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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 6 (1974), S. 197-211 
    ISSN: 1436-4646
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Given an existing network, a list of arcs which could be added to the network, the arc costs and capacities, and an available budget, the problem considered in this paper is one of choosing which arcs to add to the network in order to maximize the maximum flow from a sources to a sinkt, subject to the budgetary constraint. This problem appears in a large number of practical situations which arise in connection with the expansion of electricity or gas supply, telephone, road or rail networks. The paper describes an efficient tree-search algorithm using bounds calculated by a dynamic programming procedure which are very effective in limiting the solution space explicitly searched. Computational results for a number of medium sized problems are described and computing times are seen to be very reasonable.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1572-9338
    Keywords: Vehicle routing ; Lagrangian relaxation ; shortest path relaxation ; dynamic programming relaxation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract We consider the basic Vehicle Routing Problem (VRP) in which a fleet ofM identical vehicles stationed at a central depot is to be optimally routed to supply customers with known demands subject only to vehicle capacity constraints. In this paper, we present an exact algorithm for solving the VRP that uses lower bounds obtained from a combination of two relaxations of the original problem which are based on the computation ofq-paths andk-shortest paths. A set of reduction tests derived from the computation of these bounds is applied to reduce the size of the problem and to improve the quality of the bounds. The resulting lower bounds are then embedded into a tree-search procedure to solve the problem optimally. Computational results are presented for a number of problems taken from the literature. The results demonstrate the effectiveness of the proposed method in solving problems involving up to about 50 customers and in providing tight lower bounds for problems up to about 150 customers.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 21 (1981), S. 19-46 
    ISSN: 1436-4646
    Keywords: Traveling Salesman Problem ; Assignment Problem ; Branch and Bound ; Lagrangean Relaxation ; Hamiltonian Circuits ; Arc Premiums/Penalties
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We describe an algorithm for the asymmetric traveling salesman problem (TSP) using a new, restricted Lagrangean relaxation based on the assignment problem (AP). The Lagrange multipliers are constrained so as to guarantee the continued optimality of the initial AP solution, thus eliminating the need for repeatedly solving AP in the process of computing multipliers. We give several polynomially bounded procedures for generating valid inequalities and taking them into the Lagrangean function with a positive multiplier without violating the constraints, so as to strengthen the current lower bound. Upper bounds are generated by a fast tour-building heuristic. When the bound-strengthening techniques are exhausted without matching the upper with the lower bound, we branch by using two different rules, according to the situation: the usual subtour breaking disjunction, and a new disjunction based on conditional bounds. We discuss computational experience on 120 randomly generated asymmetric TSP's with up to 325 cities, the maximum time used for any single problem being 82 seconds. This is a considerable improvement upon earlier methods. Though the algorithm discussed here is for the asymmetric TSP, the approach can be adapted to the symmetric TSP by using the 2-matching problem instead of AP.
    Type of Medium: Electronic Resource
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