In recent years, increscent emissions in the city of Beijing due to expanded population, accelerated industrialization and inter-regional pollutant transportation have led to hazardous atmospheric pollution issues. Al...
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In recent years, increscent emissions in the city of Beijing due to expanded population, accelerated industrialization and inter-regional pollutant transportation have led to hazardous atmospheric pollution issues. Although a number of anthropogenic control measures have been put into use, frequent/severe haze events have still challenged regional governments. In this study, a hybrid population-production pollution nexus model (PPP) is proposed for air pollution management and air quality planning (AMP) with the aim to coordinate human activities and environmental protection. A fuzzy-stochastic mixed quadratic programming method (FSQ) is developed and introduced into a PPP for tackling atmospheric pollution issues with uncertainties. Based on the contribution of an index of population-production pollution, a hybrid PPP-based AMP model that considers employment structure, industrial layout pattern, production mode, pollutant purification efficiency and a pollution mitigation scheme have been applied in Beijing. Results of the adjustment of employment structure, pollution mitigation scheme, and green gross domestic product under various environmental regulation scenarios are obtained and analyzed. This study can facilitate the identification of optimized policies for alleviating population production-emission conflict in the study region, as well as ameliorating the hazardous air pollution crisis at an urban level. (C) 2017 Elsevier Ltd. All rights reserved.
Inventory management under uncertainty is a widely investigated field and many different types of inventory models have been used to address inventory problems in practice. However, a look at the literature reveals th...
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Inventory management under uncertainty is a widely investigated field and many different types of inventory models have been used to address inventory problems in practice. However, a look at the literature reveals that few papers are devoted to inventory planning and management in environments characterised by uncertainty resulting from extreme events. In this paper a fuzzy-stochastic multi objective modelling approach is used to address the problem of managing inventory in an environment characterised by uncertainty. The model is applied specifically to the military environment and determines the required stock level for a single item, based on three different scenarios. A numerical example is provided for the sake of illustration and the reliability of the model tested through simulation. The results are compared with those obtained from the well known (r, Q) and (s, S) inventory models in the literature. This comparison showed that the hybrid model presented in this paper is more reliable in extreme scenarios. (C) 2016 Elsevier Ltd. All rights reserved.
In this paper, a fuzzy-stochastic optimization model is developed for an intermodal fleet management system of a large international transportation company. The proposed model integrates various strategic, tactical an...
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In this paper, a fuzzy-stochastic optimization model is developed for an intermodal fleet management system of a large international transportation company. The proposed model integrates various strategic, tactical and operational level decisions simultaneously. Since real-life fleet planning problems may involve different types of uncertainty jointly such as randomness and fuzziness, a hybrid chance-constrained programming and fuzzy interactive resolution-based approach is employed. Therefore, stochastic import/export freight demand and fuzzy transit times, truck/trailer availabilities, the transport capacity of Ro-Ro vessels, bounds on block train services, etc. can also be taken into account concurrently. In addition to minimize overall transportation costs, optimization of total transit times and CO2 emission values are also incorporated in order to provide sustainable fleet plans by maximizing customer satisfaction and environmental considerations. Computational results show that effective and efficient fleet plans can be produced by making use of the proposed optimization model.
Multi-item stochastic and fuzzy-stochastic inventory models are formulated under total budgetary and space constraints. Here, the inventory costs are directly proportional to the respective quantities, unit purchase/p...
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Multi-item stochastic and fuzzy-stochastic inventory models are formulated under total budgetary and space constraints. Here, the inventory costs are directly proportional to the respective quantities, unit purchase/production cost is inversely related to the demand and replenishment/production rate is assumed to vary directly with demand. Shortages are allowed but fully backlogged. Here, for both models, demand and budgetary resource are assumed to be random. In fuzzy-stochastic model, in addition to the above assumptions, available storage space and total expenditure are imprecise in nature. Impreciseness in the parameters have been expressed with the help of linear membership functions. Assuming random variables to be independent and to follow normal distributions, the models have been formulated as stochastic and fuzzy-stochastic non-linear programming problems. The stochastic problem is first reduced to the equivalent single objective or multiple objectives problems following chance-constraint method. The problem with single objective is solved by a gradient-based technique whereas fuzzy technique is applied to the multi-objective one. In the same way, the fuzzy-stochastic programming problem is first reduced to a corresponding equivalent fuzzy non-linear programming problem and then it is solved by fuzzy non-linear programming (FNLP) following Zimmermann technique. The models are illustrated numerically and the results of different models are compared. (C) 2003 Elsevier Ltd. All rights reserved.
This paper studies the effect of the open innovation concept in the product design process and supply chain master planning. Complex uncertainties caused by using outbound resources within co-design processes, financi...
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This paper studies the effect of the open innovation concept in the product design process and supply chain master planning. Complex uncertainties caused by using outbound resources within co-design processes, financial challenges between the collaborating parties, and integrating outbound innovative designs with supply chain tactical planning problem are taken into account. To this end, a robust fuzzy-stochastic optimization model is proposed which can integrate the product design with the main activities of a multi-product supply chain. The proposed model is able to cope with different type of uncertainties including random, epistemic and deep uncertainties. Integrating the financial and physical flows, using a novel pricing mechanism, and considering the outside-in innovations in the product design process are the outstanding contributions of the proposed model. Furthermore, to cover both the short-term and long-term success criteria, ambidexterity of the studied supply chain is taken into account via two conflicting explorative and exploitive objectives. Results indicate the superiority of the presented model and its ability in supporting managerial decisions in the mid-term planning process.
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