Meta-heuristic based Construction Supply Chain Modelling and Optimization

Driven by the severe competition within the construction industry, the necessity of improving and optimizing the performance of construction supply chain has been aroused. This thesis proposes three problems with regard to the construction supply chain optimization from three perspectives, namely, deterministic single objective optimization, stochastic optimization and multi-objective optimization respectively. Mathematical models for each problem are constructed accordingly and meta-heuristic algorithms are developed and applied for resolving these three problems

Optimization Model that Minimizes the Penalty Caused by Delayed Delivery of Construction Projects

Purpose: This work aims to minimize the penalty generated by delays in delivering construction projects operated by a single machine.   Theoretical framework: The fundamental issues to develop this work are optimization and its application to delays in construction projects operated in their initial stage by a single machine.   Design/Methodology/Approach: We presented a construction case study, developed an optimization model, implemented a computational optimization tool and obtained the optimal sequence to perform the tasks.     Findings: The numerical results demonstrated the model’s usefulness in minimizing the penalty generated by delays in the delivery of projects.   Research, practical & social implications: The benefit of this study is to help managers or decision-makers schedule their construction projects with limited resources and deadlines per activity to minimize penalty costs of delay.   Originality/Value: Considering that the construction sector generates the largest labor force in Peru, the study has an important social value by providing a tool to improve operations and incentivize construction companies to continue operating. In addition, it provides a substantial basis for future work by applying optimization in a specific area of civil engineering. To our knowledge, no researcher or company in Peru has yet addressed this study

A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio

Reactive scheduling to treat disruptive events in the MRCPSP

Esta tesis se centra en diseñar y desarrollar una metodología para abordar el MRCPSP con diversas funciones objetivo y diferentes tipos de interrupciones. En esta tesis se exploran el MRCPSP con dos funciones objetivo, a saber: (1) minimizar la duración del proyecto y (2) maximizar el valor presente neto del proyecto. Luego, se tiene en cuenta dos tipos diferentes de interrupciones, (a) interrupción de duración, e (b) interrupción de recurso renovable. Para resolver el MRCPSP, en esta tesis se proponen tres estrategias metaheurísticas: (1) algoritmo memético para minimizar la duración del proyecto, (2) algoritmo adaptativo de forrajeo bacteriano para maximizar el valor presente neto del proyecto y (3) algoritmo de optimización multiobjetivo de forrajeo bacteriano (MBFO) para resolver el MRCPSP con eventos de interrupción. Para juzgar el rendimiento del algoritmo memético y de forrajeo bacteriano propuestos, se ha llevado a cabo un extenso análisis basado en diseño factorial y diseño Taguchi para controlar y optimizar los parámetros del algoritmo. Además se han puesto a prueba resolviendo las instancias de los conjuntos más importantes en la literatura: PSPLIB (10,12,14,16,18,20 y 30 actividades) y MMLIB (50 y 100 actividades). También se ha demostrado la superioridad de los algoritmos metaheurísticos propuestos sobre otros enfoques heurísticos y metaheurísticos del estado del arte. A partir de los estudios experimentales se ha ajustado la MBFO, utilizando un caso de estudio.DoctoradoDoctor en Ingeniería Industria

Data-driven Warehouse Management in Global Supply Chains

Warehouse management has emerged as a determinant for success of global supply chain management. This thesis focuses on how to solve warehouse challenges in global supply chain management (SCM) that is characterized by large volume uncertainty, great responsiveness needs and complex order-fulfilment collaboration with other functionalities. We employ data analytic methods to exploit the rich data information obtained from detailed registration of daily warehouse operations to address these challenges. By providing actual application examples in real-world situations we showcase the potency of such data-driven warehouse management. In this dissertation, data-driven warehouse management is presented by four-steps in the time horizon of warehouse operations: Long-term opportunities (for the coming years) are examined by predictive analytics for expanding cross-border e-commerce in the European Union. Mid-term demand for spare parts during the end-of-life phase (of several months) are forecasted by means of data-driven modelling for installed base. Short-term operational opportunity (weekly or daily) are presented by employing detailed productivity data to sustain effective operation of variable warehouse resources. Real-time (hourly or shorter) data applications are introduced for job priority allocation to improve daily responsiveness in warehouse order fulfilment. All these data analytic methods can be incorporated in warehouse management systems where practitioners can tune the specific strategies according to their warehouse constraints, including location cost, labour cost, time criticality, and freight company flexibility. In this way, data analytics at the warehouse level offers great opportunities for managing increasing uncertainties and performance requirements in global SCM