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

Assessment of Quay and Yard Transshipment Operations Under Proximity Limitations in Multi-Terminal Container Ports

The assignment of storage locations and space has a considerable impact on the performance of container terminals. This holds especially in multi-terminal transshipment ports where the planning of inbound and outbound container flows needs to consider space limitations and travel distances for reallocations, causing both intra- and inter-terminal transports. Thus, in this work, we study the impact of closeness limitations on quay and yard areas when conducting transshipment operations at multi-terminal transshipment ports. In doing so, a mathematical formulation and several scenarios covering different distance policies for limiting the allocation of containers before vessel loading or unloading operations are assessed. At a tactical level, this paper provides insights on assignment decisions while assessing distance-based policies that can be incorporated in practice

A genetic algorithm for robust berth allocation and quay crane assignment

Scheduling problems usually obtain the optimal solutions assuming that the environment is deterministic. However, actually the environment is dynamic and uncertain. Thus, the initial data could change and the initial schedule obtained might be unfeasible. To overcome this issue, a proactive approach is presented for scheduling problems without any previous knowledge about the incidences that can occur. In this paper, we consider the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems where a typical objective is to minimize the service time. The robustness is introduced within this problem by means of buffer times that should be maximized to absorb possible incidences or breakdowns. Therefore, this problem becomes a multi-objective optimization problem with two opposite objectives: minimizing the total service time and maximizing the robustness or buffer time

Robust scheduling for Berth Allocation and Quay Crane Assignment Problem

[EN] Decision makers must face the dynamism and uncertainty of real-world environments when they need to solve the scheduling problems. Different incidences or breakdowns, for example, initial data could change or some resources could become unavailable, may eventually cause the infeasibility of the obtained schedule. To overcome this issue, a robust model and a proactive approach are presented for scheduling problems without any previous knowledge about incidences. This paper is based on proportionally distributing operational buffers among the tasks. In this paper, we consider the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems. The dynamism and uncertainty are managed by assessing the robustness of the schedules. The robustness is introduced by means of operational buffer times to absorb those unknown incidences or breakdowns. Therefore, this problem becomes a multiobjective combinatorial optimization problem that aims to minimize the total service time, to maximize the buffer times, and to minimize the standard deviation of the buffer times. To this end, a mathematical model and a new hybrid multiobjective metaheuristic is presented and compared with two well-known multiobjective genetic algorithms: NSGAII and SPEA2+.This work has been partially supported by by the Spanish Government under research project MINECO TIN2013-46511-C2-1-P, the project PIRSES-GA-2011-294931 (FP7-PEOPLE-2011-IRSES), and the predoctoral FPU fellowship (AP2010-4405).Rodríguez Molins, M.; Salido Gregorio, MA.; Barber Sanchís, F. (2014). Robust scheduling for Berth Allocation and Quay Crane Assignment Problem. Mathematical Problems in Engineering. 2014(1):1-17. https://doi.org/10.1155/2014/834927S11720141Imai, A., Chen, H. C., Nishimura, E., & Papadimitriou, S. (2008). The simultaneous berth and quay crane allocation problem. 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An Integrated Quay Crane Assignment and Scheduling Problem. Computers & Industrial Engineering, 73, 115-123. doi:10.1016/j.cie.2013.12.012Park, Y.-M., & Kim, K. H. (2003). A scheduling method for Berth and Quay cranes. OR Spectrum, 25(1), 1-23. doi:10.1007/s00291-002-0109-zZhang, C., Zheng, L., Zhang, Z., Shi, L., & Armstrong, A. J. (2010). The allocation of berths and quay cranes by using a sub-gradient optimization technique. Computers & Industrial Engineering, 58(1), 40-50. doi:10.1016/j.cie.2009.08.002Lambrechts, O., Demeulemeester, E., & Herroelen, W. (2007). Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. Journal of Scheduling, 11(2), 121-136. doi:10.1007/s10951-007-0021-0Hendriks, M., Laumanns, M., Lefeber, E., & Udding, J. T. (2010). Robust cyclic berth planning of container vessels. OR Spectrum, 32(3), 501-517. doi:10.1007/s00291-010-0198-zHan, X., Lu, Z., & Xi, L. (2010). 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The berth allocation problem at port terminals : a column generation framework

Le problème d'allocation de postes d'amarrage (PAPA) est l'un des principaux problèmes de décision aux terminaux portuaires qui a été largement étudié. Dans des recherches antérieures, le PAPA a été reformulé comme étant un problème de partitionnement généralisé (PPG) et résolu en utilisant un solveur standard. Les affectations (colonnes) ont été générées a priori de manière statique et fournies comme entrée au modèle %d'optimisation. Cette méthode est capable de fournir une solution optimale au problème pour des instances de tailles moyennes. Cependant, son inconvénient principal est l'explosion du nombre d'affectations avec l'augmentation de la taille du problème, qui fait en sorte que le solveur d'optimisation se trouve à court de mémoire. Dans ce mémoire, nous nous intéressons aux limites de la reformulation PPG. Nous présentons un cadre de génération de colonnes où les affectations sont générées de manière dynamique pour résoudre les grandes instances du PAPA. Nous proposons un algorithme de génération de colonnes qui peut être facilement adapté pour résoudre toutes les variantes du PAPA en se basant sur différents attributs spatiaux et temporels. Nous avons testé notre méthode sur un modèle d'allocation dans lequel les postes d'amarrage sont considérés discrets, l'arrivée des navires est dynamique et finalement les temps de manutention dépendent des postes d'amarrage où les bateaux vont être amarrés. Les résultats expérimentaux des tests sur un ensemble d'instances artificielles indiquent que la méthode proposée permet de fournir une solution optimale ou proche de l'optimalité même pour des problème de très grandes tailles en seulement quelques minutes.The berth allocation problem (BAP) is one of the key decision problems at port terminals and it has been widely studied. In previous research, the BAP has been formulated as a generalized set partitioning problem (GSPP) and solved using standard solver. The assignments (columns) were generated a priori in a static manner and provided as an input to the optimization model. The GSPP approach is able to solve to optimality relatively large size problems. However, a main drawback of this approach is the explosion in the number of feasible assignments of vessels with increase in problem size which leads in turn to the optimization solver to run out of memory. In this research, we address the limitation of the GSPP approach and present a column generation framework where assignments are generated dynamically to solve large problem instances of the berth allocation problem at port terminals. We propose a column generation based algorithm to address the problem that can be easily adapted to solve any variant of the BAP based on different spatial and temporal attributes. We test and validate the proposed approach on a discrete berth allocation model with dynamic vessel arrivals and berth dependent handling times. Computational experiments on a set of artificial instances indicate that the proposed methodology can solve even very large problem sizes to optimality or near optimality in computational time of only a few minutes

Development of the framework for a lean, energy efficient, and environmentally friendly port: umm qasr port as a Case Study

The research focus is to examine rigorously how the implementation of Lean within the Umm Qasr Port improves the operation processes and to explore the Lean impact on environment improvement and energy efficiency management. In this research, the ROPMEE model has been developed by the researcher to evaluate the service quality in the cargo delivery process in the Port of Umm Qasr as it covers all the functional and non-functional areas in the cargo delivery process compared to other quality dimensions. The findings confirm that the process quality dimension is the most influential factor in service quality in the Port of Umm Qasr. The reasons for the poor performance of current practices adopted by the port are the use of traditional ways of information flow and a decision-making process that requires more time and steps within the whole process. The lack of smooth process flow is a potential cause of bottlenecks within port operation that create serious problems not only for the customer but also for the port itself. In this research, a visual representation is created of how the current value stream map for different port processes has been established on the identification and elimination of non- value-added activity or “waste” involved in delivering services in Umm Qasr port for customers. A VSM tool was applied to visually map the cargo handling flow, ship entrance, ship maneuvering and cargo clearance to display the current and future states of processes in a way that highlights opportunities for improvement. Based on the defined and classified waste according to the seven deadly wastes of Lean, this research suggests a future value stream map for port processes. The impact of the identified wastes has been quantified in terms of cost, carbon dioxide emissions working time efficiency, and energy consumption cost. This research is the first attempt to develop a Lean port model for improving port processes, as there have been no previous studies aimed at providing a holistic framework for improving port performance, which can be used by other ports. Implementing the Lean approach requires a gradual shift in work culture by involving all port employees and customers in the continuous improvement process and changing the service delivery from a push to pull system

Managing vessel arrival uncertainty in container terminals: a machine learning approach

A container terminal is a complex system where a broad range of operations are carried out involving a wide array of resources that need to interact over a 24 hour operating cycle. Since the various activities are mutually related to each other, there is a need not only to maximise the efficiency of each one, but also to ensure proper coordination, hence to solve integrated decision-making problems. Several factors can affect the quality of the services provided and the overall efficiency. Vessel arrival uncertainty further complicates the task of the planners and, as a result, of the effectiveness of the planning itself, in particular at the operational level. Each arrival produces high peak loads for other terminal activities, as well as for the supporting arrival activities (pilotage, towage, etc.) and hinterland transportation (waiting, congestion etc.). Deviating arrivals only worsen this peak load. On a daily level, the actual time of arrival of the vessels often deviates from the scheduled time. Despite contractual obligations to notify the Estimated Time of Arrival (ETA) at least 24 hours before the arrival, ship operators often have to adapt and update the latest ETA due to unexpected circumstances. This aspect results in a last-minute change of plans in terminal operations resulting in higher costs. In fact, the ability to predict the actual time of a vessel’s arrival in a port 24 hours in advance is fundamental for the related planning activities for which the decision-making processes need to be constantly adapted and updated. Moreover, disruptions in container flows and operations caused by vessel arrival uncertainty can have cascade effects within the overall supply chain and network within which the port is part. Although vessel arrival uncertainty in ports is a well-known problem for the scientific community, the literature review highlights that in the maritime sector the specific instruments for dealing with this problem are extremely limited. The absence of a reference model that specifies the relationship between vessel arrival uncertainty and the involved variables resulted in the application of a specific machine learning approach within the Knowledge Discovery in Database process. This V approach, that abandons all prior assumptions about data distribution shape, is based on the self-learning concept according to which the relation between an outcome variable Y and the set of predictors X is directly identified from the historical collected data. The approach has been validated thanks to two different case studies: the container terminal of Cagliari, located in the Mediterranean basin, and one of the main container terminals of Antwerp, located at the North Sea. Depending on the framework and planning purposes several estimates can provide useful information on vessel arrivals. Sometimes, it can be useful for planners to infer a quantitative estimate of the delay/advance in minutes, sometimes it may be useful to have a qualitative estimate, even only knowing whether or not an incoming vessel is likely to arrive before or after the scheduled ETA. For this reason a two-step instrument is proposed is made up of two different modules. The fitted algorithmic models used to obtain predictions are Logistic Regression, CART (Classification and Regression Trees) and Random Forest. All the proposed models are able to learn from experience, following the well-known Data Mining paradigm “learning from data”. From a practical point of view, the probability, associated to the continuous estimation, of specifically identifying the work-shift of the incoming vessel is calculated. In all predictions Random Forest algorithms still show the best performance. This aspect can help planners, in the daily strategy decision making process, in order to improve the use of the human, mechanical and spatial resources required for handling operations. This could maximise terminal efficiency and minimise terminal costs, hence improving terminal competitiveness. Moreover, the interpretation of the discovered knowledge, made it possible to evaluate the most discriminating variables of the analysis, even thanks to graphical visualisation of the Importance-plots

MODELLING CONTAINER LOGISTICS PROCESSES IN CONTAINER TERMINALS: A CASE STUDY IN ALEXANDRIA

This study aims to optimize the logistics processes of container terminals. Potentially powerful pipe-flow models of container terminal logistics processes have been neglected to date and modelling of terminals is rare. Because research which adopts a pipe flow and dynamic operational perspective is rare, a case application in Alexandria, Egypt collated empirical container and information flows using interviews and company records to describe its logistics processes and model container and information flows. The methodology used includes qualitative and quantitative methods and a descriptive methodology proceeds sequentially. Primary and secondary data were presented as a pipe flow model to show interrelations between the company’s resources and to identify bottlenecks. Simulation modelling used Simul8 software. Operational level modelling of both import and export flows simulated the actual inbound and outbound flows of containers from entry to exit. The import logistics process includes activities such as unloading vessels by quay cranes, moving containers by tractors to yard cranes to go for storage where customs procedures take place before exiting the terminal by customer’s truck. The export logistics process includes the activities associated with customers’ trucks, lifters, storage yards, tractors and quay cranes. The model takes into account the uncertainties in each activity. This study focuses on operational aspects rather than cost issues, and considers container flows rather than vessel flows. Although the simulated model was not generalized, implementation elsewhere is possible. Following successful validation of a base simulation model which reproduces the case company’s historical scenario, scenario testing empowered the case company to pro-actively design and test the impact of operational changes on the entire logistics process. The study evaluates a typical container terminal logistics system including both import and export containers in the presence of multiple uncertainties in terminal operations (e.g. quay crane operations, tractor operations, yard crane operations). Sensitivity testing and scenario analysis can empower terminal managers to make decisions to improve performance, and to guide terminal planners, managers, and operators in testing future investment scenarios before implementation.Arab Academy for Science, Technology and Maritime Transpor