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The Capacitated m-Ring-Star Problem (CmRSP) is the problem of designing a set of rings that pass through a central depot and through some transition points and/or customers, and then assigning each nonvisited customer to a visited point or customer. The number of customers visited and assigned to a ring is bounded by an upper limit: the capacity of the ring. The objective is to minimize the total routing cost plus assignment costs. The problem has practical applications in the design of urban optical telecommunication networks. This paper presents and discusses two integer programming formulations for the CmRSP. Valid inequalities are proposed to strengthen the linear programming relaxation and are used as cutting planes in a branch-and-cut approach. The procedure is implemented and tested on a large family of instances, including real-world instances, and the good performance of the proposed approach is demonstrated.

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The vehicle routing problem with simultaneous distribution and collection (VRPSDC) is the variation of the capacitated vehicle routing problem that arises when the distribution of goods from a depot to a set of customers and the collection of waste from the customers to the depot must be performed by the same vehicles of limited capacity and the customers can be visited in any order. We study how the branch-and-price technique can be applied to the solution of this problem and in particular we compare two different ways of solving the pricing subproblem: exact dynamic programming and state space relaxation. By applying a bi-directional search we experimentally prove its effectiveness in solving the subproblem. We also devise suitable branching strategies for both the exact and the relaxed approach and we report on an extensive set of computational experiments on benchmark instances with both simple and composite demands.

We consider the classical problem of scheduling n tasks with given processing time on m identical parallel processors so as to minimize the maximum completion time of a task. We introduce lower bounds, approximation algorithms and a branch-and-bound procedure for the exact solution of the problem. Extensive computational results show that, in many cases, large-size instances of the problem can be solved exactly. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

We study the strip packing problem, in which a set of two-dimensional rectangular items has to be packed in a rectangular strip of fixed width and infinite height, with the aim of minimizing the height used. The problem is important because it models a large number of real-world applications, including cutting operations where stocks of materials such as paper or wood come in large rolls and have to be cut with minimum waste, scheduling problems in which tasks require a contiguous subset of identical resources, and container loading problems arising in the transportation of items that cannot be stacked one over the other.The strip packing problem has been attacked in the literature with several heuristic and exact algorithms, nevertheless, benchmark instances of small size remain unsolved to proven optimality. In this paper we propose a new exact method that solves a large number of the open benchmark instances within a limited computational effort. Our method is based on a Benders' decomposition, in which in the master we cut items into unit-width slices and pack them contiguously in the strip, and in the slave we attempt to reconstruct the rectangular items by fixing the vertical positions of their unit-width slices. If the slave proves that the reconstruction of the items is not possible, then a cut is added to the master, and the algorithm is reiterated.We show that both the master and the slave are strongly -hard problems and solve them with tailored preprocessing, lower and upper bounding techniques, and exact algorithms. We also propose several new techniques to improve the standard Benders' cuts, using the so-called combinatorial Benders' cuts, and an additional lifting procedure. Extensive computational tests show that the proposed algorithm provides a substantial breakthrough with respect to previously published algorithms.