Maritime distribution of stone products

Transcription

1 Maritime distribution of stone products Johan Oppen Molde University College, Norway

2 Outline Introduction Problem description Solution methods Conclusions

3 Introduction NorStone is a Norwegian producer of stone products The company is currently cooperating with Møre Research Institute The purpose is to search for new logistic solutions and cost savings in the value chain So far, we have an initial report and a master thesis The master thesis contains an initial mathematical model, which is non-linear and quite large At this point in time, we have just begun the work This presentation will be of the type this is what the problem looks like, and this is what we want to do to solve it

4 Problem description When you buy stone products, most of the price is related to logistics The land owner does not get much per ton of solid rock The industry is characterized by large volumes of cheap products and huge logistic costs Inventory is an important issue, it takes a lot of space to store thousands of tons of many different products Buying or leasing storage space is costly, even in Norway Most of the transportation is done by bulk vessels

5 About NorStone NorStone is part of Heidelberg Cement Group, which is the world leader in aggregates and one of the three largest producers of cement and concrete Aggregates are sand and gravel used in concrete and asphalt Heidelberg Cement Group has employees in 50 countries NorStone has 172 employees (2008) and runs 13 plants and terminals in Norway tons produced in 2007

6 About NorStone Main products: Aggregates (sand, gravel) for concrete and asphalt Railway gravel Offshore materials, products for coverage of underwater pipes and preparing the seabed for installations Road building materials

7 The production process Removing of vegetation and soil Drilling and blasting Transportation from quarry to production Production: crushing and sorting/sifting, the finished products are stored in large bins Loading and transport Unloading at customers or terminals

8 Pile of stone after blasting

9 Robust equipment

10 Production and storage

11 Loading of bulk vessel

12 What are the logistic challenges? Demand is varying during the year, there is limited inventory capacity This means there is an inventory management problem here: Should the company invest in more inventory capacity to be able to store more products for the peak season, or should they try to sell more products in the low season?

13 What are the logistic challenges? The company has terminals which serve local customers, products are transported to the terminals by bulk vessels from plants less than 100 miles away The terminals may be viewed as extra inventory, and instead of the terminals placing orders for replenishment, the whole problem could be solved as an inventory routing problem This means that production and inventories at the plants, forecasted and actual demand from customers, together with inventory capacities at the terminals should be considered as parts of the same planning problem and thus solved simultaneously This is a challenging problem, but (hopefully) possible to solve

14 What are the logistic challenges? Utilization of vessels should be improved, sailings in ballast should be reduced Whenever two vessels of the same type meet, both sailing in ballast, there is a problem Routing can probably be improved Currently very short planning horizon (1-3 days), should be extended

15 What are the logistic challenges? Alternative vessel types should be considered By using tugboats and barges, loading capacity and engine power are decoupled Instead of a bulk vessel spending six hours unloading tons, a tugboat can leave the barge behind for unloading and immediately proceed to pick up another barge for the next load New and more environmental-friendly bulk vessels Larger vessels? More efficient unloading equipment on board?

16 Solution methods How can we find solutions to this problem? Some possibilities: Linearize and refine the mathematical model and use AMPL/CPLEX or some other math programming package Reformulate the model and use column generation Develop one or more heuristics In the end, we want to solve real-world problems based on a model that is close enough to reality

17 AMPL/CPLEX Linearizing the model is probably possible Linearization will lead to many new variables and a very large model May work for toy-sized instances Useful for validating the model Useless for real-world instances

18 Column generation Reformulate the problem into a set covering problem A solution is a combination of feasible vessel schedules Global constraints on inventory and port access Solving subproblems is likely to be the bottleneck May work even for real-world instances We have some experience from using CG on rich VRPs with inventory constraints, perhaps it will work also at sea?

19 Heuristics Heuristics always work Additional constraints and problem size can normally be handled No guarantees on solution quality Experience shows that relatively good solutions are found quickly for many problems Many methods to choose from: Tabu Search, Genetic Algorithms, Simulated Annealing,...

20 Conclusions We are dealing with an inventory, fleet design/fleet size and mix, and distribution problem This is a real-world problem We have to solve the correct problem, meaning that we cannot simplify too much So far, we have an initial mathematical model The model needs to be extended Then we need to develop solution methods We have a lot of interesting work to do

21 Finally Any comments, questions or suggestions are welcome Thank you for your attention!