VMI versus information sharing: an analysis under static uncertainty strategy with fill rate constraints

A vendor-managed inventory (VMI) relationship between a downstream retailer and an upstream vendor consists of two distinct components: (i) information sharing (IS) and (ii) a shift in decision-making responsibility. This study compares these two components of VMI in a two-stage serial supply chain based on the ‘static uncertainty’ strategy under dynamic and random demand with fill rate constraints. Numerical experiments are conducted using analytical models to identify the conditions where the incremental value of VMI over IS is significant. The results provide guidelines relevant to academia and supply chain practitioners in taking VMI adoption decision above and beyond IS according to their specific business environment.     

Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.     

Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms

This paper aims to solve the balanced multi-robot task allocation problem. Multi-robot systems are becoming more and more significant in industrial, commercial and scientific applications. Effectively allocating tasks to multi-robots i.e. utilizing all robots in a cost effective manner becomes a tedious process. The current attempts made by the researchers concentrate only on minimizing the distance between the robots and the tasks, and not much importance is given to the balancing of work loads among robots. It is also found from the literature that the multi-robot system is analogous to Multiple Travelling Salesman Problem (MTSP). This paper attempts to develop mechanism to address the above two issues with objective of minimizing the distance travelled by ‘m’ robots and balancing the work load between ‘m’ robots equally. The proposed approach has two fold, first develops a mathematical model for balanced multi-robot task allocation problem, and secondly proposes a methodology to solve the model in three stages. Stage I groups the ‘N’ tasks into ‘n’ clusters of tasks using K-means clustering technique with the objective of minimizing the distance between the tasks, stage II calculates the travel cost of robot and clusters combination, stage III allocates the robot to the clusters in order to utilise all robot in a cost effective manner.     

Ascorbic acid imprinted polymer-modified graphite electrode: A diagnostic sensor for hypovitaminosis C at ultra trace ascorbic acid level

A new kind of molecularly imprinted polymer-modified graphite electrode was fabricated by “grafting-to” approach, incorporating sol–gel technique, for the detection of acute deficiency in serum ascorbic acid level (SAAL), manifesting hypovitaminosis C. The modified electrode exhibited ascorbic acid (AA) oxidation at less positive potential (0.0 V) than the earlier reported methods, resulting in a limit of detection as low as 6.13 ng mL⁻¹ (RSD = 1.2%, S/N = 3). The diffusion coefficient (1.096 × 10⁻⁵ cm² s⁻¹), rate constant (7.308 s⁻¹), and Gibb's free energy change (−12.59 kJ mol⁻¹) due to analyte adsorption, were also calculated to explore the kinetics of AA oxidation. The proposed sensor was found to enhance sensitivity substantially so as to detect ultra trace level of AA in the presence of other biologically important compounds (dopamine, uric acid, etc.), without any cross interference and matrix complications from biological fluids and pharmaceutical samples.     

Variational sensitivity analysis and design optimization

Variational method (VM) is employed to derive the co-state equations, boundary (transversality) conditions, and functional sensitivity derivatives. The converged solutions of the state equations together with the steady state solution of the co-state equations are integrated along the domain boundary to uniquely determine the functional sensitivity derivatives with respect to the design function. The application of the variational method to aerodynamic shape optimization problems is demonstrated on internal flow problems at supersonic Mach number range of 1.5. Optimization results for flows with and without shock phenomena are presented. The study shows that while maintaining the accuracy of aerodynamical objective function and constraint within the reasonable range for engineering prediction purposes, variational method provides a substantial gain in computational efficiency, i.e., computer time and memory, when compared with the finite difference computations.     

A study on measuring surface deformation of the L’Aquila region using the StaMPS technique

This research compares two time-series interferometric synthetic aperture radar (InSAR) methods, namely persistent scatterer SAR interferometry (PS-InSAR) and small baseline subset (SBAS) to retrieve the deformation signal from pixels with different scattering characteristics. These approaches are used to estimate the surface deformation in the L’Aquila region in Central Italy where an earthquake of magnitude Mw 6.3 occurred on 6 April 2009. Fourteen Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) images, covering the pre-seismic, co-seismic, and post-seismic period, are used for the study. Both the approaches effectively extract measurement pixels and show a similar deformation pattern in which the north-west and south-east regions with respect to the earthquake epicentre show movement in opposite directions. The analysis has revealed that the PS-InSAR method extracted more number of measurement points (21,103 pixels) as compared to the SBAS method (4886 pixels). A comparison of velocity estimates shows that out of 833 common pixels in both the methods, about 62% (517 pixels) have the mean velocity difference below 3 mm year^?1 and nearly 66% pixels have difference below 5 mm year^?1. It is concluded that StaMPS-based PS-InSAR method performs better in terms of extracting more number of measurement pixels and in the estimation of mean line of sight (LOS) velocity as compared to SBAS method.     

Two-echelon fuzzy stochastic supply chain for the manufacturer–buyer integrated production–inventory system

This paper deals with two-echelon integrated procurement production model for the manufacturer and the buyer integrated inventory system. The manufacturer procures raw material from outside suppliers (not a part of supply chain) then proceed to convert it as finished product, and finally delivers to the buyer, who faces imprecise and uncertain, called fuzzy random demand of customers. The manufacturer and the buyer work under joint channel, in which a centralized decision maker makes all decisions to optimize the joint total relevant cost (JTRC) of entire supply chain. In this account, in one production cycle of the manufacturer we determine an optimal multi-ordering policy for the buyer. To be part of this, we first derive the JTRC in stochastic framework, and then extend it in fuzzy stochastic environment. In order to scalarize the fuzzy stochastic JTRC, we use an evaluation method wherein randomness is estimated by probabilistic expectation and fuzziness is estimated by possibilistic mean based on possibility evaluation measure. To derive the optimal policies for both parties, an algorithm is proposed. A numerical illustration addresses the situations of paddy procurement, conversion to rice and fulfillment of uncertain demand of rice. Furthermore, sensitivity of parameters is examined to illustrate the model and algorithm.     

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives, and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multiobjective optimization, genetic algorithms, and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multiobjective approaches show promising results in fitness convergence, design diversity, and user satisfaction metrics.     

Rolling System Design Using Evolutionary Sequential Process Optimization

The design of a rolling system is a multistage process optimization problem involving sequential relationship between consecutive stages. This relationship is peculiar to sequential processes in which the output stock of one stage serves as the input stock into the deforming tool of the other stage. This paper describes the optimization of a real-life rolling system design using a genetic algorithm (GA)-based approach capable of dealing with the sequential nature of this problem. It presents a mathematical model of a real-life rolling system design and explains the proposed optimization approach. Even in the presence of multiple stages, the proposed approach identifies a variety of near-optimal design solutions from which one could be finally chosen based on designer's preferences. It is also shown that the obtained solutions dominate the designs reported in literature.     

Electrical transport in copper chromite catalyst

The electrical conductivity, σ, and thermoelectric power, S, of copper chromite (CuCr₂O₄) are reported in the temperature range 295 to 815 K. A break (T _B) in the slope of the plot of log σ against T ^?1 was observed around 556 K. Apart from this break, the curves are linear, and their slopes correspond to activation energies of 0.60 eV (T _B<556 K) and 1.22 eV (T _B>556 K). A break (T′ _B) in the slope of the -S against T ^?1 plot was also observed round 556 K. Apart from the break at this temperature, the S against T ^?1 curves are linear. At T′ _B>556, S can be expressed by the relation $$S = - \left[ {\left( {\frac{{0.65 eV}}{{{\text{2}}eT}}} \right) + (0.42) mV K^{ - {\text{1}}} } \right]$$ The mechanism involved in the electrical transport is the hopping of holes from Cr⁴⁺ centres to neighbouring Cr³⁺ ions. The typical hopping mobility of the holes is of the order of 10⁶ m² V^?1 sec^?1. The mobility activation energy of the holes in CuCr₂O₄ decreases with temperature due to the smoothing of the potential barriers between Cr⁴⁺ and Cr³⁺ sites.