Sustainable two stage supply chain management: A quadratic optimization approach with a quadratic constraint

Designing a supply chain to comply with environmental policy requires awareness of how work and/or production methods impact the environment and what needs to be done to reduce those environmental impacts and make the company more sustainable. This is a dynamic process that occurs at both the strategic and operational levels. However, being environmentally friendly does not necessarily mean improving the efficiency of the system at the same time. Therefore, when allocating a production budget in a supply chain that implements the green paradigm, it is necessary to figure out how to properly recover costs in order to improve both sustainability and routine operations, offsetting the negative environmental impact of logistics and production without compromising the efficiency of the processes to be executed. In this paper, we study the latter problem in detail, focusing on the CO2 emissions generated by the transportation from suppliers to production sites, and by the production activities carried out in each plant. We do this using a novel mathematical model that has a quadratic objective function and all linear constraints except one, which is also quadratic, and models the constraint on the budget that can be used for green investments caused by the increasing internal complexity created by large production flows in the production nodes of the supply network. To solve this model, we propose a multistart algorithm based on successive linear approximations. Computational results show the effectiveness of our proposal

A bi-objective model for scheduling green investments in two-stage supply chains

Investing in green technologies to increase sustainability in supply chains has become a common practice for two reasons: the first is directly related to the defense of the environment and people’s health to smooth the emissions of pollutants; the second is the increasing consumer awareness of green products. Despite the higher costs of producing with green technologies and processes, there is also a higher markup on the price of products which rewards the former costs. This study proposes a mathematical model for scheduling green investments over time in a two-stage supply chain to minimize the impact of production on the environment and the economic costs deriving from the investment. The resulting bi-objective model has nonlinear constraints and is solved using a commercial solver. Given its complexity, we propose an upper-bound heuristic and a lower-bound model to reduce the optimality gap attained at a given time limit. Tests on synthetic instances have been conducted, and an example demonstrates the applicability and efficacy of the proposed model

A Cloud-based System to Protect Against Industrial Multi-risk Events☆

Abstract Industrial areas frequently present a high concentration of production operations which are source of anthropic risks. For this reason Smart Public Safety is receiving an increasing attention from industry, research and authorities. Moreover, due the consequences of global warming, these areas could be subject to risk events with increased probability with respect to the past. Information technologies enable an innovative approach towards safety management, which relies on the evolution of tools for environmental monitoring and citizens' interaction. This work presents the preliminary results of the Italian research project SIGMA - sensor Integrated System in cloud environment for the Advanced Multi-risk Management. The proposed system includes a continuous monitoring of the different information sources, thus reducing human control as much as possible. At the same time, the communication system manages multiple data flows in a flexible way, adapting itself to different working scenarios, enabling smarter applications. SIGMA intends to acquire, integrate and compute heterogeneous data, coming from various sensor networks in order to provide useful insights for the monitoring, forecasting and management of risk situations through services provided to citizens and businesses, both public and private. Based on the integration of different interoperating components, the system is able to provide a complete emergency management framework through simulations/optimizations and heterogeneous data manipulation tools. The prototype solution is detailed by a use case application in an industrial area located in the region of Sicily, Italy. In particular, web based modular applications connected through SIGMA allow the monitoring of the industrial environment through data gathering from different sensor networks, such as outdoor sensors mounted in the surroundings of large industrial areas, and support of the design of the logistics network aimed at covering the industrial risks

A Cooperative Model to Improve Hospital Equipments and Drugs Management

Abstract. The cost of services provided by public and private healthcare systems is nowadays becoming critical. This work tackles the criticalities of hospital equipments and drugs management by emphasizing its implications on the whole healthcare system efficiency. The work presents a multi-agent based model for decisional cooperation in order to address the problem of integration of departments, wards and personnel for improving equipments, and drugs management. The proposed model faces the challenge of (i) gaining the benefits deriving from successful collaborative models already used in industrial systems and (ii) transferring the most appropriate industrial management practices to healthcare systems

Palmitoylethanolamide exerts neuroprotective effects in mixed neuroglial cultures and organotypic hippocampal slices via peroxisome proliferator-activated receptor-α

<p>Abstract</p> <p>Background</p> <p>In addition to cytotoxic mechanisms directly impacting neurons, β-amyloid (Aβ)-induced glial activation also promotes release of proinflammatory molecules that may self-perpetuate reactive gliosis and damage neighbouring neurons, thus amplifying neuropathological lesions occurring in Alzheimer's disease (AD). Palmitoylethanolamide (PEA) has been studied extensively for its anti-inflammatory, analgesic, antiepileptic and neuroprotective effects. PEA is a lipid messenger isolated from mammalian and vegetable tissues that mimics several endocannabinoid-driven actions, even though it does not bind to cannabinoid receptors. Some of its pharmacological properties are considered to be dependent on the expression of peroxisome proliferator-activated receptors-α (PPARα).</p> <p>Findings</p> <p>In the present study, we evaluated the effect of PEA on astrocyte activation and neuronal loss in models of Aβ neurotoxicity. To this purpose, primary rat mixed neuroglial co-cultures and organotypic hippocampal slices were challenged with Aβ_1-42and treated with PEA in the presence or absence of MK886 or GW9662, which are selective PPARα and PPARγ antagonists, respectively. The results indicate that PEA is able to blunt Aβ-induced astrocyte activation and, subsequently, to improve neuronal survival through selective PPARα activation. The data from organotypic cultures confirm that PEA anti-inflammatory properties implicate PPARα mediation and reveal that the reduction of reactive gliosis subsequently induces a marked rebound neuroprotective effect on neurons.</p> <p>Conclusions</p> <p>In line with our previous observations, the results of this study show that PEA treatment results in decreased numbers of infiltrating astrocytes during Aβ challenge, resulting in significant neuroprotection. PEA could thus represent a promising pharmacological tool because it is able to reduce Aβ-evoked neuroinflammation and attenuate its neurodegenerative consequences.</p

A large neighborhood search based matheuristic for the tourist cruises itinerary planning

The planning of itineraries for tourist cruises is a complex process where several features, such as vessel selection, port services, and requirements for point of interest to be inserted in each tour, must be addressed. The present work models the tour planning problem as a variant of vehicle routing problem considering specific constraints such as: fixed number of tours, not mandatory visits of all nodes, multiple time windows, possibility to choose among different travel speed values. The resulting mathematical formulation lead to a complex model for which commercial solvers fail to solve large instances in a reasonable time. To overcome this issue we propose a Large Neighborhood Search based matheuristic, in which an over-constrained version of the mathematical model is used to exhaustively and efficiently explore large neighborhoods. Test results performed on a real case instances demonstrate effectiveness of the proposed approach

A GREEN FINANCE BI-OBJECTIVE MODEL IN A THREE STAGE SUPPLY CHAIN

In this paper, we study a novel green financing model for a three-stage supply chain. The model considers nonlinear relations among CO2 emissions, budget spent for emission control, and quantity of products moved towards and worked in the facilities. Moreover, the model, besides the minimization of CO2 emissions, aims at balancing the commodity flow over the different facilities. The latter objective is represented by the linear combination of two quadratic penalty functions: one associated with the arc flows and the other with the entering flows at the facilities, respectively. The model is solved on both synthetic instances and a realistic network, demonstrating its effectiveness as a tool for strategically supporting green financing decisions in supply chains

A view of operations research applications in Italy, 2018

This book presents expert descriptions of the successful application of operations research in both the private and the public sector, including in logistics, transportation, product design, production planning and scheduling, and areas of social interest. Each chapter is based on fruitful collaboration between researchers and companies, and company representatives are among the co-authors. The book derives from a 2017 call by the Italian Operations Research Society (AIRO) for information from members on their activities in promoting the use of quantitative techniques, and in particular operations research techniques, in society and industry. A booklet based on this call was issued for the annual AIRO conference, but it was felt that some of the content was of such interest that it deserved wider dissemination in more detailed form. This book is the outcome. It equips practitioners with solutions to real-life decision problems, offers researchers examples of the practical application of operations research methods, and provides Master’s and PhD students with suggestions for research development in various fields