Ranga V. Ramasesh

Dynamic job shop scheduling: A survey of simulation research

Scheduling of job shops has been extensively researched over the last three decades and it continues to attract the interests of both academic researchers and practitioners. This paper provides a state-of-the-art survey of the simulation-based research on dynamic job shop scheduling with a distinct emphasis on two important aspects. First, job-shop simulation modeling and experimental considerations are discussed with a review of the different approaches taken in the literature. Second, a review of the different studies is provided with a focus on their findings on the job shop performance criteria of interest. This paper is aimed at both practicing managers and researchers in the area of job shop scheduling because of the following considerations: 1. (i) The number of factors that characterize a job shop is very large and there is considerable variation in modeling and experimentation across the vast number of studies reported. Important research findings relevant to these issues reviewed in this paper would facilitate comparison of the currently available research results and their extension or application to other situations.2. (ii) A review of the various studies focusing on some of the more important performance criteria should help practitioners in the selection of scheduling rules to match the criterion particularly relevant to their shops.3. (iii) Recent studies which have modeled job shops as dual-resource constrained systems and assembly shops have been included in this survey for a more comprehensive coverage. A detailed bibliography is included and every effort has been made to make the survey up-to-date.

Measurement of manufacturing flexibility: A value based approach

Abstract Flexibility is emerging as one of the key competitive strengths in todays manufacturing systems. Measuring the flexibility of manufacturing systems is important to operations managers engaged in decision making on strategic issues related to flexibility. Flexibility is widely recognized as a multidimensional attribute. Most of the studies reported in the literature have focused on measuring separate dimensions independently and also have tended to be non-financial in nature. As a result, many of these measures have only limited application in strategic decision making. In this paper we consider the measurement of the aggregate flexibility of a manufacturing system by investigating the joint effect of the flexibilities on a variety of dimensions when measurement for a medium-term time horizon is appropriate. We propose a value-based approach in which flexibility is measured by the ability of a manufacturing system to generate high net revenues consistently across all conceivable states of the nature in which it may be called upon to function. We suggest the ratio of the mean to the standard deviation of the distribution of optimal net revenues as the flexibility measure. We develop a stochastic mathematical programming formulation that facilitates the modeling of the manufacturing flexibility on different dimensions and the determination of the distribution of the net optimal revenues across all possible states. We exploit the special structure of our formulation to efficiently solve large problems that arise in some practical situations. We illustrate the application of the model using a numerical example which shows that the value of each type of flexibility varies depending on how it enhances the revenues generated by the system. We also find that synergistic benefits can be gained by combining different types of flexibilities. Our model provides an approach to determine the aggregate value of manufacturing flexibility and to explore the trade-offs that may exist among the different dimensions of flexibility.

Agility in manufacturing systems: an exploratory modeling framework and simulation

It has been widely recognized that agility enables manufacturing systems to respond to dynamic and unpredictable changes in today’s competitive environment. Develops a quantitative analysis framework and a simulation methodology to explore the value of agility in financial terms. Addresses the issues pertaining to the assessment of how an agile system performs in an environment of unanticipated changes, the comparison between two or more systems with different designs and hence different agility levels and the justification of investments in agility. Proposes an exploratory framework for a structured analysis of the various segments of the manufacturing system in which agility at different levels is built‐in through different pathways and links it to a set of aggregate performance measures. Then develops a simulation model that captures dynamic and unanticipated changes in the operating environment and facilitates performance appraisal and investment justification decisions using a quantitative financial metric.

Lot streaming in multistage production systems

Abstract Lot streaming is a procedure in which a production lot is split into smaller sub-lots and moved to the next processing stage so that operations at successive stages of a multistage manufacturing system can be overlapped in time. Lot streaming reduces the manufacturing lead time and thereby provides an opportunity to lower the costs of holding work-in-process inventories. In this paper, we present an economic production lot size model that minimizes the total relevant cost when lot streaming is used. Using illustrative numerical examples, we show that our model can yield significant cost economies compared to the traditional approaches.

Multiple sourcing: The determination of lead times

In a multiple-sourcing environment, the order quantity may be split concurrently among vendors. This paper presents a technique for the estimation of parameters of the lead time distributions in such systems based on order statistics. Reductions in lead times due to multiple sourcing are also studied for the two-vendor case.

Note: Dual sourcing with nonidentical suppliers

We analyze a dual-sourcing inventory model with exponential lead times and constant unit demand in which the order quantity is split in some proportion between two sources of supply. Unlike earlier studies, we do not require that the two sources be identical in terms of the lead-time parameters or the supply prices. We compare the expected total annual costs for the two-source and the traditional single-source models over a wide range of parameter values. We confirm the findings of earlier studies that, under stochastic lead times, dual sourcing yields savings in holding and shortage costs that could outweigh the incremental ordering costs. With this more general model, we demonstrate that savings from dual sourcing are possible even where the mean or the variability of the second source is higher.

Economic justification of advanced manufacturing technology

Despite the far reaching implications of the advanced manufacturing technologies (AMT) for the maintenance and enhancement of a firms competitive position, the financial justification of investments in AMT has not been easy. Recent research in manufacturing strategy, financial theory, management accounting and organization decision making suggests that the AMT-justification process should involve (i) a strategic analysis to ascertain the strategic fit and the competitive advantage of AMT, (ii) an economic analysis to ascertain the financial viability of AMT and (iii) an understanding of the dynamics of organizational decision making to overcome the barriers to the adoption of AMT. Focusing on the economic analysis we present a concise review of the state of the art and propose a four-level justification framework for investment in AMT. At Level 1 of this framework, we analyze the easily quantifiable benefits and costs using the traditional discounted cash flow (DCF) models. We examine the limitations of the traditional DCF analysis and discuss some refinements suggested in the literature. At Level 2, we develop and illustrate with a numerical example, a stochastic mathematical programming model to quantify the strategic benefits such as flexibility and quality. At Level 3, we quantify the benefits of the time series linkages between the project currently being justified and a related future project using a learning curve model. Finally, at Level 4 we focus on a qualitative assessment of the benefits which were not included in the evaluation at the first three levels.

Order crossover in inventory systems

Order crossovers are usually ignored in iid lead-time inventory systems by using a facilitating assumption, which leads to a one-cycle analysis and an upper bound on the true cost. But the calculation of the probabilities of order crossover can shed light on the magnitude of the departure from the true cost. Here, we first introduce the crossover issue within the context of total cost in the one-vendor continuous review, reorder point, constant demand model so that the need for the calculation of these probabilities is made evident. Second, we present closed-form expressions for the probabilities in one- and two-vendor models. And third, we verify by simulation that our analytical approximation of the true cost is indeed tight

Adapting the newsvendor model for infinite-horizon inventory systems

Abstract We address inventory decisions in the context of the “reorder-point, order-quantity” policy in infinite-horizon, stochastic lead-time demand inventory systems in which the parameters may be non-stationary. We develop a heuristic policy based on the simple economic order quantity (EOQ) model and the one-period newsvendor model. Using illustrative numerical examples, we demonstrate that our simple and easy-to-understand heuristic policy performs nearly as well as the optimal policy derived from complicated mathematical procedures. Our heuristic policy is simple, easy-to-implement, and flexible in that it can be easily adapted to situations when the parameters of the inventory system may change. Analytical modeling under non-stationary parameters would be extremely complex and the derivation of closed form optimal policies is mathematically intractable in most cases. In such cases, practitioners will find our heuristic policy an attractive option.

Procurement under uncertain supply lead times -- A dual- sourcing technique could save costs

Abstract Inventory system costs tend to be higher if uncertainty prevails in the supply lead times and consequently in the demand during the lead time because of the possibility of shortages or the need to maintain larger safety stocks. In this paper we explore a dual-sourcing technique in which the replenishment order quantity for the inventoried item with stochastic lead times is split between two sources of supply, and split-orders are placed simultaneously. It is shown that this results in effective lead times with lower variability and hence savings in the inventory holding and shortage costs are possible. If these savings exceed the incremental ordering costs associated with procurement from two sources overall cost economies result. In this paper, the theoretical concepts underlying the dual-sourcing technique and its implications for implementation in procurement management are presented along with an overview of the recent research in the area of dual sourcing.