Heuristic Procedures to Solve Sequencing and Scheduling Problems in Automobile Industry

With the growing trend for greater product variety, mixed-model assembly nowadays is commonly employed in many industries, which can enable just-in-time production for a production system with high variety. Efficient production scheduling and sequencing is important to achieve the overall material supply, production, and distribution efficiency around the mixed-model assembly line. This research addresses production scheduling and sequencing on a mixed-model assembly line for products with multiple product options, considering multiple objectives with regard to material supply, manufacturing, and product distribution. This research also addresses plant assignment for a product with multiple product options as a prior step to scheduling and sequencing for a mixed-model assembly line. This dissertation is organized into three parts based on three papers. Introduction and literature review Part 1. In an automobile assembly plant many product options often need to be considered in sequencing an assembly line with which multiple objectives often need to be considered. A general heuristic procedure is developed for sequencing automobile assembly lines considering multiple options. The procedure uses the construction, swapping, and re-sequencing steps, and a limited search for sequencing automobile assembly lines considering multiple options. Part 2. In a supply chain, production scheduling and finished goods distribution have been increasingly considered in an integrated manner to achieve an overall best efficiency. This research presents a heuristic procedure to achieve an integrated consideration of production scheduling and product distribution with production smoothing for the automobile just-in-time production assembly line. A meta-heuristic procedure is also developed for improving the heuristic solution. Part 3. For a product that can be manufactured in multiple facilities, assigning orders to the facility is a common problem faced by industry considering production, material constraints, and other supply-chain related constraints. This paper addresses products with multiple product options for plant assignment with regard to multiple constraints at individual plants in order to minimize transportation costs and costs of assignment infeasibility. A series of binary- and mixed-integer programming models are presented, and a decision support tool based on optimization models is presented with a case study. Summary and conclusion

Capability of APSIM-Oryza to stimulate lowland rice-based farming systems under nitrogen treatments in a tropical climate

Rice is the most important crop in Asia and the staple food for most of the world’s population. Due to the overwhelming importance of this crop, modelling rice-based farming systems will provide valuable help to compare experimental research findings across regions, extrapolate field experimental data to wider environments, develop management recommendations and decision-support systems, explore effects of climate change and adaptation options, and prediction of crop yield. There is an increasing demand for the capability to simulate rice-based cropping systems, especially in Asia. Such a system capability will allow expanded investigation of nitrogen dynamics, crop sequencing, intercropping, crop residue management and soil and water management. Incorporation of the ORYZA2000 rice model(Bouman and van Laar, 2006) into APSIM (Agricultural Production Systems Simulator (APSIM-Oryza) together with recent work on carbon and nitrogen dynamics in transitional flooded/non-flooded systems(Gaydon et al., 2009) has facilitated long-term simulation of lowland rice-based farming systems scenarios. However, the capability of APSIM-Oryza to simulate rice-based crop sequences involving other crops has undergone limited testing to this point and under a variety of crop management practices and cropping systems. In this paper, we detail testing of the APSIM-Oryza simulation model against an experimental dataset involving lowland rice-rice-soybean crop rotation in West Nusa Tenggara Province(NTB) Indonesi

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

Business Groups in Emerging Markets - Financial Control and Sequential Investment

Business groups in emerging markets perform better than unaffiliated firms. One explanation is that business groups substitute some functions of missing institutions, for example, enforcing contracts. We investigate this by setting up a model where firms within the business group are connected to each other by a vertical production structure and an internal capital market. Thus, the business group’s organizational mode and the financial structure allow a self-enforcing contract to be designed. Our model of a business group shows that only sequential investments can solve the ex post moral hazard problem. We also find that firms may prefer not to integrate

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Advanced resource planning as decision support module to ERP.

In this paper, we show that the planning and decision-support capabilities of the MPC (Manufacturing Planning and Control) system, which forms the core of any ERP (Enterprise Resource Planning) package, may be substantively enhanced by including a Decision Support Module (DSM) as an add-on at the midterm planning level. This DSM, called Advanced Resource Planning (ARP), serves as parameter setting process as well as tool for improving the structure of the ERP system itself. The ultimate goal of the DSM is to yield realistic information both for scheduling, sales and marketing, strategic and operational decision making and suppliers and customers.

Business Groups in Emerging Markets-Financial Control & Sequential Investment

Business groups in emerging markets perform better than unaffiliated firms. One explanation is that business groups substitute some functions of missing institutions, for example, enforcing contracts. We investigate this by setting up a model where firms within the business group are connected to each other by a vertical production structure and an internal capital market. Thus, the business group’s organizational mode and the financial structure allow a self-enforcing contract to be designed. Our model of a business group shows that only sequential investments can solve the ex post moral hazard problem. We also find that firms may prefer not to integrate.http://deepblue.lib.umich.edu/bitstream/2027.42/57210/1/wp830 .pd

21st century trade agreements: implications for long-run development policy

This repository item contains a single issue of The Pardee Papers, a series papers that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. The Pardee Papers series features working papers by Pardee Center Fellows and other invited authors. Papers in this series explore current and future challenges by anticipating the pathways to human progress, human development, and human well-being. This series includes papers on a wide range of topics, with a special emphasis on interdisciplinary perspectives and a development orientation.This paper examines the extent to which the emerging world trading regime leaves nations the “policy space” to deploy effective policy for long-run diversification and development and the extent to which there is a convergence of such policy space under global and regional trade regimes. We examine the economic theory of trade and long-run growth and underscore the fact that traditional theories lose luster in the presence of the need for long-run dynamic comparative advantages and when market failures are rife. We then review a “toolbox” of policies that have been deployed by developed and developing countries past and present to kick-start diversity and development with the hope of achieving longrun growth. Next, we examine the extent to which rules under the World Trade Organization (WTO), trade agreements between the European Union (EU) and developing countries, trade agreements between the United States (US) and developing countries, and those among developing countries (South-South, or S-S, agreements) allow for the use of such policies. We demonstrate that there is a great divergence among trade regimes over this question. While S-S agreements provide ample policy space for industrial development, the WTO and EU agreements largely represent the middle of the spectrum in terms of constraining policy space choices. On the far end, opposite S-S agreements, US agreements place considerably more constraints by binding parties both broadly and deeply in their trade commitments. Rachel Denae Thrasher holds a master’s degree in International Relations and a law degree, both from Boston University, and she is a Research Fellow at the Frederick S. Pardee Center for the Study of the Longer-Range Future. Her recent research has focused on policy issues related to regional trade agreements, multilateral environmental agreements (MEAs) and on global forests governance. Kevin P. Gallagher is an Assistant Professor in the Department of International Relations and Research Fellow at the Frederick S. Pardee Center for the Study of the Longer-Range Future, both at Boston University. He is also a fellow at the Global Development and Environment Institute at Tufts University. He has written extensively on trade and global development. Also see related publication The Future of the WTO, by Kevin Gallagher

Firm Performance and the Political Economy of Corporate Governance: Survey Evidence for Bulgaria, Hungary, Slovakia and Slovenia

Using survey data for 220 traditional manufacturing firms over 7 years of transition and 4 CEE countries, we find firms that produced for the EU market under planning consistently outperform those that produced for the CMEA market. Within the previously CMEA market, the best firms were selected to outside privatisation and outperformed insider/state owned firms. Outside privatisation was resisted in EU oriented firms and ownership was found to have no effect on performance. We argue that insider/state ownership in previously CMEA and EU markets builds up political support for the market system during its initial stages, ensuring its long-term success.http://deepblue.lib.umich.edu/bitstream/2027.42/39722/3/wp338.pd