• Energy Research

In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The considerable amount of energy required for heating, cooling, and lighting as well as material handling equipment (MHE) in warehouses represents about 20% of the overall logistical costs. The reduction of warehouses’ energy consumption would thus lead to a significant benefit from an environmental point of view. In this context, sustainable strategies allowing the minimization of the cost of energy consumption due to MHE represent a new challenge in warehouse management. Consistent with this purpose, a two-step optimization model based on integer programming is developed in this paper to automatically identify an optimal schedule of the material handling activities of electric mobile MHEs (MMHEs) (i.e., forklifts) in labor-intensive warehouses from profit and sustainability perspectives. The resulting scheduling aims at minimizing the total cost, which is the sum of the penalty cost related to the makespan of the material handling activities and the total electricity cost of charging batteries. The approach ensures that jobs are executed in accordance with priority queuing and that the completion time of battery recharging is minimized. Realistic numerical experiments are conducted to evaluate the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. The obtained results show the effectiveness of the model in identifying the optimal battery-charging schedule for a fleet of electric MMHEs from economic and environmental perspectives simultaneously.

In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The considerable amount of energy required for heating, cooling, and lighting as well as material handling equipment (MHE) in warehouses represents about 20% of the overall logistical costs. The reduction of warehouses’ energy consumption would thus lead to a significant benefit from an environmental point of view. In this context, sustainable strategies allowing the minimization of the cost of energy consumption due to MHE represent a new challenge in warehouse management. Consistent with this purpose, a two-step optimization model based on integer programming is developed in this paper to automatically identify an optimal schedule of the material handling activities of electric mobile MHEs (MMHEs) (i.e., forklifts) in labor-intensive warehouses from profit and sustainability perspectives. The resulting scheduling aims at minimizing the total cost, which is the sum of the penalty cost related to the makespan of the material handling activities and the total electricity cost of charging batteries. The approach ensures that jobs are executed in accordance with priority queuing and that the completion time of battery recharging is minimized. Realistic numerical experiments are conducted to evaluate the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. The obtained results show the effectiveness of the model in identifying the optimal battery-charging schedule for a fleet of electric MMHEs from economic and environmental perspectives simultaneously.

A worldwide trend is leading the international community to explore the possible paths for the transition from a linear to a circular economic model. Recent studies identified the main enabling factors to facilitate this transition, among which closed loop logistics models and new service-based and function-oriented business models play a significant role. The diffusion of new business models relating to the use of a product rather than its ownership can indeed reduce material consumption and the related environmental impacts, while customer loyalty and company revenues are expected to increase. However, the overall impacts of such models on the environmental, economic and social perspectives are not clear, as several uncertainty factors arise with the implementation of non-ownership models. Therefore, the aim of this work is to evaluate impacts on sustainability dimensions due to the transition from an ownership-based to a product-as-a-service based model in direct and reverse supply chain of a large appliance product, i.e. washing machines. The analysis is carried out developing a fuzzy cognitive map model to quantify direct and indirect effects on the social, economic and environmental dimensions due to the adoption of a new business model. First, both the traditional and the leasing supply chain models are investigated. Then, the key drivers and their causal relationships affecting systems performance are identified and discussed. Results stress out the great potential of such a change in the improving of the sustainable dimensions highlighting the enabling strategies viable by a policy maker to facilitate the transition to a product-as-a-service based model.

A worldwide trend is leading the international community to explore the possible paths for the transition from a linear to a circular economic model. Recent studies identified the main enabling factors to facilitate this transition, among which closed loop logistics models and new service-based and function-oriented business models play a significant role. The diffusion of new business models relating to the use of a product rather than its ownership can indeed reduce material consumption and the related environmental impacts, while customer loyalty and company revenues are expected to increase. However, the overall impacts of such models on the environmental, economic and social perspectives are not clear, as several uncertainty factors arise with the implementation of non-ownership models. Therefore, the aim of this work is to evaluate impacts on sustainability dimensions due to the transition from an ownership-based to a product-as-a-service based model in direct and reverse supply chain of a large appliance product, i.e. washing machines. The analysis is carried out developing a fuzzy cognitive map model to quantify direct and indirect effects on the social, economic and environmental dimensions due to the adoption of a new business model. First, both the traditional and the leasing supply chain models are investigated. Then, the key drivers and their causal relationships affecting systems performance are identified and discussed. Results stress out the great potential of such a change in the improving of the sustainable dimensions highlighting the enabling strategies viable by a policy maker to facilitate the transition to a product-as-a-service based model.

This paper investigates the impact of the adoption of public support on the performance of public–private partnership (PPP) projects as perceived and measured by the different actors involved. In particular, the public support investigated by this study is put-or pay contracts, which are often used in PPP projects financed through project finance to optimize risk allocation. In order to quantify the benefit gained by each party with and without the put-or-pay contract, cash flows of the project have been modeled by using the concept of real option, defined as the right without the obligation to make an action if it is convenient to do so. This concept enabled us to model and quantify the inner flexibility mechanism of put-or-pay contracts. With a put-or-pay agreement signed between the municipality, a (private) owner, and operator of a disposal facility, the owner of the facility has the faculty, without any obligation, to require the payment of penalty, if the municipality fails to meet its obligations. This means that the owner of the facility holds a series of European put options that can be exercised if it is convenient for the holder. The developed model has been used for studying the effectiveness of put-or-pay contracts for financing the treatment plant of a special dispose through project finance, i.e., the plant for disposal of marine plant posidonia.

This paper investigates the impact of the adoption of public support on the performance of public–private partnership (PPP) projects as perceived and measured by the different actors involved. In particular, the public support investigated by this study is put-or pay contracts, which are often used in PPP projects financed through project finance to optimize risk allocation. In order to quantify the benefit gained by each party with and without the put-or-pay contract, cash flows of the project have been modeled by using the concept of real option, defined as the right without the obligation to make an action if it is convenient to do so. This concept enabled us to model and quantify the inner flexibility mechanism of put-or-pay contracts. With a put-or-pay agreement signed between the municipality, a (private) owner, and operator of a disposal facility, the owner of the facility has the faculty, without any obligation, to require the payment of penalty, if the municipality fails to meet its obligations. This means that the owner of the facility holds a series of European put options that can be exercised if it is convenient for the holder. The developed model has been used for studying the effectiveness of put-or-pay contracts for financing the treatment plant of a special dispose through project finance, i.e., the plant for disposal of marine plant posidonia.

During the last years, due to the strict regulations on waste landfilling, anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW) is increasingly considered a sustainable alternative for waste stabilization and energy recovery. AD can reduce the volume of OFMSW going to landfill and produce, at the same time, biogas and compost, all at a profit. The uncertainty about the collected quantity of organic fraction, however, may undermine the economic-financial sustainability of such plants. While the flexibility characterizing some AD technologies may prove very valuable in uncertain contexts since it allows adapting plant capacity to changing environments, the investment required for building flexible systems is generally higher than the investment for dedicated equipment. Hence, an adequate justification of investments in these flexible systems is needed. This paper presents the results of a study aimed at investigating how different technologies may perform from technical, economic and financial standpoints, in presence of an uncertain organic fraction quantity to be treated. Focusing on two AD treatment plant configurations characterized by a technological process with different degree of flexibility, a real options-based model is developed and then applied to the case of the urban waste management system of the Metropolitan Area of Bari (Italy). Results show the importance of pricing the flexibility of treatment plants, which becomes a critical factor in presence of an uncertain organic fraction. Hence, it has to be taken into consideration in the design phase of these plants.