A Flexible Inventory Model for Municipal Solid Waste Recycling

Most of the United States have laws mandating the recycling of municipal solid waste (MSW). In order to comply, municipalities recycle quotas of materials, without regard to fluctuating prices. An inventory system is proposed that allows municipalities to be sensitive to materials prices as they recycle in accordance with state mandates. A dynamic model is developed; it uses historical secondary material prices as exogenous inputs to minimize the net present value of MSW recycling system cost. The model provides a cost-effective method for municipalities to achieve their MSW recycling targets. The savings is approximately $1.43 per ton of MSW generated based on total MSW management costs of$13.5 per ton. The model also allows one to investigate the effectiveness of various strategies for increasing the recycling rate. These strategies include: reducing the transportation cost for recyclables, supporting the market price of selected secondary materials, and landfill bans on selected materials. This model may also be used to investigate the effect of market price changes on the portfolio of materials held in inventory for recycling.Municipal Solid Waste, Recycling, Inventory, Optimization

Simulation Based Study of Safety Stocks under Short-Term Demand Volatility in Integrated Device Manufacturing.

© IEOM Society InternationalA problem faced by integrated device manufacturers (IDMs) relates to fluctuating demand and can be reflected in long-term demand, middle-term demand, and short-term demand fluctuations. This paper explores safety stock under short term demand fluctuations in integrated device manufacturing. The manufacturing flow of integrated circuits is conceptualized into front end and back end operations with a die bank in between. Using a model of the back-end operations of integrated circuit manufacturing, simulation experiments were conducted based on three scenarios namely a production environment of low demand volatility and high capacity reliability (Scenario A), an environment with lower capacity reliability than scenario A (Scenario B), and an environment of high demand volatility and low capacity reliability (Scenario C). Results show trade-off relation between inventory levels and delivery performance with varied degree of severity between the different scenarios studied. Generally, higher safety stock levels are required to achieve competitive delivery performance as uncertainty in demand increases and manufacturing capability reliability decreases. Back-end cycle time are also found to have detrimental impact on delivery performance as the cycle time increases. It is suggested that success of finished goods safety stock policy relies significantly on having appropriate capacity amongst others to support fluctuations

Evaluating demand driven MRP: a case based simulated study

This study evaluates the assumption underpinning Material Requirements Planning (MRP), buffer management and DDMRP before analysing the case company and evaluating the potential benefits, utilizing simulated data from the existing ERP system. The purpose of this research is to evaluate DDMRP in the context of improving the performance of a printing ink manufacturing company. The main issues the company is facing using a traditional MRP system include poor due-date performance, stock levels not corresponding to the actual market needs and overall system instability leading to inefficiencies. The findings indicate the potential of DDMRP to improve system stability and product availability

Long Term Comparison of Alternative Range Livestock Management Strategies Across Extended Droughts and Cyclical Prices

Variable precipitation, more importantly drought, impacts profitability for livestock management. Financial outcomes from management decisions related to forage shortages can be exacerbated by price variability. This research examines alternative management strategies to determine the potential profitability and riskiness over a long-term horizon and across various drought event scenarios. Results indicate that late calving can be a promising strategy, but it also can result in higher variability in profits as compared to some of the other strategies analyzed. Retaining ownership of steer calves over the winter, with the option to sell if forage supplies become scarce, outperforms both partial liquidation and summer feeding, and it results in less profit variability than late calving or early weaning.Production Economics, Productivity Analysis,

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

A multi-agent growth model based on the von Neumann-Leontief framework

This paper presents a discrete-time growth model to describe the dynamics of a multi-agent economy, and the model consists of production process, exchange process, price and technology adjustment processes etc. Technologies of agents in each period are represented by a technology matrix pair, and some properties of Perron-Frobenius eigenvalues and eigenvectors of technology matrix pairs are discussed. An exchange model is also developed to serve as the exchange part of the growth model. And equilibrium paths of the growth model are proved to be balanced growth paths sharing a unique normalized price vector. Though this paper focuses mainly on the case of n agents and n goods, the growth model can also deal with the case of m agents and n goods. A numerical example with 6 agents and 4 goods is given, which describes the dynamics of a two-country economy and has endogenous price fluctuations and business cycles.von Neumann’s expanding economic model, input-output model, dynamic general equilibrium, disequilibrium, multi-country economic model

Optimal Dynamic Procurement Policies for a Storable Commodity with L\'evy Prices and Convex Holding Costs

In this paper we study a continuous time stochastic inventory model for a commodity traded in the spot market and whose supply purchase is affected by price and demand uncertainty. A firm aims at meeting a random demand of the commodity at a random time by maximizing total expected profits. We model the firm's optimal procurement problem as a singular stochastic control problem in which controls are nondecreasing processes and represent the cumulative investment made by the firm in the spot market (a so-called stochastic "monotone follower problem"). We assume a general exponential L\'evy process for the commodity's spot price, rather than the commonly used geometric Brownian motion, and general convex holding costs. We obtain necessary and sufficient first order conditions for optimality and we provide the optimal procurement policy in terms of a "base inventory" process; that is, a minimal time-dependent desirable inventory level that the firm's manager must reach at any time. In particular, in the case of linear holding costs and exponentially distributed demand, we are also able to obtain the explicit analytic form of the optimal policy and a probabilistic representation of the optimal revenue. The paper is completed by some computer drawings of the optimal inventory when spot prices are given by a geometric Brownian motion and by an exponential jump-diffusion process. In the first case we also make a numerical comparison between the value function and the revenue associated to the classical static "newsvendor" strategy.Comment: 28 pages, 3 figures; improved presentation, added new results and section

Performance optimization of a leagility inspired supply chain model: a CFGTSA algorithm based approach

Lean and agile principles have attracted considerable interest in the past few decades. Industrial sectors throughout the world are upgrading to these principles to enhance their performance, since they have been proven to be efficient in handling supply chains. However, the present market trend demands a more robust strategy incorporating the salient features of both lean and agile principles. Inspired by these, the leagility principle has emerged, encapsulating both lean and agile features. The present work proposes a leagile supply chain based model for manufacturing industries. The paper emphasizes the various aspects of leagile supply chain modeling and implementation and proposes a new Hybrid Chaos-based Fast Genetic Tabu Simulated Annealing (CFGTSA) algorithm to solve the complex scheduling problem prevailing in the leagile environment. The proposed CFGTSA algorithm is compared with the GA, SA, TS and Hybrid Tabu SA algorithms to demonstrate its efficacy in handling complex scheduling problems