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In a globalized market, forest management plans play an important role in the sustainability of forest enterprises. Several optimization processes have therefore been developed to support decision-making in forestry operations. However, important issues remain to be addressed, such as planning the allocation of harvesting areas and scheduling the harvesting teams that are contracted for these purposes. Harvesting schedules include different time scales and natural constraints, so that finding optimal or even good quality ones constitutes a highly complex combinatorial optimization problem. Efficient planning of harvesting operations can significantly reduce the costs associated with logistics and improve the economic performance of companies in the sector. In Uruguay, almost 75% of total forest harvesting operations for pulp production are carried out by contractor companies, so they are an important player in the supply chain. This study aims to optimize the allocation and routing of the harvesting equipment of forest contractors, which must be located at the sites to be harvested during the year. Numerical experiments over a case study based on realistic data have shown that realistic-sized instances can be resolved by standard mathematical programming software in a reasonable time. The mathematical programming model can also be useful to evaluate potential gains in joint planning by several contractors with respect to the costs incurred by separate planning; as illustrated also with numerical examples over the same case study. This model can be used to support annual forest harvest scheduling and equipment allocation for corporate contractors, leading to better quality plans and improvement opportunities.

A multistage stochastic capacitated discrete procurement problem with lead times, cancellation and postponement is addressed. The problem determines the procurement of a product under uncertain demand at minimal expected cost during a time horizon. The supply of the product is made through the purchase of optional distinguishable orders of fixed size with delivery time. Due to the unveiling of uncertainty over time it is possible to make cancellation and postponement corrective decisions on order procurement. These decisions involve costs and times of implementation. A model of the problem is formulated as an extension of a discrete capacitated lot-sizing problem under uncertain demand and lead times through a multi-stage stochastic mixed-integer linear optimization approach. Valid inequalities are generated, based on a conventional inequalities approach, to tighten the model formulation. Experiments are performed for several problem instances with different uncertainty information structure. Their results allow to conclude that the incorporation of a subset of the generated inequalities favor the model solution.

This paper introduces the product-mold-machine manufacturing problem, which is related to the manufacturing process of tires, plastics, ceramics, and glass. Each product has its own shape and mechanical properties obtained from processing a specific mold on a machine. Each machine can process a set of molds at the same time and, hence, the processing time on a machine depends on the longest processing time among the set of molds on the machine. The objective is to find a production plan that minimizes the total number of periods needed to meet the demand of each product, considering a limited number of machines, molds, changeover times, and incompatibilities constraints. We provide a mixed integer linear programming model for the problem and propose two constructive heuristics of polynomial-time, which provide upper bounds on the optimal value and easy-to-implement feasible solutions. Numerical experiments carried out over instances based on real data show the effectiveness of the proposed approaches.

For systems that are suitable to be modelled by continuous Markov chains, dependability analysis is not always straightforward. When such systems are large and complex, it is usually impossible to compute their dependability measures exactly. An alternative solution is to estimate them by simulation, typically by Monte Carlo simulation. But for highly reliable systems standard simulation can not reach satisfactory accuracy levels (measured by the variance of the estimator) within reasonable computing times. Conditional Monte Carlo with Intermediate Estimations (CMIE) is a simulation method proposal aimed at making accurate estimations of dependability measures on highly reliable Markovian systems. The basis of CMIE is introduced, the unbiasedness of the corresponding estimator is proven, and its variance is shown to be lower than the variance of the standard estimator. A variant of the basic scheme, that applies to large and highly reliable multicomponent systems, is introduced. Some experimental results are shown. [ABSTRACT FROM AUTHOR]

Consider a communication network with perfect nodes, links with independent failures, special nodes K called terminals, and a diameter d . The corresponding d-diameter constrained K-reliability ( d -DCKR) is the probability that the K terminals remain connected by paths composed by d hops or less. This problem has valuable applications in hop-constrained communication. The general d -DCKR evaluation is NP-Hard. However, we prove that the computational complexity of the 2-DCKR is linear in | K | when | K | is fixed, and an analytic expression for the target probability is provided. We introduce two Monte Carlo-based heuristics to tackle the general d -DCKR problem. The first one connects the target problem with polynomial interpolation theory, while the second counts subgraphs. Numerical experiments with Petersen, dodecahedron and a series-parallel graph confirm the effectiveness of both approaches. The article concludes with a discussion of trends for future work.

Dependability modeling and evaluation is an important aspect of complex computer and telecommunication networks design. One of the most flexible tools is Markov chain modeling, which allows representing dynamic behavior and interactions between the different system components.

The operation of a system, such as a vehicle, communication network or automatic process, heavily depends on the correct operation of its components. A Stochastic Binary System (SBS) mathematically models the behavior of on-off systems, where the components are subject to probabilistic failures. Our goal is to understand the reliability of the global system.

The article presents information on the 1st American Latin Ibero Association (ALIO) on Operations Research/Institute for Operations Research and the Management Sciences (INFORMS) Workshop on Operations Research (OR) Education in Montevideo, Uruguay, in November 2006. The workshop was held in conjunction with the 2006 Latin-Ibero-American Conference on Operations Research. The event included demonstrations and presentations on effective methods for quantitative learning.