This paper addresses a shipments-planning problem faced by Omya Hustadmarmor, a producer of large volume slurry products. From a single producing plant with limited production and tank storage capacity, multiple products
are transported by different vessels to a number of tank farms that also have limited inventory capacity. The objective is to minimize the total distribution cost such that capacity constraints are not violated, i.e. neither stopping
production at the plant, nor running out of stock at the tank farms. This Inventory-Routing Problem is solved by the use of a mixed integer-programming model and the application of a metaheuristic approach to obtain optimal
solutions. At the company, the solution method was implemented successfully in a Decision Support System and resulted in a much more flexible and predictable supply chain. Today’s business environment is characterized by an increasing complexity in manufacturing and supply chain management (Booth 1996). Huge numbers of choices and persistent time and margin pressures make managerial decisions more difficult. In addition, new enterprise applications and software are generating overwhelming amounts of data. Turning this data into
decision-supportive information by applying theoretical models in practice is one of the tasks of Operations Research (Robinson 2000). For almost 35 years, the ‘Institute for Operations Research and Management Sciences’
(INFORMS) honors extraordinary practical achievements and success stories in Operations Research with the ‘Franz Edelman Award’. By bringing together top examples of innovation from different international organizations, the possibilities of Operations Research should be demonstrated and serve as a continuous source for new ideas and optimization. Among the finalists of the 2006 ‘Franz Edelman Competition’ is the European company Omya Hustadmarmor.
Table of Contents
1 Introduction
1.1 Relevance
1.2 Research Objective and Outline
2 About Omya Hustadmarmor
2.1 Company Information
2.2 Supply Chain Management
3 Overcoming the Decision Difficulty
3.1 Operational Challenges
3.1.1 Complexity Drivers in the Supply Chain
3.1.2 Coherence of Production and Distribution Planning
3.2 The Inventory-Routing-Optimization Problem
3.2.1 Mixed-Integer Linear Programming
3.2.2 A Nonlinear Objective Function
3.2.3 Finding the Solution with a Metaheuristic Method
3.3 The Decision Support System
3.3.1 Design and Implementation
3.3.2 Beneficial Effects
4 Management Implications
4.1 Innovativeness of the Approach
4.2 Transferability to Other Industries
5 Conclusion and Outlook
Research Objectives and Key Topics
This paper examines how Omya Hustadmarmor successfully addresses increasing supply chain complexity by developing an innovative Decision Support System (DSS) for maritime inventory routing. The central research goal is to analyze the company's approach in integrating transportation, inventory, and production planning and to explore the transferability of this optimization model to other industries facing similar logistical challenges.
- Analysis of inventory-routing problems (IRP) in a maritime context.
- Implementation of mixed-integer programming and metaheuristic algorithms.
- Development and beneficial effects of a Decision Support System (DSS).
- Evaluation of operational efficiency through integrated supply chain planning.
- Discussion on the transferability of the model to other industries, such as natural gas logistics.
Excerpt from the Book
3.2.1 Mixed-Integer Linear Programming
In a first approach, the optimization problem was modeled as a linear mixed-integer program (MIP), i.e. the minimization (or maximization) of a linear objective function subject to linear constraints with partly integer variables (Silver et al. 1998). The objective function (1) was defined as minimizing the overall distribution cost, concretely the sum of transportation cost and inventory cost.
However, in this first approach, the linear mixed-integer linear programming could not deliver optimal solutions. This was due to the high number of uncertainties and operational challenges interrelated with the planning procedures.
Summary of Chapters
1 Introduction: Provides an overview of the increasing complexity in supply chain management and introduces Omya Hustadmarmor as the central case study.
2 About Omya Hustadmarmor: Offers basic company information and a detailed description of the firm's supply chain management.
3 Overcoming the Decision Difficulty: Analyzes operational challenges and the theoretical model of the inventory-routing-optimization problem, including the implementation of the Decision Support System.
4 Management Implications: Discusses the innovativeness of the developed approach compared to existing literature and examines its transferability to other industries.
5 Conclusion and Outlook: Summarizes the key findings and highlights the project's success in achieving complexity reduction and stability within the supply chain.
Keywords
Operations Research, Supply Chain Management, Inventory-Routing Problem, Omya Hustadmarmor, Decision Support System, Maritime Logistics, Metaheuristic, Mixed-Integer Programming, Transportation Planning, Production Planning, Optimization, Inventory Management, Genetic Algorithm, Fleet Utilization, Complexity Reduction.
Frequently Asked Questions
What is the core focus of this research paper?
The paper focuses on the supply chain optimization efforts of Omya Hustadmarmor, specifically how the company addresses complex maritime inventory-routing challenges through the implementation of a new Decision Support System.
What are the central themes of the work?
The main themes include maritime transportation logistics, integrated planning of production and distribution, inventory optimization, and the practical application of operations research models.
What is the primary research objective?
The objective is to analyze how the company overcomes supply chain complexity through its innovative planning model and to discuss the potential for applying this successful approach to other business sectors.
Which scientific methods are utilized in the paper?
The research employs a mixed-integer programming model alongside metaheuristic approaches, specifically a memetic algorithm consisting of a greedy heuristic and a genetic algorithm to find optimal transportation plans.
What is covered in the main part of the document?
The main section covers the identification of operational challenges, the theoretical formulation of the Inventory-Routing Problem (IRP), the design of the Decision Support System, and the subsequent quantitative and qualitative benefits for the firm.
Which keywords best describe this study?
The study is best characterized by terms such as Operations Research, Inventory-Routing Problem, Decision Support System, Maritime Logistics, and Supply Chain Optimization.
Why did the initial linear model fail to produce optimal results?
The initial mixed-integer linear programming approach was unsuccessful because it could not adequately account for the high level of uncertainties and the complex, interrelated nature of the operational planning variables.
How did the team resolve the nonlinear difficulties in the optimization?
The team adopted a metaheuristic approach, specifically a memetic algorithm, which combines local search heuristics with exact methods to manage the time-varying complexity of the planning process more effectively.
What specific long-term benefits resulted from the DSS implementation?
The DSS enabled more reliable 28-day planning, leading to cost savings, better fleet utilization, reduced raw material waste, and improved strategic decision-making regarding market expansion and infrastructure investments.
How is the transferability of the model illustrated?
The paper illustrates transferability by using the example of the liquefied natural gas (LNG) supply chain, demonstrating how the DSS principles could help other companies with similar bulk transportation needs improve their logistical efficiency.
- Quote paper
- Dipl.-Kfm. Simon Straßburger (Author), 2007, Omya Hustadmarmor optimizes its Supply Chain, Munich, GRIN Verlag, https://www.grin.com/document/112971
