Domain Decomposition of Optimal Control Problems for Dynamic Networks of Elastic Strings

We consider optimal control problems related to exact- and approximate controllability of dynamic networks of elastic strings. In this note we concentrate on problems with linear dynamics, no state and no control constraints. The emphasis is on approximating target states and velocities in part of the network using a dynamic domain decomposition method (d³m) for the optimality system on the network. The decomposition is established via a Uzawa-type saddle-point iteration associated with an augmented Lagrangian relaxation of the transmission conditions at multiple joints. We consider various cost functions and prove convergence of the infinite dimensional scheme for an exemplaric choice of the cost. We also give numerical evidence in the case of simple exemplaric networks.     

Ein Verfahren zur Optimierung von Nachrichtenübertragungsnetzen mit Mehrwegeführung

Within the framework of long-term planning of telecommunication transmission networks, an economic solution has to be calculated for the future network structure. In this structure, the cost of routing the circuits required between the exchanges must be optimized. For reliability reasons, it is necessary to route over several edge-disjoint paths. The paper describes the importance of long-term planning of telecommunication transmission networks. It is shown that this problem can be formulated by a model with a concave objective function and linear constraints. For this model, an algorithm is developed whose numeric behaviour is described. Practical applications of the procedure are indicated.     

Urban rapid transit network design: accelerated Benders decomposition

This paper presents an urban rapid transit network design model, which consists of the location of train alignments and stations in an urban traffic context. The design attempts to maximize the public transportation demand using the new infrastructure, considering a limited budget and number of transit lines. The location problem also incorporates the fact that users can choose their transportation mode and trips. In real cases, this problem is complex to solve because it has thousands of binary variables and constraints, and cannot be solved efficiently by Branch and Bound. For this reason, some algorithms based on Benders decomposition have been defined in order to solve it. These algorithms have been compared in test networks. The project has been supported by the research project 70029/T05, from the Spanish “Ministerio de Fomento” and the research project TRA2005-09068-C03-01, from the Spanish “Ministerio de Educación y Ciencia”.     

A Decomposition Method for Transfer Line Life Cycle Cost Optimisation

A new method to search best parameters of a transfer line so that the cost of each manufactured part will be minimised. The synchronised transfer lines with parallel machining are considered. Such lines are widely used in mass and large-scale mechanical production. The objective is to minimise the line life cycle cost per part under the given productivity and technological constraints. The design decisions to be optimised are: number of spindles and workstations. This will be accomplished by defining subsets of tasks which are performed by one spindle head and cutting conditions for each spindle. The paper focuses on a mathematical model of the problem and methods used to solve it. This model is formulated in terms of mixed (discrete and non-linear) programming and graph theory. A special decomposition scheme based on the parametric decomposition technique is proposed. For solving the sub-problems obtained after decomposition, a Branch-and-Bound algorithm as well as a shortest path technique are used.     

On routing in VLSI design and communication networks

In this paper, we study the global routing problem in VLSI design and the multicast routing problem in communication networks. First we propose new and realistic models for both problems. In the global routing problem in VLSI design, we are given a lattice graph and subsets of the vertex set. The goal is to generate trees spanning these vertices in the subsets to minimize a linear combination of overall wirelength (edge length) and the number of bends of trees with respect to edge capacity constraints. In the multicast routing problem in communication networks, a graph is given to represent the network, together with subsets of the vertex set. We are required to find trees to span the given subsets and the overall edge length is minimized with respect to capacity constraints. Both problems are APX-hard. We present the integer linear programming (LP) formulation of both problems and solve the LP relaxations by the fast approximation algorithms for min-max resource-sharing problems in [K. Jansen, H. Zhang, Approximation algorithms for general packing problems and their application to the multicast congestion problem, Math. Programming, to appear, doi:10.1007/s10107-007-0106-8] (which is a generalization of the approximation algorithm proposed by Grigoriadis and Khachiyan [Coordination complexity of parallel price-directive decomposition, Math. Oper. Res. 2 (1996) 321-340]). For the global routing problem, we investigate the particular property of lattice graphs and propose a combinatorial technique to overcome the hardness due to the bend-dependent vertex cost. Finally, we develop asymptotic approximation algorithms for both problems with ratios depending on the best known approximation ratio for the minimum Steiner tree problem. They are the first known theoretical approximation bound results for the problems of minimizing the total costs (including both the edge and the bend costs) while spanning all given subsets of vertices.     

Modeling of building evacuation problems by network flows with side constraints

In this paper we model building evacuations by network flows with side constraints. Side constraints come from variable arc capacities on some arcs which are functions of flows in incident arcs. In this context we study maximum flow, minimum cost, and minimax objectives. For some special structured networks we propose ‘greedy’ algorithms for solving these problems. For more general network structures, solution procedures are recommended which take advantage of the network structures of the problems.     

Railway delay management with passenger rerouting considering train capacity constraints

Delay management for railways is concerned with the question of whether a train should wait for a delayed feeder train or depart on time. The answer should not only depend on the length of the delay but also consider other factors, such as capacity restrictions. We present an optimization model for delay management in railway networks that accounts for capacity constraints on the number of passengers that a train can effectively carry. While limited capacities of tracks and stations have been considered in delay management models, passenger train capacity has been neglected in the literature so far, implicitly assuming an infinite train capacity. However, even in open systems where no seat reservation is required and passengers may stand during the journey if all seats are occupied, physical space is naturally limited, and the number of standing seats is constrained for passenger safety reasons. We present a mixed-integer nonlinear programming formulation for the delay management problem with passenger rerouting and capacities of trains. Our model allows the rerouting of passengers missing their connection due to delays or capacity constraints. We linearize the model in exact and approximate ways and experimentally compare the different approaches with the solution of a reference model from the literature that neglects capacity constraints. The results demonstrate that there is a significant impact of considering train capacity restrictions in decisions to manage delays.     

Cost optimal allocation of rail passenger lines

We consider the problem of cost optimal railway line allocation for passenger trains for the Dutch railway system. At present, the allocation of passenger lines by Dutch Railways is based on maximizing the number of direct travelers. This paper develops an alternative approach that takes operating costs into account. A mathematical programming model is developed which minimizes the operating costs subject to service constraints and capacity requirements. The model optimizes on lines, line types, routes, frequencies and train lengths. First, the line allocation model is formulated as an integer nonlinear programming model. This model is transformed into an integer linear programming model with binary decision variables. An algorithm is presented which solves the problem to optimality. The algorithm is based upon constraint satisfaction and a Branch and Bound procedure. The algorithm is applied to a subnetwork of the Dutch railway system for which it shows a substantial cost reduction. Further application and extension seem promising.     

Network synthesis under survivability constraints

Telecommunication networks are subject to link and equipment failures. Since failures cannot be entirely avoided, networks have to be designed so as to survive failure situations. In this paper, we are interested in designing low cost survivable networks. Given point-to-point traffic demands and a cost/capacity function for each link, we aim at finding the minimum cost capacities satisfying the given demands and survivability requirements. A survivability model that reroutes interrupted traffic using all the available capacities on the network is presented and studied. In the proposed model, capacity and flow assignments for each network operating state are jointly optimized. We prove the -hardness of the optimisation problem defined by dual constraints. Then, we propose a polynomial relaxation along with a fast heuristic to compute a feasible solution of the problem from its relaxed optimal solution. Our solution approaches are tested on a set of problem instances.Received: September 2002, Revised: July 2003, AMS classification: 90C05     

Optimization of transmission network for homogeneous good market

We examine a model of a perfect competitive homogeneous good market with a network structure. Such a structure is typically important for energy resources: natural gas, oil and electricity. Local markets are connected by transmission lines with limited capacities and given cost functions for capacity increments. We consider the total welfare optimization problem and provide a method that determines optimal investments in the transmission system expansion for some types of the networks. In particular, we study the case where the market is divided into two submarkets with binding transmission line flow constraints between the submarkets. We obtain efficient algorithms for determination of the transmission systems optimal expansion. We conclude with the impact of the results and the outlook to future studies.     

Developing work schedules for an inter-city transit system with multiple driver types and fleet types

In developing work schedules, the job assignment flexibility exploits the variety of available skills, thus enabling the assignment of workers to perform different jobs. In this study, we investigate the problem of finding the mix of primary and secondary jobs in short term work schedules to meet, at minimum cost, the daily service requirements of an inter-city bus transit firm in Andra Pradesh India operating multiple fleet types. We formulate the problem as a set covering model with resource allocation constraints. We develop a branch-and-price procedure to solve the model. Computational results are provided.     

Benders Decomposition for a Location-Design Problem in Green Wireless Local Area Networks

We consider a problem arising in the design of green (or energy-saving) wireless local area networks (GWLANs). Decisions on both location and capacity dimensioning must be taken simultaneously. We model the problem as an integer program with nonlinear constraints and derive valid inequalities. We handle the nonlinearity of the formulation by developing a Benders decomposition algorithm. We propose various ways to improve the Benders master problem and the feasibility cuts.     

带组约束可靠性网络最优化问题的精确算法

本文提出了一种求解带组约束串-并网络系统最优冗余问题的精确算法.该算法利用拉格朗日松驰和Dantzig-Wolfe分解法得到问题的上界,并结合动态规划求解子问题.算法采用一种有效的切割和剖分方法,以逐步缩小对偶间隙和保证收敛性.数值结果表明该算法对于求解带组约束可靠性最优化问题是很有效的.     

Two-stage stochastic programming under multivariate risk constraints with an application to humanitarian relief network design

In this study, we consider two classes of multicriteria two-stage stochastic programs in finite probability spaces with multivariate risk constraints. The first-stage problem features multivariate stochastic benchmarking constraints based on a vector-valued random variable representing multiple and possibly conflicting stochastic performance measures associated with the second-stage decisions. In particular, the aim is to ensure that the decision-based random outcome vector of interest is preferable to a specified benchmark with respect to the multivariate polyhedral conditional value-at-risk or a multivariate stochastic order relation. In this case, the classical decomposition methods cannot be used directly due to the complicating multivariate stochastic benchmarking constraints. We propose an exact unified decomposition framework for solving these two classes of optimization problems and show its finite convergence. We apply the proposed approach to a stochastic network design problem in the context of pre-disaster humanitarian logistics and conduct a computational study concerning the threat of hurricanes in the Southeastern part of the United States. The numerical results provide practical insights about our modeling approach and show that the proposed algorithm is computationally scalable.

     

On the inverse problem of minimum spanning tree with partition constraints

In this paper we first discuss the properties of minimum spanning tree and minimum spanning tree with partition constraints. We then concentrate on the inverse problem of minimum spanning tree with partition constraints in which we need to adjust the weights of the edges in a network as less as possible so that a given spanning tree becomes the minimum one among all spanning trees that satisfy the partition restriction. Based on the calculation of maximum cost flow in networks, we propose a strongly polynomial algorithm for solving the problem.The author gratefully acknowledges the partial support of Croucher Foundation.     

Optimal generation scheduling for constrained multi-area hydrothermal power systems with cascaded reservoirs

The optimization problem in this paper is targeted at large-scale hydrothermal power systems. The thermal part of the system is a multi-area power pool with tie-line constraints, and the hydro part is a set of cascaded hydrostations. The objective is to minimize the operation cost of the thermal subsystem. This is an integer nonlinear optimization process with a large number of variables and constraints. In order to obtain the optimal solution in a reasonable time, we decompose the problem into thermal and hydro subproblems. The coordinator between these subproblems is the system Lagrange multiplier. For the thermal subproblem, in a multi-area power pool, it is necessary to coordinate the area generations for reducing the operation cost without violating tie limits. For the hydro subsystem, network flow concepts are adopted to coordinate water usage over the entire study time span, and the reduced gradient method is used to overcome the linear characteristic of the network flow method in order to obtain the optimal solution. In this study, load forecasting errors and forced outages of generating units are incorporated in system reliability requirements. Three case studies for the proposed method are presented.     

Disruption management for resource-constrained project scheduling

In this paper, we study the problem of how to react when an ongoing project is disrupted. The focus is on the resource-constrained project scheduling problem with finish–start precedence constraints. We begin by proposing a classification scheme for the different types of disruptions and then define the constraints and objectives that comprise what we call the recovery problem. The goal is to get back on track as soon as possible at minimum cost, where cost is now a function of the deviation from the original schedule. The problem is formulated as an integer linear program and solved with a hybrid mixed-inter programming/constraint programming procedure that exploits a number of special features in the constraints. The new model is significantly different from the original one due to the fact that a different set of feasibility conditions and performance requirements must be considered during the recovery process. The complexity of several special cases is analysed. To test the hybrid procedure, 554 20-activity instances were solved and the results compared with those obtained with CPLEX. Computational experiments were also conducted to determine the effects of different factors related to the recovery process.     

On the analysis of optimization problems in arc-dependent networks

This paper is concerned with the design and analysis of algorithms for optimization problems in arc-dependent networks. A network is said to be arc-dependent if the cost of an arc $a$ depends upon the arc taken to enter $a$. These networks are fundamentally different from traditional networks in which the cost associated with an arc is a fixed constant and part of the input. We first study the arc-dependent shortest path (ADSP) problem, which is also known as the suffix-1 path-dependent shortest path problem in the literature. This problem has a polynomial time solution if the shortest paths are not required to be simple. The ADSP problem finds applications in a number of domains, including highway engineering, turn penalties and prohibitions, and fare rebates. In this paper, we are interested in the ADSP problem when restricted to simple paths. We call this restricted version the simple arc-dependent shortest path (SADSP) problem. We show that the SADSP problem is NP-complete. We present inapproximability results and an exact exponential algorithm for this problem. We also extend our results for the longest path problem in arc-dependent networks. Additionally, we explore the problem of detecting negative cycles in arc-dependent networks and discuss its computational complexity. Our results include variants of the negative cycle detection problem such as longest, shortest, heaviest, and lightest negative simple cycles.²     

A new branch-and-price algorithm for the traveling tournament problem

The traveling tournament problem (ttp) consists of finding a distance-minimal double round-robin tournament where the number of consecutive breaks is bounded. For solving the problem exactly, we propose a new branch-and-price approach. The starting point is a new compact formulation for the ttp. The corresponding extensive formulation resulting from a Dantzig-Wolfe decomposition is identical to one given by Easton, K., Nemhauser, G., Trick, M., 2003. Solving the traveling tournament problem: a combined interger programming and constraint programming approach. In: Burke, E., De Causmaecker, P. (Eds.), Practice and Theory of Automated Timetabling IV, Volume 2740 of Lecture Notes in Computer Science, Springer Verlag Berlin/Heidelberg, pp. 100–109, who suggest to solve the tour-generation subproblem by constraint programming. In contrast to their approach, our method explicitly utilizes the network structure of the compact formulation: First, the column-generation subproblem is a shortest-path problem with additional resource and task-elementarity constraints. We show that this problem can be reformulated as an ordinary shortest-path problem over an expanded network and, thus, be solved much faster. An exact variable elimination procedure then allows the reduction of the expanded networks while still guaranteeing optimality. Second, the compact formulation gives rise to supplemental branching rules, which are needed, since existing rules do not ensure integrality in all cases. Third, non-repeater constraints are added dynamically to the master problem only when violated. The result is a fast exact algorithm, which improves many lower bounds of knowingly hard ttp instances from the literature. For some instances, solutions are proven optimal for the first time.     

Iterative algorithms for the curfew planning problem

The curfew planning problem is to design an annual timetable for railway track maintenance teams. Each team is capable of handling certain types of repairs and replacement jobs. The jobs are combined into a set of projects according to their locations and types. The timetable shows which project should be worked on by each team on a weekly basis throughout an entire year. Our objective is to design a schedule with minimum network disruption due to ongoing maintenance projects that require absolute curfew. Absolute curfew projects are those that cause complete closure of the rail traffic. For tackling this problem, we develop four optimization-based iterative algorithms. We also present very promising computational results obtained within a few minutes using data provided by a major North American railroad.