37,845 research outputs found
Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals
Tesis por compendioDespite the continuous evolution in computers and information technology, real-world
combinatorial optimization problems are NP-problems, in particular in the domain of
planning and scheduling. Thus, although exact techniques from the Operations Research
(OR) field, such as Linear Programming, could be applied to solve optimization problems,
they are difficult to apply in real-world scenarios since they usually require too much computational
time, i.e: an optimized solution is required at an affordable computational time.
Furthermore, decision makers often face different and typically opposing goals, then resulting
multi-objective optimization problems. Therefore, approximate techniques from
the Artificial Intelligence (AI) field are commonly used to solve the real world problems.
The AI techniques provide richer and more flexible representations of real-world (Gomes
2000), and they are widely used to solve these type of problems. AI heuristic techniques
do not guarantee the optimal solution, but they provide near-optimal solutions in a reasonable
time. These techniques are divided into two broad classes of algorithms: constructive
and local search methods (Aarts and Lenstra 2003). They can guide their search processes
by means of heuristics or metaheuristics depending on how they escape from local optima
(Blum and Roli 2003). Regarding multi-objective optimization problems, the use of AI
techniques becomes paramount due to their complexity (Coello Coello 2006).
Nowadays, the point of view for planning and scheduling tasks has changed. Due to
the fact that real world is uncertain, imprecise and non-deterministic, there might be unknown
information, breakdowns, incidences or changes, which become the initial plans
or schedules invalid. Thus, there is a new trend to cope these aspects in the optimization
techniques, and to seek robust solutions (schedules) (Lambrechts, Demeulemeester, and
Herroelen 2008).
In this way, these optimization problems become harder since a new objective function
(robustness measure) must be taken into account during the solution search. Therefore,
the robustness concept is being studied and a general robustness measure has been developed
for any scheduling problem (such as Job Shop Problem, Open Shop Problem,
Railway Scheduling or Vehicle Routing Problem). To this end, in this thesis, some techniques
have been developed to improve the search of optimized and robust solutions in
planning and scheduling problems. These techniques offer assistance to decision makers
to help in planning and scheduling tasks, determine the consequences of changes, provide
support in the resolution of incidents, provide alternative plans, etc.
As a case study to evaluate the behaviour of the techniques developed, this thesis focuses
on problems related to container terminals. Container terminals generally serve
as a transshipment zone between ships and land vehicles (trains or trucks). In (Henesey
2006a), it is shown how this transshipment market has grown rapidly. Container terminals
are open systems with three distinguishable areas: the berth area, the storage yard,
and the terminal receipt and delivery gate area. Each one presents different planning and
scheduling problems to be optimized (Stahlbock and Voß 2008). For example, berth allocation,
quay crane assignment, stowage planning, and quay crane scheduling must be
managed in the berthing area; the container stacking problem, yard crane scheduling, and
horizontal transport operations must be carried out in the yard area; and the hinterland
operations must be solved in the landside area.
Furthermore, dynamism is also present in container terminals. The tasks of the container
terminals take place in an environment susceptible of breakdowns or incidences. For
instance, a Quay Crane engine stopped working and needs to be revised, delaying this
task one or two hours. Thereby, the robustness concept can be included in the scheduling
techniques to take into consideration some incidences and return a set of robust schedules.
In this thesis, we have developed a new domain-dependent planner to obtain more effi-
cient solutions in the generic problem of reshuffles of containers. Planning heuristics and
optimization criteria developed have been evaluated on realistic problems and they are
applicable to the general problem of reshuffling in blocks world scenarios.
Additionally, we have developed a scheduling model, using constructive metaheuristic
techniques on a complex problem that combines sequences of scenarios with different
types of resources (Berth Allocation, Quay Crane Assignment, and Container Stacking
problems). These problems are usually solved separately and their integration allows
more optimized solutions.
Moreover, in order to address the impact and changes that arise in dynamic real-world
environments, a robustness model has been developed for scheduling tasks. This model
has been applied to metaheuristic schemes, which are based on genetic algorithms. The
extension of such schemes, incorporating the robustness model developed, allows us to
evaluate and obtain more robust solutions. This approach, combined with the classical
optimality criterion in scheduling problems, allows us to obtain, in an efficient in way,
optimized solution able to withstand a greater degree of incidents that occur in dynamic
scenarios. Thus, a proactive approach is applied to the problem that arises with the presence
of incidences and changes that occur in typical scheduling problems of a dynamic real world.Rodríguez Molins, M. (2015). Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48545TESISCompendi
Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems
We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multiagent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level
A solution approach for dynamic vehicle and crew scheduling
In this paper, we discuss the dynamic vehicle and crew schedulingproblem and we propose a solution approach consisting of solving asequence of optimization problems. Furthermore, we explain why itis useful to consider such a dynamic approach and compare it witha static one. Moreover, we perform a sensitivity analysis on ourmain assumption that the travel times of the trips are knownexactly a certain amount of time before actual operation.We provide extensive computational results on some real-world datainstances of a large public transport company in the Netherlands.Due to the complexity of the vehicle and crew scheduling problem,we solve only small and medium-sized instances with such a dynamicapproach. We show that the results are good in the case of asingle depot. However, in the multiple-depot case, the dynamicapproach does not perform so well. We investigate why this is thecase and conclude that the fact that the instance has to be splitin several smaller ones, has a negative effect on the performance.transportation;vehicle and crew scheduling;large-scale optimization;dynamic planning
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems
This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version
A hybrid CFGTSA based approach for scheduling problem: a case study of an automobile industry
In the global competitive world swift, reliable and cost effective production subject to uncertain situations, through an appropriate management of the available resources, has turned out to be the necessity for surviving in the market. This inspired the development of the more efficient and robust methods to counteract the existing complexities prevailing in the market. The present paper proposes a hybrid CFGTSA algorithm inheriting the salient features of GA, TS, SA, and chaotic theory to solve the complex scheduling problems commonly faced by most of the manufacturing industries. The proposed CFGTSA algorithm has been tested on a scheduling problem of an automobile industry, and its efficacy has been shown by comparing the results with GA, SA, TS, GTS, and hybrid TSA algorithms
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