No matter if it is the assignment of departments to empty rooms in a building, machines to manufacturing cells, factories to geographical regions, products to racks in a warehouse, sensors to devices, edge computers inside the internet of things network, public transport stations to different zones in a city, you may need to model and […]
In this article, I propose a classification for static facilities in supply chains, i.e., those that are not mobile. Furthermore, as these facilities are static, their location becomes essential, and selecting a location is accompanied by strategic costs. Supply chains of physical items, e.g., physical products, were considered for the classification presented below. A brief […]
In this article I present an exemplary application of monte-carlo simulation for warehouse allocation. Monte-carlo simulation is a very popular technique when it comes to risk assessment. In previous posts I have introduced implementations of monte-carlo simulations in Python and R. I e.g. used monte-carlo simulation to assess the risk associated with price developments of commodities […]
One of the duties I frequently performed as an operations research analyst in consulting projects was optimizing companies’ supply chain network designs. A supply chain is a network that connects suppliers with customers to procure materials, transform them into final products, and deliver these products to customers. Supply chain management is a key function of […]
Providing a coding example for how to conduct spatial proximity customer clustering, applicable e.g. when searching for multiple centers of gravity (i.e. when wanting to locate multiple warehouses). The logic and approach is the same as in any kind of distance based clustering problem. I willk apply k-means clustering for grouping customers based on their spatial distance. […]
In a previous post I showed how a single warehouse location problem can be approached by locating the warehouse near its center of mass. In this post we want to locate multiple warehouses at their center of mass. We will use the functions already defined in previous posts. In http://www.supplychaindataanalytics.com/single-warehouse-problem-locating-warehouse-at-center-of-mass-using-r/ I provided a function for locating a […]
Providing a coding example for how to conduct spatial proximity customer clustering, applicable e.g. when searching for multiple centers of gravity (i.e. when wanting to solve a multiple warehouse location problem). The logic and approach is the same as in any kind of distance based clustering problem. I willk apply k-means clustering for grouping customers based on […]
Operations research provides a range of algorithms for determining exact optimal solutions to discrete warehouse location problems. However, these algorithms require a cost matrix, specifying the cost for supplying customer i from warehouse j, for each customer and each warehouse. Below, I propose an algorithm for specifying this cost matrix such that one can apply […]
In this post I provide a coding example of how a group of customers can be assigned to one warehouse each, considering a set of fixed warehouses with unlimited capacity. The underlying assumption is that there are no fixed costs and that costs only depend on the euclidean distance between customer and warehouse. Furthermore, no […]
In a previous post I demonstrated how one can assign customers to the nearest warehouse using a well-known clustering algorithm. In this post I will demonstrate how a single warehouse location problem can be approached by applying center of mass calculation in R. The idea is to locate the warehouse in right in the average […]