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

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.The authors wish to thank three anonymous referees for their comments and valuable suggestions to improve the paper. The first author acknowledges Ciˆencia 2007 of FCT (Foundation for Science and Technology) Portugal for the fellowship grant C2007-UMINHO-ALGORITMI-04. Financial support from FEDER COMPETE (Operational Programme Thematic Factors of Competitiveness) and FCT under project FCOMP-01-0124-FEDER-022674 is also acknowledged

Developing lean and responsive supply chains : a robust model for alternative risk mitigation strategies in supply chain designs

This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC’s network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet

Rift Valley Fever – epidemiological update and risk of introduction into Europe

Rift Valley fever (RVF) is a vector-borne disease transmitted by a broad spectrum of mosquito species, especially Aedes and Culex genus, to animals (domestic and wild ruminants and camels) and humans. Rift Valley fever is endemic in sub-Saharan Africa and in the Arabian Peninsula, with periodic epidemics characterised by 5–15 years of inter-epizootic periods. In the last two decades, RVF was notified in new African regions (e.g. Sahel), RVF epidemics occurred more frequently and low-level enzootic virus circulation has been demonstrated in livestock in various areas. Recent outbreaks in a French overseas department and some seropositive cases detected in Turkey, Tunisia and Libya raised the attention of the EU for a possible incursion into neighbouring countries. The movement of live animals is the most important pathway for RVF spread from the African endemic areas to North Africa and the Middle East. The movement of infected animals and infected vectors when shipped by flights, containers or road transport is considered as other plausible pathways of introduction into Europe. The overall risk of introduction of RVF into EU through the movement of infected animals is very low in all the EU regions and in all MSs (less than one epidemic every 500 years), given the strict EU animal import policy. The same level of risk of introduction in all the EU regions was estimated also considering the movement of infected vectors, with the highest level for Belgium, Greece, Malta, the Netherlands (one epidemic every 228–700 years), mainly linked to the number of connections by air and sea transports with African RVF infected countries. Although the EU territory does not seem to be directly exposed to an imminent risk of RVFV introduction, the risk of further spread into countries neighbouring the EU and the risks of possible introduction of infected vectors, suggest that EU authorities need to strengthen their surveillance and response capacities, as well as the collaboration with North African and Middle Eastern countries.info:eu-repo/semantics/publishedVersio

The sero-epidemiology of Rift Valley fever in people in the Lake Victoria Basin of western Kenya

Rift Valley fever virus (RVFV) is a zoonotic arbovirus affecting livestock and people. This study was conducted in western Kenya where RVFV outbreaks have not previously been reported. The aims were to document the seroprevalence and risk factors for RVFV antibodies in a community-based sample from western Kenya and compare this with slaughterhouse workers in the same region who are considered a high-risk group for RVFV exposure. The study was conducted in western Kenya between July 2010 and November 2012. Individuals were recruited from randomly selected homesteads and a census of slaughterhouses. Structured questionnaire tools were used to collect information on demographic data, health, and risk factors for zoonotic disease exposure. Indirect ELISA on serum samples determined seropositivity to RVFV. Risk factor analysis for RVFV seropositivity was conducted using multi-level logistic regression. A total of 1861 individuals were sampled in 384 homesteads. The seroprevalence of RVFV in the community was 0.8% (95% CI 0.5–1.3). The variables significantly associated with RVFV seropositivity in the community were increasing age (OR 1.2; 95% CI 1.1–1.4, p<0.001), and slaughtering cattle at the homestead (OR 3.3; 95% CI 1.0–10.5, p = 0.047). A total of 553 slaughterhouse workers were sampled in 84 ruminant slaughterhouses. The seroprevalence of RVFV in slaughterhouse workers was 2.5% (95% CI 1.5–4.2). Being the slaughterman, the person who cuts the animal’s throat (OR 3.5; 95% CI 1.0–12.1, p = 0.047), was significantly associated with RVFV seropositivity. This study investigated and compared the epidemiology of RVFV between community members and slaughterhouse workers in western Kenya. The data demonstrate that slaughtering animals is a risk factor for RVFV seropositivity and that slaughterhouse workers are a high-risk group for RVFV seropositivity in this environment. These risk factors have been previously reported in other studies providing further evidence for RVFV circulation in western Kenya

On the influence of market structure in modelling the US copper industry

Many of the contemporary models used to describe the behavior of the mineral industries assume a competitive market i.e. one in which market price is equal to marginal production cost. One such recent model of the worldwide copper industry is the MIDAS-II model developed for the Bureau of Mines [3, 4]. This model is used to project production and prices up through the year 2000. The purpose of this paper is to demonstrate the importance of the assumed market structure in the construction of these forecasts. If the market structure of the US copper industry is assumed to be comprised of a few large firms (an oligopoly), then forecasts based upon exactly the same data base differ significantly from the competitive market assumption.

Conservative linear programming with mixed multiple objectives

In an ordinary linear program a single objective vector is constructed and one attempts to choose a decision vector to optimize this objective. Often multiple criteria exist or exact estimates for the components of a single objective vector are not entirely clear. For these cases a conservative decision-maker may want to choose an alternative that maximizes the objective value under the worst foreseeable circumstances. Herein we develop a unified framework for applying the maximin criterion to problems with various degrees of uncertainty attached to the objective vector. Three cases are solved via linear programming: (1) Complete Information, (2) Partial Information, and (3) Total Ignorance. It is shown that the functional value of the maximin solution decreases in a convex manner with increasing uncertainty. In addition certain relationships between maximin and efficient solutions are provided. Finally, an extension to integer constrained decision variables is presented.