Supply Chain Management became a popular term in the mid-1990s but, even today, no clear definition of the term has emerged. Instead, for most academics and practitioners, supply chain management is a broad term that covers many functions, including but not limited to manufacturing, warehousing, and transportation, as well as supplier relationship management, inventory management, pricing, and customer service.
As globalization has increased the scope and the complexity of supply chains, developing a supply chain strategy has become critical to a company's success. For some companies, a supply chain strategy that operates across multiple areas is necessary to achieve competitive advantage. For other companies, a supply chain strategy that focuses on a single area is sufficient to achieve operational excellence. In either case, optimization plays an important role.
This case studies collection highlights discrete optimization models in three areas that fall under the umbrella of supply chain: manufacturing, location analysis, and transportation. Please note that not all of the topics are serious in nature!
- Cutting Stock Problem
Given paper rolls of fixed width and a set of orders for rolls of smaller widths, the objective of the Cutting Stock Problem is to determine how to cut the rolls into smaller widths to fulfill the orders to minimize the scrap.
- Open-Pit Mining with Integer Programming
This example on the Gurobi Optimization website solves a simple mining problem of how to extract minerals from an open pit in order to generate the most profit.
- Project Scheduling with CPM
Given a list of activities required to complete a project along with the duration of each activity and the dependencies between activities, the objective of the Critical Path Method (CPM) is to determine the sequence of activities that minimizes the latest completion time.
- Production Scheduling with Piecewise-Linear Objectives
This example on the Gurobi Optimization website solves a simple production scheduling problem and demonstrates the use of piecewise-linear objectives in Gurobi.
- Air Ambulance Reassignment Problem
The objective of the Air Ambulance Reassignment Problem is to determine a minimum cost assignment of helicopters to sites to satisfy the projected demand for the next time period.
- Cell Tower Coverage
This example on the Gurobi Optimization website solves a simple covering problem to determine how to build cell towers to provide signal to the largest number of people.
- Facility Location
This example on the Gurobi Optimization website solves a simple facility location problem to determine where to build warehouses to supply a large number of supermarkets.
- Offshore Wind Farming
This example on the Gurobi Optimization website solves the problem of how to minimize the cost of laying underwater cables to collect electricity produced by an offshore wind farm.
- Quadratic Assignment Problem (interactive NEOS demo)
The objective of the Quadratic Assignment Problem is to assign \(n\) facilities to \(n\) locations in such a way as to minimize the assignment cost, which is a function of flow and distance.
- Bar Crawl Optimization Problem
The objective of the Bar Crawl Optimization Problem is to select a set of bars and determine an ordering of them in such a way as to maximize the total benefit minus the total distance subject to a budget constraint.
- The Traveling Salesman Problem
This example on the Gurobi Optimization website solves an instance of the well-known traveling salesman problem.
- Multiple Traveling Salesman Problem
The Multiple Traveling Salesman Problem (mTSP) is a generalization of the Traveling Salesman Problem (TSP) in which more than one salesman is allowed. Given a set of cities, one depot where \(m\) salesmen are located, and a cost metric, the objective of the \(m\)TSP is to determine a tour for each salesman such that the total tour cost is minimized and that each city is visited exactly once by only one salesman.
- Routing Tanker Trucks
This example on the Gurobi Optimization website solves a simple vehicle routing problem to determine how to deliver diesel fuel by tanker truck to customers while driving the fewest miles.