AHP model for optimum distribution network selection in food industry

Efficient supply chain distribution network design must take into account various dimensions of performance and product characteristics.The appropriate choice of distribution network results in customer needs being satisfied at the lowest possible cost. Investigators have recently begun to realize that the decision in the supply chain distribution network design must be driven by an extensive set of performance metrics and the characteristics of the products. In this thesis, cost and service factor performance metrics were regarded as the decision criteria for optimizing supply chain distribution network design. Qualitative and quantitative factors were considered in selecting the optimum delivery network design by using Analytic Hierarchy Process (AHP) methodology. After aggregating the ideas of a group of experts and customers, the selection decision is made. Sensitivity analysis was performed to show the robustness and consistency of the model. The results of the analysis illustrate the model is found to be stable and robust and the ketchup sauce manufacturers can select their suitable and optimum distribution network designs according to this study

Solution strategies for a supply chain deterministic model

To most firms, intelligent supply chain decisions are essential to achieve competitiveness in an environment characterized with increasing globalization and relentless changes. As the supply chain concept largely evolved from traditional logistics management, practitioners and researchers have historically focused on the individual processes of a supply chain. A wide array of mathematical models have been developed to tackle such functional issues as inventory level, lead-time performance, delivery performance, customer satisfaction and so on. This research presents a model that aims to evaluate and optimize the overall performance of the supply chain. The key nodes and variables in the model are composed of plant location and production volume, storage location and volume, transportation mode and volume. Optimization of the model is to minimize the total supply chain operation cost, expressed as the sum of production cost, storage cost, transportation cost and lost-sale cost. Our methodology is a three-phased approach. First, we build a mixed integer-programming model of 3-tier supply chain with multi-plant, multi-warehouse, and multi-retailer, multi-period and restricted capacity. This mathematical model is solved by CPLEX/OPL. Due to excessive computation time to reach the Optimal Solution, we introduce the second phase to develop heuristic solutions to reduce the computation time. In the final phase, we evaluate the proposed heuristic solutions. Result analysis shows that the computation time is generally exponentially correlated to the data size in seeking Optimal Solutions, whereas it generally follows the polynomial distribution when the Heuristic Solutions are applied. Consequently, Heuristic Solution is preferred for models with large size data

A Methodology For Minimizing The Oscillations In Supply Chains Using System Dynamics And Genetic Algorithms

Supply Chain Management (SCM) is a critically significant strategy that enterprises depend on to meet challenges that they face because of highly competitive and dynamic business environments of today. Supply chain management involves the entire network of processes from procurement of raw materials/services/technologies to manufacturing or servicing intermediate products/services to converting them into final products or services and then distributing and retailing them till they reach final customers. A supply chain network by nature is a large and complex, engineering and management system. Oscillations occurring in a supply chain because of internal and/or external influences and measures to be taken to mitigate/minimize those oscillations are a core concern in managing the supply chain and driving an organization towards a competitive advantage. The objective of this thesis is to develop a methodology to minimize the oscillations occurring in a supply chain by making use of the techniques of System Dynamics (SD) and Genetic Algorithms (GAs). System dynamics is a very efficient tool to model large and complex systems in order to understand their complex, non-linear dynamic behavior. GAs are stochastic search algorithms, based on the mechanics of natural selection and natural genetics, used to search complex and non-linear search spaces where traditional techniques may be unsuitable

Global supply chain optimization: a machine learning perspective to improve caterpillar's logistics operations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Supply chain optimization is one of the key components for the effective management of a company with a complex manufacturing process and distribution network. Companies with a global presence in particular are motivated to optimize their distribution plans in order to keep their operating costs low and competitive. Changing condition in the global market and volatile energy prices increase the need for an automatic decision and optimization tool. In recent years, many techniques and applications have been proposed to address the problem of supply chain optimization. However, such techniques are often too problemspecific or too knowledge-intensive to be implemented as in-expensive, and easy-to-use computer system. The effort required to implement an optimization system for a new instance of the problem appears to be quite significant. The development process necessitates the involvement of expert personnel and the level of automation is low. The aim of this project is to develop a set of strategies capable of increasing the level of automation when developing a new optimization system. An increased level of automation is achieved by focusing on three areas: multi-objective optimization, optimization algorithm usability, and optimization model design. A literature review highlighted the great level of interest for the problem of multiobjective optimization in the research community. However, the review emphasized a lack of standardization in the area and insufficient understanding of the relationship between multi-objective strategies and problems. Experts in the area of optimization and artificial intelligence are interested in improving the usability of the most recent optimization algorithms. They stated the concern that the large number of variants and parameters, which characterizes such algorithms, affect their potential applicability in real-world environments. Such characteristics are seen as the root cause for the low success of the most recent optimization algorithms in industrial applications. Crucial task for the development of an optimization system is the design of the optimization model. Such task is one of the most complex in the development process, however, it is still performed mostly manually. The importance and the complexity of the task strongly suggest the development of tools to aid the design of optimization models. In order to address such challenges, first the problem of multi-objective optimization is considered and the most widely adopted techniques to solve it are identified. Such techniques are analyzed and described in details to increase the level of standardization in the area. Empirical evidences are highlighted to suggest what type of relationship exists between strategies and problem instances. Regarding the optimization algorithm, a classification method is proposed to improve its usability and computational requirement by automatically tuning one of its key parameters, the termination condition. The algorithm understands the problem complexity and automatically assigns the best termination condition to minimize runtime. The runtime of the optimization system has been reduced by more than 60%. Arguably, the usability of the algorithm has been improved as well, as one of the key configuration tasks can now be completed automatically. Finally, a system is presented to aid the definition of the optimization model through regression analysis. The purpose of the method is to gather as much knowledge about the problem as possible so that the task of the optimization model definition requires a lower user involvement. The application of the proposed algorithm is estimated that could have saved almost 1000 man-weeks to complete the project. The developed strategies have been applied to the problem of Caterpillar’s global supply chain optimization. This thesis describes also the process of developing an optimization system for Caterpillar and highlights the challenges and research opportunities identified while undertaking this work. This thesis describes the optimization model designed for Caterpillar’s supply chain and the implementation details of the Ant Colony System, the algorithm selected to optimize the supply chain. The system is now used to design the distribution plans of more than 7,000 products. The system improved Caterpillar’s marginal profit on such products by a factor of 4.6% on average.Caterpillar Inc