Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows

Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.

Vehicle routing optimization algorithm based on time windows and dynamic demand

To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.

Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows

The time dependent vehicle routing problem with time windows（TDVRPTW） is considered. A multi-type ant system（MTAS） algorithm hybridized with the ant colony system（ACS）and the max-min ant system（MMAS） algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection（NNS） mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm＇s good performance with all these strategies on classic vehicle routing problem with time windows（VRPTW） benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.

A GA approach to vehicle routing problem with time windows considering loading constraints

As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry.

Research on Vehicle Routing Problem with Soft Time Windows Based on Hybrid Tabu Search and Scatter Search Algorithm

With the expansion of the application scope of social computing problems,many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes,cultures,and the emotional needs of customers.The actual soft time window vehicle routing problem,speeding up the response of customer needs,improving distribution efficiency,and reducing operating costs is the focus of current social computing problems.Therefore,designing fast and effective algorithms to solve this problem has certain theoretical and practical significance.In this paper,considering the time delay problem of customer demand,the compensation problem is given,and the mathematical model of vehicle path problem with soft time window is given.This paper proposes a hybrid tabu search(TS)&scatter search(SS)algorithm for vehicle routing problem with soft time windows(VRPSTW),which mainly embeds the TS dynamic tabu mechanism into the SS algorithm framework.TS uses the scattering of SS to avoid the dependence on the quality of the initial solution,and SS uses the climbing ability of TS improves the ability of optimizing,so that the quality of search for the optimal solution can be significantly improved.The hybrid algorithm is still based on the basic framework of SS.In particular,TS is mainly used for solution improvement and combination to generate new solutions.In the solution process,both the quality and the dispersion of the solution are considered.A simulation experiments verify the influence of the number of vehicles and maximum value of tabu length on solution,parameters’control over the degree of convergence,and the influence of the number of diverse solutions on algorithm performance.Based on the determined parameters,simulation experiment is carried out in this paper to further prove the algorithm feasibility and effectiveness.The results of this paper provide further ideas for solving vehicle routing problems with time windows and improving the efficiency of vehicle routing problems and have strong applicability.

An Alternative Algorithm for Vehicle Routing Problem with Time Windows for Daily Deliveries

This study attempts to solve vehicle routing problem with time window (VRPTW). The study first identifies the real problems and suggests some recommendations on the issues. The technique used in this study is Genetic Algorithm (GA) and initialization applied is random population method. The objective of the study is to assign a number of vehicles to routes that connect customers and depot such that the overall distance travelled is minimized and the delivery operations are completed within the time windows requested by the customers. The analysis reveals that the problems experienced in vehicle routing with time window can be solved by GA and retrieved for optimal solutions. After a thorough study on VRPTW, it is highly recommended that a company should implement the optimal routes derived from the study to increase the efficiency and accuracy of delivery with time insertion.

A novel genetic algorithm for vehicle routing problem with time windows

A novel genetic algorithm with multiple species in dynamic region is proposed,each of which occupies a dynamic region determined by the weight vector of a fuzzy adaptive Hamming neural network. Through learning and classification of genetic individuals in the evolutionary procedure,the neural network distributes multiple species into different regions of the search space. Furthermore,the neural network dynamically expands each search region or establishes new region for good offspring individuals to continuously keep the diversification of the genetic population. As a result,the premature problem inherent in genetic algorithm is alleviated and better tradeoff between the ability of exploration and exploitation can be obtained. The experimental results on the vehicle routing problem with time windows also show the good performance of the proposed genetic algorithm.

Generalized multiple time windows model based parallel machine scheduling for TDRSS

The scheduling efficiency of the tracking and data relay satellite system（TDRSS）is strictly limited by the scheduling degrees of freedom（DoF）,including time DoF defined by jobs＇ flexible time windows and spatial DoF brought by multiple servable tracking and data relay satellites（TDRSs）.In this paper,ageneralized multiple time windows（GMTW）model is proposed to fully exploit the time and spatial DoF.Then,the improvements of service capability and job-completion probability based on the GMTW are theoretically proved.Further,an asymmetric path-relinking（APR）based heuristic job scheduling framework is presented to maximize the usage of DoF provided by the GMTW.Simulation results show that by using our proposal 11%improvement of average jobcompletion probability can be obtained.Meanwhile,the computing time of the time-to-target can be shorten to 1/9 of the GRASP.

Solving open vehicle problem with time window by hybrid column generation algorithm

This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.

Rolling horizon scheduling algorithm for dynamic vehicle scheduling system

Dynamic exclusive pickup and delivery problem with time windows (DE-PDPTW), aspecial dynamic vehicle scheduling problem, is proposed. Its mathematical description is given andits static properties are analyzed, and then the problem is simplified asthe asymmetrical travelingsalesman problem with time windows. The rolling horizon scheduling algorithm (RHSA) to solve thisdynamic problem is proposed. By the rolling of time horizon, the RHSA can adapt to the problem'sdynamic change and reduce the computation time by dealing with only part of the customers in eachrolling time horizon. Then, its three factors, the current customer window, the scheduling of thecurrent customer window and the rolling strategy, are analyzed. The test results demonstrate theeffectiveness of the RHSA to solve the dynamic vehicle scheduling problem.

Self-adaptive large neighborhood search algorithm for parallel machine scheduling problems

A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.

An improved estimation of distribution algorithm for multi-compartment electric vehicle routing problem

The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process,are employed.A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost,distribution cost,time window penalty cost and charging service cost.To solve the problem,an estimation of the distribution algorithm based on Lévy flight(EDA-LF)is proposed to perform a local search at each iteration to prevent the algorithm from falling into local optimum.Experimental results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA algorithm.In addition,when comparing with existing algorithms,the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale instances.Further experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles.

On Vehicle Routing Problems (VRP) with a Focus on Multiple Priorities

This paper discusses the concept of priorities based on Time and Quantity, which arise on the occasion of vehicle routing. It explains the interconnectivity between the priorities based on Time and Quantity and formulates a dynamic that shows the fusion of Time and Quantity into the Vehicle Routing Problem’s objective function. The paper focuses on the development of an expanded VRP objective function in which the priorities based on Time and Quantities are imbedded thus opens a vista of knowledge, aggregating and modelling the priorities as a mean to reduce transportation costs that lead to an organized and more timely deliveries of goods employing various of today’s proposed logistic systems coupled with widely used positioning systems.

Adaptive subsequence adjustment with evolutionary asymmetric path-relinking for TDRSS scheduling

Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.

Improved Multi-Agent System for the Vehicle Routing Problem with Time Windows

The vehicle routing problem with time windows （VRPTW） involves assigning a fleet of limited capacity vehicles to serve a set of customers without violating the capacity and time constraints. This paper presents a multi-agent model system for the VRPTW based on the internal behavior of agents and coordination among the agents. The system presents a formal view of coordination using the traditional contract-net protocol （CNP） that relies on the basic loop of agent behavior for order receiving, order announcement, bid calculation, and order scheduling followed by order execution. An improved CNP method based on a vehicle selection strategy is used to reduce the number of negotiations and the negotiation time. The model is validated using Solomon＇s benchmarks, with the results showing that the improved CNP uses only 30% as many negotiations and only 70% of the negotiation time of the traditional CNP.

Fleet Scheduling for Multiple Plants with Limited Storage Capacity

Some manufacturers replace traditional warehouses with shipping areas at the scattered plants for holding the finished products in order to reduce land usage and inventory cost.The limited storage capacity of such shipping area leads to challenges of scheduling vehicles for pickup since the overflow of storage space is prohibited.A heuristic rule is developed for splitting the continuous arrival of inventory at a plant into a sequence of discrete tasks for pickup.In this way,the original problem can be converted into a multiple trip vehicle routing problem with time window(MTVRPTW).Subsequently,a modified tabu search(TS)algorithm is applied for deriving the schedule.Finally,an industry case of an electric apparatus manufacturer is studied to demonstrate and validate the developed optimization approach,and the results imply good performance of the developed tool.

Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution tasks.The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW.Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles,the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce.However,practically,it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles(EVs)in actual operation.Hence,we propose the robust optimization model based on a route-related uncertain set to tackle this problem.Moreover,adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem.The effectiveness of the method is verified by experiments,and the influence of uncertain demand and uncertain parameters on the solution is further explored.

A Coordination-Based Algorithm for Dedicated Destination Vehicle Routing in B2B E-Commerce

This paper proposes a solution to the open vehicle routing problem with time windows(OVRPTW)considering third-party logistics(3PL).For the typical OVRPTW problem,most researchers consider time windows,capacity,routing limitations,vehicle destination,etc.Most researchers who previously investigated this problem assumed the vehicle would not return to the depot,but did not consider its final destination.However,by considering 3PL in the B2B e-commerce,the vehicle is required back to the nearest 3PL location with available space.This paper formulates the problem as a mixed integer linear programming(MILP)model with the objective of minimizing the total travel distance.A coordinate representation particle swarm optimization(CRPSO)algorithm is developed to obtain the best delivery sequencing and the capacity of each vehicle.Results of the computational study show that the proposed method provides solution within a reasonable amount of time.Finally,the result compared to PSO also indicates that the CRPSO is effective.

Modeling for UAV resource scheduling under mission synchronization

Unmanned aerial vehicle（UAV） resource scheduling means to allocate and aggregate the available UAV resources depending on the mission requirements and the battlefield situation assessment.In previous studies,the models cannot reflect the mission synchronization;the targets are treated respectively,which results in the large scale of the problem and high computational complexity.To overcome these disadvantages,a model for UAV resource scheduling under mission synchronization is proposed,which is based on single-objective non-linear integer programming.And several cooperative teams are aggregated for the target clusters from the available resources.The evaluation indices of weapon allocation are referenced in establishing the objective function and the constraints for the issue.The scales of the target clusters are considered as the constraints for the scales of the cooperative teams to make them match in scale.The functions of the intersection between the ＂mission time-window＂ and the UAV ＂arrival time-window＂ are introduced into the objective function and the constraints in order to describe the mission synchronization effectively.The results demonstrate that the proposed expanded model can meet the requirement of mission synchronization,guide the aggregation of cooperative teams for the target clusters and control the scale of the problem effectively.

Research on Refined Oil Distribution Plan Based on Dynamic Time Window

Based on the analysis of the refined oil distribution plan which includes the various vehicles models-single oil-multiple gas stations. This paper puts forward the dynamic time window of oil supplementation based on every moment, and establishes the mathematical model of the refined oil distribution plan, using C language, taking various circumstances of the model into account to find the optimal solution through several operations. This process can make the refined oil distribution plan and the distribution route is more reasonable. At the same time, the distribution cost is lowest. Through the analysis of the experimental results, the validity and algorithm of the model are proved.