A decomposition algorithm for robust lot sizing problem with remanufacturing option

In this paper, we propose a decomposition procedure for constructing robust optimal production plans for reverse inventory systems. Our method is motivated by the need of overcoming the excessive computational time requirements, as well as the inaccuracies caused by imprecise representations of problem parameters. The method is based on a min-max formulation that avoids the excessive conservatism of the dualization technique employed by Wei et al. (2011). We perform a computational study using our decomposition framework on several classes of computer generated test instances and we report our experience. Bienstock and Özbay (2008) computed optimal base stock levels for the traditional lot sizing problem when the production cost is linear and we extend this work here by considering return inventories and setup costs for production. We use the approach of Bertsimas and Sim (2004) to model the uncertainties in the input

The decision rule approach to optimization under uncertainty: methodology and applications

Dynamic decision-making under uncertainty has a long and distinguished history in operations research. Due to the curse of dimensionality, solution schemes that naïvely partition or discretize the support of the random problem parameters are limited to small and medium-sized problems, or they require restrictive modeling assumptions (e.g., absence of recourse actions). In the last few decades, several solution techniques have been proposed that aim to alleviate the curse of dimensionality. Amongst these is the decision rule approach, which faithfully models the random process and instead approximates the feasible region of the decision problem. In this paper, we survey the major theoretical findings relating to this approach, and we investigate its potential in two applications areas

Landscape transformation alters functional diversity in coastal seascapes

Ecography published by John Wiley & Sons on behalf of Nordic Society Oikos The ecological impacts of landscape modification and urbanisation have transformed the composition of plant and animal assemblages, and altered the condition of ecosystems globally. Landscape transformation influences the spatial distribution of species and ecological functions by selecting for generalist species with wide ecological niches, which can adapt to opportunities in highly-modified environments. These effects of landscape modification can shape functional diversity on land, but it is not clear whether they have similar functional consequences in the sea. We used estuaries as a model system to test how landscape transformation alters functional diversity in coastal seascapes, and measured how variation in level of urbanisation, catchment modification and habitat loss influenced fish diversity across thirty-nine estuaries in eastern Australia. Fish were surveyed with baited remote underwater video stations and functional diversity was indexed with three metrics that describe variation in the functional traits and niche space of assemblages. The extent of landscape transformation in the catchment of each estuary was associated with variation in the functional diversity of estuarine fish assemblages. These effects were, however, not what we expected as functional diversity was highest in modified estuaries that supported a large area of both urban and grazing land in their catchments, were bordered by a small area of natural terrestrial vegetation and that contained a moderate area of mangroves. Zoobenthivores and omnivores dominated assemblages in highly-modified estuaries, and piscivorous fishes were common in natural waterways. Our results demonstrate, that the modification and urbanisation of ecosystems on land can alter functional diversity in the sea. Intense landscape transformation appears to select for abundant generalists with wide trophic niches, and against species with specialised diets, and we suggest that these changes might have fundamental consequences for ecosystem functioning in estuaries, and other highly modified seascapes. © 2019 The Authors

Robust shift generation in workforce planning

In this paper we apply robust optimization techniques to the shift generation problem in workforce planning. At the time that the shifts are generated, there is often much uncertainty in the workload predictions. We propose a model to generate shifts that are robust against this uncertainty. An adversarial approach is used to solve the resulting robust optimization model. In each iteration an integer nonlinear knapsack problem is solved to calculate the worst case workload scenario. We apply the approach to generate shifts in a real-life Air Traffic Controller workforce planning problem. The numerical results show the value of our approach

Decision Under Ignorance: A Comparison of Existing Criteria: .

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