A time staged linear programming model for production loading problems with import quota limit in a global supply chain

a b s t r a c t Globalization has ushered in a new era when more and more companies are expanding their manufacturing operations on a global scale. This poses some special challenges and raises certain issues. This paper examines production loading problems that involve import quota limits in the global supply chain network. Import quota, which is imposed by importing countries (mostly in North America and Europe), requires that certain types of products imported into these countries are against valid quotas held by the exporters. Globally loading of production, therefore, requires new methods and techniques, which are different from those used in domestic loading of production. This paper presents a time staged linear programming model for production loading problems with import limits to minimize the total cost, consisting of raw materials cost, machine cost, labour cost, overtime cost, inventory cost, outsourcing cost and quota related costs. To enhance the practical implications of the proposed model, different managerial production loading plans are evaluated according to expected changes in future production policies and situations. A series of computational results demonstrate the effectiveness of the proposed model

Robust global supply chain planning under uncertainty

The New World Economy presents business organizations with some special challenges that they have never met before, when they manage their activities in the global supply chain network. Business managers find that traditional managerial approaches, techniques and principles are no longer effective in dealing with these challenges. This dissertation is a study of how to solve new problems emerging in the global supply chain network. Three main issues identified in the global supply chain network are: production loading problems for global manufacturing, logistics problems for global road transport and container loading problems for global air transport. These problems involve a higher level of uncertainty and risk. Three types of dual-response strategies have been developed to hedge the uncertainty and short lead time in the above three problems. These strategies are: a dual-response production loading strategy for global manufacturing, a dual-response logistics strategy for global road transport and a dual-response container loading strategy for global air transport. In order to implement these strategies, the two-stage stochastic recourse programming models have been formulated. The computational results show that the two-stage stochastic recourse models have an advantage in comparison to the corresponding deterministic models for the three issues. However, the two-stage stochastic recourse models lack the ability of handling risk, which is particularly important in today's highly-competitive environment. We thus develop a robust optimization framework for dealing with uncertainty and risk. The robust optimization framework consists of a robust optimization model with solution robustness, a robust optimisation model with model robustness and a robust optimization model with trade-off between solution robustness and model robustness. Each type of the robust optimization models represents a different measure of performance in terms of risk and cost. A series of experiments demonstrate that the robust optimization models can create a global supply chain planning system with more flexibility, reliability, agility, responsiveness and lower risk

Proactive model to determine information technologies supporting expansion of air cargo network

Shippers and recipients expect transportation companies to provide more than just the movement of a package between points; certain information must be available to them as well, to enable forecasts and plans within the supply chain. The transportation companies also need the information flow that undergirds a transportation grid, to support ad-hoc routing and strategic structural re-alignment of business processes. This research delineates the information needs for an expanding air cargo network, then develops a new model of the information technologies needed to support expansion into a new country. The captured information will be used by shippers, recipients, and the transportation provider to better guide business decisions. This model will provide a method for transportation companies to balance the tradeoffs between the operating efficiencies, capital expenditures, and customer expectations of their IT systems. The output of the model is a list of technologies – optimized by cost – which meet the specific needs of internal and external customers when expanding air cargo networks into a new country

Effect of variable shipping frequency on production-distribution policy in a vendor-buyer integrated system

This paper investigates the effect of variable shipping frequency on production-distribution policy in a vendor-buyer integrated system. In a recent article Chiu et al. [1] derived the optimal replenishment lot size for an economic production quantity problem with multi-delivery and quality assurance, based on an assumption that the number of shipment is a given constant. However, in a vendor-buyer integrated system in supply chain environment, joint determination of replenishment lot size and number of shipments may help such a system to gain significant competitive advantage in terms of becoming a low-cost producer as well as having tight linkage to customer. For this reason, the present study extends the work of Chiu et al. [1] by considering shipping frequency as one of the decision variables and incorporating customer’s stock holding cost into system cost analysis. Hessian matrix equations are employed to certify the convexity of cost function that contains two decision variables, and the effect of variable shipping frequency on production-distribution policy is investigated. A numerical example is provided to demonstrate practical usage of the research result

Risk measurement in the global supply chain using monte-carlo simulation

Abstract Nowadays, logistics and supply chain management (SCM) is critical to compete in the current turbulent markets. In addition, in the global context, there are many uncertainties which affect on the market. One of the most important risks is supplier disruption. The first step to cope with these uncertainties is quantifying them. In this regard many researches have focused on the problem but measurement of the risk in the global SCM is yet a challenge. In the uncertain conditions, simulation is a good tool to study the system. This paper aims to study a global supply chain with related risks and measurement of the risks using simulation. Global aspects considered in the paper are: 1-currency exchange rate, 2-extended leadtime for abroad supplies, 3-regional and local uncertainties. In this regard, two popular risk measurement approaches (VaR and CVaR) are used in the simulation of uncertainties in the global supply chain. Results showed that adopting risk averse behavior to cope with the uncertainties leads to the lower stockouts and also higher costs

Planning in Global chemical supply chains with regulatory factors

Ph.DDOCTOR OF PHILOSOPH

HR Selection Distortions: A theoretical framework for the Fiji Public Service

Despite being frequently perceived as a pertinent issue necessary to critically examine how incumbents are selected on merit, HR selection distortions is typically illdefined and poorly explained in much debate, hence, more precision in terms of contextualization of practice is needed. Through explaining and synthesizing the work of a number of scholars from different disciplines, the paper develops a theoretical framework for a meta- analysis, which begins with an exploration of the relationship between HR selection, networking and relational ties, employee’s justice perceptions, group heterogeneity and worker performance in Fiji’s public service institutions. The theoretical framework provides the leeway for the research questions to be answerable and the postulated hypotheses testable. However, more needs to be done to explain not only the nature and emergence of HR selection distortions but also the very real problems it faces in sustaining itself, let alone transforming the hiring processes in Fiji’s public service. The value of the paper lies in its theoretical innovation, drawing on a range of disciplines, and its attempt to situate HR selection distortions precisely, conceptually, theoretically, and practically

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

Scenario Optimization Approach for Designing Biomass Supply Chain

Commercialization of biofuel industry is highly dependent on development of efficient biomass supply chain system. The effect of uncertain parameters on biomass supply chain needs to be investigated for realistic and robust economic assessment of the system. The present study focuses on the development of scenario optimization model for maximizing profit of biomass supply to biorefinery under weather uncertainty. The model determines material flow, number of harvest units, in-field transportation units, transportation units, storage method, and allocation of units to sites. The applicability of proposed model is demonstrated by developing a case study for Abengoa ethanol biorefinery at Hugoton, Kansas.The scenario optimization model developed in the present study has the ability to determine material flows along with the number of harvesting units, in-field transportation units, and transportation units required by the biorefinery while considering the weather uncertainty. The optimization model takes into consideration the purchasing and deployment of assets, with a highly seasonal production of biomass. The case study for Abengoa Biorefinery at Hugoton, Kansas presents the practical application of the model. It was concluded that harvest work hours influence the major cost related decisions in biomass supply chain. The results also indicate that yield of biomass is a crucial factor in determining the profit of biomass supply to the biorefinery. The modeling approach can be also extended to large-scale application. The direction for future research should be to consider different types of biomass feedstocks and conversion processes into the model.Department of Agricultural Economic