No abstract

No abstract

No abstract

The rise in the worldwide demand of forest products of the last decades predicts an expansion of the forest harvesting industry. In this context, the Argentinian Northeastern Region (NEA) is considered a promising land since the local forest harvesting industry has one of the largest growing rates in the world. Despite its potential, this region faces some challenging obstacles: budget shortage, trade barriers and poor logistic infrastructure. For instance, traditionally the forest products are delivered by truck, which is from three to five times more expensive than other means of transport, like maritime or river transport. This is why in this paper, after a revision of the most recent advances in the worldwide supply chain management practices in the forest industry, recommendations for Argentina in order to overcome its main drawbacks in the forest sector are presented.

The separation at the source of Municipal Solid Waste (MSW) is an initiative that facilitates the subsequent recycling work and contributes to palliate the negative impacts of the traditional unsorted collection system. This paper presents a multi-objective integer linear programming model of the determination of the optimal location of assorted waste bins in an urban area. We consider, jointly, the objectives of minimizing the investment cost and the average distance from the dwellings to the bins. The model was applied in simulated instances of an Argentinian medium-size city, contributing to the transition from the current door-to-door based system to a community bins system. To solve this problem, we apply both the weighting method, which has been used to solve similar problems in the literature, and a novel version of the augmented ε-constraint method (AUGMECON2). The results over simulated scenarios show that, in general, AUGMECON2 has a better performance, yielding a larger number of efficient solutions at lower computation times.Growing Science Ltd. All rights reserved. 7

Supply chain management problems are widespread across all economic activities. We analyze here how to address these in the case of the forest industry, which in emerging economies such as Argentina is subject to high logistic costs and faces problems of biological and economic sustainability. In this work, we analyze a management model covering from the schedule of harvesting activities and the transportation of raw materials to the final transformation at several industrial plants. Since this involves more than one objective, single-criterion mathematical programming methods are not appropriate. Here, instead, we introduce an extended goal programming formulation of the problem, able to yield good solutions in a computationally efficient way. We consider four goals: the maximization of the net present value of the production, the minimization of interannual variations in harvests, the maximization of carbon capture in the form of forest biomass, and the minimization of variations in the mean annual distance covered in transportation to the industrial plants. We apply this theoretical model to derive solutions for an actual Argentinean company. We show that the model reaches the target levels of the different goals, except for carbon balance, which is negative in all of the scenarios under evaluation.

In this paper we investigate the use of lot streaming in non-permutation flowshop scheduling problems. The objective is to minimize the makespan subject to the standard flowshop constraints, but where it is now permitted to reorder jobs between machines. In addition, the jobs can be divided into manageable sublots, a strategy known as lot streaming. Computational experiments show that lot streaming reduces the makespan up to 43% for a wide range of instances when compared to the case in which no job splitting is applied. The benefits grow as the number of stages in the production process increases but reach a limit. Beyond a certain point, the division of jobs into additional sublots does not improve the solution.

The daily production planning of sawmills is a critical task in pursuing the optimal exploitation of forest resources. Production planning determines which logs are to be processed, taking into account their characteristics with the aim of satisfying the demand for final products. Logs are turned into lumber when they are cut according to a set of available cutting patterns (CPs). The development of efficient production planning is a key factor in improving the productivity of sawmills, and mathematical modeling is a suitable technique to achieve this objective. In this paper, a mixed integer linear programming (MILP) model for optimal daily production planning in sawmills is proposed. The model involves a set of CPs for each type of log, which is obtained through an exhaustive algorithm, attaining all possible feasible CPs. The proposed approach determines the optimal number of logs of each type to be cut, the selected CPs to be used, material inventory, demand fulfillment, and other industrial and commercial issues with the objective of maximizing the firm’s benefit, in reasonable computational time, considering the size of the problem.