Lower and upper bounds for a capacitated plant location problem with multicommodity flow

In this paper, we introduce a capacitated plant location problem with multicommodity flow. Given a set of potential plant sites and a set of capacitated arcs linking plants, transshipment points and customers, the aim is to determine where to locate plants and how to move flows from open plants to customers through a set of transshipment points. This model extends the classical capacitated plant location problem by introducing a multicommodity flow problem in the distribution issue. The combination of the location problem and the flow distribution problem is reasonable and realistic since both of them belong to strategic planning horizons. We propose a Lagrangean-based method, including a Lagrangean relaxation, a Lagrangean heuristic and a subgradient optimization, to provide lower and upper bounds of the model. Then, we employ a Tabu search to further improve upper bounds provided by the Lagrangean procedure. The computational results demonstrate that our solution method is effective since gaps between the upper and lower bound are on average around 2%.     

A scatter search-based heuristic to locate capacitated transshipment points

We consider a fixed charge two-stage location problem in which a given number of intermediate transshipment points are to be located between the supply plants and the customer locations. Both plants and transshipment points are capacitated. Scatter search is a population-based heuristic that has been applied to several combinatorial optimization problems. We develop an efficient scatter search-based heuristic approach with hybrid improvements including local search and path-relinking routines. Computational results demonstrate the effectiveness of the heuristic even for realistic problems with larger instances and tighter capacities.     

A GRASP with adaptive large neighborhood search for pickup and delivery problems with transshipment

The pickup and delivery problem (PDP) has been studied extensively for applications ranging from courier, cargo and postal services, to public transportation. The work presented here was inspired by a daily route planning problem at a regional air carrier who was trying to determine the benefits of transshipment. Accordingly, a primary goal of this paper is identify the circumstances under which measurable cost saving can be achieved when one aircraft transports a request from its origin to an intermediate point and a second aircraft picks it up and delivers it to its final destination. In structuring the analysis, we describe a unique way to model this transshipment option on a directed graph and introduce a specialized two-route insertion heuristic that considers when to exploit this option. Based on the new representation, most existing heuristics for the PDP can be readily extended to handle transshipments.To find solutions, we developed a greedy randomized adaptive search procedure (GRASP) with several novel features. In the construction phase, shipment requests are inserted into routes until all demand is satisfied or no feasible insertion exists. In the improvement phase, an adaptive large neighborhood search algorithm is used to modify portions of the feasible routes. Specialized removal and insertion heuristics were designed for this purpose. In the absence of test cases in the literature, we also developed a procedure for randomly generating problem instances. Testing was done on 56 existing PDP instances which have 50 requests each, and on 50 new data sets with 25 requests each and one transshipment location. For the former, the performance and solution quality of the GRASP were comparable to the best known heuristics. For the latter, GRASP found the solutions within 1% of optimality on 88% of the instances.     

Multi-dimensional visual tracking using scatter search particle filter

Multi-dimensional visual tracking (MVT) problems include visual tracking tasks where the system state is defined by a high number of variables corresponding to multiple model components and/or multiple targets. A MVT problem can be modeled as a dynamic optimization problem. In this context, we propose an algorithm which hybridizes particle filters (PF) and the scatter search (SS) metaheuristic, called scatter search particle filter (SSPF), where the optimization strategies from SS are embedded into the PF framework. Scatter search is a population-based metaheuristic successfully applied to several complex combinatorial optimization problems. The most representative optimization strategies from SS are both solution combination and solution improvement. Combination stage enables the solutions to share information about the problem to produce better solutions. Improvement stage makes also possible to obtain better solutions by exploring the neighborhood of a given solution. In this paper, we have described and evaluated the performance of the scatter search particle filter (SSPF) in MVT problems. Specifically, we have compared the performance of several state-of-the-art PF-based algorithms with SSPF algorithm in different instances of 2D articulated object tracking problem and 2D multiple object tracking. Some of these instances are from the CVBase’06 standard database. Experimental results show an important performance gain and better tracking accuracy in favour of our approach.     

基于改进GA的救援物资中转网点定位

针对大规模救援物资调运的多目标中转运输网点定位问题,考虑运输费用、中转网点的作业变动费用和运输时间,建立一个救援物资中转运输网点的非线性多目标混合整数规划模型。为有效求解该模型,提出一种基于矩阵编码的遗传算法,利用费用矩阵标杆的寻优导向信息提高遗传变异算子的局部搜索能力,提高全局收敛速度。通过算例分析验证该模型和算法的有效性。     

A hybrid scatter search meta-heuristic for delay-constrained multicast routing problems

This paper investigates the first hybrid scatter search and path relinking meta-heuristic for the Delay-Constrained Least-Cost (DCLC) multicast routing problem. The underpinning mathematic model of the DCLC multicast routing problem is the constrained Steiner tree problem in graphs, a well known NP-complete problem. After combining a path relinking method as the solution combination method in scatter search, we further explore two improvement strategies: tabu search and variable neighborhood search, to intensify the search in the hybrid scatter search algorithm. A large number of simulations on some benchmark instances from the OR-library and a group of random graphs of different characteristics demonstrate that the improvement strategy greatly affects the performance of the proposed scatter search algorithm. The hybrid scatter search algorithm intensified by a variable neighborhood descent search is highly efficient in solving the DCLC multicast routing problem in comparison with other algorithms and heuristics in the literature.     

On solving unreliable planar location problems

In this paper, we generalize conventional P-median location problems by considering the unreliability of facilities. The unreliable location problem is defined by introducing the probability that a facility may become inactive. We proposed efficient solution methods to determine locations of these facilities in the unreliable location model. Space-filling curve-based algorithms are developed to determine initial locations of these facilities. The unreliable P-median location problem is then decomposed to P 1-median location problems; each problem is solved to the optimum. A bounding procedure is used to monitor the iterative search, and to provide a consistent basis for termination. Extensive computational tests have indicated that the heuristics are efficient and effective for solving unreliable location problems.Scope and purposeThis paper addresses an important class of location problems, where p unreliable facilities are to be located on the plane, so as to minimize the expected travel distance or related transportation cost between the customers and their nearest available facilities. The unreliable location problem is defined by introducing the probability that a facility may become inactive. Potential application of the unreliable location problem is found in numerous areas. The facilities to be located can be fire station or emergency shelter, where it fails to provide service during some time window, due to the capacity or resource constraints. Alternatively, the facilities can be telecommunication posts or logistic/distribution centers, where the service is unavailable due to breakdown, repair, shutdown of unknown causes. In this paper, we prescribed heuristic procedures to determine the location of new facilities in the unreliable location problems. The numerical study of 2800 randomly generated instances has shown that these solution procedures are both efficient and effective, in terms of computational time and solution quality.     

G-PaMeLA: A divide-and-conquer approach for joint channel assignment and routing in multi-radio multi-channel wireless mesh networks

The performance of Multi-Radio Multi-Channel Wireless Mesh Networks (MRMC-WMNs) based on the IEEE 802.11 technology depends significantly on how the channels are assigned to the radios and how traffic is routed between the access points and the gateways. In this paper we propose an algorithmic approach to this problem, for which, as far as we know, no optimal polynomial time solutions have been put forward in the literature. The core of our scheme consists of a sequential divide-and-conquer technique which divides the overall Joint Channel Assignment and Routing (JCAR) problem into a number of local optimization sub-problems that are executed sequentially. We propose a generalized scheme called Generalized Partitioned Mesh network traffic and interference aware channeL Assignment (G-PaMeLA), where the number of sub-problems is equal to the maximum number of hops to the gateway, and a customized version which takes advantage of the knowledge of the topology. In both cases each sub-problem is formulated as an Integer Linear Programming (ILP) optimization problem. An optimal solution for each sub-problem can be found by using a branch-and-cut method. The final solution is obtained after a post-processing phase, which improves network connectivity. The divide-and-conquer technique significantly reduces the execution time and makes our solution feasible for an operational WMN. With the help of a detailed packet level simulation, the G-PaMeLA technique is compared with several state-of-the-art JCAR algorithms. Our results highlight that G-PaMeLA performs much better than the others in terms of packet loss rate, collision probability and fairness among traffic flows.     

Cut-and-solve: An iterative search strategy for combinatorial optimization problems

Branch-and-bound and branch-and-cut use search trees to identify optimal solutions to combinatorial optimization problems. In this paper, we introduce an iterative search strategy which we refer to as cut-and-solve and prove optimality and termination for this method. This search is different from traditional tree search as there is no branching. At each node in the search path, a relaxed problem and a sparse problem are solved and a constraint is added to the relaxed problem. The sparse problems provide incumbent solutions. When the constraining of the relaxed problem becomes tight enough, its solution value becomes no better than the incumbent solution value. At this point, the incumbent solution is declared to be optimal. This strategy is easily adapted to be an anytime algorithm as an incumbent solution is found at the root node and continuously updated during the search.Cut-and-solve enjoys two favorable properties. Since there is no branching, there are no “wrong” subtrees in which the search may get lost. Furthermore, its memory requirement is negligible. For these reasons, it has potential for problems that are difficult to solve using depth-first or best-first search tree methods.In this paper, we demonstrate the cut-and-solve strategy by implementing a generic version of it for the Asymmetric Traveling Salesman Problem (ATSP). Our unoptimized implementation outperformed state-of-the-art solvers for five out of seven real-world problem classes of the ATSP. For four of these classes, cut-and-solve was able to solve larger (sometimes substantially larger) problems. Our code is available at our websites.     

A tabu search heuristic for the hybrid flowshop scheduling with finite intermediate buffers

This paper investigates the hybrid flowshop scheduling with finite intermediate buffers, whose objective is to minimize the sum of weighted completion time of all jobs. Since this problem is very complex and has been proven strongly NP-hard, a tabu search heuristic is proposed. In this heuristic there are two main features. One is that a scatter search mechanism is incorporated to improve the diversity of the search procedure. And the other is that a permutation of N jobs representing their processing order in the first stage instead of a complex complete schedule is used to denote a solution. Computational experiments on randomly generated instances with different structures show that the proposed tabu search heuristic can provide good solutions compared to both the lower bounds and the algorithm proposed for this problem in a lately published literature.     

Hybrid imperialist competitive algorithm,variable neighborhood search,and simulated annealing for dynamic facility layout problem

Today’s manufacturing plants tend to be more flexible due to rapid changes in product mix and market demand. Therefore, this paper investigates the problem of location and relocation (when there are changes incurred to the material flows between departments) manufacturing facilities such that the total cost of material flows and relocation costs are minimized. This problem is known as the dynamic facility layout problem (DFLP), which is a general case of static facility layout problem. This paper proposes a robust and simply structured hybrid technique based on integrating three meta-heuristics: imperialist competitive algorithms, variable neighborhood search, and simulated annealing, to efficiently solve the DFLP. The novel aspect of the proposed algorithm is taking advantage of features of all above three algorithms together. To test the efficiency of our algorithm, a data set from the literature is used for the experimental purpose. The results obtained are quite promising in terms of solution quality for most of the test problems.     

Exact approaches for static data segment allocation problem in an information network

In a large distributed database, data are geographically distributed across several separate servers (or data centers). This helps in distributing load in the access network. It also helps to serve data locally where it is required. There are various approaches based on the granularity of data for efficient data distribution in a communication network. The file allocation problem (FAP) locates files to servers, the segment allocation problem (SAP) locates database segments, and the mirror location problem (MLP) locates replicas of the entire database. The placement of such data to multiple servers can be modeled as an optimization problem. The major decisions influencing optimization involves the location of servers, allocation of content and assignment of users. In this paper, we study the segment allocation problem (SAP), which is also known as the partial mirroring problem. This approach is more tractable than the file allocation problem in realistic cases and also eliminates the overhead of (constant) update costs that is incurred in the mirror location problem. Our contribution is two-fold: Firstly, earlier works on SAP assume pre-defined segments. We build a data partitioning method using well-known facility location models. We quantify the performance of the partitioning method. We show that the method partitions the database within a reasonable limit of error. Secondly, we introduce a new model for the segment allocation problem in which the segments are completely connected to each other by high-bandwidth links and contains a cost benefit for inter-segment traffic flows. We formulate this problem as an MILP and build exact solution approaches to solve large scale problems. We demonstrate some structural properties of the problem that make it solvable, using a Benders decomposition algorithm. Computational results validate the superiority of the decomposition approach.     

三层物流网络选址-路径优化及混合启发式算法研究*

为同时解决转运、分配、选址和车辆路径问题,在考虑车辆载重和行驶距离约束,配送中心处理能力约束的基础上,构建了一个多产品三层物流网络选址-路径模型,以总成本最小为目标,提出一种基于贪婪随机自适应搜索算法和里程节约算法的混合启发式算法,给出了该算法的步骤和伪代码。实验结果表明该算法具有可行性,并且与其他算法比较而言,算法具有高效性。     

An Efficient Motion Strategy to Compute Expected-Time Locally Optimal Continuous Search Paths in Known Environments

In this paper we address the problem of finding time-optimal search paths in known environments. In particular, we address the problem of searching a known environment for an object whose unknown location is characterized by a known probability density function (PDF). With this formulation, the time required to find the object is a random variable induced by the choice of search path together with the PDF for the object's location. The optimization problem we consider is that of finding the path that minimizes the expected value of the time required to find the object. As the complexity of the problem precludes finding an exact optimal solution, we propose a two-level, heuristic approach to finding the optimal search path. At the top level, we use a decomposition of the workspace based on critical curves to impose a qualitative structure on the solution trajectory. At the lower level, individual segments of this trajectory are refined using local numerical optimization methods. We have implemented the algorithm and present simulation results for the particular case when the object's location is specified by the uniform PDF.     

A GRASP algorithm based new heuristic for the capacitated location routing problem

Abstract

In this paper, the capacitated location-routing problem (CLRP) is studied. CLRP is composed of two hard optimisation problems: the facility location problem and the vehicle routing problem. The objective of CLRP is to determine the best location of multiple depots with their vehicle routes such that the total cost of the solution is minimal. To solve this problem, we propose a greedy randomised adaptive search procedure. The proposed method is based on a new heuristic to construct a feasible CLRP solution, and then a local search-based simulated annealing is used as improvement phase. We have used a new technique to construct the clusters around the depots. To prove the effectiveness of our algorithm, several LRP instances are used. The results found are very encouraging.     

Locating sensors to observe network arc flows: Exact and heuristic approaches

The problem of optimally locating sensors on a traffic network to monitor flows has been an object of growing interest in the past few years, due to its relevance in the field of traffic management and control. Sensors are often located in a network in order to observe and record traffic flows on arcs and/or nodes. Given traffic levels on arcs within the range or covered by the sensors, traffic levels on unobserved portions of a network can then be computed. In this paper, the problem of identifying a sensor configuration of minimal size that would permit traffic on any unobserved arcs to be exactly inferred is discussed. The problem being addressed, which is referred to in the literature as the Sensor Location Problem (SLP), is known to be NP-complete, and the existing studies about the problem analyze some polynomial cases and present local search heuristics to solve it. In this paper we further extend the study of the problem by providing a mathematical formulation that up to now has been still missing in the literature and present an exact branch and bound approach, based on a binary branching rule, that embeds the existing heuristics to obtain bounds on the solution value. Moreover, we apply a genetic approach to find good quality solutions. Extended computational results show the effectiveness of the proposed approaches in solving medium-large instances.     

An improved hybrid particle swarm optimization algorithm for fuzzy p-hub center problem

The p-hub center problem is useful for the delivery of perishable and time-sensitive system such as express mail service and emergency service. In this paper, we propose a new fuzzy p-hub center problem, in which the travel times are uncertain and characterized by normal fuzzy vectors. The objective of our model is to maximize the credibility of fuzzy travel times not exceeding a predetermined acceptable efficient time point along all paths on a network. Since the proposed hub location problem is too complex to apply conventional optimization algorithms, we adapt an approximation approach (AA) to discretize fuzzy travel times and reformulate the original problem as a mixed-integer programming problem subject to logic constraints. After that, we take advantage of the structural characteristics to develop a parametric decomposition method to divide the approximate p-hub center problem into two mixed-integer programming subproblems. Finally, we design an improved hybrid particle swarm optimization (PSO) algorithm by combining PSO with genetic operators and local search (LS) to update and improve particles for the subproblems. We also evaluate the improved hybrid PSO algorithm against other two solution methods, genetic algorithm (GA) and PSO without LS components. Using a simulated data set of 10 nodes, the computational results show that the improved hybrid PSO algorithm achieves the better performance than GA and PSO without LS in terms of runtime and solution quality.     

混合分散搜索算法求解带容量约束车辆路径问题

设计一种解决带容量约束车辆路径问题的混合分散搜索算法. 在基本分散搜索的基础上, 保留参考集更新策略和组合策略的全局搜索能力. 采用随机插入法作为解的多样性产生方法, 以扩大搜索空间, 避免陷入局部最优.应用简化的变邻域搜索作为改进策略进行局部开发, 引入邻域半径减少策略提高开发效率. 对改进后的新种群实施精英保留策略, 保证算法收敛. 实验结果分析表明, 混合分散搜索算法优于所对比的算法, 寻优能力可靠.

     

TARS: traffic-aware route search

In a traffic-aware route search (TARS), the user provides start and target locations and sets of search terms. The goal is to find the fastest route from the start location to the target via geographic entities (points of interest) that correspond to the search terms, while taking into account variations in the travel speed due to changes in traffic conditions, and the possibility that some visited entities will not satisfy the search requirements. A TARS query may include temporal constraints and order constraints that restrict the order by which entities are visited. Since TARS generalizes the Traveling-Salesperson Problem, it is an NP-hard problem. Thus, it is unlikely to find a polynomial-time algorithm for evaluating TARS queries. Hence, we present in this paper three heuristics to answer TARS queries—a local greedy approach, a global greedy approach and an algorithm that computes a linear approximation to the travel speeds, formulates the problem as a Mixed Integer Linear Programming (MILP) problem and uses a solver to find a solution. We provide an experimental evaluation based on actual traffic data and show that using a MILP solver to find a solution is effective and can be done within a limited running time in many real-life scenarios. The local-greedy approach is the least effective in finding a fast route, however, it has the best running time and it is the most scalable.