An Integrated distribution system for Deteriorating Items via an Artificial Intelligence Method

This paper proposes a n-manufacturer-one- distributor-n-retailer single-period inventory model for deteriorating items that integrates three levels of distribution system. In order to achieve long-term benefits and global optimum of the system, the different facilities develop their partnership through information sharing and strategic alliances. The mathematical model describes how the integrated approach to decision making can achieve global optimum. Due to the complexity of the non-linear problems, it is not possible to find the global optimum analytically. Annealing is the physical process of heating up a solid until it melts followed by cooling it down until it crystallizes into a state with perfect lattice. Following this physical phenomenon, a Artificial Intelligence methodSimulated Annealing (SA), has been developed to find the global optimum for a complex cost surface through stochastic search process. A computer program in C-language has been developed for this purpose and is implemented to derive the optimum decision for the decision maker. Numerical examples and a sensitivity analysis are given to validate the results of the system. The proposed model has potential application in product distribution inventory systems

Optimization and sensitivity analysis of computer simulation models by the score function method

Experimental Design;Simulation;Optimization;Queueing Theory

Procurement policy and allocation policy of common component in a two-echelon assembled-to-stock system

Ph.DDOCTOR OF PHILOSOPH

Monte Carlo DEA and budget allocation for data collection: An application to measure supply chain efficiency

Ph.DDOCTOR OF PHILOSOPH

Optimal Location of Biomethane Gas Manufacturing Plants and Allocation of Feedstock and Liquified Carbon Product

Biomethane gas (BMG), known for its sustainability, low environmental impact, and high profitability, has received wide attention in recent years. To facilitate the process of making strategic plans for building a BMG production system, this dissertation leverages the mathematical modeling and optimization techniques to minimize the supply chain cost for such a system. Typical elements in a BMG production system consist of the local farms that produce the feedstock, the hubs that collect and store the feedstock produced by farms, the reactors that generate BMG from the feedstock transported from the hubs, the condensers that liquefy the BMG from the reactors, and the delivery points that act as end distributors and accept the liquefied BMG from condensers. The logistics of a BMG production system can be divided into four stages: farm-to-hub (F2H) stage, hub-to-reactor (H2R) stage, reactor-to-condenser (R2C) stage, and condenser-to-delivery point (C2DP) stage. Depending on the variation on the elements and stages of a BMG production system, four supply chain configurations for BMG facility locations are proposed with increasing level of complexity: single-stage, single-reactor system (SS-SRS); single-stage, multi-reactor system (SS-MRS); three-stage, multi-facility system (TS-MFS); and four-stage, multi-facility system (FS-MFS). The objective for each configuration is to locate facilities optimally and to design the transportation/pipeline connecting network such that the supply chain cost, including the total of feedstock costs, labor costs, facilities building costs, and transportation/pipeline layout costs are minimized. A systematic approach, containing mathematical modeling and heuristic design, is proposed for each configuration. Numerical experiments are conducted for each designed heuristic to verify its performance

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success