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IIM Indore

Operation Management and Quantitative Techniques

Indian automotive manufacturers rely mainly on direct truck deliveries from their assembly plants to the customersacross the country. As India has an extensive coastline, companies should be able to develop e_x000E_cient distribution withcoastal shipping. We select a major Indian automotive manufacturer based in southern India with the major ports inIndia considered as potential roll-on/roll-oterminals to analyze the modal shift from primary road based distributionto coastal intermodal shipping. The computational study of a mathematical model along with the analysis of currentregulatory framework estimates current potential and suggests requirements for a successful modal shift.

World University root node

WORKING PAPER

Enhancing the role of coastal shipping in outbound automotive logistics-a viability study

We present a Lax–Friedrichs type algorithm to numerically integrate a class of nonlocal and nonlinear systems of conservation laws in several space dimensions. The convergence of the approximate solutions is proved, also providing the existence of solution in a slightly more general setting than in other results in the current literature. An application to a crowd dynamics model is considered. This numerical algorithm is then used to test the conjecture that as the convolution kernels converge to a Dirac δ, the nonlocal problem converges to its non-nonlocal analogue.

Fulltext

SIAM Journal on Numerical Analysis

Nonlocal systems of conservation laws in several space dimensions

The multi-tiered cross-market dependency structures among capital market worldwide have a highly complex architecture which makes international portfolio management a challenging exercise. The challenge in recent times has been magnified given that the strengths of linkage structures have increased over the past decades. Understanding the complex interdependencies in a temporal scale and identifying the backbone structure in this interdependent system will aid in unearthing the avenues from where diversification benefits may possibly arise. With this objective in mind, the current study attempts to mathematically formulate the cross-market connectivity into weighted network models and elucidate the backbone structures by deploying a global threshold filtering approach. The present work investigates the dependency structure based on weekly-data series belonging to forty-three global markets. The weighted networks depicting cross-market relationships are filtered and visually inspected to decipher the significant connectivity structures. The study identifies that average cross-market linkage strengths increased during market stress conditions. The study also identified a disjointed set of markets wherein one can direct the investments to cushion oneself from systemic risk impacts.

Frontiers in Artificial Intelligence and Applications

Elucidation of the backbone structure of cross-market dependency network of world market indices: A global threshold filtering approach

India has emerged as a major automotive manufacturing hub. But this massive and complex market presents logistical challenges, especially at a time when there is increasing interest in sustainable logistics solutions. One option: Transport more autos by water—but the nation's waterway systems are underdeveloped.

Supply Chain Management Review

Roadblocks to Sustainable Logistics in India's Auto Industry

In this paper k-obnoxious facility location problem has been modeled as a pure planner location problem.  Area restriction concept has been incorporated by inducting a convex polygon in the constraints set. A linear programming iterative algorithm for k- obnoxious facility locations has been developed. An upper bound has been incorporated in the algorithm to get the  optimal solution. Also the concept of upper bound has reduced  the number of linear programming problems to solved in the algorithm. Rectilinear distance norm has been considered as the distance measure as it is more appropriate to the various realistic situations. 

Transportation Management

Solving k-obnoxious facility location problem on a plane

In this paper, we define a fractional negative binomial process (FNBP) by replacing the Poisson process by a fractional Poisson process (FPP) in the gamma subordinated form of the negative binomial process. It is shown that the one-dimensional distributions of the FPP and the FNBP are not infinitely divisible. Also, the space fractional Pólya process (SFPP) is defined by replacing the rate parameter λ by a gamma random variable in the definition of the space fractional Poisson process. The properties of the FNBP and the SFPP and the connections to PDEs governing the density of the FNBP and the SFPP are also investigated.

Probability and Mathematical Statistics

Fractional negative binomial and pólya processes

I congratulate Professor Caron and Professor Fox for a well-written paper that establishes a novel statistical network modeling framework, which uses completely random measures to model graphs with various levels of sparsity. Although it has been made clear in the paper that the characteristics of the underlying completely random measure, e.g., the discount parameter $σ$ < 1 of the generalized gamma process, play a crucial role in determining the sparsity levels of the generated graphs, I should like to call attention to the potentially important role played by the link function f(x) = 1 − e−x, in generating unweighted undirected sparse graphs with zij |wi, wj ∼ Bernoulli[f(2wiwj)] for i 6= j. With this link function, the contribution of zij to the negative log-likelihood of the model can be expressed aswhich quickly explodes towards ∞ as the product wiwj approaches zero when zij = 1. Thus the choice of this link function implies an inductive bias towards fitting nonzero edges zij = 1, while not strongly penalizing zero edges zij = 0 even if their corresponding products wiwj are large. The same link function, which is referred to as the Bernoulli-Poisson link, has also been used in Zhou (2015), which constructs nonparametric Bayesian network models for overlapping community detection and missing link prediction, allowing the computation to scale linearly with the number of edges, rather than quadratically with the number of nodes. It would be of interest to articulate the role of this specific link function in supporting sparse graphs under the proposed framework.In addition to controlling for sparse graphs how the number of edges increases as a function of the number of nodes, another topic worth further investigation is how to introduce structured sparsity patterns to the graph adjacency matrices, including modeling dense on-diagonal but sparse off-diagonal blocks, sparse on-diagonal but dense off-diagonal blocks, or a mixture of both. It would be interesting to find out whether the new notion of exchangeability could be maintained while achieving these network modeling goals.

arXiv

Discussion on “Sparse graphs using exchangeable random measures” by François Caron and Emily B. Fox

A comprehensive understanding of the population growth of a variety of pests is often crucial for efficient crop management. Our motivating application comes from calibrating a two-delay blowfly (TDB) model which is used to simulate the population growth of Panonychus ulmi (Koch) or European red mites that infest on apple leaves and diminish the yield. We focus on the inverse problem, that is, to estimate the set of parameters/inputs of the TDB model that produces the computer model output matching the field observation as closely as possible. The time series nature of both the field observation and the TDB outputs makes the inverse problem significantly more challenging than in the scalar valued simulator case.
In spirit, we follow the popular sequential design framework of computer experiments. However, due to the time-series response, a singular value decomposition based Gaussian process model is used for the surrogate model, and subsequently, a new expected improvement criterion is developed for choosing the follow-up points. We also propose a new criterion for extracting the optimal inverse solution from the final surrogate. Three simulated examples and the real-life TDB calibration problem have been used to demonstrate higher accuracy of the proposed approach as compared to popular existing techniques.

ARXIV.ORG

A sequential design approach for calibrating a dynamic population growth model

This paper addresses the scheduling problem in assembly job shop systems with machine breakdowns. The present study intends to rank the dispatching rules that are applied in an assembly job shop production system. A simulation model of an assembly job shop is developed for the purpose of this ranking and eight dispatching rules from the literature are incorporated in the simulation model. The product structures considered in this study are single-level assembly structure, two-level assembly structures and three level assembly structures. The machines are subjected to non-availability due to of breakdowns. Five performance measures are considered for analysis. The performance of each dispatching rule for each performance measure is calculated individually. To identify which of the dispatching rule provides the optimum result when all the performance measures are equally important, grey relational analysis is adopted to rank the dispatching rules. The results indicate that earliest completion time rule performs better in comparison with the other rules investigated in this study for single-level structure with multiple objectives.

International Journal of Advanced Operations Management

Simulation modelling and analysis of dispatching rules in an assembly job shop production system with machine breakdowns

Much of the research in perishable items inventory management has focused on the first-in-first-out issue process. However, motivated by the technical characteristics of the blood unit issue process, we model an order-up-to-level policy under periodic review setting with random issue of items from inventory. We provide empirical evidence in support of the random issuing assumption using real data on serial numbers of blood units issued from a blood bank. For general demand distribution we derive exact expressions for per period expected shortage, expected wastage and expected cost as functions of the policy parameters R (order-up-to-level) and T (review period). Since the exact model becomes computationally burdensome with increase in the number of periods of life of the perishable item an approximate model for the random issuing process is developed. The accuracy of the approximation is affirmed using simulation analysis. A gradient search-based heuristic is provided to identify the optimum policy parameters for the approximate model. A real life application of the model is demonstrated in determining the optimum frequency and order-up-to-level for blood collection at a blood bank.