An improved negotiation-based approach for collecting and sorting operations in waste management and recycling

This paper addresses the problem of optimal planning for collection, sorting, and recycling operations. The problem arises in industrial waste management, where distinct actors manage the collection and the sorting operations. In a weekly or monthly plan horizon, they usually interact to find a suitable schedule for servicing customers but with a not well-defined scheme. We proposal an improved negotiation-based approach using an auction mechanism for optimizing these operations. Two interdependent models are presented: one for waste collection by a logistics operator and the other for sorting operations at a recycling plant. These models are formulated as mixed-integer linear programs where costs associated with sorting and collection are to be minimized, respectively. We describe the negotiation-based approach involving an auction where the logistics operator bids for collection time slots, and the recycling plant selects the optimal bid based on the integration of sorting and collection costs. This approach aims to achieve an optimization of the entire waste management process. Computational experiments are presente

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 Quadratic-Linear Bilevel Programming Approach to Green Supply Chain Management

Green Supply Chain Management requires coordinated decisions between the strategic and operational organization layers to address strict green goals. Furthermore, linking CO2 emissions to supply chain operations is not always easy. This study proposes a new mathematical model to minimize CO2 emissions in a three-layered supply chain. The model foresees using a financial budget to mitigate emissions contributions and optimize supply chain operations planning. The three-stage supply chain analyzed has inbound logistics and handling operations at the intermediate level. We assume that these operations contribute to emissions quadratically. The resulting bilevel programming problem is solved by transforming it into a nonlinear mixed-integer program by applying the Karush-Kuhn-Tucker conditions. We show, on different sets of synthetic data and on a case study, how our proposal produces solutions with a different flow of goods than a modified linear model version. This results in lower CO2 emissions and more efficient budget expenditure

Unregulated Cap-and-Trade Model for Sustainable Supply Chain Management

Cap-and-trade models have been largely studied in the literature when it comes to reducing emissions in a supply chain. In this paper, further pursuing the goal of analyzing the effectiveness of cap-and-trade strategies in reducing emissions in supply chains, we propose a mathematical model for sustainable supply chain management. This optimization program aims at reducing emissions and supply chain costs in an unregulated scenario w.r.t. the cap definition, i.e., trading CO2 is allowed but no formal limit on the CO2 emissions is imposed. Also, we considered an initial budget for technological investments by the facilities in the considered supply chain, allowing plants to reduce their unit production emissions at a different unit production cost. For this model, differently from what exists in the literature, we derive some theoretical conditions guaranteeing that, if obeyed, the emissions over time have a non-increasing trend meaning that decreasing caps over time can be attained with a self-regulated scenario. Computational results show the effectiveness of our approach

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

A Cooperative Model to Improve Hospital Equipments and Drugs Management

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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