Branch and bound algorithms for the bus evacuation problem

The bus evacuation problem (BEP) is a vehicle routing problem that arises in emergency planning. It models the evacuation of a region from a set of collection points to a set of capacitated shelters with the help of buses, minimizing the time needed to bring the last person out of the endangered region.     

An algorithm for the optimization of nonbifurcated flows in computer communication networks

For practical reasons, many computer communications networks currently in operation use single path routing, also called nonbifurcated routing: the traffic of every end to end communication is constrained to follow at most one single path at a time. The determination of optimal nonbifurcated flows is therefore a problem which confronts many network designers. In this paper, two important features of the classical nonbifurcated flow deviation algorithm, originally proposed by Fratta et al. are improved: the path length metric and the starting flow calculation. Tested on different examples, the essential gain is brought by a heuristic which provides a starting flow which is already a reasonable approximation of the global nonbifurcated optimum. Compared with the initial algorithm, this starting flow leads, with less computing time, to solutions which are closer to the optimum.     

An optimization model for the container pre-marshalling problem

In most container yards around the world, containers are stacked high to utilize yard space more efficiently. In these yards, one major factor that affects their operational efficiency is the need to re-shuffle containers when accessing a container that is buried beneath other containers. One way to achieve higher loading efficiency is to pre-marshal the containers in such a way that it fits the loading sequence. In this research, we present a mathematical model for the container pre-marshalling problem. With respect to a given yard layout and a given sequence that containers are loaded onto a ship, the model yields a plan to re-position the export containers within the yard, so that no extra re-handles will be needed during the loading operation. The optimization goal is to minimize the number of container movements during pre-marshalling. The resulting model is an integer programming model composed of a multi-commodity flow problem and a set of side constraints. Several possible variations of the model as well as a solution heuristic are also discussed. Computation results are provided.     

A constrained binary knapsack approximation for shortest path network interdiction

A modified shortest path network interdiction model is approximated in this work by a constrained binary knapsack which uses aggregated arc maximum flow as the objective function coefficient. In the modified shortest path network interdiction problem, an attacker selects a path of highest non-detection probability on a network with multiple origins and multiple available targets. A defender allocates a limited number of resources within the geographic region of the network to reduce the maximum network non-detection probability between all origin-target pairs by reducing arc non-detection probabilities and where path non-detection probability is modeled as a product of all arc non-detection probabilities on that path. Traditional decomposition methods to solve the shortest path network interdiction problem are sensitive to problem size and network/regional complexity. The goal of this paper is to develop a method for approximating the regional allocation of defense resources that maintains accuracy while reducing both computational effort and the sensitivity of computation time to network/regional properties. Statistical and spatial analysis methods are utilized to verify approximation performance of the knapsack method in two real-world networks.     

Exploratory research on reading cognition and escape-route planning using building evacuation plan diagrams

The purpose of evacuation plan diagrams is for readers to comprehend and then plan an evacuation route. However, comprehending such diagrams involves complex issues that have yet to be addressed by research. This study aims to investigate how Taiwanese people interpret evacuation plan diagrams in their buildings. Issues of interest include the amount of time that it takes for a member of the general public to read a diagram and the time that they spend planning their escape route. Correlated and influencing factors are analyzed. The floor plan of an actual department store was used as the diagram for cognitive testing. A method of stimulated measurement was conducted over the Internet. The results of the experiment showed that the time it takes to plan an escape route is about 1.1 to 2 times longer than its reading time. This indicates that there is a significant time difference between diagram interpretation and stimulated planning. It was found that the longer it takes to read a diagram, the longer it takes to plan an escape route. In addition, to understand the difference between interpretations by the general public versus those with an architectural background, an analysis showed that the general public takes two to three times longer than architectural professionals to read a diagram and plan an escape route. Consequently, improvements in reading diagrams could help in the planning of a more efficient escape route. Furthermore, through our analysis, we found that the design of diagram symbols must satisfy conventional use and also that diagrams must avoid the use of metaphorical and abstract symbols. Diagrams that follow our guidelines will generally result in more effective and efficient conveyance of the intended message, thereby assisting in an emergency.     

Complexity and design of QoS routing algorithms in wireless mesh networks

Quality of service (QoS) provisioning in wireless mesh networks (WMNs) is an open issue to support emerging multimedia services. In this paper, we study the problem of QoS provisioning in terms of end-to-end bandwidth allocation in WMNs. It is challenging due to interferences in the networks. We consider widely used interference models and show that except a few special cases, the problem of finding a feasible path is NP-complete under the models. We propose a k-shortest path based algorithmic framework to solve this problem. We also consider the problem of optimizing network performance by on-line dynamic routing, and adapt commonly used conventional QoS routing metrics to be used in WMNs. We find the optimal solutions for these problems through formulating them as optimization models. A model is developed to check the existence of a feasible path and another to find the optimal path for a demand; moreover, an on-line optimal QoS routing algorithm is developed. Comparing the algorithms implemented by the proposed framework with the optimization models shows that our solution can find existing feasible paths with high probability, efficiently optimizes path lengths, and has a comparable performance to the optimal QoS routing algorithm. Furthermore, our results show that contrary to wireline networks, minimizing resource consumption should be preferred over load distribution even in lightly loaded WMNs.     

A model for evacuation risk assessment with consideration of pre- and post-disaster factors

In urban areas, the occurrence of disasters can cause extensive damage to human society. For this reason, evacuation, regarded as a critical course of action to relocate people and property, helps to alleviate loss of life and property to a great extent. Risk associated with evacuation is an abstract concept that cannot be easily conceptualized. This paper develops a model for assessing and visualizing the risks associated with the evacuation process in response to potential catastrophes. Understanding of evacuation risk, the potential for losing transport connections and the difficulty of transferring rescue resources, was previously limited by considering pre-disaster factors only. This study mitigates such limitation by extending previous research to include the contingent post-disaster factors that have received scant attention to date. Two contingent post-disaster factors: the spatial impact of the disaster and the potential for traffic congestion caused by the evacuee routing behaviors, are discussed in detail and integrated into the model along with other pre-disaster factors. A case study on the transportation network of Beijing, China is used to demonstrate the value of the model. This paper asserts that the notion of evacuation risk is not a static evaluation of such factors as road vulnerability; rather it involves a dynamic process where contingent factors associated with disastrous events play a role. This model can help city emergency planners to identify urban infrastructures that may hinder an efficient evacuation process because of their deficient configuration.     

A three-stage approach for the resource-constrained shortest path as a sub-problem in column generation

This paper focuses on the common scenario in which the resource-constrained shortest path problem (RCSP) on an acyclic graph is a sub-problem in the context of column generation. It proposes a pseudo-polynomial time, three-stage solution approach. Stages 1 (preprocessing) and 2 (setup) are implemented one time to transform RCSP into a shortest path problem, which is solved by stage 3 (iterative solution) at each column generation iteration, each time with different arc costs. This paper analyzes certain properties related to each stage as well as algorithm complexity. Computational tests compare the performances of this method, a state-of-the-art label-setting algorithm, and CPLEX optimization software on four classes of instances, each of which involves either one or multiple resources, and show that the new method is effective. The new method outperforms the label-setting algorithm when resource limitations are tight—as can be expected in practice, and outperforms CPLEX for all tested instances. The label-setting algorithm outperforms CPLEX for all single-resource RCSP instances and almost all multiple-resource RCSP instances.     

An efficient integer programming formulation for the assignment of base stations to controllers in cellular networks

In mobile networks, the assignment of base stations to controllers when planning the network has a strong impact on network performance. In a previous paper, the authors formulated the assignment of base stations to packet controllers in GSM-EDGE Radio Access Network (GERAN) as a graph partitioning problem, which was solved by a heuristic method. In this paper, an exact method is presented to find optimal solutions that can be used as a benchmark. The proposed method is based on an effective re-formulation of the classical integer linear programming model of the graph partitioning problem, which is solved by the branch-and-cut algorithm in a commercial optimization package. Performance assessment is based on an extensive set of problem instances built from data of a live network. Preliminary analysis shows some properties of the graphs in this problem justifying the limitations of heuristic approaches and the need for more sophisticated methods. Results show that the proposed method outperforms classical heuristic algorithms used for benchmarking, even under runtime constraints. Likewise, it improves the efficiency of exact methods previously applied to similar problems in the cellular field.     

Detecting infeasibility and generating cuts for mixed integer programming using constraint programming

We study a hybrid MIP/CP solution approach in which CP is used for detecting infeasibilities and generating cuts within a branch-and-cut algorithm for MIP. Our framework applies to MIP problems augmented by monotone constraints that can be handled by CP. We illustrate our approach on a generic multiple machine scheduling problem, and present a number of computational experiments.     

Mobile robot path planning using artificial bee colony and evolutionary programming

In this paper, an evolutionary approach to solve the mobile robot path planning problem is proposed. The proposed approach combines the artificial bee colony algorithm as a local search procedure and the evolutionary programming algorithm to refine the feasible path found by a set of local procedures. The proposed method is compared to a classical probabilistic roadmap method (PRM) with respect to their planning performances on a set of benchmark problems and it exhibits a better performance. Criteria used to measure planning effectiveness include the path length, the smoothness of planned paths, the computation time and the success rate in planning. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed method are also shown.     

Efficient solution techniques for the integrated coverage, sink location and routing problem in wireless sensor networks

Sensors are tiny electronic devices having limited battery energy and capability for sensing, data processing and communicating. They can collectively behave to provide an effective wireless network that monitors a region and transmits the collected information to gateway nodes called sinks. Most of the applications require the operation of the network for long periods of times, which makes the efficient management of the available energy resources an important concern. There are three major issues in the design of sensor networks: sensor deployment or the coverage of the sensing area, sink location, and data routing. In this work, we consider these three design problems within a unified framework and develop two mixed-integer linear programming formulations. They are difficult to solve exactly. However, it is possible to compute good feasible solutions of the sink location and routing problems easily, when the sensors are deployed and their locations in the sensor field become known. Therefore, we propose a tabu search heuristic that tries to identify the best sensor locations satisfying the coverage requirements. The objective value corresponding to each set of sensor locations is calculated by solving the sink location and routing problem. Computational tests carried out on randomly generated test instances indicate that the proposed hybrid approach is both accurate and efficient.     

A two-stage stochastic programming approach for project planning with uncertain activity durations

This paper investigates the problem of setting target finish times (due dates) for project activities with random durations. Using two-stage integer linear stochastic programming, target times are determined in the first stage followed by the development of a detailed project schedule in the second stage. The objective is to balance (1) the cost of project completion as a function of activity target times with (2) the expected penalty incurred by deviating from the specified values. It is shown that the results may be significantly different when deviations are considered, compared to when activities are scheduled as early as possible in the traditional way. For example, the optimal target completion time for a project may be greater than the makespan of the early-start schedules under any scenario. To find solutions, an exact algorithm is developed for the case without a budget constraint and is used as a part of a heuristic when crashing is permitted. All computational procedures are demonstrated on a set of 150 benchmark problems consisting of 90 activities each.     

REUSE: A combined routing and link scheduling mechanism for wireless mesh networks

Increasing the capacity of wireless mesh networks has motivated numerous studies. In this context, the cross-layer optimization techniques involving joint use of routing and link scheduling are able to provide better capacity improvements. Most works in the literature propose linear programming models to combine both mechanisms. However, this approach has high computational complexity and cannot be extended to large-scale networks. Alternatively, algorithmic solutions are less complex and can obtain capacity values close to the optimal. Thus, we propose the REUSE algorithm, which combines routing and link scheduling and aims to increase throughput capacity in wireless mesh networks. Through simulations, the performance of the proposal is compared to a developed linear programming model, which provides optimal results, and to other proposed mechanisms in the literature that also deal with the problem algorithmically. We observed higher values of capacity in favor of our proposal when compared to the benchmark algorithms.     

A global approach for designing reliable WDM networks and grooming the traffic

In this paper, we address the global problem of designing reliable wavelength division multiplexing (WDM) networks including the traffic grooming. This global problem consists in finding the number of optical fibers between each pair of optical nodes, finding the configuration of each node with respect to transponders, finding the virtual topology (i.e., the set of lightpaths), routing the lightpaths, grooming the traffic (i.e, grouping the connections and routing them over the lightpaths) and, finally, assigning wavelengths to the lightpaths. Instead of partitioning the problem into subproblems and solving them successively, we propose a mathematical programming model that addresses it as a whole. Numerical results are presented and analyzed.     

OPQR-G: algorithm for efficient QoS partition and routing in multiservice IP networks

This paper considers the combined problem of optimal QoS partition and routing (problem QPQR-G) for a QoS framework in which a performance dependent cost function is associated with each network element and the QoS metric is additive (e.g. delay, jitter). This problem has been addressed in the context of unicast connections and multicast trees only. Here we consider the problem for a more general case of a multicommodity flow network. Also, it is assumed that the performance dependent cost functions are non-increasing and are of general integer type. The goal is to determine primary paths between the origin and destination (OD) pairs and QoS partitions on the links so that the overall cost in the network is minimised while all OD pair QoS requirements are satisfied. As the problem is NP-complete, we concentrate on the development of an efficient heuristic algorithm. In addition, two LP-based algorithms were developed, that use the optimisation tool ILOG™ CPLEX 7.1 LP for solving the problem OPQR-G. The numerical results obtained for various test network scenarios are very close to the optimal. The problem addressed in this paper provides the basis for the solution of many interesting and practical engineering problems, such as dimensioning and admission control/resource reservation in IP networks that support service differentiation.     

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely, net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number.     

A cutting plane approach for integrated planning and scheduling

In this paper we propose a branch-and-cut algorithm for solving an integrated production planning and scheduling problem in a parallel machine environment. The planning problem consists of assigning each job to a week over the planning horizon, whereas in the scheduling problem those jobs assigned to a given week have to be scheduled in a parallel machine environment such that all jobs are finished within the week. We solve this problem in two ways: (1) as a monolithic mathematical program and (2) using a hierarchical decomposition approach in which only the planning decisions are modeled explicitly, and the existence of a feasible schedule for each week is verified by using cutting planes. The two approaches are compared with extensive computational testing.     

Practical and flexible path planning for car-like mobile robot using maximal-curvature cubic spiral

This paper presents a nonholonomic path planning method, aiming at taking into considerations of curvature constraint, length minimization, and computational demand, for car-like mobile robot based on cubic spirals. The generated path is made up of at most five segments: at most two maximal-curvature cubic spiral segments with zero curvature at both ends in connection with up to three straight line segments. A numerically efficient process is presented to generate a Cartesian shortest path among the family of paths considered for a given pair of start and destination configurations. Our approach is resorted to minimization via linear programming over the sum of length of each path segment of paths synthesized based on minimal locomotion cubic spirals linking start and destination orientations through a selected intermediate orientation. The potential intermediate configurations are not necessarily selected from the symmetric mean circle for non-parallel start and destination orientations. The novelty of the presented path generation method based on cubic spirals is: (i) Practical: the implementation is straightforward so that the generation of feasible paths in an environment free of obstacles is efficient in a few milliseconds; (ii) Flexible: it lends itself to various generalizations: readily applicable to mobile robots capable of forward and backward motion and Dubins’ car (i.e. car with only forward driving capability); well adapted to the incorporation of other constraints like wall-collision avoidance encountered in robot soccer games; straightforward extension to planning a path connecting an ordered sequence of target configurations in simple obstructed environment.