A Simulated transportation problem for improved operational planning: An application in the trucking industry

This paper demonstrates the use of a simulated transportation method as an approach to developing an instructional guide that is operationally flexible and feasible for planning truck rental needs in a way to minimize costs. The informational impact of this approach aids not only the internal operations, but also external relations with truck renting companies by providing a more accurate measure of the anticipated demand on rental equipment, thus allowing the rental companies an opportunity to meet this demand.The results of the research conducted indicate the simulated transportation method is a reliable and valid planning instrument for minimizing transportation cost and generating useful information for management decision making.     

Design of an improved quantum-inspired evolutionary algorithm for a transportation problem in logistics systems

Logistics faces great challenges in vehicle schedule problem. Intelligence Technologies need to be developed for solving the transportation problem. This paper proposes an improved Quantum-Inspired Evolutionary Algorithm (IQEA), which is a hybrid algorithm of Quantum-Inspired Evolutionary Algorithm (QEA) and greed heuristics. It extends the standard QEA by combining its principles with some heuristics methods. The proposed algorithm has also been applied to optimize a problem which may happen in real life. The problem can be categorized as a vehicle routing problem with time windows (VRPTW), which means the problem has many common characteristics that VRPTW has, but more constraints need to be considered. The basic idea of the proposed IQEA is to embed a greed heuristic method into the standard QEA for the optimal recombination of consignment subsequences. The consignment sequence is the order to arrange the vehicles for the transportation of the consignments. The consignment subsequences are generated by cutting the whole consignment sequence according to the values of quantum bits. The computational result of the simulation problem shows that IQEA is feasible in achieving a relatively optimal solution. The implementation of an optimized schedule can save much more cost than the initial schedule. It provides a promising, innovative approach for solving VRPTW and improves QEA for solving complexity problems with a number of constraints.     

基于改进初始解的遗传算法的布局设计方法

带性能约束的三维布局问题属于具有很强应用背景的组合优化问题,进行了基于全局的布局求解方法的探索。由于NP完全问题的计算复杂性,使得遗传算法求解问题的全局最优解时效率较低。改进了遗传算法的初始解,对提高算法的效率进行了研究。并以旋转卫星舱布局的简化模型为背景,建立了多目标优化数学模型。实例结果与传统遗传算法以及乘子法的计算结果比较,表明该算法具有较好的求解效率。     

An integral solution to a nonlinear diffusion problem

The generalized Galerkin method (or the method of integral relations) is applied to the type of problem described by quasilinear parabolic equations. As an example the problem of nonlinear transient slab diffusion with a general reservoir boundary condition is worked out. The integral relations are given for an arbitrary number of strips, and solutions using up to seven strips have been obtained in order to investigate the convergence of the method.     

An extension to the single bottleneck transportation problem

In the existing models of bottleneck transportation problem (BTP), the transport time along a given path is taken as a fixed charge. In most practical situations, however, the transport time usually varies with the amount of transport. In order to make up this defect, an extended BTP model is built in which transport time t_ij is determined by a function of the amount to be transported rather than a constant. A heuristic recursive algorithm is proposed for the solution of the model. Furthermore, a special case of the model (that the supplies at sources are sufficient) is studied and a set of formulae of optimal solution are derived.     

Well posedness and numerical solution for a non-local pseudohyperbolic initial boundary value problem

ABSTRACT

In this paper, we study a non-local initial boundary value problem for a one-dimensional pseudohyperbolic equation. We first establish the existence and uniqueness of strong solution, then a numerical solutions for the system will be derived by using the finite-difference method.     

An optimal solution to a dock door assignment problem

This paper addresses issues at the shipping/receiving dock of a shipping company, where trucks arrive from vendors to have their shipments unloaded, sorted, and reloaded onto trucks going to the customers. The assignment of dock doors to incoming and outgoing trucks determines the efficiency of the dock operation. A bilinear programming model is proposed to solve the assignment problem.     

A model with a solution algorithm for the cash transportation vehicle routing and scheduling problem

Cash transportation vehicle routing and scheduling are essential for security carriers to minimize their operating costs and ensure safe cash conveyance. In real operations, to increase cash conveyance safety, there must be significant variation in daily cash transportation vehicle routes and schedules, making such vehicle routes and schedules difficult to formulate. However, for convenient planning purposes, security carriers normally plan such routes and schedules based on personal experience, without considering variations in routes and schedules from a system perspective. As a result, the obtained routes and schedules are neither safe nor efficient for transporting cash. In this study, a model is developed where the time–space network technique is utilized to formulate the potential movements of cash transportation vehicles among all demand points in the dimensions of time and space. This model incorporates a new concept of similarity of time and space for routing and scheduling, which is expected to help security carriers formulate more flexible routing and scheduling strategies. This is helpful to reduce the risk of robbery. Mathematically, the model is formulated as an integer multiple-commodity network flow problem. A solution algorithm, based on a problem decomposition/collapsing technique, coupled with the use of a mathematical programming software, is developed to efficiently solve the problem. The case study results show that our model and solution algorithm could be useful references for security carriers in actual practice.     

Efficient solution algorithms for a time-critical reliable transportation problem in multigraph networks with FIFO property

Relief distribution in urban environments is one of the major activities in emergency logistics management. The effective and time-saving dispatching process in affected areas is pivotal in rescue operations. In this study, we formulate a reliable time-dependent vehicle routing problem with time windows in a multigraph based network. In such networks, there exist parallel arcs with multiple attributes between nodes. The purpose of the provided model is to minimize delays in delivering prioritized items in disaster response operations. It also controls the minimum reliability of each route. Controlling the reliability in relief distribution gives this assurance that emergency packages on vehicles can reach their destinations safely and in a timely manner. In order to solve the problem, a novel restricted dynamic programming is applied to the problem through the giant-tour representation. The proposed algorithm can reach the optimal solution when utilized in an unrestricted way. In addition, a modified caching genetic algorithm and a three-phase optimization method based on the tabu search heuristic are provided to deal with larger instances in reasonable computation times. Finally, a real transportation case is presented to illustrate the potential applicability of the model in urban environments. The results accentuate the efficiency of the proposed methods and show the significance of multigraph to accelerate the distribution operations for reliable emergency logistics planning.     

An improved MIP-based approach for a multi-skill workforce scheduling problem

This paper deals with scheduling complex tasks with an inhomogeneous set of resources. The problem is to assign technicians to tasks with multi-level skill requirements. Here, the requirements are merely the presence of a set of technicians that possess the necessary capabilities. An additional complication is that a set of combined technicians stays together for the duration of a work day. This typically applies to scheduling of maintenance and installation operations. We build schedules by repeated application of a flexible matching model that selects tasks to be processed and forms groups of technicians assigned to combinations of tasks. The underlying mixed integer programming (MIP) model is capable of revising technician-task allocations and performs very well, especially in the case of rare skills.     

An improved initial basis for the Simplex algorithm

A lot of research has been done to find a faster (polynomial) algorithm that can solve linear programming (LP) problems. The main branch of this research has been devoted to interior point methods (IPM). The IPM outperforms the Simplex method in large LPs. However, there is still much research being done in order to improve pivoting algorithms. In this paper, we present a new approach to the problem of improving the pivoting algorithms: instead of starting the Simplex with the canonical basis, we suggest as initial basis a vertex of the feasible region that is much closer to the optimal vertex than the initial solution adopted by the Simplex. By supplying the Simplex with a better initial basis, we were able to improve the iteration number efficiency of the Simplex algorithm in about 33%.     

An improved monkey algorithm for a 0-1 knapsack problem

The 0-1 knapsack problem is a classic combinational optimization problem. However, many exiting algorithms have low precision and easily fall into local optimal solutions to solve the 0-1 knapsack problem. In order to overcome these problems, this paper proposes a binary version of the monkey algorithm where the greedy algorithm is used to strengthen the local search ability, the somersault process is modified to avoid falling into local optimal solutions, and the cooperation process is adopted to speed up the convergence rate of the algorithm. To validate the efficiency of the proposed algorithm, experiments are carried out with various data instances of 0-1 knapsack problems and the results are compared with those of five metaheuristic algorithms.     

An out-of-kilter based heuristic for the integer multicommodity transportation problem

A new formulation of the multicommodity transportation problem is introduced whereby all supply and demand constraints and in addition, a subset of the capacity constraints are incorporated into an equivalent single commodity, uncapacitated network. Solution of this network problem generally yields stronger lower bounds than one obtains by solving the individual single commodity transportation problems independently. A heuristic algorithm using this formulation is developed for the integer problem and limited computational experience indicates that the new formulation does provide a significant advantage over the unconstrained approach and solutions that are generally within seven percent of the lower bound. The application of this formulation in solving the continuous problem is also discussed.     

针对选址问题的一种遗传算法改进探究

选址问题是现代地理信息资源配置的重要研究领域之一,通用性强、鲁棒性高的遗传算法可以较好地解决这类问题。常用方法是使用二进制编码的遗传算法对栅格数据地图进行选址。为克服二进制编码的标准遗传算法在解决选址问题过程中易陷入早熟的缺点,在研究了使用不同算子、引入观测概念这两大类解决标准遗传算法陷入早熟问题的方法后,针对选址问题的特点,选择了引入多样性测度与应用小生境技术对遗传算法进行改进,并深入探究了引入多样性测度与应用小生境技术后,遗传算法解决选址问题的过程中准确性、在线性能函数、离线性能函数的改善;接着提出了进一步改进小生境技术的方法,使得遗传群体中的每一个个体都参与遗传操作,并且避免了两个相同的个体参与交叉操作的情况。最后通过地图选址实验,将改进的小生境遗传算法与多样性测度结合,成功提高了遗传算法的性能。