Green sustainable supply chain under cap and trade regulation involving Government introspection

This paper investigates Government intervention in a three-echelon supply chain comprising one manufacturer and one retailer. Government is the top level member trying to reduce environmental impacts based on the amount of carbon emission during the production process. Government controls the chain by collecting tax from the retailer which is indirectly paid by the customer and paying subsidy/imposing ne on the manufacturer. Government encourages manufacturer to reduce carbon emission by contributing some subsidy and also makes an e ort to generate Government net revenue (GNR) by imposing tax. The GNR is generated by collecting tax from the retailer on the sold product and penalty from the manufacturer at the trading price for the extra amount of emissions. The retail price is decided based on the selling price, tax and greening level. We aim to determine optimal levels of pricing, greening and amount of tax to be levied. The models for both linear and iso-elastic demand patterns are developed. The aim of this piece research is two-fold: (i) review the existent literature on the relationship between environ- mental collaboration and sustainability performance and (ii) render a tenable prototype of supply chain to illuminate the relationship between sustainability and profitability. According to the aforesaid goals this paper has carried out a detailed empirical research by using advanced structural equation modelling approaches. The research findings will be particularly important for manufacturing companies struggling to find techniques to achieve sustainability performance. Also it will aid the supply chains in developing environmental collaboration with the Govt. in order to attain the targets of GSCM.

Rebalancing Autonomous Vehicles using Deep Reinforcement Learning

The shared autonomous mobility-on-demand (AMoD) system is a promising business model in the coming future which provides a more efficient and affordable urban travel mode. However, to maintain the efficient operation of AMoD and address the demand and supply mismatching, a good rebalancing strategy is required. This paper proposes a reinforcement learning-based rebalancing strategy to minimize passengers’ waiting in a shared AMoD system. The state is defined as the nearby supply and demand information of a vehicle. The action is defined as moving to a nearby area with eight different directions or staying idle. A 4.6 4.4 km2 region in Cambridge, Massachusetts, is used as the case study. We trained and tested the rebalancing strategy in two different demand patterns: random and first-mile. Results show the proposed method can reduce passenger’s waiting time by 7% for random demand patterns and 10% for first-mile demand patterns.

Dynamic stop pooling for flexible and sustainable ride sharing

Ride sharing -- the bundling of simultaneous trips of several people in one vehicle -- may help to reduce the carbon footprint of human mobility. However, the complex collective dynamics pose a challenge when predicting the efficiency and sustainability of ride-sharing systems. Standard door-to-door ride sharing services trade reduced route length for increased user travel times and come with the burden of many stops and detours to pick up individual users. Requiring some users to walk to nearby shared stops reduces detours, but could become inefficient if spatio-temporal demand patterns do not well fit the stop locations. Here, we present a simple model of dynamic stop pooling with flexible stop positions. We analyze the performance of ride sharing services with and without stop pooling by numerically and analytically evaluating the steady state dynamics of the vehicles and requests of the ride sharing service. Dynamic stop pooling does a-priori not save route length, but occupancy. Intriguingly, it also reduces the travel time, although users walk parts of their trip. Together, these insights explain how dynamic stop pooling may break the trade-off between route lengths and travel time in door-to-door ride sharing, thus enabling higher sustainability and service quality.

New goods demand patterns in digital economy environment: The case of electric vehicles in Russia

The trend of using electric vehicles is changing the automotive industry. Electric cars are becoming the most environmentally friendly replacement for combustion vehicles. Knowing the preferences of potential consumers will allow developing effective solutions to create demand for this product. Generating demand should be based on estimating its potential and shaping the consumer profile of this type of transport for market of each country. New goods need special methods to generate demand, since their potential buyers have difficulties in purchase decision making. This paper presents results of a study on prospects in Russia for such new goods as electric vehicles. The study identified factors that ultimately determine the interest of those Russian consumers who have the financial ability to purchase electric vehicles in the near future in electric vehicles. The study demonstrates that consumer prejudices are still there against difficulties in operating electric vehicles. The study confirmed that a number of factors affect the purchase of an electric car in Russia. Expanding the presence of electric vehicles in carsharing companies will significantly improve experience in using this type of transport by potential users. Generating the demand for electric vehicles by applying influence marketing tools is the one of the best solutions.

Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

Purpose - Investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns. Design/Methodology/Approach - This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network. Findings - This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance. Research limitations/implications - This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity, and post-processing requirements. Originality/value - This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency

Motivated by maximizing spot instances in cloud shared systems, in this work, we consider the problem of taking advantage of unused resources in highly dynamic cloud environments while preserving users’ performance. We introduce an online model for sharing resources that captures basic properties of cloud systems, such as unpredictable users’ demand patterns, very limited feedback from the system, and service level agreement (SLA) between the users and the cloud provider. We provide a simple and efficient algorithm for the single-resource case. For any demand patterns, our algorithm guarantees near-optimal resource utilization as well as high users’ performance compared with their SLA baseline. In addition to this, we validate empirically the performance of our algorithm using synthetic data and data obtained from Microsoft’s systems.

Modelling and Incorporating the Variable Demand Patterns to the Calibration of Water Distribution System Hydraulic Model

Calibration of a water distribution system (WDS) hydraulic model requires adjusting several parameters including hourly or sub-hourly demand multipliers, pipe roughness and settings of various hydraulic components. The water usage patterns or demand patterns in a 24-h cycle varies with the customer types and can be related to many factors including spatial and temporal factors. The demand patterns can also vary on a daily basis. For an extended period of hydraulic simulation, the modelling tools allows modelling of the variable demand patterns using daily multiplication factors. In this study, a linear modelling approach was used to handle the variable demand patterns. The parameters of the linear model allow modelling of the variable demand patterns with respect to the baseline values, and they were optimised to maximise the association with the observed data. This procedure was applied to calibrate the hydraulic model developed in EPANET of a large drinking water distribution system in regional South Australia. Local and global optimisation techniques were used to find the optimal values of the linear modelling parameters. The result suggests that the approach has the potential to model the variable demand patterns in a WDS hydraulic model and it improves the objective function of calibration.

Analysis of Demand Changes in a University Counseling Center Telehealth System

The Counseling and Wellness Center (CWC) offers various types of mental health appointments for students at the University of Florida. The CWC is implementing a new walk-in system for student appointments to increase the timeliness and accessibility of first appointments. Due to the COVID-19 pandemic, the CWC shifted to offer telehealth appointments, primarily through Zoom. The research objective is to conduct a data analysis of historical appointment data before the shift to telehealth and after the shift to telehealth to understand how appointment demand changed during the pandemic. The data analysis breaks down the data by appointment type, weekday, and time of day. This project collaborates with staff at the Counseling and Wellness Center and has the goal of helping the CWC better understand demand patterns, so they can better anticipate appointment demand and serve the UF student population in a timely manner.

Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

PurposeThe study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.Design/methodology/approachThis work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.FindingsThis research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.Research limitations/implicationsThis research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.Originality/valueThis research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.