem is a combinatorial constraint-satisfaction problem (CSP) that remains unsolved for many array sizes greater than 30. In order to reduce the time required to solve large instances, we present an Ant Colony Optimization algorithm called m-Dimensional Relative Ant Colony Optimization ($$m$$ m DRACO) for combinatorial CSPs, focusing specifically on the Costas-array problem. This paper introduces the optimizations included in $$m$$ m DRACO, such as map-based association of pheromone with arbitrary-length component sequences and relative path storage. We assess the quality of the resulting $$m$$ m DRACO framework on the Costas-array problem by computing the efficiency of its processor utilization and comparing its run time to that of an ACO framework without the new optimizations. $$m$$ m DRACO gives promising results; it has efficiency greater than 0.5 and reduces time-to-first-solution for the $$m = 16$$ m = 16 Costas-array problem by a factor of over 300.

An adaptive simulated annealing and artificial fish swarm algorithm for the optimization of multi-depot express delivery vehicle routing

In this paper, the Capacitated Vehicle Routing Problem (CVRP) of multi-depot express delivery is investigated based on the actual express delivery business in Beijing and driving intention-based road network. An Adaptive Simulated Annealing and Artificial Fish Swarm Algorithm (A-SAAFSA) is proposed to solve the CVRP. The basic ideas are use a “certainty” probability to accept the worst solution through the Metropolis criterion in the search process, and a strategy of adjusting the swimming direction to avoid falling into the local optimal solution. Moreover, an adaptive visual strategy, which adjusts the visual range adaptively in real time according to the current solution quality, is used to ensure the efficient searching and accuracy of the algorithm. Experimental results show that the A-SAAFSA algorithm outperforms four well-known algorithms, namely simulated annealing and artificial fish swarm algorithm, artificial fish swarm algorithm, simulated annealing algorithm, and genetic algorithm.

Study on Customized Shuttle Transit Mode Responding to Spatiotemporal Inhomogeneous Demand in Super-Peak

Instantaneous mega-traffic flow has long been one of the major challenges in the management of mega-cities. It is difficult for the public transportation system to cope directly with transient mega-capacity flows, and the uneven spatiotemporal distribution of demand is the main cause. To this end, this paper proposed a customized shuttle bus transportation model based on the “boarding-transfer-alighting” framework, with the goal of minimizing operational costs and maximizing service quality to address mega-transit demand with uneven spatiotemporal distribution. The fleet application is constructed by a pickup and delivery problem with time window and transfer (PDPTWT) model, and a heuristic algorithm based on Tabu Search and ALNS is proposed to solve the large-scale computational problem. Numerical tests show that the proposed algorithm has the same accuracy as the commercial solution software, but has a higher speed. When the demand size is 10, the proposed algorithm can save 24,000 times of time. In addition, 6 reality-based cases are presented, and the results demonstrate that the designed option can save 9.93% of fleet cost, reduce 45.27% of vehicle waiting time, and 33.05% of passenger waiting time relative to other existing customized bus modes when encountering instantaneous passenger flows with time and space imbalance.

A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

In this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

Optimization-assisted water supplement mechanism with energy efficiency in IoT based greenhouse

Remarkable resource management and energy efficiency improvements can be achieved in greenhouses using innovative technological advancements and modern agricultural methods. Deployment of Internet of Things (IoT) and optimization algorithms in greenhouse farming is highly desirable for real-time monitoring and controlling various parameters with optimal solutions. However, IoT based greenhouses require more energy as compared to traditional farming. This paper proposes an optimal greenhouse water supplement mechanism with efficient energy consumption based on IoT and optimization techniques. The first contribution of this study is to gather the actual water and soil moisture levels from the greenhouse and tank using IoT devices. Secondly, the formulation and deployment of an objective function to compute the optimal water and soil moisture levels for greenhouse and tank based on user-desired settings, the system constraints and actual sensing values. We applied a rule-based expert system to activate water pumps with the required flow rate and operational duration to achieve efficient energy consumption. To prove the effectiveness of the proposed concept, embedded IoT devices and objective function for optimization are deployed as well as, a number of experiments are conducted to provide the optimal water and soil moisture levels in a real greenhouse and water tank environment.

A Dynamic Route-Planning System Based on Industry 4.0 Technology

Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.

Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm

This study studies the problem of on-orbit maintenance task planning for space-robot clusters. Aiming at the problem of low maintenance efficiency of space-robot cluster task-planning, this study proposes a cluster-task-planning method based on energy and path optimization. First, by introducing the penalty-function method, the task planning problem of the space-robot cluster under limited energy is analyzed, and the optimal-path model for task planning with comprehensive optimization of revenue and energy consumption are constructed; then, the maintenance task points are clustered to reduce the scale of the problem, thus reducing the difficulty of solving the problem; finally, a modified differential evolution algorithm is proposed to solve the problem of space-robot cluster task-planning, improve the performance of space-robot cluster task-assignment and path planning. Simulation results show that the proposed optimal-path model of space-robot cluster and the modified differential evolution algorithm can effectively solve the task-planning problem of spatial robot clusters.