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IIM Indore

Operation Management and Quantitative Techniques

We present a location–allocation problem for wastewater treatment in a cluster of processing units. The problem involves installing wastewater treatment plants of appropriate capacities at suitable locations and allocating processing units to these plants. The operational cost of treatment plants is a nonconvex function of wastewater volume and pollutant concentration. This problem is formulated as a non-convex mixed integer <a href="https://www.sciencedirect.com/topics/computer-science/nonlinear-programming">nonlinear programming</a> (MINLP) problem. For the general class of this problem, solution strategies based on adapted <a href="https://www.sciencedirect.com/topics/mathematics/mccormick">McCormick</a> envelopes and also piece-wise linear approximation are presented. For a special case of this problem, an exact convexification strategy is proposed and an outer approximation based branch-and-cut approach is presented as an exact solution method. For solving larger instances, we present a matheuristic solution method based on multi-start neighborhood search incorporated into a MINLP solution approach. A real-world case study is presented to illustrate the results of the model along with a multi-objective analysis.

World University root node

Computers & Operations Research

Capacitated facility location–allocation problem for wastewater treatment in an industrial cluster

We develop a two-part high-dimensional Bayesian modelling approach to analyse the relationship between saving behaviour and health. In contrast to the existing literature, our approach allows different data-generating processes for the decision to save and the amount saved, and therefore unveils a more detailed picture of the relationship between financial behaviour and health than previous work. We explore different measures of saving, including monthly saving behaviour and the stock of financial assets held. Further, we exploit British panel data, which includes an extensive range of biomarkers. Our second contribution lies in comparing the effects of these objective measures of health with commonly used self-assessed health measures. We find that health is a significant determinant of saving behaviour and financial asset holding, and that biomarker measures have differential impacts on saving behaviour compared to self-reported health measures.

Fulltext

The European Journal of Finance

Saving behaviour and health: A high-dimensional Bayesian analysis of British panel data

This work examines the role of a non-price variable, shelf-space in inter-brand competition. The retailer introduces a private brand to control the prices of the national brand, which acts as a monopoly in the absence of any competition from other brands. The private brands compete with national brands for demand, which is a function of the allocated shelf space and the brand price. We propose a game-theoretic model involving a national brand and a private brand who compete horizontally in the supermarkets for shelf space. A comparative study is undertaken through two different cases. In the first case, the retailer promotes the private brand manufactured by itself followed by the second case wherein retailer sells the private brand produced by another manufacturer. The retailer allocates shelf space to both the national as well as private brand simultaneously to capture the competition. The manufacturer of the national brand acts as the stackelberg given its power of setting wholesale prices. The equilibrium of the profit maximization game is expressed in terms of prices, shelf-spaces and profit margin of the players. This work is an attempt to gain insights on the conditions of allocation which are profitable for the manufacturer taking into account the consumer preferences between competing brands. The study also indicates that manufacturer’s profits reduces when they compete for space and make the retailer dominant, when the latter is only involved in shelf space allocation without any in-house production. We contribute as building blocks to understand how shelf-space limitation intensifies the manufacturer-retailer competition and also obtain managerial insight on disproportionate distribution of profits.

Journal of Revenue and Pricing Management

Shelf space allocation game with private brands: a profit-sharing perspective

We study the problem of assigning doctors to existing, non-operational Primary Health Centers (PHCs). We do this in the presence of clear guidelines on the maximum population that can be served by any PHC, and uncertainties in the availability of the doctors over the planning horizon. We model the problem as a robust capacitated multi-period maximal covering location problem with server uncertainty. Such supply-side uncertainties have not been accounted for in the context of multi-period facility location in the extant literature. We present an MIP formulation of this problem, which turns out to be too difficult for an off-the-shelf solver like CPLEX. We, therefore, present several dominance rules to reduce the size of the model. We further propose a Benders decomposition based solution method with several refinements that exploit the underlying structure of the problem to solve it extremely efficiently. Our computational experiments show one of the variants of our Benders decomposition based method to be on average almost 1000 times faster, compared to the CPLEX MIP solver, for problem instances containing 300 demand nodes and 10 facilities. Further, while the CPLEX MIP solver could not solve most of the instances beyond 300 demand nodes and 10 facilities even after 20 hours, two of our variants of Benders decomposition could solve instances upto the size of 500 demand nodes and 15 facilities in less than 0.5 hour, on average.

European Journal of Operational Research

Capacitated multi-period maximal covering location problem with server uncertainty

https://arxiv.org/abs/2101.

Bayesian inference in high-dimensional models

The problem of precision matrix estimation in a multivariate Gaussian model is fundamental to network estimation. Although there exist both Bayesian and frequentist approaches to this, it is difficult to obtain good Bayesian and frequentist properties under the same prior-penalty dual, complicating justification. It is well known, for example, that the Bayesian version of the popular lasso estimator has poor posterior concentration properties. To bridge this gap for the precision matrix estimation problem, our contribution is a novel prior-penalty dual that closely approximates the popular graphical horseshoe prior and penalty and performs well in both Bayesian and frequentist senses. A chief difficulty with the horseshoe prior is a lack of closed form expression of the density function, which we overcome in this article, allowing us to directly study the penalty function. In terms of theory, we establish posterior convergence rate of the precision matrix that matches the oracle rate, in addition to the frequentist consistency of the maximum a posteriori estimator. In addition, our results also provide theoretical justifications for previously developed approaches that have been unexplored so far, e.g. for the graphical horseshoe prior. Computationally efficient Expectation Conditional Maximization and Markov chain Monte Carlo algorithms are developed respectively for the penalized likelihood and fully Bayesian estimation problems, using the same latent variable framework. In numerical experiments, the horseshoe-based approaches echo their superior theoretical properties by comprehensively outperforming the competing methods. A protein-protein interaction network estimation in B-cell lymphoma is considered to validate the proposed methodology.

arXiv preprint arXiv:2104.

Precision Matrix Estimation under the Horseshoe-like Prior-Penalty Dual

This paper presents a case study for an assembly job shop encountered in a hydraulic components manufacturing industry. A simulation model is developed from the data collected and checked the performance of the existing dispatching rule used. Five performance measures are considered for analysis. The performance of each dispatching rule for each performance measure is calculated individually. An Order Review Release (ORR) policy namely Backward Scheduling with Interval Release policy is used for the experimentation. The analysis of results reveals that integrating dispatching rules with ORR policy provides better system performance than using dispatching rules alone. The present study confirms that other dispatching rules other than the existing rule gives better performance.

Materials Today: Proceedings

Scheduling of an assembly job shop: A case study based on hydraulic manufacturing industry

The health system is intricate due to its dynamic nature and critical service requirements. The involvement of multiple layers of health service providers quadrupled this complexity and results in a complicated operating environment. Simulation is often considered an apt technique to model and study complex systems in the literature. The popularity of simulation in the healthcare domain had only accelerated with time and resulted in a large number of articles intended to solve myriad healthcare problems. This article analyzes healthcare simulation literature of the past decade (2007-2016) that addresses operations management issues in various healthcare service delivery levels and categorizes the literature accordingly. In the next step, we attempt to assimilate the entire literature to capture specific health issues addressed, operations management concepts applied, and simulation methods used, and identify major research gaps. Finally, we develop the research agenda from dividing these gaps into the contextual, conceptual, and methodological genre that is consistent with the previous state-of-the-art literature reviews in operations management. Furthermore, this article demonstrates other minute aspects such as "sources of funding"and "tools used for the research"to maintain coherence with the previous reviews in the healthcare simulation. The objective of this work is twofold: To connect the knowledge continuum to the present, and to provide potential research directions for future academicians. © 2020 ACM.

ACM Transactions on Modeling and Computer Simulation

Application of Simulation in Healthcare Service Operations

This paper focuses on social cloud formation, where agents are involved in a closeness-based conditional resource sharing and build their resource sharing network themselves. The objectives of this paper are: (1) to investigate the impact of agents' decisions of link addition and deletion on their local and global resource availability, (2) to analyze spillover effects in terms of the impact of link addition between a pair of agents on others' utility, (3) to study the role of agents' closeness in determining what type of spillover effects these agents experience in the network, and (4) to model the choices of agents that suggest with whom they want to add links in the social cloud. The findings include the following. Firstly, agents' decision of link addition (deletion) increases (decreases) their local resource availability. However, these observations do not hold in the case of global resource availability. Secondly, in a connected network, agents experience either positive or negative spillover effect and there is no case with no spillover effects. Agents observe no spillover effects if and only if the network is disconnected and consists of more than two components (sub-networks). Furthermore, if there is no change in the closeness of an agent (not involved in link addition) due to a newly added link, then the agent experiences negative spillover effect. Although an increase in the closeness of agents is necessary in order to experience positive spillover effects, the condition is not sufficient. By focusing on parameters such as closeness and shortest distances, we provide conditions under which agents choose to add links so as to maximise their resource availability.

Resource Availability in the Social Cloud: An Economics Perspective

We consider a production inventory problem of a deteriorating product that begins to deteriorate immediately on production. Two models of three echelon supply chain are developed whose rate of demand, production and deterioration are assumed to be constant. The second model allows backlogging of the demand, while the first model does not allow backlogging. In both models, the producer's cycle starts with zero stock and the production continues till the stock level reaches an optimal value. There will be demand and deterioration and consequently stock level falls to zero during the non-production phase. The demand subsequently received is backordered in the second model and a fraction of it is met by restarting the production process. Optimal lot sizes of distributor and retailer is identified within each of the producer's cycle. A numerical example is illustrated. Comparison between the models shows that backordering reduces the total supply chain cost. Copyright © 2021 Inderscience Enterprises Ltd.