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Woll aton Rd. Being an active member of the OR Society is all about promoting a healthy subject area and ensuring that it remains vibrant.
DSE Consulting Ltd. David Buxton has had a varied career history. Starting out as a Geographer before moving into Analytical roles and then into senior positions in general management.
Recently, David move into academia, before taking the risky step to strike out and founded dseConsulting.
Enjoying commercial success and a reputation as a simulation expert, last year he joined forces to also launch decisionLab, styled as the modern approach to OR consultancy.
These business ventures and the previous experiences have each given different insights into the role of OR in business, and had led David to believe that an entrepreneurial spirit in Operational Research is an essential component.
Perhaps we should focus more on risk taking? And what about those characteristics associated with the most successful entrepreneurs: interpersonal skills, the ability to persuade, the ability to lead and motivate, charisma?
Are those skills traditionally valued and developed in OR? In the end, does it really matter? Perhaps other disciplines are better suited to the cut and thrust of a commercially-driven world - we can let them take the plaudits whilst we continue with the decision support.
But does this threaten to marginalise OR? And in a competitive job-market what can an OR graduate do stand out?
Using examples from his own experience, David will highlight the importance of the inner entrepreneur. Worked 15 years in the Dept for Education in its various guises manly as an OR analyst, but also in policy, working across a wide range of education policies.
He has also been an active member across the wider OR community - regularly attending the OR Soc Conferences, delivering plenary talks in and He is currently chair of the OR in schools taskforce and carries out a wide variety of outreach activities.
His research interests are in game theory and queueing theory. Her role is to raise awareness of OR among young people and their teachers.
OR can help encourage young people to continue studying maths by showing them some applications of the maths they are learning and also highlights a potential career for someone who enjoys maths.
Abstract Participants in Science, Technology, Engineering and Mathematics STEM subjects need to actively encourage and enthuse young school children to explore the various possibilities available to them.
Operational Research is no different. In this talk we will present what the OR society already does in means of outreach activities as well as playing a game audience participation is required used to introduce game theory to school children.
It is hoped that this talk will encourage and enthuse young Operational Researchers to participate in such events and further the great work done by the Society to disseminate the subject.
Please introduce yourself and, if applicable, provide the chair with a copy of your presentation to upload onto the seminar room computer.
Please ensure that you are familiar with the equipment before the start of your talk. Anyone going over this time will be asked to stop by the chair.
You should familiarise yourself with the equipment and ensure there are no obvious problems which would prevent the stream from running to schedule.
You should introduce yourself to the speakers. They will provide you with electronic copies of their presentations to be loaded onto the seminar room computer.
The speaker has 20 minutes for presentation followed by 5 minutes of questions and answers.
Each talk is followed by a 5 minute break for the comfort of the audience and to allow for movement between streams. Please, do not move the next talk forward.
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Scan the following image with your mobile phone QR-Code reader required, e. Journals and case studies Free O.
Nurse rostering in a Danish hospital Jonas Baeklund 2. A bilevel model for valves location in water distribution systems Andrea Peano 3. A nice use for MCDA in public health: potential new approaches to decision making in the national institute for health and clinical excellence Brian Reddy 2.
Stabilizing policies for employment portals Hanyi Chen and Burak Buke 2. Assortment planning with substitution effects Jochen Schurr 3.
A dynamic programming heuristic for the quadratic knapsack problem Franklin Djeumou Fomeni and Adam Letchford 3.
Compact formulations of the Steiner traveling salesman problem Saeideh D. Nasiri, Adam Letchford and Dirk O. Periodical vehicle routing problem due to driver familiarity Matthew Soulby and Jason Atkin 3.
The impact of on time delivery on supply chain management: are third party logistics providers worth the investment?
Yolanda Silvera and Rebecca DeCoster 2. Point process models for assessing the reliability of desalination plant equipments subject to failures due to red tide events Ahmed Al Hinai and B.
Reliability assessment of subsea systems in ultra-deepwater oil and gas developments Anietie Umofia, Stephen Okonji and Shaomin Wu 2.
An improved choice function heuristic selection for cross domain heuristic search John Drake and Ender Ozcan 2. Markovian analysis of stochastic serial multi echelon supply chains Despoina Ntio and Michael Vidalis 2.
Hybrid lateral transshipments in multi-item inventory networks Sandra Rauscher and Kevin Glazebrook 2. Vehicle routing with dependent vehicles Edward Kent and Jason Atkin 2.
Capacity planning for motorail transportation Pascal Lutter 3. Best next sample Sergio Morales Enciso 2. Wallace and Zhan Pang 3. The design of transportation networks: a multi objective model combining equity, efficiency and efficacy Maria Barbati 2.
Organization of a public service through the solution of a districting problem Carmela Piccolo and Sabrina Graziano 3. Modelling the uncertainty of data and the robust shortest path Michael Clark and Andrew J.
A non-identical parallel machines scheduling problem with multi-objective minimization Jean Respen, Nicolas Zufferey and Edoardo Amaldi 2.
An exact algorithm for the uncertain version of parallel and identical machines scheduling problem with interval processing times and total completion time criterion Marcin Siepak 2.
Modelling retail sales for retail price optimization Timo P. The transition to an energy sufficient economy: a system dynamics model for energy policy evaluation in Nigeria Timothy Mbasuen and Richard C.
Darton 2. On the Peter principle: an agent based investigation into the consequential effects of social networks and behavioural factors Angelico Fetta, Paul Harper, Vincent Knight, Israel Vieira and Janet Williams 3.
A meta-parameter analysis of boosting for time series forecasting Devon Barrow and Sven Crone 2.
Long-term reserve warranty forecasting with neural network models Shuang Xia and Shaomin Wu 3. In other words, selfish users do not exhibit socially optimal behaviour.
It can be shown that, under both types of optimal customer behaviour, the equality of average queue-joining rates for the observable and unobservable models can be induced by a particular relationship between the system parameters.
However, this equality of queue-joining rates results in differences with respect to other performance measures, such as mean waiting times and busy periods.
It can also be shown that the two types of model observable and unobservable cannot simultaneously share the same selfishly and socially optimal queue-joining rates, regardless of the choice of input parameters.
This change made the electricity market competitive and introduced several new subjects. We present linear mathematical model of total daily profit maximization subject to flow constraints.
It is assumed that the demand and supply are known and some of them are arranged. Possible transmission capacities are known but also additional capacities can be purchased.
All trading, transmission prices and amounts are subject of auctions. First, we present energy trading problem as directed multiple-source and multiple-sink network and then model it using linear programming.
Also, we provide one realistic example which is slightly changed in order to save confidentiality of the given data.
The performance measures under consideration are those which are imposed on universities from outside, such as statutory performance indicators and newspaper league tables, and which bring rewards in SCOR the form of either status or funding.
The existing literature provides evidence that such measures are causing tension within institutions, but there has to date been no attempt to examine that tension using the tools of management science.
The main tool used here is Data Envelopment Analysis. This model is used first to determine the production possibility set for a group of universities and then to explore the options open to them.
Game Theory provides a range of structures which model such interactive decisions and can aid a decision-maker in determining optimal strategies.
The results of the DEA model are therefore processed using a typical league table construction and then evaluated through the lens of Game Theory.
They are interested in improving the control planning by the use of optimization techniques. We build up an integrated model, consisting of a tour planning and a duty rostering part.
The tours should guarantee a network-wide control whose intensity is proportional to given spatial and time dependent traffic distributions.
We model this using a spacetime network and formulate the associated optimization problem as a Multi-Commodity Flow Problem in an integer programming IP formulation.
The rostering part is needed, since we must assign a crew to each tour. All duties of a crew member must fit in a feasible roster.
It is also modeled as a Multi-Commodity Flow Problem in a directed acyclic graph, where specific paths correspond to feasible rosters for one month.
To the best of our knowledge there is no approach in the literature that integrates vehicle routing and duty rostering planning into one model yet, and there is no model of Toll Enforcement Optimization.
With our approach all legal rules can be modeled. We present computational results which document the practicability of our proposal.
When used at airports like Heathrow, where during busy times aircraft are arranged in stacks and are released predominantly from the bottom level of each stack, such algorithms could lead to solutions that achieve very good objective function values, but are not always feasible in practice.
The main goal of this work is to find feasible arrival sequences by considering the sequencing and control problems 23 Abstracts Friday simultaneously.
An exact multi-objective dynamical programming algorithm was developed for this purpose. Its effect on the arrival sequence and some potential benefits that can be achieved at Heathrow through slight tactical changes have been investigated.
This presentation focuses in particular on the impact which varying the number of release levels has on the arrival sequence, both in terms of runway utilisation and of waiting time per aircraft.
Furthermore, we will consider the effects of extending the lengths of the trajectories from the stack to the runway for some of the aircraft.
This allows the aircraft that remain in the stacks to ladder down faster into the release levels and as a consequence, may result in better sequences becoming feasible.
However, during peak demand hours, such strategies are unable to provide continuous green indications. They might worsen prevalent traffic conditions if not revised to avoid queue overflows.
Signal settings cycle time, green times, phase sequences and offsets can be calculated with different objectives in mind.
Offsets are used essentially to synchronise signals. It is often considered that progression schemes maximisation of throughput by co-ordination are only achievable during light traffic conditions, and the use of other approaches based on minimisation of disutility functions, e.
A critical challenge is the staging of static queues and platoons incoming groups of vehicles to avoid progression disruptions and overflowing queues.
Therefore, a suitable control method would be able to cope with traffic outbursts, accommodating moving vehicles, and discharging queues regularly.
The proposed methodology examines the interactions between platoons and queues, considering both progression and fair allocation of green times.
For this purpose, a set of experiments using a traffic micro-simulation are proposed to reproduce scenarios fluctuating between low and high peaks of demand.
Measures of effectiveness will be analysed based on progression bandwidth, platoon composition, and queue interactions, while considering side road conditions.
Our approach studies this phenomena from the perspective of vehicle groups rather than network or individual performance indicators, which may provide new insights to the problem of finding adequate traffic signal settings at arterial roads.
The problem is highly constrained and comprises a large set of different constraints. A branch-and-price method for solving the problem exactly is proposed.
The master problem is to assign schedules to the nurses, and its linear relaxation is solved by means of column generation.
A number of specific algorithms for handling these constraints are proposed. The method is very flexible regarding the rules a schedule should comply with, which is a key concern when creating solution methods for nurse rostering problems.
Computational tests show that optimal solutions can be found for instances with a two weeks planning period in a reasonable amount of computing time.
To drain a pipe, some of the isolation valves already present in the network are closed, in such a way that the smallest part of network containing the damaged pipe is disconnected from each of the reservoirs feeding the WDS.
In this way, only that subnetwork is isolated and drained. Such subnetworks are called network sectors and their shape is determined by valves positioning.
Isolation valves are usually located at the extremes of the pipes. Since valves are expensive, WDSs can be equipped with just a limited number of valves, whose location poses a challenging optimization problem.
When any pipe is equally like to require maintenance, the best valve location is the one which minimizes the maximum undelivered demand in case a sector needs to be isolated.
Undelivered demand accounts not only for the demand of the sector which requires maintenance, which is drained on purpose, but also of the demand of any other sector which gets disconnected from the reservoirs as a secondary effect of isolating the first sector.
This issue makes this real-life problem much more complex than a pure graph partitioning problem. We propose a bilevel mixed-integer linear programming model, where the outer level models valves location and the inner level models water flow.
We show how to reformulate the bilevel model as a single level mixed-integer linear model, and test our approach on a real life WDS.
This method relies on well-centred and suboptimal dual solutions to generate new columns in a column generation framework.
Theoretical support is provided to show that the method converges to an optimum if such optimum exists.
Additionally, computational comparisons with the standard column generation and the analytic centre cutting plane method are presented. The problems on which we have performed the comparisons are the cutting stock problem, the vehicle routing problem with time windows and the capacitated lot sizing problem with setup times.
The method shows consistently reductions in the number of outer iterations as well as CPU time when solving the relaxations of these combinatorial optimization problems after applying a Dantzig-Wolfe reformulation.
The results are encouraging showing that the method is an attractive general purpose column generation method. Its dynamic adjustable optimality tolerance and its natural stabilization features make it a competitive option in this context.
The performance of the method is enhanced when larger instances are considered. Compromise solutions thus neatly fit with the concept of Pareto optimality.
Chair: Izabela Komenda 3. Lower bound improvements of penalty param1. It combines evidence-based and health economic approaches to medicine with equity concerns in order to adequately prioritise interventions, attempting to reduce variations in levels of treatment between local regions and encourage best practice across the health system.
However, for a number of reasons it is challenging to adequately quantify, model and describe the multiple effects of public health interventions.
As a result there is uncertainty around their cost effectiveness; this adds to the complexity of the decision making process.
Multi-criteria decision analysis MCDA relates to the methods and procedures by which concerns about multiple conflicting criteria can be formally incorporated into the management process and is used to provide a framework to help decision makers structure complex decisions.
Such situations are common in public health scenarios. This presentation will describe some of the most common difficulties of measuring cost effectiveness in public health and explain why MCDA techniques may be an appropriate method to overcome some of these issues.
Linear bilevel programming problems are the most studied class of bilevel problems and have been widely used to represent real life applications in which there are hierarchical decisional structures.
The case in which a subset or all the variables are discrete represents another important class of problems on which we focused as they enable to better describe applications and problems with an inner combinatorial nature.
We consider a particular class of linear bilevel problems in which the variables controlled by the leader are required to be discrete.
This equivalence is true for a sufficiently large value of this parameter. A valid lower bound for it is already known. We provide two improvements of this existing lower bound and experiment the two new lower bounds on a set of test problems.
The parameters proposed are better from a theoretical point of view because they assure a reduction of the penalty used to force the integrality requirements on the upper level variables.
This implies the possibility to solve larger instances increasing the applicability of bilevel models. The basic concept in multicriteria optimization is Pareto optimality: a solution is Pareto optimal, if improving one objective is impossible without worsening another.
However, in general the number of Pareto optimal solutions is exponential. To choose a single, well-balanced Pareto optimal solution, Yu proposed compromise solutions.
A compromise solution is a feasible solution closest to the ideal point. The ideal point is the component-wise optimum over all feasible solutions in objective space.
Compromise solutions are always Pareto optimal. Using different weighted norms, the compromise solution can attain any point in the Pareto set.
The latter does not hold for other concepts, e. In particular, compromise solutions find well-balanced Pareto solutions that may escape weighted sum methods.
Compromise solutions and the slightly more general reference point methods are widely used in state-of-the-art software tools. Still, there are very few theoretical results backing up these methods.
We establish a strong connection between approximating the Pareto set and approximating compromise solutions.
In particular, we show that an approximate Pareto set always contains an approximate compromise solution. Each given employee can match with a specific employer with probability q independently.
This system resembles assembly-like queueing systems. We model it as a two-dimensional Continuous Time Markov Chain and analyze its properties.
Our main focus is on stabilizing policies for such portals. Next, we will introduce the Accept the Shortest Queue policy, which forces constraint on the acceptance of customers.
The system will accept employees employers only when they have a smaller or equal number than employers employees.
Under this policy, the system is ergodic for any set of arrival rates, but the rate of rejecting customers is exceptionally large.
Therefore we consider how to modify it to enable us to accpet more customers without losing stability.
In traditional formulations the objective is to minimise the cost for real power output subject to technical constraints such as load balance, spinning reserve and minimum up-and downtimes of individual units.
Deterministic variants of this problem are well known and researched and are be- ing solved successfully in the energy industry, mainly due to good predictability of future electricity demand.
However, due to increased demand uncertainty and fluctuating wind supplies it becomes more and more important to incorporate uncertainty explicitly in the problem formulation.
Stochastic unit commitment problems are large scale multistage mixed integer optimisation programmes which are excruciatingly hard to solve.
In this talk I will give an outline of the problem formulation and describe the scenario decomposition techniques we use to make the problem more tractable.
Some subset selection problems on a retail level, arising for example in internet advertising or in the apparel industry, face besides the obvious resource limitation further challenges that are due to high uncertainty in demand.
This ability raises the question how one should optimally allocate show room space to products in order to maximize profit over the entire selling season.
We present a dynamic programming DP approach with Bayesian learning to study assortment decisions in a limited horizon setting.
A particular challenge when accounting for substitution effects is the fact that the DP is no longer weakly coupled and hence cannot be decomposed into single-product sub-problems.
Further, the Bayesian learning scheme becomes incomparably more complex as compared to existing models that assume independence among products.
We will briefly discuss the simplification approach via a one-step look ahead of the DP and a Monte-Carlo integration scheme for the expected cumulated future profits.
Further, computational results will be provided that demonstrate the large-scale feasibility and performance of the approach.
Visitors start from a hotel with desire to visit all interesting sites in a city exactly once and to come back to the hotel.
The tested benchmarks are combined from three real instances made using tourist maps of cities of Venice, Belgrade and Koper and two instances of modified cases from TSPLIB.
We introduced and compared two exact methods for solving the TVP. The quadratic knapsack problem, on the other hand, is NP-hard in the strong sense, which makes it unlikely that it can be solved in pseudo-polynomial time.
We show however that the dynamic programming approach to the linear knapsack problem can be modified to yield an effective heuristic for the quadratic version.
The standard integer programming formulation of the STSP has an exponential number of constraints, just like the standard formulation of the TSP.
On the other hand, there exist several known compact formulations of the TSP, i. In this study, we present three of these compact formulations specifically the commodity flow formulations and timestage formulations for the TSP and show how they can be adapted to the STSP.
We also compare these formulations in terms of the strength of their resulting linear programming relaxation bound.
A carrier must serve two type of customers: the importers receive loaded containers from a depot and return empty containers, whereas the exporters receive empty containers and ship loaded containers to the depot.
Unlike classical drayage problems, what is original in this vehicle routing problem is the impossibility to separate trucks and containers during customer service and the opportunity to carry more than one container in some trucks.
In order to address this special split backhaul problem, we propose a mathematical model based on a node-arc formulation.
Nodes represent the depot, importers and exporters. Arcs represent links between nodes, but, due to precedence constraints, arcs from exporters to importers are not considered.
Since this problem is NP hard, this research investigates approximate algorithms and, whenever it is possible, compares approximate solutions to exact ones.
In this work we propose a two phase approach. In the first phase, we separate importers and exporters and determine separate routes using an efficient metaheuristic.
In the second phase, we propose several heuristics to merge these routes. Finally, we compare our solutions to the real decisions of the carrier who has motivated this problem.
Not only will familiarity decrease service and travel times, due to knowing the roads, building entrances, delivery points and so on, but the rapport which can be built up between driver and customer also SCOR has value.
When driver learning is applied to the Vehicle Routing Problem VRP a trade off occurs between route length and the driver familiarity within the route.
To accommodate this, formulations have been applied in the past which group customers that consistently occur in the same route into delivery areas.
Determining whether the delivery areas should remain the same over time or be adapted to account for the periodical nature of the orders needs to be considered.
For the normal VRP where driver familiarity is ignored the main focus is usually upon the route length, so taking account of the periodical nature and more predictable elements of the stochastic customer demand is of little consequence.
Information obtained from the vehicle routing software and consultancy company, Optrak, has allowed for a more accurate, rich representation of the VRP encountered by delivery companies, considering the gain in driver familiarity and the periodical nature of the customer demand.
Initial research concentrated on the static variant of the problem. We produced several variations of a general heuristic for constructing an initial feasible solution, and used neighbourhood search operators to make improvements.
This was extended into an implementation of a Tabu Search heuristic, varying reconstruction heuristics and a Branch and Bound method to improve the results.
Analyses have been performed across a range of benchmark datasets, including both clustered and random allocations of pickup and delivery locations.
The approaches used give results which are competitive with the state of the art. Our current research is dedicated to solving the dynamic problem, where a time stamp is allocated to each request and the request does not become known to the system until that time.
Investigations have been performed to identify when the algorithms should be updated to incorporate the arrival of new requests.
Datasets with both varying degrees of urgency and proportion of dynamic requests have been examined along with various different waiting strategies and the design of new evaluation functions.
Competitive results have been achieved across a range of benchmark datasets. Most existing research on supply chain performance incorporates the use of quantitative analysis, but this has been found to not be entirely appropriate when it comes to the area of supplier relationships and supplier performance management.
The major focus of this research is on the area of on time delivery, and its effects on the supplier relationship. It has long been thought that all that is required for an effective supply chain is an investment in technology; it was not felt that there was a requirement to do anything 1 27 Abstracts Saturday as the technology installed will do everything to improve efficiencies.
But with new technologies and globalisation of the markets, this way of thinking has been proving to be wrong. The leading scholars in the area of supply chain management have also been emphasising this new reality, when HBR convened a panel of leading thinkers in the field of supply chain management, technology was not the major issue on their minds it was people and relationship management.
This paper, through data collected emphasises the relevance and importance of performance measurement in supply chains, especially those indicators affecting on time delivery.
Data collected thus far indicates the advantages and disadvantages of third party logistic providers to the on time delivery process.
The objective is to maximise the throughput of high priority goods under capacity restriction. Simulation will be applied to assess the performance of the model.
The theoretical forecast errors both their mean and variance are derived for the aggregated and nonaggregated demand in order to contrast the relevant forecasting performances.
The theoretical analysis is followed by experimentation with real world data. The results indicate that performance improvements achieved through the aggregation strategy are a function of the aggregation level, the smoothing constant value and the process parameter; they also show that for high positive autocorrelation, aggregation does not add any value.
However, there are crucial differences between the humanitarian and the commercial logistics. One of the differences is that the conditions in which humanitarian relief organizations operate are extremely chaotic and instead of minimizing inventory or transportation costs the objective is rather to minimize lead times for aid items in order to reduce suffer of disaster victims.
One of the challenges humanitarian operations are faced with are the so-called unsolicited donations. In-kind donations often do not meet the relief needs of an affected population, but are nevertheless pushed into local warehouses of the disaster areas.
During the Haiti earthquake for example, the number of flights to the Port-au-Prince airport increased from 13 up to during the first three days of response.
Thus, the airport was faced with serious bottlenecks and unsolicited donations occupied very limited warehouse capacity in the airport.
Over the past years the number of research contributions to humanitarian logistics has risen significantly.
However, methods of operations research have not yet widely been applied in this field. In this work a queuing model will be presented which illustrates the effects of unsolicited donations on the Haiti relief operation.
As both demand for, and public expectation of, EMS is escalating in the Western world, the provision of an efficient and effective service is a significant challenge for many nations.
A particular difficulty for planners is to allocate often limited resources whilst managing increasing demand for services, in a way to ensure high levels of geographical coverage and to improve key performance targets.
Our work considers a novel time series approach to forecasting the daily demand exerted upon WAST using the model-free technique of Singular Spectrum Analysis; and shows that in addition to being more flexible in approach, the predictions generated using this technique compare favourably to forecasts obtained from conventional methods.
We progress to use this technique to predict demand at a regional level, and use a range of approximation and numerical time-dependent priority queueing theory techniques to obtain staffing level recommendations.
Our research contributes to the ongoing study of time-dependent multi-server queues through developing the techniques to cope with a priority system where life-threatening emergencies are treated with precedence.
Ultimately we aim to develop a time-dependent and priority workforce capacity planning tool to optimise ambulance deployment strategies and generate rosters for crew members.
There are clear indications of worsen seawater quality during periods of red tides in the Sultanate of Oman. In this study a failure mode and effect analysis FMEA is conducted on the critical equipment in the Plant to identify the classes of failure modes and their effects on the RO Plant smooth operation.
The failure and maintenance history data for the period is evaluated and more emphasis is focused on plant failures during the periods of the Red tide incidents.
A stochastic point process model is considered for reliability analysis in this study. A standard statistical approach is used for reliability analysis by fitting the Weibull distribution to the data sets.
The results obtained are presented on a distribution overview. SCOR Abstracts Saturday The results presented here is to set a pace of further reliability modelling to schedule cost effective preventive maintenance actions on the RO Desalination Plant equipment.
This is accomplished by applying it to the analysis of accident reports from hazardous industries and by performing a safety risk study for an external organisation.
A mixed-modelling approach, combining system dynamics and methods used in the risk and reliability field e. The output will be a set of guidelines that can support risk analysts in their future risk studies.
This drive introduces a significant increase in the cost of hydrocarbon search and also presents unprecedented challenges. The challenges include safety, environment, flow assurance and equipment reliability.
Deepwater conditions inherently dictate the development of these fields by means of subsea production systems since traditional surface facilities such as steel-piled jacket might be either technically unfeasible or uneconomical due to the water depth.
Maintaining systems located in the ultra deep-water requires specialised and expensive vessels, which need to be equipped with robotic devices due to the water depths.
Any requirement to intervene or repair an installed subsea system is thus normally very expensive and may result in considerable economic production loss.
High equipment reliability is therefore required in order to safeguard the environment and personnel, and to make the exploitation of hydrocarbons with subsea technology economically feasible.
This paper investigates reliability assessment for subsea systems operated in the ultra-deepwater. With API 17N as a key focus, the paper looks at the control system for this type of developments and the reliability assessment.
Unlike traditional meta-heuristic techniques, a hyper-heuristic operates on a search space of heuristics rather than directly on the search space of solutions.
A single-point based search selection hyper-heuristic framework relies on two key components, a heuristic selection method and a move acceptance method.
Operating on a single candidate solution until some termination criteria is met, low-level heuristics are repeatedly selected and applied producing a new solution, then a decision is made as to whether to accept this new solution or not.
The Choice Function is an elegant heuristic selection method which scores heuristics based on a combination of three different measures and applies the heuristic with the highest rank.
Each of these measures are weighted appropriately to provide sufficient intensification and diversification of the heuristic search process.
Different methods in the literature have been proposed to manage these weightings, here we will describe a new method loosely based on reinforcement learning.
Using the HyFlex software benchmark framework developed to support CHeSC the first Cross-domain Heuristic Search Challenge we have tested and compared this new method for controlling such parameters to previous approaches.
Overall, there are two types of analyses that inform safety risk management of hazardous systems: safety risk assessment and postmortem accident analysis.
The review of the literature suggests that the methods currently used in these analyses need to be extended to account for more recent understandings of the impact of people who operate, maintain and control these systems.
Also, most of the methods are static, sequential and usually ignore feedback structures within which human decision-making operate.
Thus, they are not robust enough to capture the dynamic impact of human and organisational factors on the safety risk.
A system dynamics approach, which has been used to analyse feedback systems and their behaviour over time, provides a range of structures that can help to overcome these problems.
Constructing the magic square has been considered as a hard computational problem domain. There has been a growing interest in the development of such general methodologies which searches the space of heuristics rather than the space of the solutions.
This study presents a methodology to construct magic squares using a selection hyper-heuristic based on a random descent heuristic selection method.
The results show that the approach could be an effective methodology to construct magic squares. The results will be reported in more details at the conference.
In contrast to my previous work and other similar works, in this case a more complex inventory model with four stages is developed. Furthermore, the Erlang distribution allows the model to depict the replenishment process in a better way compared to other methods that have been utilized in the past.
This extended model combined with the two and three stage inventory systems leads to the construction of a framework for the inventory decision making process.
Specifically, three serial inventory systems with two, three and four stage respectively have been researched. The demand and the replenishment process are stochastic.
Orders follow S,s policy. The replenishment processes follow the Erlang distribution. The external demand is distributed by pure Poisson process which means that the amount that each customer asks is one unit.
Last but not least the last upstream node is always considered saturated. The supply networks are modeled as continuous Markov processes with discrete states.
The structures of the transition matrices of those systems are explored and computational algorithms are developed to generate them for different values of systems parameters.
Mat lab software is used for any computation. Various performance measures derive from steady state probabilities such as fill rate, average inventory in systems and cycle time.
Each node supplies only one downstream node and is replenished by only one upstream node. Each node, except for the most upstream, faces supply uncertainty and the last node retailer additionally faces external demand that follows the pure Poisson distribution.
Each node,except for the most upstream, which is saturated- follows a continuous review ordering policy. If the upstream has insufficient stock, then the orders are partially satisfied and the rest is lost.
To calculate these measures the supply chain is modeled as a continuous time Markov process with discrete states.
Our major task is to figure out how the performance measures are influenced when we expand our system, adding one node at a time.
A computational algorithm is developed to generate the transition matrices different values of system characteristics and by them the stationary probability distribution.
After determining the performance measures, we compare the numerical results in order to reveal the optimal ordering policy.
Allowing lateral transshipments in multilocation inventory networks permits lower levels of safety stock thereby cutting costs while maintaining or improving service levels.
Usually, such movements of stock are either carried out in a reactive manner responding to a stock-out in the system, or preventively to rebalance inventory levels.
Recent results show that using a hybrid version of these two approaches can yield further improvements. This lies in the fact that shipment costs in many cases consist of a higher fixed and a lower variable part.
The rebalancing of inventory levels can thus be achieved at an often negligible additional cost if a reactive transshipment is made.
We extend this idea and implement it in a multi-item setup. Our model describes a multi-location inventory network facing compound, non-homogeneous Poisson demand.
Instances of demand have an underlying discrete, multivariate distribution. We derive a quasi-myopic policy by applying a dynamic programming policy improvement step to a no-transshipment policy.
Transshipment costs are modelled with a knapsack-like structure to accommodate different types of items. We carry out an extensive simulation study to show the benefit of modelling multi-item transshipments against operating single item models in parallel.
Although the distances are discrete between two nodes, the travel times may not be discrete because the traffic has uncertain nature.
It is important to delivery order to customers in specified time window, so handling the uncertainty is more critical. Also the vehicles are capacitated so they must be loaded below their capacity.
Chance constrained model based random key represented genetic algorithm is developed and numerical experiments are solved to show effectiveness of the proposed algorithm.
The efficiency of the solver procedure is crucial for solving large-scale problems. The solution is obtained by an iterative procedure, where each iteration can be represented by a basis of the linear equation system.
During an iteration some vectors must be multiplied by the inverse of the actual basis. In order to speed up these operations, proper basis handling procedures must be applied.
Two methodologies exist in the state-of-the-art literature, the product form of the inverse PFI and LU factorization.
The majority of the LU methods is widely used, because iterations can be done without the need of re-factorization, and the PFI can serve about iteration without re-inversion in order to provide numerical stability.
In our work we revisited the PFI and implemented it in such a way that hundreds or sometimes even few thousands of iterations can be done 1 SCOR Abstracts Saturday without losing accuracy.
The novelty of our approach is in the processing of the non-triangular part of the basis, based on block triangularization algorithms. The resulting inverse of the modified algorithm performs way better than those found in the literature.
These results can shed new light on the usefulness of the PFI. Neiman prozessor violett an schlafe, beste hem erst..
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