Centers of Gravity Calculator is a web app for running advanced Center of Gravity analysis in supply chain network design (read: determination locations of distribution warehouses that minimize costs of transportation). This web app is used worldwide by logistic service providers, consultancies, and multinationals. All its solvers are proprietary solvers.

Lowering the operational costs of a waste industry company

The waste industry supply chain is shown schematically below: Waste volumes are split across household, business/government, industry/agriculture, construction/demolition, and others. Waste processing volumes are divided across incineration, landfill, and recycling. Supply and demand play a part, and are regulated by waste trade. Such trade exists because some waste companies focus on collection, while others focus on processing. Profit margins can change rapidly due to the tax system, legislation, support for recycling, and new process technology. Transportation and processing costs account for more than 50% of total costs. Minimizing these costs is thus important in increasing the low profit margins.

Which processing centers to use and how should waste flow?

Planning question is which processing centers to use, and how should waste flow, to minimize transportation and processing costs. Waste is collected and temporarily stored at Transfer Centers; the collection itself is not included in the model. Several types of waste are collected with various characteristics, such as chloride percentage. Waste is generally processed in three steps, each representing a different type of process. Step 1 is carried out in one of the Processing Centers 1, step 2 in a Processing Center 2, and step 3 in one of the Final Processing Centers. Sometimes not all the steps are necessary, and some centers can carry out more than one step. In total, there are more than a hundred possible ways to process waste. In terms of modeling, each center has its own location, start up costs, and operational costs. Each center has a fixed processing capacity, but also other technical constraints such as only being capable of processing a waste flow with a chloride percentage within a certain band. To match these constraints, several types of waste can be mixed but there are no fixed recipes for waste flows. Transportation costs depend on waste characteristics, transport distance and time, and truck costs per mile and per hour. All the Transfer Centers are fully owned, but the company does hire additional processing capacity from others. Contractual obligations - non-technical constraints - limit the options in deciding whether or not to outsource a waste processing step. Planning need is to optimize the usage of the various Centers and the flows of waste such that the transportation and processing costs are minimized while taking into account the situation described above (technical and non-technical constraints).

The planning tool answers the planning question

The planning tool provides an answer to the planning question and is used on both tactical (monthly planning) and strategic levels (longer term what-if scenarios). The core of the planning tool is a mathematical Mixed Integer Programming model defined in AIMMS that uses the mathematical solver CPLEX. AIMMS runs in the background, and is connected to a user interface built in MS Excel using Visual Basic for Applications (VBA). The planning tool process consists of the following five steps.

1. Data entry
   • Waste parameters and waste quantities collected at all the Transfer Centers
   • Transport cost parameters
   • Center parameters: cost parameters, location, technical restrictions (e.g., chloride limits), and non-technical restrictions (e.g., minimum volume to be processed due to contractual obligations)
   
   
2. Data validation / Network generation
   • Validation of a-priori assignments, et cetera.
   • Automatic retrieval of journey times and distances between locations by a separate interface (VBA) with TLN Planner (route planner used by the Dutch transport sector)
   • The network (approximately 600 nodes connected by 30,000 arcs) is then built up automatically (VBA) in the appropriate format, and then delivered to the standard AIMMS-Excel interface.
   
   
3. Network calculation
   • Using the mathematical solvers underpinning AIMMS (a.o. CPLEX), the following are calculated: waste flows (what amount of what waste from where to where) through the network, shadow prices, transportation costs, and processing costs. The costs are then minimized.
   
   
4. Reporting
   • On costs, waste flows, activated centers, et cetera.
   • On shadow prices (for example, what would costs have been if more/less processing capacity were available)
   • On integrated costs, calculated by running through all the steps and assigning costs to each step according to volume or otherwise - and summing all the costs (VBA)
   
   
5. Validation by planners
   • Apart from the tactical planning - resulting in a plan minimizing monthly costs - the tool is also used for strategic planning by running different scenarios. The shadow prices derived are useful in negotiations over long-term prices to be charged by other waste processing companies for hiring their processing capacity.
   
   

Results

The planning tool has reduced yearly operational costs considerably. Having a complete view of the supply chain, decisions are no longer suboptimal. Nowadays, waste is transported from one plant to another if the decrease in total processing costs exceeds the increase in transportation costs. Previously, waste would rarely cross regional borders since regional managers did not have an overall view of the full chain. The cost reductions mainly stem from better utilizing the processing locations. Furthermore, even the total transportation costs have been reduced.

Due to its success, the application has won an innovation award within the mother company.

This application routes transport flows through a huge European network of roads, waterways, rail roads, air lanes, and terminals, while minimizing costs, kilometers, time, CO2 emissions, or a combination - by applying Dijkstra's Shortest Path algorithm. One can define scenarios (e.g., "what happens with costs and CO2 emissions if we forbid a certain modality") and compare scenario results. This provides insight into the possibilities of a modal shift towards more sustainable transport.

The application interfaces with Google Earth for geographical visualization. Its underlying network database (a.o. highway road segments) is bought from a third party (and reworked before use).

This application shows the impact on inventories and product availability of different planning methods. Inputs are minimum batch size per product, production speeds per product, total production hours available, setup times between products, product demands, and forecasts. The user selects from several planning and scheduling rules, thus creating a scenario. The simulation creates detailed production schedules (for 52 weeks) - per scenario - and reports on inventory costs and product availability. The simulation makes clear which planning and scheduling rules result in the lowest inventory costs and highest product availability.

Two operations have different workload profiles (in this case related to flight schedules), with workload peaking at different times of the day. How much idle time (shift hours -/- workload hours) can be saved if both operations are merged?

To answer this question, shifts were created for both workload profiles separately and also for the combined workload profile, using a custom-built shifts generator that first generates all possible shifts and then sets the quantity of each shift such that the workload is covered against minimal shift hours. Total idle time of the two separate operations could then be compared to that of the merged operation.

Below simulator has been derived from an extended version used to determine how many service desks to open - for each hour of the day - to keep the average client waiting time acceptable. Parameters can be adjusted via ⚙ button.