410 research outputs found
Synthesising robust schedules for minimum disruption repair using linear programming
An off-line scheduling algorithm considers resource, precedence, and synchronisation requirements of a task graph, and generates a schedule guaranteeing its timing requirements. This schedule must, however, be executed in a dynamic and unpredictable operating environment where resources may fail and tasks may execute longer than expected. To accommodate such execution uncertainties, this paper addresses the synthesis of robust task schedules using a slack-based approach and proposes a solution using integer linear programming (ILP). Earlier we formulated a time slot based ILP model whose solutions maximise the temporal flexibility of the overall task schedule. In this paper, we propose an improved, interval based model, compare it to the former, and evaluate both on a set of random scenarios using two public domain ILP solvers and a proprietary SAT/ILP mixed solver
Systematic approaches for synthesis, design and operation of biomass-based energy systems
A biomass-based energy system (BES) is a utility facility which produces cooling, heat and power simultaneously from biomass. By having a BES installed on-site, industrial processes can reduce energy costs by locally producing heat, cooling and power for process and work place requirements. However, several barriers have hindered development of BESs in the energy industry. These barriers include doubts over its operational uncertainties (e.g., seasonal biomass supply, equipment reliability, etc.), the misconception that generating energy from biomass is only a marginal business and the lack of successful cooperative partnerships within the industry. According to literature, such barriers are due to the lack of frameworks that address design aspects and demonstrate the economic viability of a BES.
This thesis presents systematic approaches and frameworks to design a BES. These approaches emphasise on integrating synthesis, design and operation decision making for a BES during its preliminary design phase. Firstly, a systematic approach is presented to synthesise a BES considering seasonal variations in biomass supply and energy demand. In this approach, a multi-period optimisation model is formulated to perform technology and design capacity selection by considering seasonal variations in biomass supply and energy demand profiles. This approach is then extended to systematically allocate equipment redundancy within the BES in order to maintain a reliable supply of energy. In this approach, k-out-of-m system modelling and the principles of chance-constrained programming are integrated in a multi-period optimisation model to simultaneously screen technologies based on their respective equipment reliability, capital and operating costs. The model also determines equipment capacities, along with the total number of operating (and stand-by) equipment based on various anticipated scenarios in a computationally efficient manner. Following this, a systematic approach is developed to simultaneously screen, size and allocate redundancy within a BES considering its operational strategies (e.g., following electrical load or following thermal load).
Subsequently, a systematic framework on Design Operability Analysis (DOA) is developed to analyse BES designs in instances of failure. This framework provides a stepwise procedure to evaluate proposed BES designs under scenarios of disruption and analyse their true feasible operating range. Knowledge of the feasible operating range enables designers to determine and validate if a BES design is capable of meeting its intended operations. Following this, a systematic Design Retrofit Analysis (DRA) framework is presented to debottleneck and retrofit existing BES designs in cases where energy demands are expected to vary in the future. The presented framework re-evaluates an existing BES design under disruption scenarios and determines its real-time feasible operating range. The real-time feasible operating range will allow designers to determine whether debottlenecking is required. If debottlenecking is required, the framework provides systematic debottlenecking and retrofit guidelines for BES designs. The design of a BES is then extended further to consider its interaction in an eco-industrial park (EIP). Since heat, cooling and power are essentially required in most industrial processes, a BES can be more economically attractive if synthesised for an EIP. As such, an optimisation-based negotiation framework is developed to analyse the potential cost savings allocation between participating plants in an EIP coalition. This framework combines the principles of rational allocation of benefits with the consideration of stability and robustness of an EIP coalition to changes in cost assumptions. Lastly, possible extensions and future opportunities for this research work are highlighted at the end of this thesis
Operational Research: Methods and Applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
Stochastic Scheduling of Wind-Integrated Power Systems
The cost of balancing supply and demand will increase as power systems are decarbonised,
because the requirement for operating reserve will increase with the wind
penetration, while the flexible fossil-fuel generators, which have been the traditional
providers of reserve, will be displaced. While these costs can be mitigated through increased
interconnection, energy storage, and demand-side market participation, a fundamental
review of system operational policy is also needed to ensure that the available
reserves are scheduled optimally. Stochastic Unit Commitment can find the commitment
and dispatch decisions that minimise the expected system costs, including the potential
costs of unserved energy, given the short-term uncertainties of wind and other variables.
It therefore has the potential to provide the most efficient possible paradigm for the operation
of wind-integrated systems. Because the systemâs ability to respond to wind fluctuations
is constrained by intertemporal limitations of the other components, time domain
simulations are needed to assess the performance of different operational strategies or
generator fleet characteristics. However, Stochastic Unit Commitment has demanding
computational requirements that can render it impractical for long-term simulations of a
large power system.
This thesis develops a new tool for simulating the operation of large, wind-integrated
power systems using stochastic scheduling, with the emphasis on computational efficiency.
Embedded within it are new models for characterising time series of aggregated
wind output and wind forecast errors; these models are integrated with a Stochastic Unit
Commitment algorithm within a Monte Carlo framework. We explore simplifications
that can mitigate the computational burden without unduly compromising the quality
of the analysis. Simulations with the tool show that fully stochastic scheduling can reduce
operating costs by around 4% relative to traditional deterministic approaches, in a
system with a 50% wind penetration
Systematic approaches for synthesis, design and operation of biomass-based energy systems
A biomass-based energy system (BES) is a utility facility which produces cooling, heat and power simultaneously from biomass. By having a BES installed on-site, industrial processes can reduce energy costs by locally producing heat, cooling and power for process and work place requirements. However, several barriers have hindered development of BESs in the energy industry. These barriers include doubts over its operational uncertainties (e.g., seasonal biomass supply, equipment reliability, etc.), the misconception that generating energy from biomass is only a marginal business and the lack of successful cooperative partnerships within the industry. According to literature, such barriers are due to the lack of frameworks that address design aspects and demonstrate the economic viability of a BES.
This thesis presents systematic approaches and frameworks to design a BES. These approaches emphasise on integrating synthesis, design and operation decision making for a BES during its preliminary design phase. Firstly, a systematic approach is presented to synthesise a BES considering seasonal variations in biomass supply and energy demand. In this approach, a multi-period optimisation model is formulated to perform technology and design capacity selection by considering seasonal variations in biomass supply and energy demand profiles. This approach is then extended to systematically allocate equipment redundancy within the BES in order to maintain a reliable supply of energy. In this approach, k-out-of-m system modelling and the principles of chance-constrained programming are integrated in a multi-period optimisation model to simultaneously screen technologies based on their respective equipment reliability, capital and operating costs. The model also determines equipment capacities, along with the total number of operating (and stand-by) equipment based on various anticipated scenarios in a computationally efficient manner. Following this, a systematic approach is developed to simultaneously screen, size and allocate redundancy within a BES considering its operational strategies (e.g., following electrical load or following thermal load).
Subsequently, a systematic framework on Design Operability Analysis (DOA) is developed to analyse BES designs in instances of failure. This framework provides a stepwise procedure to evaluate proposed BES designs under scenarios of disruption and analyse their true feasible operating range. Knowledge of the feasible operating range enables designers to determine and validate if a BES design is capable of meeting its intended operations. Following this, a systematic Design Retrofit Analysis (DRA) framework is presented to debottleneck and retrofit existing BES designs in cases where energy demands are expected to vary in the future. The presented framework re-evaluates an existing BES design under disruption scenarios and determines its real-time feasible operating range. The real-time feasible operating range will allow designers to determine whether debottlenecking is required. If debottlenecking is required, the framework provides systematic debottlenecking and retrofit guidelines for BES designs. The design of a BES is then extended further to consider its interaction in an eco-industrial park (EIP). Since heat, cooling and power are essentially required in most industrial processes, a BES can be more economically attractive if synthesised for an EIP. As such, an optimisation-based negotiation framework is developed to analyse the potential cost savings allocation between participating plants in an EIP coalition. This framework combines the principles of rational allocation of benefits with the consideration of stability and robustness of an EIP coalition to changes in cost assumptions. Lastly, possible extensions and future opportunities for this research work are highlighted at the end of this thesis
Bus route design and frequency setting for public transit systems
Thesis (PhD)--Stellenbosch University, 2022.ENGLISH ABSTRACT: The availability of effective public transport systems is increasingly becoming an urgent problem
in urban areas worldwide due to the traffic congestion caused by private vehicles. The careful
design of such a transport system is important because, if well designed, such a system can
increase the comfort of commuters and ensure that they arrive at their destinations timeously.
A well-designed public transport system can also result in considerable cost savings for the
operator of the system.
The problem considered in this dissertation is that of designing three mathematical models
for aiding a bus company in deciding upon efficient bus transit routes (facilitated by the first
two models) and setting appropriate frequencies for buses along these routes (facilitated by the
third model). The design criteria embedded in the first model (for designing bus routes) are the
simultaneous pursuit of minimising the expected average passenger travel time and minimising
the system operatorâs cost (measuring the latter as the sum total of all route lengths in the
system). The first model takes as input an origin-destination demand matrix for a specified set
of bus stops, along with the corresponding road network structure, and returns as output a set of
bus route solutions. The decision maker can then select one of these route sets subjectively, based
on the desired trade-off achieved between the aforementioned transit system design criteria. This
bi-objective minimisation problem is solved approximately in three distinct stages â a solution
initialisation stage, an intermediate analysis stage and an iterative metaheuristic search stage
during which high-quality trade-off solutions are sought. A novel procedure is introduced for the
solution initialisation stage aimed at effectively generating high-quality initial feasible solutions.
Two metaheuristics are adopted for the solution implementation, namely a dominance-based
multi-objective simulated annealing algorithm and an improved non-dominated sorting genetic
algorithm.
The second model is a novel approach towards establishing high-quality bus routes resembling
a reference set of bus routes (typically the currently operational bus routes) to varying degrees,
providing the decision maker with bus route design alternatives that may be implemented incre mentally in order to limit the disruption experienced by passengers in the bus transit network.
The objectives pursued in this model are the simultaneous minimisation of the expected aver age passenger travel time and the minimisation of a reference-route-to-design-route similarity
measure. The second model takes the same input as the first model above, with the addition of
a reference route set with which to compare alternative design routes in terms of similarity, and
provides as output a set of trade-off solutions according to this modelâs design criteria. The same
three-stage approximate solution methodology described above is adopted for this model, and
the same two metaheuristic implementations are utilised to solve instances of this new model.
In the third model, high-quality bus frequencies are sought for each bus route in pursuit of min imising the expected average travel time for passengers (including waiting time, transfer time
and travel time) and simultaneously minimising the total number of buses required by an operator to maintain the specified frequencies. The third model takes as input all the data required by
the first model, along with a route set for which frequencies should be set, and returns as output
a set of bus frequencies at which buses should operate along the various routes, based on a de sired trade-off between the aforementioned two design criteria. The solution approach adopted
for this bi-objective minimisation problem again conforms to the three aforementioned distinct
stages, with the exception that only a non-dominated sorting genetic algorithm is designed for
solving it.
The first and third models are finally applied to a special case study involving real data in order
to showcase the practical applicability of the modelling approach.AFRIKAANSE OPSOMMING: Die beskikbaarheid van doeltreffende openbare vervoerstelsels word wËereldwyd toenemend ân
dringende probleem in stedelike gebiede as gevolg van die verkeersopeenhopings wat deur private
voertuie veroorsaak word. Die noukeurige ontwerp van so ân vervoerstelsel is belangrik, want
as dit goed ontwerp is, kan so ân stelsel die gemak van pendelaars verhoog en verseker dat
hul betyds by hul bestemmings aankom. ân Goed-ontwerpte openbare vervoerstelsel kan ook
aansienlike kostebesparings vir die stelseloperateur tot gevolg hËe.
Die probleem wat in hierdie proefskrif oorweeg word, is die ontwerp van drie wiskundige modelle
om ân busonderneming daartoe in staat te stel om besluite oor doeltreffende busvervoerroetes
(die eerste twee modelle) en die geskikte frekwensies vir busse langs hierdie roetes (die derde
model) te neem. Die ontwerpkriteria in die eerste model (vir die ontwerp van busroetes) is
die gelyktydige strewe daarna om die verwagte gemiddelde reistyd van passasiers te minimeer
en die koste van die stelseloperateur te minimeer (laasgenoemde gemeet as die somtotaal van
alle roetelengtes in die stelsel). Die eerste model neem as toevoer ân oorsprong-bestemming aan vraagmatriks vir ân spesifieke stel bushaltes, tesame met die ooreenstemmende padnetwerkstruk tuur, en lewer as afvoer ân versameling busroetestelle. Die besluitnemer kan dan een van hierdie
roetestelle subjektief kies, gebaseer op die gewenste afruiling tussen die bogenoemde ontwerpkri teria. Hierdie twee-doelige minimeringsprobleem word in drie verskillende fases benaderd opgelos
â ân oplossingsinisialiseringsfase, ân intermediËere analise-fase en ân iteratiewe metaheuristiese
soekfase waartydens afruilingssoplossings van hoše gehalte gesoek word. ân Nuwe prosedure word
vir die oplossingsinisialiseringsfase daargestel wat daarop gemik is om aanvanklike haalbare
oplossings van hoše gehalte op ân doeltreffende wyse te genereer. Twee meteheuristieke word
vir die oplossing van die model gebruik, naamlik ân dominansie-gebaseerde meer-doelige ge simuleerde temeperingsalgoritme en ân verbeterde nie-gedomineerde sorteer-genetiese algoritme.
Die tweede model is ân nuwe benadering om busroetes van hoše gehalte te vestig wat in verskil lende mates ooreenkomste met ân verwysingstel busroetes (tipies die huidige stel operasionele
roetes) toon, en bied die besluitnemer alternatiewe vir busroetes wat geleidelik gešımplementeer
kan word om die ontwrigting van passasiers in die busvervoernetwerk te beperk. Die doele wat in
hierdie model nagestreef word, is die gelyktydige minimering van die verwagte gemiddelde passas ier se reistyd en die minimering van ân verwysingsroete-na-ontwerp-roete ooreenkomsmaatstaf.
Die tweede model neem dieselfde toevoere as die eerste model hierbo, met die byvoeging van ân
verwysingsroete waarmee alternatiewe ontwerproetestelle in terme van ooreenkoms vergelyk kan
word, en bied as afvoer ân stel afruilingsoplossings volgens die model se ontwerpkriteria. Die selfde drie-fase benaderde oplos-singsmetode hierbo beskryf, word op hierdie model toegepas, en
dieselfde twee metaheuristiese implementerings word gebruik om gevalle van hierdie nuwe model
op te los.
In die derde model word busfrekwensies van hoše gehalte vir elke busroete gesoek om die verwagte
gemiddelde reistyd van passasiers (insluitend wagtyd, oorklimtyd en werklike reistyd) te minimeer en terselfdertyd die totale aantal busse wat ân operateur benodig, te minimeer terwyl die
gespesifiseerde frekwensies gehandhaaf word. Die derde model neem dieselfde toevoerdata as die
eerste model, tesame met ân roete waarvoor frekwensies vasgestel moet word, en lewer as afvoer
ân stel busfrekwensies waarteen busse langs die verskillende roetes ontplooi moet word, gebaseer
op ân gewenste afruiling tussen die bogenoemde twee ontwerpkriteria. Die oplossingsbenadering
wat op hierdie tweedoelige minimeringsprobleem toegepas word, volg weer die bogenoemde drie
fases, met die uitsondering dat slegs ân nie-gedomineerde sorteer-genetiese algoritme ontwerp
word om dit op te los.
Die eerste en derde modelle word uiteindelik op ân spesiale gevallestudie toegepas wat op werklike
data gebaseer is om sodoende die praktiese toepaslikheid van die modelleringsbenadering te
illustreer.Doctora
Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold:
Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction
STRATEGIC PLANNING OF CIRCULAR SUPPLY CHAINS WITH MULTIPLE DOWNGRADED MARKET LEVELS: A METHODOLOGICAL PROPOSAL
Recent legislation has recognized the importance of adopting Circular Economy (CE) principles in supply chain (SC) restructuring. The primary objective is to create circular supply chains (CSCs) that effectively reintegrate end-of-life (EOL) products into production networks through processes such as reusing, remanufacturing, and recycling. This paradigm shift toward circularity aims to enhance resource efficiency, extend product lifecycle, and minimise waste, thereby aligning firms with sustainable practices while providing them with a competitive advantage.
In line with the goals of the CE, this study focuses on the design and optimisation of strategic decisions within a circular supply chain (CSC). To achieve this aim, a bi-objective mixed-integer linear programming (MILP) model is developed. This model represents a significant contribution as it offers a compact and generalized formulation for dealing with CSC design problems.
The proposed MILP model encompasses several key decision variables and considerations. It determines the optimal number of downgraded market levels to be activated, the location of forward and treatment facilities as well as the optimal product flow within the CSC. Furthermore, the model takes into account the cannibalisation effects associated with the demand for both new and recovered products, ensuring a comprehensive analysis of the system dynamics.
To solve the complex mathematical model, the augmented epsilon-constraint (AUGMECON2) method is employed. The utilisation of this method enables decision-makers to obtain practical solutions within reasonable time frames. The computational results obtained from applying the MILP model illustrate its encouraging potential and effectiveness in dealing with strategic decision-making problems within CSCs
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