Dr Debajyoti Biswas

A static surge pricing model for slot-based on-demand service platforms. This contains all the datasets pertaining to the numerical tests for this model.

The COVID-19 pandemic has had an unprecedented impact on global health and the economy since its inception in December, 2019 in Wuhan, China. Non-pharmaceutical interventions (NPI) like lockdowns and curfews have been deployed by affected countries for controlling the spread of infections. In this paper, we develop a Mixed Integer Non-Linear Programming (MINLP) epidemic model for computing the optimal sequence of NPIs over a planning horizon, considering shortages in doctors and hospital beds, under three different lockdown scenarios. We analyse two strategies - centralised (homogeneous decisions at the national level) and decentralised (decisions differentiated across regions), for two objectives separately - minimization of infections and deaths, using actual pandemic data of France. We linearize the quadratic constraints and objective functions in the MINLP model and convert it to a Mixed Integer Linear Programming (MILP) model. A major result that we show analytically is that under the epidemic model used, the optimal sequence of NPIs always follows a decreasing severity pattern. Using this property, we further simplify the MILP model into an Integer Linear Programming (ILP) model, reducing computational time up to 99%. Our numerical results show that a decentralised strategy is more effective in controlling infections for a given severity budget, yielding up to 20% lesser infections, 15% lesser deaths and 60% lesser shortages in healthcare resources. These results hold without considering logistics aspects and for a given level of compliance of the population. (C) 2022 Elsevier B.V. All rights reserved.

‘On-demand home services’ is a fast-growing industry where online platforms match independent service professionals with customers seeking aid for household tasks. In this paper, we study the assignment and routing of service professionals for serving customers of an on-demand home services platform considering the Triple Bottom Line (TBL) criteria for ensuring sustainability in operations. We characterize this as the Home Services Assignment and Routing Problem with the Triple Bottom Line (HSARP-TBL) and implement a Mixed Integer Linear Programming (MILP) model for solving it. We assign service professionals to customers based on their desired time slots and also transport modes for each customer visit by a professional, considering either combinations of public transport or a personal vehicle for each professional’s tour. The objective is to minimize costs due to time window violations and uncovered customers, catering to the economic pillar of the TBL. We incorporate additional constraints related to the TBL by improving customer satisfaction based on the ratings of assigned professionals to customers, with and without subscription (economic), controlling emissions due to transportation of professionals (environmental) and ensuring equity in service allocation and net earnings between professionals (social). For tackling large instances we implement a Hybrid Genetic Search (HGS) algorithm adapting it to our problem setting. We demonstrate that the HGS outperforms the MILP model systematically for large instances in terms of solution value and computational time. Finally, we observe that for some instances, without worsening the primary economic objective, all the TBL indicators can be improved. •We study the routing of on-demand home services.•We introduce a triple bottom line approach.•We propose a mathematical formulation.•We propose a Hybrid Genetic Search algorithm.•We perform extensive computational tests.

•Wholesale price contract mitigates misalignment between the players’ preferences.•When manufacturer is leader, insurer is indifferent to promotional policies.•In no-contract setting preferences are different depending on the cost of production. A product marketed as smart home insurance combines home insurance and “smart” home products, at an attractive price, so that customers are better protected from hazards and hence the insurance company suffers fewer losses. Our study analyzes two policies offered by insurers to promote the adoption of smart products: a discount on insurance either with or without offering a free smart product to customers. We examine the insurer’s preferences with regard to these two policies for two types of interactions with the smart product manufacturer (SPM): the no-contract setting and the wholesale-price contract setting. A Nash model allows us to compare the players’ pricing decisions regarding both types of policies and different interactions. For the wholesale-price contract, we find that the insurer always prefers the free smart product policy (the insurer incurs the cost of the smart product). In the no-contract setting, the insurer prefers the free smart product unless the product development cost coefficient is high. We observe that the change in an insurer’s decisions—namely, those that are affected by hazard and customer characteristics—varies with the type of interaction. Finally, when an SPM is the market leader, we find that insurers are indifferent between the two promotional policies because the same profit results in both cases.

Datasets for Numerical tests for Static Time-based Pricing for Two-sided on-demand service platforms

This research proposes a game theory model in a supply chain (SC) involving one manufacturer and one retailer. The SC works in a global market in which consumers are located worldwide and subject to traceability issues that can create distrust of the product quality. This issue can be resolved by im-plementing blockchain technology which provides benefits in terms of high traceability along with low transaction costs. However, blockchain negatively impacts the environment because of their high energy consumption. Therefore, in this study, we capture the trade-offs between traceability and sustainability for blockchain adoption by characterizing a game theory model. Our findings show that high levels of distrust pushes firms to avoid the implementation of blockchain. In such circumstances, blockchain is not sufficient to make consumers recognize the product quality and trust the firms' practices. In contrast, low levels of distrust can make blockchain an economically suitable technology conditioned to minimal environmental damages; otherwise, firms need to carefully evaluate the trade-offs between distrust and sustainability. Since the adoption of blockchain leads to an increase in prices and decrease of distrust, two factors determine whether to pursue this technology or not: low consumer sensitivity to price and high sensitivity to quality. In this study, we develop three specific cases where we model: 1) the direct impact of blockchain on distrust, 2) a stochastic distrust term, and 3) a Stackelberg game. Each case confirms our results and strengthens the robustness of our findings. (c) 2022 Elsevier B.V. All rights reserved.

Local Energy Communities (LECs) are an essential component of the clean energy transition in Europe. However, differences in terminology and interpretation across disciplines and among stakeholder groups present challenges to their effective development and operation. This paper explores three key concepts frequently encountered in LEC research and practice: prosumers and self-consumption, flexibility and demand response, and uncertainty. We provide both a Power Systems and an Operations Research (OR) perspective on each term, highlighting how varying definitions can lead to misunderstandings and misaligned decision models. By promoting greater clarity and shared understanding of these terms, we aim to support more effective interdisciplinary collaboration and the development of robust decision support tools for the design and operation of LECs.

The dataset contains synthetically generated data about customers willingness-to-pay (WTP) and expectation-to-be-paid (ETP) of service professionals across different time slots in order to compute the optimal surge price for the on-demand services platform.

Large university campuses are micro-societies and mini-cities with significant opportunities to lead by example in the clean energy transition. They often used Building Energy Management Systems (BEMS) to monitor and control energy supply and demand. However, this data could be further exploited to provide a better understanding of the performane of renewables in specific geographical regions. In this paper we leverage empirical data from a public university BEMS for research purposes, aiming to provide insights for citizen groups such as energy communities who need support to make long term planning decisions. We evaluate statistical and machine learning models of photovoltaic (PV) power generation from PV arrays on university buildings, and provide a tool to estimate PV generation to support energy community planning decisions.

Datasets for testing a static surge pricing model for revenue management of on-demand service platforms.