N. Hemachandra

Perishable inventory management and dynamic pricing using RFID technology

In price-sensitive markets, price promotions coupled with an appropriate item replenishment strategy can be effective in controlling the total costs of servicing the market. In supply chains that handle perishable products, inventory management is already a complex problem and the management of products in a dynamic-pricing environment is even more challenging. Monitoring and control of time-sensitive products can be facilitated by the application of radio frequency identification (RFID) technology, which enables non-contact, real-time data collection and efficient interfacing with the management control system in the supply chain. This paper describes an integrated framework for inventory management and pricing in a discrete time (periodic review and ordering) framework, and describes an efficient algorithm, including a new approximation, for the related optimization problem. We then propose a suitable architecture for the application of RFID technology in this context, to realize the potential benefits.

Min-Max Fair Power Flow Tracing for Transmission System Usage Cost Allocation: A Large System Perspective

Power flow tracing has been suggested as an approach for evaluating 1) transmission system usage (TSU) cost and 2) loss (MW) cost for generator and load entities in the system. Recently, optimal power flow tracing methods have been proposed to “explicitly” model fairness constraints in the tracing framework. This paper, further, strengthens the tracing-compliant min-max fair cost allocation approach. The min-max model proposed in this paper is robust. It addresses concerns like scalability, numerical stability and termination in a finite number of steps while searching the optimal solution. We also propose a methodology to model DISCOMs and GENCOs as coalition within min-max framework. Case studies on an all India network of 1699 nodes and comparison with average participation and marginal participation methods bring out the better conflict resolution feature of the proposed approach. A method to model HVDC lines within the marginal participation scheme is also proposed. Quantitative and qualitative comparison of various TSU cost allocation methods on such a large system is another noteworthy contribution of the paper.

Performance analysis and buffer allocations in some open assembly systems

This paper considers a finite capacity fork-join queueing model for open assembly systems with arrival and departure synchronizations and presents an approach for enumerating the state space and obtaining the steady state probabilities of the same for such a model under exponential assumptions. Several performance measures like the throughput of the system, fraction of arrivals that actually enter into the system, utilizations of the work stations, etc., are obtained. Further, design of such fork-join systems in terms of buffer size configurations for maximizing the throughput of the system, or for minimizing the mean waiting time of a typical job, or for minimizing the Work-In-Process of the system is done using the above performance prediction approach. Such optimal configurations exhibit some interesting features.

Business excellence through supply chain flexibility in Indian industries: an investigative study

Supply Chain Flexibility (SCF) has been a topic of interest for academicians and practitioners. It is evident from the published literature that this problem, poses considerable challenges to the various segments of the Supply Chain (SC). In order to be competitive, the Indian manufacturing industries have already started implementing flexibility in different segments of the SC by several means and ways. A survey was carried out among the Indian manufacturing organisations to learn about their experience and perceptions related to SCF. The issues of importance identified are the triggers (stimuli) which force an organisation to seek higher flexibility, the means (enablers) which facilitate the realisation of SCF and the hurdles (inhibitors) which hinder progress in SCF. The data collected clearly revealed that different organisations perceive SCF differently and also practice various policies. The data also analysed using ANOVA and Factor Analysis (FA) helped in establishing the various trends and perceptions that are in vogue. The details of the study, analysis and the results are presented in this paper.

Service levels, system cost and stability of production–inventory control systems

Production-inventory control system models have been analysed in the literature either in terms of their stability against demand fluctuations or in terms of their service level and cost performance under uncertain demand. This article analyses the production-inventory system performance in terms of service level (i.e. order fill rate) and average system costs, under stable settings of the control parameters. The classical automatic pipeline variable inventory and order-based production control system has been modified by explicitly modelling safety stock to help achieve higher services levels in the face of random demand. The stability of the system is affected by the control parameters: fractional rates of adjustment of work-in-process and inventory. However, the service level and average cost are affected by the control parameters as well as the smoothing factor in demand forecasting. This article puts forward five propositions which give light to general system performance based on the parameters selection. Intensive simulation experiments have also been carried out to reveal the performance variations within the stable region, leading to further insights on the system behaviour. The managerial insights which can assist proper tuning of systems to help achieve the desired performances have been discussed.

Stochastic approximation algorithms for constrained optimization via simulation

We develop four algorithms for simulation-based optimization under multiple inequality constraints. Both the cost and the constraint functions are considered to be long-run averages of certain state-dependent single-stage functions. We pose the problem in the simulation optimization framework by using the Lagrange multiplier method. Two of our algorithms estimate only the gradient of the Lagrangian, while the other two estimate both the gradient and the Hessian of it. In the process, we also develop various new estimators for the gradient and Hessian. All our algorithms use two simulations each. Two of these algorithms are based on the smoothed functional (SF) technique, while the other two are based on the simultaneous perturbation stochastic approximation (SPSA) method. We prove the convergence of our algorithms and show numerical experiments on a setting involving an open Jackson network. The Newton-based SF algorithm is seen to show the best overall performance.

Stochastic Algorithms for Discrete Parameter Simulation Optimization

We present two efficient discrete parameter simulation optimization (DPSO) algorithms for the long-run average cost objective. One of these algorithms uses the smoothed functional approximation (SFA) procedure, while the other is based on simultaneous perturbation stochastic approximation (SPSA). The use of SFA for DPSO had not been proposed previously in the literature. Further, both algorithms adopt an interesting technique of random projections that we present here for the first time. We give a proof of convergence of our algorithms. Next, we present detailed numerical experiments on a problem of admission control with dependent service times. We consider two different settings involving parameter sets that have moderate and large sizes, respectively. On the first setting, we also show performance comparisons with the well-studied optimal computing budget allocation (OCBA) algorithm and also the equal allocation algorithm.

Join Minimum Cost Queue For Multiclass Customers: Stability And Performance Bounds

We consider a system of K parallel queues providing different grades of service through each of the queues and serving a multiclass customer population. Service differentiation is achieved by specifying different join prices to the queues. Customers of class j define a cost function pij(ci,xi) for taking service from queue i when the join price for queue i is ci and congestion in queue i is xi and join the queue that minimizes pij(·,·). Such a queuing system will be called the “join minimum cost queue” (JMCQ) and is a generalization of the join shortest queue (JSQ) system. Non-work-conserving (called Paris Metro pricing system) and work-conserving (called the Tirupati system) versions of the JMCQ are analyzed when the cost to an arrival of joining a queue is a convex combination of the join price for that queue and the expected waiting time in that queue at the arrival epoch. Our main results are for a two-queue system.

Optimal auctions for multi-unit procurement with volume discount bids

In this paper, we design an optimal procurement mechanism for procuring multiple units of a single homogeneous item based on volume discount bids submitted by rational and intelligent suppliers. We develop an elegant auction mechanism, VD-OPT, that minimises the cost to the buyer, satisfying at the same time: Bayesian Incentive Compatibility (BIC); interim individual rationality.

Discrete parameter simulation optimization algorithms with applications to admission control with dependent service times

We propose certain discrete parameter variants of well known simulation optimization algorithms. Two of these algorithms are based on the smoothed functional (SF) technique while two others are based on the simultaneous perturbation stochastic approximation (SPSA) method. They differ from each other in the way perturbations are obtained and also the manner in which projections and parameter updates are performed. All our algorithms use two simulations and two- timescale stochastic approximation. As an application setting, we consider the important problem of admission control of packets in communication networks under dependent service times. We consider a discrete time slotted queueing model of the system and consider two different scenarios - one where the service times have a dependence on the system state and the other where they depend on the number of arrivals in a time slot. Under our settings, the simulated objective function appears ill-behaved with multiple local minima and a unique global minimum characterized by a sharp dip in the objective function in a small region of the parameter space. We compare the performance of our algorithms on these settings and observe that the two SF algorithms show the best results overall. In fact, in many cases studied, SF algorithms converge to the global minimum.