A variable neighborhood descent with ant colony optimization to solve a bilevel problem with station location and vehicle routing

Applied Intelligence - Electric vehicles are becoming popular in transport systems due to subsidies provided by the governments and the reduction of environmental issues. A bilevel optimization...     

基于第三方带软时间窗约束的车辆路径问题研究

在分析电商企业的“自建物流+第三方物流”配送模式的基础上,对自建物流成本和第三方物流成本分别展开研究,并在自建物流成本中设计了软时间窗惩罚函数.建立了基于第三方带软时间窗约束的车辆路径模型,设计了基于自然数序列的改进遗传算法对模型进行求解,改进交叉与变异操作来保护优秀基因,提出了种群扩张机制.最后,算例结果表明模型可以有效减少物流配送成本,提高配送效率,改进遗传算法还在提高计算时间方面有显著的成效.     

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives—to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon’s and Gehring and Homberger’s benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with the two-tailed Wilcoxon test for verifying the statistical significance of the results.     

Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem

This paper investigates integrated flexible job shop problem (FJSP) with preventive maintenance (PM) activities under the multi-objective optimization approaches. Finding compromise solutions between the production objectives and maintenance ones is under consideration. In order to carry out the maintenance activities, reliability models are employed. This paper attempts to simultaneously optimize two objectives: the minimization of the makespan for the production part and the minimization of the system unavailability for the maintenance part. For doing it so, two decisions are taken at the same time: finding the appropriate assignment of n jobs on m machines in order to minimize the makespan and deciding when to execute the PM activities in order to minimize the system unavailability. Both the maintenance activity numbers and maintenance intervals are not fixed in advance. Four multi-objective optimization methods are compared to find the Pareto-optimal front in the flexible job-shop problem case. Promising the obtained results, a benchmark with a large number of test instances (more than 4800) and meticulous care is employed.     

A hub location problem with fully interconnected backbone and access networks

This paper considers the design of two-layered fully interconnected networks. A two-layered network consists of clusters of nodes, each defining an access network and a backbone network. We consider the integrated problem of determining the access networks and the backbone network simultaneously. A mathematical formulation is presented, but as the linear programming relaxation of the mathematical formulation is weak, a formulation based on the set partitioning model and column generation approach is also developed. The column generation subproblems are solved by solving a series of quadratic knapsack problems. We obtain superior bounds using the column generation approach than with the linear programming relaxation. The column generation method is therefore developed into an exact approach using the branch-and-price framework. With this approach we are able to solve problems consisting of up to 25 nodes in reasonable time. Given the difficulty of the problem, the results are encouraging.     

Joint scheduling and routing algorithm with load balancing in wireless mesh network

Wireless mesh network (WMN) is a promising solution for last mile broadband internet access. Mesh nodes or mesh routers are connected via wireless links to form a multi-hop backbone infrastructure and improving throughput is the primary goal. While previous works mainly focused on either link level protocol design or complex mathematical model, in this paper, we investigate the performance gains from jointly optimizing scheduling and routing in a multi-radio, multi-channel and multi-hop wireless mesh network. Then, two optimization objectives are addressed by considering wireless media contention and spatial multiplexing. The first objective is to maximize throughput by exploiting spatial reuse while the second one is to ensure fairness among different links. We design a cross-layer algorithm by considering both MAC layer and network layer. Simulation results show that our joint optimization algorithm can significantly increase throughput as well as fairness.     

Spiking neural firefly optimization scheme for the capacitated dynamic vehicle routing problem with time windows

Neural Computing and Applications - A number of technological improvements have prompted a great concern on ‘dynamism’ in vehicle routing problems (VRP). In real-world applications, the...     

Multiple- and single-objective approaches to laminate optimization with genetic algorithms

In this paper an application of a genetic algorithm to a material- and sizing-optimization problem of a plate is described. This approach has obvious advantages: it does not require any derivative information and it does not impose any restriction, in terms of convexity, on the solution space. The plate optimization problem is firstly formulated as a constrained mixed-integer programming problem with a single objective function. An alternative multiobjective formulation of the problem in which some constraints are included as additional objectives is also presented. Some numerical results are included that show the appropriateness of the algorithm and of the mathematical model for the solution of this optimization problem, as well as the superiority of the multiobjective approach.     

Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem

This paper deals with the joint production and maintenance scheduling problem according to a new bi-objective approach. This method allows the decision maker to find compromise solutions between the production objectives and maintenance ones. Reliability models are used to take into account the maintenance aspect of the problem. The aim is to simultaneously optimize two criteria: the minimization of the makespan for the production part and the minimization of the system unavailability for the maintenance side. Two decisions are taken at the same time: finding the best assignment of n jobs to m machines in order to minimize the makespan and deciding when to carry out the preventive maintenance actions in order to minimize the system unavailability. The maintenance actions numbers as well as the maintenance intervals are not fixed in advance. Two evolutionary genetic algorithms are compared to find an approximation of the Pareto-optimal front in the parallel machine case. On a large number of test instances (more than 5000), the obtained results are promising.     

Lagrangian duality applied to the vehicle routing problem with time windows

This paper considers the vehicle routing problem with time windows, where the service of each customer must start within a specified time interval. We consider the Lagrangian relaxation of the constraint set requiring that each customer must be served by exactly one vehicle yielding a constrained shortest path subproblem. We present a stabilized cutting-plane algorithm within the framework of linear programming for solving the associated Lagrangian dual problem. This algorithm creates easier constrained shortest path subproblems because less negative cycles are introduced and it leads to faster multiplier convergence due to a stabilization of the dual variables. We have embedded the stabilized cutting-plane algorithm in a branch-and-bound search and introduce strong valid inequalities at the master problem level by Lagrangian relaxation. The result is a Lagrangian branch-and-cut-and-price (LBCP) algorithm for the VRPTW. Making use of this acceleration strategy at the master problem level gives a significant speed-up compared to algorithms in the literature based on traditional column generation. We have solved two test problems introduced in 2001 by Gehring and Homberger with 400 and 1000 customers respectively, which to date are the largest problems ever solved to optimality. We have implemented the LBCP algorithm using the ABACUS open-source framework for solving mixed-integer linear-programs by branch, cut, and price.     

Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows

Today, companies need to collect and to deliver goods from and to their depots and their customers. This problem is described as a Vehicle Routing Problem with Mixed Linehaul and Backhaul customers (VRPMB). The goods delivered from the depot to the customers can be alternated with the goods picked up. Other variants of VRP added to VRPMB are Heterogeneous fleet and Time Windows. This paper studies a complex VRP called HVRPMBTW which concerns a logistic/transport society, a problem rarely studied in literature. In this paper, we propose a Particle Swarm Optimization (PSO) with a local search. This approach has shown its effectiveness on several combinatorial problems. The adaptation of this approach to the problem studied is explained and tested on the benchmarks. The results are compared with our previous methods and they show that in several cases PSO improves the results.     

Equipment life time optimization with respect to operational expenses

The subject of investigations is the equipment operational model, where the operational expenses are represented by planned replacement expenses and losses due to failures. The two replacement strategies are considered: “planned replacement at the end of predetermined life time or after a failure” and “planned replacement at the end of predetermined life time.” In the second case only working capacity is restored after failure. The optimal lifetime value is shown to depend on the ratio of planned replacement expenses and losses due to failure. The rated conditions for the optimal equipment lifetime and for the gamma-percentile life are obtained providing the distribution of time to failure is determined by the Weibull law. The expediency of the equipment group replacement is discussed.     

Vehicle routing problem with a heterogeneous fleet and time windows

In this paper, a problem variant of the vehicle routing problem with time windows is introduced to consider vehicle routing with a heterogeneous fleet, a limited number of vehicles and time windows. A method that extends an existing tabu search procedure to solve the problem is then proposed. To evaluate the performance of the proposed method, experiments are conducted on a large set of test cases, which comprises several benchmark problems from numerous problem variants of the vehicle routing problem with a heterogeneous fleet. It is observed that the proposed method can be used to give reasonably good results for these problem variants. In addition, some ideas are presented to advance the research in heuristics, such as fair reporting standards, publication of benchmark problems and executable routines developed for algorithmic comparison.     

CP and IP approaches to cancer radiotherapy delivery optimization

We consider the problem of decomposing an integer matrix into a positively weighted sum of binary matrices that have the consecutive-ones property. This problem is well-known and of practical relevance. It has an important application in cancer radiation therapy treatment planning: the sequencing of multileaf collimators to deliver a given radiation intensity matrix, representing (a component of) the treatment plan. Two criteria characterise the efficacy of a decomposition: the beam-on time (the length of time the radiation source is switched on during the treatment), and the cardinality (the number of machine set-ups required to deliver the planned treatment). Minimising the former is known to be easy. However finding a decomposition of minimal cardinality is NP-hard. Progress so far has largely been restricted to heuristic algorithms, mostly using linear programming, integer programming and combinatorial enumerative methods as the solving approaches. We present a novel model, with corresponding constraint programming and integer programming formulations. We compare these computationally with previous formulations, and we show that constraint programming performs very well by comparison.     

考虑时间窗与随机航行时间的船舶航线规划

良好的航线设计是船运公司降低运营成本,提高服务质量的关键。船舶运输中存在航行时间不确定,码头资源稀缺等特点,基于此将其航线问题抽象为考虑时间窗与随机旅行时间的多重流动旅行商问题。针对航行时间的随机性,设计了线性近似方法,提出了虚拟时间窗的概念,构建了初始模型与修正模型;给出了该问题的一个算例,验证了模型与算法的有效性和合理性。     

A Simulated Annealing-based parallel multi-objective approach to vehicle routing problems with time windows

The Capacitated Vehicle Routing Problem with Time Windows (VRPTW) consists in determining the routes of a given number of vehicles with identical capacity stationed at a central depot which are used to supply the demands of a set of customers within certain time windows. This is a complex multi-constrained problem with industrial, economic, and environmental implications that has been widely analyzed in the past. This paper deals with a multi-objective variant of the VRPTW that simultaneously minimizes the travelled distance and the imbalance of the routes. This imbalance is analyzed from two perspectives: the imbalance in the distances travelled by the vehicles, and the imbalance in the loads delivered by them. A multi-objective procedure based on Simulated Annealing, the Multiple Temperature Pareto Simulated Annealing (MT-PSA), is proposed in this paper to cope with these multi-objective formulations of the VRPTW. The procedure MT-PSA and an island-based parallel version of MT-PSA have been evaluated and compared with, respectively, sequential and island-based parallel implementations of SPEA2. Computational results obtained on Solomon’s benchmark problems show that the island-based parallelization produces Pareto-fronts of higher quality that those obtained by the sequential versions without increasing the computational cost, while also producing significant reduction in the runtimes while maintaining solution quality. More specifically, for the most part, our procedure MT-PSA outperforms SPEA2 in the benchmarks here considered, with respect to the solution quality and execution time.     

A novel formulation of the dynamic balancing of five-bar linkages with applications to link optimization

In this report, we derive design equations and techniques for the dynamic balancing of five-bar linkage, using a novel and simplified approach. Firstly, in order to derive the dynamic equations of the mechanism we have applied the natural orthogonal complement method. Subsequently, an optimization method for the dynamic balancing of the linkage is proposed. The conditions of dynamic balancing of the five-bar linkage are expressed as seven equations and four inequalities, with twelve linkage parameters. The dynamic balancing of the mechanism is formulated and solved as an optimization problem under equality constraints. The application of the new approach is illustrated through a numerical example.     

Development and research of improved model of multipath adaptive routing in computer networks with load balancing

In modern computer networks, loading and bandwidth of communication links are optimized for providing high-quality service of network traffic, new services and technologies. As a rule, for this task the protocols of dynamic routing based on Dijkstra’s algorithm are used. Protocols of multipath routing for ensuring reliability and accuracy of the provided information allow to divide critical traffic and to execute load balancing along several routes at the same time. The purpose of the work is development and research of the improved multipath adaptive routing model in computer networks with load balancing. In this work, we focused on indicator of jitter optimization between the paved paths and a deviation value of reserve routes from an optimal route. Comparison and estimation of the offered algorithm with some existing approaches, and also modeling and analysis on various topologies of computer networks have been carried out.     

Applying two efficient hybrid heuristics for hub location problem with fully interconnected backbone and access networks

This paper considers the design of two-layered networks with fully interconnected backbone and access networks. The problem, a specific application of hub location to network design, is known as fully interconnected network design problem (FINDP). A novel mathematical programming formulation advantageous over an earlier formulation is presented to model the problem. Two hybrid heuristics are proposed to solve the problem, namely SA_VNS and TS_VNS which incorporate a variable neighborhood search (VNS) algorithm into the framework of simulated annealing (SA) and tabu search (TS). The proposed algorithms are able to easily obtain the optimal solutions for 24 small instances existing in the literature in addition to efficiently solve new generated medium and large instances. Results indicate that the proposed algorithms generate high quality solutions in a quite short CPU time.