companies that operate offshore.
2. Problem statement
As we have already mentioned, supply vessel planning on tactical level implies construction of circular weekly vessel sailing plans valid for a certain time period. As it can be seen on example in Figure B-1 in the appendix of this thesis, this plan consists of individual schedules for all vessels, which in turn are represented by sequences of voyages built to satisfy installations’ requests and characterized by the assigned scheduled arrival, discharge and departure times. Voyages consist of visits to installations and a supply base. A connection and corresponding to it sailing between any pair of locations we will understand as a leg.
The process of constructing and afterwards performing of these vessel sailing plans is additionally complicated by a number of factors, such as presence of working hours at the installations and the base, limitations on voyage durations (they are usually up to 2 or 3 days in practice), necessity to provide a spread of visits to installations, departures from the supply base to installations, limited capacities of a supply base and installations.
Moreover whilst constructing these sailing plans, weather uncertainty, which influence both sailing and service durations, should be taken into account; the last is currently done by means of having slacks for voyages incorporated into their durations. However, adding inaccurate slacks to durations of voyages might either lead to increase of idle time of vessels, in case slacks are too large or lead to lowered service level in case these slacks are smaller than needed. Moreover adding slacks only to the beginning or end of a voyage might still remain actual the problem of not fitting of working hours at the installations and the supply base as a result of weather uncertainty, which in turn means that it could be beneficial to add smaller slacks to each leg of the voyage and thus assign in a more accurate way arrival, discharge and departure time of vessels at installations and supply bases so as to achieve a better level of utilization of robust schedules. This means a proper statistical analysis of voyage durations and/or durations of legs of this voyage taking weather uncertainty into account. This is also important for building balanced schedules with respect to weather changes. Supply vessel sailing plans based on a proper combination of robust voyages satisfying all of the relevant constraints are considered as robust.
assumptions might (or might not) influence execution of weekly sailing plans in reality. The problem addressed in this work is to develop a tool able to simulate weekly sailing plans for typical winter and summer periods, evaluate their performance and suggest a posteriori improvements of weekly sailing plans.
Starting from the following section of this thesis we will use a simple term schedule meaning by that a vessel sailing plan (see the definitions in the ongoing section).
2.1 General definitions
In what follows the next definitions will be used:
Offshore Installation (oil platform, offshore platform, oil rig or just installation) is a large construction located off the shore, which has the facilities to drill wells, extract and process oil and natural gas, and temporarily store product until it can be brought to the shore for refining and marketing. They also might have limited working hours (time windows).
Service Request for an Offshore Installation is a number of visits to the installation within a time horizon (week) in order to satisfy its demand.
Supply Vessel (PSV, vessel) is a vehicle (ship) designed for servicing offshore oil installations. They range from 20 to 100 meters in length and accomplish a variety of tasks, among which the primary task is transportation of cargo, goods and/or personnel to and from offshore oil installations and other offshore structures.
Voyage of a Vessel is an ordered set of visits to offshore installations starting and ending at a supply base mapped by a set {location, times (arrival, discharge, departure)}.
Vessel Weekly Schedule is a set of consecutive voyages assigned to a vessel to be completed on a weekly time horizon.
Weekly supply vessels sailing plan is a set of vessels’ weekly schedules.
Schedule (supply vessels plan in general) is an ordered set of visits to offshore installations and supply bases mapped by a set {location, times (arrival, discharge, departure), vessel} during a given time horizon.
Significant Wave Height (SWH) is the mean wave height (trough to crest) of the highest third of the waves.
Wave Direction (WD) is the direction from where the wave originates in either cardinal directions or in azimuth degrees.
We also introduce the following definitions:
Evaluate a Schedule means to estimate a set of key robustness factors (parameters) like service level, average tardiness, maximal tardiness of an individual installation, average deviation from scheduled times, fuel costs etc., and their aggregated measure in order to address quality of a schedule in terms of robustness versus fuel consumption.
Weather Uncertainty (for the addressed case) is stochasticity induced by changes of wave directions and significant wave heights over time. Weather uncertainty, thus, consists of two components: wave height uncertainty and wave direction uncertainty. These factors lead to lengthening of voyages, inability to perform planned visits at estimated time and etc.
A Posteriori Improvement of a Schedule is a set of modifications of a schedule being evaluated (e.g. utilization of slacks between voyages, rerouting of voyages, swapping voyages between vessels and etc.) so as to improve its quality in terms of robustness versus fuel consumption.
2.2 Problems and objectives of the research
Thus, with respect to problem definition above the research problem is divided into the following set of sub problems to be resolved:
Formulation and substantiation of criteria for evaluation of schedule robustness
Statistical data analysis for weather modelling
Development and implementation of a simulation model for schedules’ evaluation
Evaluation of given schedules with respect to a set of chosen criteria
Development and implementation of an integrated simulation-optimization tool for a posteriori schedules’ improvement
Basing on the issues and problems stated above, we can formulate objectives of the research:
1. Generate weather data by means of estimating the parameters of the appropriate stochastic processes and/or distributions and clustering weather conditions according to some parameters and simulation of them. Note that there are two types of weather uncertainty:
a. Wave height uncertainty, its geographical and seasonal clustering and impact on vessel speed and sailing duration times. SWH might be considered by means of stochastic processes and/or probability distributions of wave height.
b. Wave direction uncertainty, its geographical and seasonal clustering and impact on vessel speed and sailing duration times. WD might be considered by means of stochastic processes and/or probability distributions of wave direction.
2. Build an event based simulation model for emulating supply vessel schedules having the modeled weather incorporated.
3. Suggest key parameters for evaluation of quality of schedules. Possible service parameters:
a. Service level for the whole schedule;
b. Service level for any subset of installations from the schedule;
c. Service level for the voyages of vessels;
d. Tardiness of arrival, discharge and departure times of the whole schedule;
e. Deviations of arrival, discharge and departure times from those scheduled;
f. Number of missed visits;
g. Number of not performed voyages;
h. Number of not performed weekly schedules;
i. Fuel consumptions and fuel costs;
4. Suggest and substantiate an aggregation criterion and/or ranking criterion based on the key parameters above in order to have an aggregated evaluation measure for the schedules.
5. Evaluate robustness of schedules.
6. Suggest and implement approaches for improvements of the given schedules (a posteriori). Possible ways to do that are listed below:
a. By means of utilizing slacks between voyages;
b. By means of swapping voyages between the vessels;
c. By means of speed adjustments with respect to the forecasted weather;
d. By means of a combination of the improvements above.
2.3 Data sources
Primary Data
It seems to be quite obvious that in the very case of our research not so many sources of primary data will most likely be used. However among them the following types of data and sources might be highlighted:
1. Information about:
- what are the main restrictions, limitations and constraints for weekly vessel schedules to be taken into account;
- technical rules for service;
- supply base policy for supply vessel departures;
2. Expert estimates analysis for:
- service times distributions at the installations and/or supply bases;
- changes of service time at the installation with respect to weather;
Secondary Data
It seems to be rather clear that most of the data used in this kind of research should be secondary data, provided by real oil companies, weather institutions and etc. Ideally such data as those represented below is needed for successful research:
1. Vessel configurations:
sizes and capacities;
economic speed and speed limits;
deadweights;
fuel consumptions;
fuel costs;
emissions of greenhouse gases.
2. Supply bases configuration:
service times for each vessel;
opening and closing hours;
geographical locations.
3. Installations data
set of installations and their geographical positions;
requests;
service durations.
4. Weather data
time series for significant wave heights on a given grid of points;
time series for wave directions on a given grid of points;
It is quite clear that getting all these data might well be a very difficult job. Hence, once the situation is thoroughly studied, relevant models built; these models will be first tested on small samples of modeled data. Once models prove to be working well on modeled data, real world data should be addressed; in case the samples are not large enough statistical data analysis and/or simulation may be addressed to generate additional inputs. In case some data is impossible to get either models should be adjusted or modeled data should be used to make relevant stubs.
3. Methodology and literature review