An adaptive evolutionary approach for real-time vehicle routing and dispatching

The quality of the convergence process in genetic algorithms depends on the specific choice of strategies and combinations of operators. In this paper, we address this problem and introduce an adaptive evolutionary approach that uses a genetic algorithm in an adaptive process. An application of this approach to the dynamic vehicle routing problem with time windows is presented. We compare the adaptive version of a hybrid genetic algorithm with the non-adaptive one with respect to the robustness and the quality of the generated solutions. The results obtained show the ability of our operator combination adaptation approach to produce solutions that are superior to hand-tuning and other adaptive methods with respect to performance sensitivity and robustness.     

A decomposition approach for the probabilistic maximal covering location-allocation problem

The maximal covering location problem (MCLP) maximizes the population that has a facility within a maximum travel distance or time. Numerous extensions have been proposed to enhance its applicability, like the probabilistic model for the maximum covering location-allocation with a constraint in waiting time or queue length for congested systems, with one or more servers per service center. This paper presents a solution procedure for that probabilistic model, considering one server per center, using a column generation and covering graph approaches. The computational tests report interesting results for network instances up to 818 vertices. The column generation results are competitive solving the instances in reasonable computational times, reaching optimality for some and providing good bounds for the difficult instances.     

A column generation approach for the maximal covering location problem

This article presents a column generation algorithm to calculate new improved lower bounds to the solution of maximal covering location problems formulated as a p‐median problem. This reformulation leads to instances that are difficult for column generation methods. The traditional column generation method is compared with the new approach, where the reduced cost criterion used at the column selection is modified by a Lagrangean/surrogate multiplier. The efficiency of the new approach is tested with real data, where computational tests were conducted and showed the impact of sparsity and degeneracy on column generation‐based methods.     

A new stochastic approach for a reliable p-hub covering location problem

A hub location problem (HLP) is a fertile research field, in the aspect of interdisciplinary studies, such as transportation, operation research, network design, telecommunication and economics. The location of hub facilities and allocation of non-hub nodes to hubs configure the backbone of HLPs. This study presents a new mathematical model for a reliable HLP by a new stochastic approach to minimize the total transportation cost and obtain maximum flows that the network can carry, when its link capacities are subject to stochastic degradations, as in a form of daily traffic, earthquake, flood, etc. We consider the road capacity reliability as a probability that ensures the maximum network capacity is greater than or equal to the total incoming flow to the network by considering the road capacity as random variable. As a result, this paper assumes that link capacities satisfy in a Truncated Erlang $(\mathit{TErl})$ distribution function. Due to complexity of the HLP, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is applied on the problem in order to achieve near-optimal solutions. Furthermore, the performance of the proposed algorithm (i.e., DE) is evaluated by the performance of the genetic algorithm (GA) applied on the given problem. Some computational experiments are presented to illustrate the effectiveness of the presented model and proposed algorithm. Finally the conclusion is provided.     

A branch-and-price approach for a multi-period vehicle routing problem

In this paper, we consider tactical planning for a class of multi-period vehicle routing problems (MPVRP). This problem involves optimizing daily product collections from several production locations over a given planning horizon. In this context, a single routing plan for the whole horizon must be prepared, and the seasonal variations in the producers’ supplies must be taken into account. Production variations over the horizon are approximated using a sequence of periods, each corresponding to a production season, while the intra-period variations are neglected. We propose a mathematical model that is based on the two-stage a priori optimization paradigm. The first stage corresponds to the design of a plan which, in the second stage, takes the different periods into account. The proposed set partitioning-based formulation is solved using a branch-and-price approach. The subproblem is a multi-period elementary shortest path problem with resource constraints (MPESPPRC), for which we propose an adaptation of the dynamic-programming-based label-correcting algorithm. Computational results show that this approach is able to solve instances with up to 60 producers and five periods.     

改进的粒子群优化算法求解车辆调度问题

采用对基本粒子群优化算法引入遗传操作来提高种群多样性,这样虽能避免产生局部极小,但收敛速度会降低,通过加入收缩因子来达到两者的均衡。优化和仿真结果表明改进算法性能更优,能有效地解决公交车辆的智能排班问题。     

Tree based searching approaches for integrated vehicle dispatching and container allocation in a transshipment hub

This study addresses the integration of Vehicle dispatching and container storage location problem with consideration of loading and unloading activities simultaneously. A MIP model is formulated to describe the interrelation between vehicle scheduling, yard crane scheduling and container storage location. A tree structure is used to represent the whole solution space. This representation has a good property as it captures the neighborhood structure and enhances the performance of local search and adaptive searching algorithms. Three variants of tree based searching approaches are developed, namely, the Nested Partitions method (NP), the Beam Search method (BS), and Stochastic Beam Search method (SBS). Extensive experiments show that these proposed methods can find a promising solution in matter of seconds for a practical problem and the Stochastic Beam Search method (SBS) method performs nearly as well as Nested Partitions method (NP) while gaining great computational efficiency. Due to this merit, SBS method is suggested to solve real time integrated vehicle dispatching problem in a relative large scale and may applied in other real time complex system scheduling.     

Path relinking approach for multiple allocation hub maximal covering problem

We consider the multiple allocation hub maximal covering problem (MAHMCP): Considering a serviced O–D flow was required to reach the destination optionally passing through one or two hubs in a limited time, cost or distance, what is the optimal way to locate p hubs to maximize the serviced flows? By designing a new model for the MAHMCP, we provide an evolutionary approach based on path relinking. The Computational experience of an AP data set was presented. And a special application on hub airports location of Chinese aerial freight flows between 82 cities in 2002 was introduced.     

A branch-and-cut approach for the vehicle routing problem with loading constraints

In this paper we describe a branch-and-cut algorithm for the vehicle routing problem with unloading constraints. The problem is to determine a set of routes with minimum total cost, each route leaving a depot, such that all clients are visited exactly once. Each client has a demand, given by a set of items, that are initially stored in a depot. We consider the versions of the problem with two and tri dimensional parallelepiped items. For each route in a solution, we also need to construct a feasible packing for all the items of the clients in this route. As it would be too expensive to rearrange the vehicle cargo when removing the items of a client, it is important to perform this task without moving the other client items. Such packings are said to satisfy unloading constraints.In this paper we describe a branch-and-cut algorithm that uses several techniques to prune the branch-and-cut enumeration tree. The presented algorithm uses several packing routines with different algorithmic approaches, such as branch-and-bound, constraint programming and metaheuristics. The careful combination of these routines showed that the presented algorithm is competitive, and could obtain optimum solutions within significantly smaller computational times for most of the instances presented in the literature.     

A mixed integer linear program and tabu search approach for the complementary edge covering problem

In this paper we study a complementary edge covering problem (CECP) as a variant of the total edge covering problem (TECP) which has application in the area of facility location. Unlike TECP, the partial cover of an edge through vertices is allowed in CECP such that in a feasible solution the entire edge will be covered. We propose a new mixed integer linear formulation for the problem. A number of size reduction rules are proposed which speed up getting optimal solution(s). Since the CECP is NP-Hard, a solution method based on tabu search is designed to search for optimal or near-optimal solutions. Computational experiments are carried out to evaluate effectiveness of the proposed mathematical formulation and the modified tabu search algorithm. Results justify that the proposed mathematical model can solves problems with up to 40 vertices and 456 edges optimally. Our computational efforts signify that the proposed tabu search is very effective and can find high quality solutions for larger problems in reasonable amount of time.     

A new approach for solving set covering problem using jumping particle swarm optimization method

The set covering problem (SCP) is a well known classic combinatorial NP-hard problem, having practical application in many fields. To optimize the objective function of the SCP, many heuristic, meta heuristic, greedy and approximation approaches have been proposed in the recent years. In the development of swarm intelligence, the particle swarm optimization is a nature inspired optimization technique for continuous problems and for discrete problems we have the well known discrete particle swarm optimization (DPSO) method. Aiming towards the best solution for discrete problems, we have the recent method called jumping particle swarm optimization (JPSO). In this DPSO the improved solution is based on the particles attraction caused by attractor. In this paper, a new approach based on JPSO is proposed to solve the SCP. The proposed approach works in three phases: for selecting attractor, refining the feasible solution given by the attractor in order to reach the optimality and for removing redundancy in the solution. The proposed approach has been tested on the benchmark instances of SCP and compared with best known methods. Computational results show that it produces high quality solution in very short running times when compared to other algorithms.     

A hybrid approach for the vehicle routing problem with three-dimensional loading constraints

This paper addresses a recently practical combinatorial problem named Three-Dimensional Loading Capacitated Vehicle Routing Problem, which combines three-dimensional loading problem and vehicle routing problem in distribution logistics. The problem requires a combinatorial optimization of a feasible loading and successive routing of vehicles to satisfy customer demands, where all vehicles must start and finish at a central depot. The goal of this combinatorial problem is to minimize the total transportation cost while serving customers. Despite its clearly practical significance in the real world distribution management, for its high combinatorial complexity, published papers on this problem in literature are very limited.     

A multilevel approach for modelling vehicle routing problem with uncertain travelling time

Vehicle routing problem is concerned with finding optimal collection or delivery routes in a transportation network, beginning and ending at a central depot, for a fleet of vehicles to serve a set of customers under some constraints. Assuming the travel times between two customers are uncertain variables, this paper proposes an uncertain multilevel programming model for a vehicle routing problem, of which the leader’s object is to minimize the total waiting times of the customers, and the followers’ objects are to minimize the waiting times of the vehicles for the beginning moments of the customers’ time windows. The uncertain multilevel programming model is transformed into a determinacy programming model, and an intelligent algorithm is designed for solving the crisp model.     

应急物流中的累计时间式多车场车辆路径问题

为提升应急救援的快速性和公平性,以最小化所有受灾点的累计等待时间为目标建立累计时间式多车场车辆路径问题(Cum-MDVRP)的模型。由于该问题具有NP-hard性质,设计了一种多起始点变邻域下降法对其进行快速求解。每次迭代中,多起始点方法通过改进的Split算法结合可行性修复程序生成随机的初始可行解,然后由变邻域下降法对其进一步改进。扩展的标准算例的测试结果验证了所提出模型和求解算法的有效性。     

A multi-agent approach for dynamic channel allocation

We propose a multi‐agent approach for dynamic channel allocation (MA‐DCA) in mobile cellular networks. Our approach assumes that each cell in a cellular network works as an agent that negotiates its bandwidth (channel) requirements with its neighbors to minimize its call drop probability. Using stochastic simulation, we compare our MA‐DCA approach with simple fixed channel allocation (FCA) and dynamic channel borrowing approaches for different call arrival rates, cellular network sizes and number of available channels. The results of our experiments show that the proposed MA‐DCA approach performs better than dynamic channel borrowing and FCA approaches.     

基于多代理系统方法的存贮路径问题研究

在分析现有研究成果的基础上,运用智能化的多代理技术建模方法,将问题抽象映射成为4类自治的Agent,构造存贮路径问题（Inventory Routing Problem,IRP）的多代理模型,通过Agent间的通信与协作完成问题的求解,为该问题的处理探索一条新的途径。