Busy Period Analysis of A Two-Stage Multi-Product System with Interstage Buffer

In a recent paper, Glock (2010a, Batch sizing with controllable production rates. International Journal of Production Research, 48 (20), 5925–5942) studied the impact of a variable production rate on the inventory build-up and the total costs in a two-stage production system. In this paper, we extend Glock's (2010a) model to a multi-stage production system where the production rate at each producing stage may be varied within given limits. We compare our model to the classical case with a fixed production rate and show that treating the production rate as a variable is an appropriate measure to reduce excessive inventory in a production system.

Learning effects and the phenomenon of moving bottlenecks in a two-stage production system

In this study, a data envelopment analysis-based approach is proposed for estimating and decomposing the potential gains from the horizontal integrations of two-stage production systems. In the first tier, the gains of the potential merger are measured by a composite efficiency index. The potential efficiency gains are then decomposed as the product of coordination efficiency and two divisional potential efficiency gains. In the second tier, the divisional potential efficiency gains are decomposed into scope, size, and learning efficiencies. We develop a multiplicative framework for decomposing potential efficiency gain in mergers of the two-stage production systems, and the coordination efficiency is considered. In the case study of commercial banks in mainland China, we develop an algorithm for estimating and decomposing potential efficiency gain for a two-stage bank production system.

In a production system with random yield, it may be more cost effective to release lots multiple times towards fulfilling a customer order. Such a decision, called the multiple lot-sizing problem, has been investigated in various contexts. This paper proposes an efficient algorithm for solving a new multiple lot-sizing problem defined in the context of a two-stage production system with non-rigid demand when its process yields are governed by interrupted geometric distributions. We formulate this problem as a dynamic program (DP) and develop lemmas to solve it. However, solving such a DP may be computationally extensive, particularly for large-scale cases with a high yield. Therefore, this study proposes an efficient algorithm for resolving computational issues. This algorithm is designed to reduce the DP network into a much simpler algorithm by combining a group of DP branches into a single one. Extensive experiments were carried out. Results indicate that the proposed reduction algorithm is quite helpful for practitioners dealing with large-scale cases characterized by high-yield.

Today's production systems are under the pressure of mass customization where customer orders may result in an explosive number of product variations. Handling information related to materials and components becomes a complex issue with the increasing variability. This paper presents an effective approach to handle product information in a relational database environment for multi-product and multi-process production systems. The approach presented in this paper aims to reduce the efforts when defining products to the production systems for the purpose of generating bills-of-materials (BOMs) and executing material requirements planning (MRP). Two algorithms are presented in this paper. The first algorithm is to retrieve information from the relational database environment to generate the BOMs. The second algorithm calculates the material requirements for all products in a customer order list. Examples are chosen from textiles since complications related to the MRP issues are at a greater extent in this branch of the industry.

This paper presents an analytical study of a two-stage production system with provision for storage of intermediate product. Strict limits are placed on the size of the intermediate product inventory, and on the rates at which product can be moved into or out of inventory. Exponential service times are assumed at stage one and Erlang distributed service times are assumed at stage two. The problem is solved by formulation as a Markov process, and the impact of the inventory constraints on the production rate of the line is investigated. The problem of balancing the system is considered at some length.

Analysis of a general Markovian two-stage continuous-flow production system with a finite buffer

The overall cotton industry continues to face a variety of sustainability challenges. Cotton remains one of the largest users of pesticides, insecticides, petroleum based fertilizers and water. In addition, the current pricing structure does not address the real costs of cotton production thereby creating, in many instances, poverty in many of the rural communities around the world which grow cotton. Genetically-modified cotton has been widely adopted in many cotton production areas and is thought by some to be the answer to increased environmental benefits (reduction of pesticides), but it has yet to deliver long term, sustainable benefits. Recent studies and information now available are indicating that both weeds and pests are becoming resistant to the technology. Organic production systems provide a holistic approach to the conservation and preservation of resources while protecting bio-diversity, increasing soil health, and offering greater food security. In this study, the sustainability of conventional cotton in the United States (U.S.) and certified organic cotton in the southern plains of Texas, U.S. are analyzed. The environmental impact of conventional and organic cotton cultivation was analyzed using a Life Cycle Assessment (LCA) and an economic analysis of the costs and returns of Texas organic cotton is also presented. In general, the LCA shows that the environmental impact for the cultivation of conventional cotton is higher than organic cotton, yet relatively similar for land use and water use. The environmental impact of conventional cotton cultivation is dominated by freshwater aquatic and terrestrial eco-toxicity as well as eutrophication and acidification due to the use of pesticides, synthetically compounded nitrogen fertilizer, phosphate fertilizer, potassium chloride, irrigation, and transportation. Analysis of costs and returns of both methods shows higher average returns above total specified expenses in organic farming relative to the conventional counterparts. . It is important to highlight however, that the total labor costs appear to dominate among the other components of total direct expenses under both organic and conventional practices. From both the case study presented and literature reviews, organic cotton production offers a range of potential advantages to cotton farmers, including, healthier soils, a diverse source of income, and higher prices.

Deploying radio frequency identification (RFID) devices or applying robust production control policy can both reduce the negative impact of inventory inaccuracy. In this paper, we investigate the problem of optimally deploying RFID devices in a multistage production system with inventory inaccuracy and robust control policy. We first model the multiple-stage production system as a Markov decision process model, in which the decision of deploying RFID devices is included. After that, an integer programming model is constructed for optimally deploying RFID device problem. Meanwhile, we propose a robust production control strategy by employing the policy iteration and the decomposition/coordination techniques, and a heuristic algorithm is proposed to solve the integer programming model. Finally, numerical experiments are conducted to examine the performance of the proposed optimal RFID deployment approach. We discover that when RFID is deployed at a certain workstation (i.e., production equipment), the benefits of deploying RFID devices can transfer to the upstream and downstream stages (i.e., reduce the production cost of both upstream and downstream stages). Furthermore, numerical experiments reveal that the deploying RFID devices evenly among all the stages of the production system will generate a better performance.

We analyze a Markov model of a two-stage production system capable of producing two part types. Each stage consists of an unreliable machine and the different stages are decoupled by two intermediate buffers of finite capacity, one for each part type. Unlike previous work, we specifically consider non-negligible machine setup times during changeovers and also assume that machine failure probabilities are dependent on the part type being produced. We assume that machine processing times, repair/failure times and setup times are exponentially distributed and may have different mean rates for each machine and for each part-type. We describe a solution method to evaluate the system performance that reduces the total number of equations to be solved from a multiplicative function to an additive function of buffer sizes. This model may then be integrated with a new decomposition method for analyzing longer lines. The results show the relative influence of different factors on system performance and thus provide guidance to the optimal choice of system parameters such as buffer sizes.

Energy and exergy examinations of a PVT based hybrid system for power, heating and potable water production: Transient modeling

Space M&A based on industrial concentration and pursuing concentration rent and spillover effect in new normal has become an important method to realize industrial up-grading, and how to evaluate value effect of space M&A with two-stage production system becomes a new project that has yet to be studies in academia. The article builds a M&A value evaluation simulation model based on DEA non-cooperation game to evaluate value effect of space M&A in financial industry in China.it shows, Space M&A with hypotactic game relationship can bring about substantial potential cost saving, and there must be system M&A valid when all subsystems are M&A valid. Optimal input-output index and optimal value index of intermediate variables based on model calculation further helps to obtain the measure of space M&A value improvement, which provides commercial banks with immediate empirical evidences to select M&A under new normal, helps to facilitate optimization of financial resources space configuration, and promotes transformation and upgrading as well as innovative driving of financial industry in China.

Optimal batch sizing in a multi-stage production system with rework consideration

In many production systems, batch jobs exist in the form of multiple, identical components. To facilitate movement between machines and simultaneous processing on several machines through several stages, the batch is usually split into unit loads or sub-lots. Such an operating mode improves resource utilization and batch completion time. Despite these benefits, published research dealing with the planning and control of unit-load-based manufacturing systems is still in the embryonic stage. In this paper, a model for the planning of production of a batch job through a multi-stage production system with multiple heterogeneous but functionally similar machines per stage is presented. The objective pursued is the minimization of batch manufacturing time. A dynamic programming based heuristic algorithm is developed for seeking a solution to the model. An example problem is described to demonstrate the procedure. The effect of changes in material-handling times, processing times, set-up times, and preparatory t...

This article introduces two real-life case studies respectively devoted to a parallel production system of a chemical company and to a production system made up of tightly interconnected machines. The objective is to highlight the most relevant features of a production system that determine the system's productivity. A calculation model that allows movement from machine-level to system-level indices is useful both during the production system design and during the performance measurement phase. The application of the presented framework provides useful results: (1) to improve analysis, and to make proper diagnosis of causes, of productivity loss and to track their evolution over the time; (2) to design and to implement specific improvement projects, aimed at removing loss causes, and thus increasing efficiency and productivity; (3) to establish proper production capacities, by focusing on bottleneck machines, to keep under control the actual system throughput; (4) to make comparisons and internal benchmarking aimed at defining machine management and productive maintenance best practices; (5) to design plant performance reporting systems and build appropriate tools to collect data from the field.