A practical model of routing problems for automated guided vehicles with acceleration and deceleration

Many packing, routing, and knapsack problems can be expressed in terms of integer linear programming models based on set covering. These models have been exploited in a range of successful heuristics and exact techniques for tackling such problems. In this paper, we show that integer linear programming models based on set covering can be very useful for their use within an algorithm called “Construct, Merge, Solve & Adapt”(CMSA), which is a recent hybrid metaheuristic for solving combinatorial optimization problems. This is because most existing applications of CMSA are characterized by the use of an integer programming solver for solving reduced problem instances at each iteration. We present applications of CMSA to the variable-sized bin packing problem and to the electric vehicle routing problem with time windows and simultaneous pickups and deliveries. In both applications, CMSA based on a set covering model strongly outperforms CMSA when using an assignment-type model. Moreover, state-of-the-art results are obtained for both considered optimization problems.

Multi-echelon distribution mechanism is common in supply chain design and logistics systems in which freight is delivered to the customers through intermediate depots (IDs), instead of using direct shipments. This primarily decreases the cost of the chain and consequences of environmental (energy consumption) and social (traffic, air pollution, etc.) logistic operations. This paper develops a novel multi-objective mixed-integer linear programming model (MOMILP) for a two-echelon green capacitated vehicle routing problem (2E-CVRP) in which environmental issues and time windows constraints are considered for perishable products delivery phase. To validate the proposed mathematical model, several numerical examples are generated randomly and solved using CPLEX solver of GAMS software. The e-constraint method is applied to the model to deal with the multi-objectiveness of the proposed model. Finally, the best Pareto solution for each problem is determined based on the reference point approach (RPA) as one of the most effective techniques to help the decision-makers.

Routing problems occur in a wide variety of situations. Due to the heterogeneity of cases we do not yet know how to manage the complexity of addressing all the relevant aspects in logistic planning and solving the variety of different problem types in a cost-efficient way. In the last decade, we have witnessed an emergence of systematic approach into managing variation within a set of related software systems. This paper presents an application of these advances from software engineering into vehicle routing: we suggest the construction of a higher-level (meta-) model of routing problems and the application of a software product line approach. The proposed approach results in a flexible product line for constructing a family of routing systems cost-efficiently.

Optimization of Chartered Buses Routes Under Uncertainties Using Probabilistic Vehicle Routing Problem Modeling

This paper focuses on aircraft routing and crew rostering problems simultaneously considering the risk of COVID-19 infection. As airports are among high-risk places in COVID-19 pandemic, the crew prefer to spend less sit time in airports and come back to their home base at the end of each duty day. In this research, an integrated model is developed to assign crew and aircraft to flights in order to achieve a fair schedule for the crew. The objective function is minimisation of the difference between crew sit times. Moreover in this model, a framework including flight hours, number of days and number of take-offs is considered for maintenance requirements. Particle swarm optimisation (PSO) is used as the solution approach. To validate the solution approach, 20 test problems were solved using GAMS and PSO. The results show that PSO improved CPU time significantly (98.279% in average) in turn of 1.902% gap with GAMS in optimum solution.

In this paper a novel approach for modelling Routing and Wavelength Assignment (RWA) problem in wavelength-routed Dense Wavelength Division Multiplexing (DWDM) optical networks is proposed. A new idea based on Artificial Bee Colony (ABC) algorithm is introduced for solving RWA problem which is known to be an NP-hard problem. In the proposed ABC-RWA approach every food source represents a possible and feasible lightpath between each original and destination node pair in demand matrix. The positions of food sources are modified by some artificial bees in the population where the aim is to discover the places of food sources. The food source with the highest nectar value seems to be a solution which is evaluated by the fitness function. The simulation results demonstrate the ability and efficiency of proposed approach for solving RWA in real-world optical networks. The proposed approach could be extended for dynamic RWA schemes in real-time applications and employed by network resilience architectures.

The two-echelon production routing problem with cross-docking satellites

The problem of evacuation is very important when a disaster occurs. Evacuation must have systematic planning and good calculation. The planning is related to determining the route and flow of the vehicle. The shortest route problem is a matter of how to travel from one point of source through the paths to the destination with the shortest distance. The shortest path is the minimum trajectory needed to reach a certain point. In addition to the trajectory that has passed the shortest route, it needs to be taken into account about the maximum flow of a network. The maximum flow problem aims to maximize the movement of a number of items or objects through certain paths from the point of source to the end point. In this paper, in the future a model of vehicle movement will be developed to evacuate victims with maximum flow and through the shortest evacuation route by taking into account the paths to be followed in order to implement an effective evacuation process.

The article deals with the development of information systems (or their components) for energy, in particular, for solving problems of resource optimization. It is shown that such problems can arise at the stage of energy production, for example, from biofuel, during routing by transport. The article presents the formulation of the transport routing problem, the proposed methods for its solution and practical software implementation. Capacity of vehicles, time windows and predicted demand are taken into account when building a model for the transport routing problem. Solving the problem will reduce the cost of transportation and storage of resources.

Background: This research aims to solve a home healthcare vehicle routing problem (HHCVRP) model that considers the social aspect of sustainability and will be implemented in smart cities. In addition to the dynamism and uncertainty caused by variations in the patient’s condition, the proposed model considers parameters and variables that enhance its practicability, such as assuming different levels of patient importance (priority). Methods: The model was solved using a metaheuristic algorithm approach via the Ant Colony Optimization algorithm and the Non-Dominated Sorting technique due to the ability of such a combination to work out with dynamic models with uncertainties and multi-objectives. Results: This study proposes a novel mathematical model by integrating body sensors on patients to keep updating their conditions and prioritizing critical conditions in service. The sensitivity analysis demonstrates that using a heart rate sensor improves service quality and patient satisfaction without affecting the energy consumed. In addition, quality costs are increased if the importance levels of patients increase. Conclusions: The suggested model can assist healthcare practitioners in tracking patients’ health conditions to improve the quality of service and manage workload effectively. A trade-off between patient satisfaction and service provider satisfaction should be maintained.

Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution

An integrated model of aircraft routing and crew rostering problems to develop fair schedule for the crew under COVID-19 condition

In actual distribution process, the traffic flow varies with time, and each consumer has multiple fuzzy windows. To minimize the total distribution cost and mean consumer dissatisfaction, this paper sets up a vehicle routing problem (VRP) model with multiple fuzzy time windows, based on time-varying traffic flow. In addition, the Ito algorithm was improved based on time-varying traffic flow. The model and algorithm were verified through example simulation, in comparison with ant colony optimization (ACO). During the simulation, the improved Ito algorithm effectively reduced the distribution cost and consumer dissatisfaction, and outperformed the ACO in solving efficiency and solution quality. The results fully demonstrate the feasibility and effectiveness of the proposed algorithm. The research findings provide a desirable solution to VRPs with multiple fuzzy windows and time-varying traffic flow in the real world.

In this paper, a two-stage stochastic mathematical model is developed for an asset protection routing problem under a wildfire. The main aim of this study is to reduce the negative impact of a wildfire. Some parameters, such as travel and service times, obtaining profit by protecting an asset, and upper bounds of time windows, are considered as stochastic parameters. Generating proper scenarios for uncertain parameters has a large impact on the accuracy of the obtained solutions. Therefore, artificial neural networks are employed to extract possible scenarios according to previous actual wildfire events. The problem cannot be solved by exact solvers for large instances, so two matheuristic algorithms are proposed in this study to solve the problem in a reasonable time. In the first algorithm, a set of feasible routes is generated based on a heuristic approach, then a route-based mathematical model is used to obtain the final solution. Also, another matheuristic algorithm based on adaptive large neighbourhood search (ALNS) is proposed. In this algorithm, routing decisions are determined using the ALNS algorithm while other decisions are achieved by solving an intermediate mathematical model. The numerical analysis confirms the efficiency of both proposed algorithms; however, the first algorithm performs more efficiently.

Taking the distribution route optimization of refined oil as background, this paper studies the inventory routing problem of refined oil distribution based on working time equilibrium. In consideration of the constraints of vehicle capacity, time window for unloading oil, service time and demand of each gas station, we take the working time equilibrium of each vehicle as goal and establish an integer programming model for the vehicle routing problem of refined oil distribution, the objective function of the model is to minimize the maximum working time of vehicles. To solve this model, a Lingo program was written and a heuristic algorithm was designed. We further use the random generation method to produce an example with 10 gas stations. The local optimal solution and approximate optimal solution are obtained by using Lingo software and heuristic algorithm respectively. By comparing the approximate optimal solution obtained by heuristic algorithm with the local optimal solution obtained by Lingo software, the feasibility of the model and the effectiveness of the heuristic algorithm are verified. The results of this paper provide a theoretical basis for the scheduling department to formulate the oil distribution plan.