Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem

[EN] This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm(GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.This paper has been partially supported by the Spanish Research Projects TIN2013-46511-C2-1-P and TIN2016-80856-R.Morillo-Torres, D.; Barber, F.; Salido, MA. (2017). Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem. Mathematical Problems in Engineering. 2017:1-15. https://doi.org/10.1155/2017/4627856S1152017Mouzon, G., Yildirim, M. B., & Twomey, J. (2007). Operational methods for minimization of energy consumption of manufacturing equipment. International Journal of Production Research, 45(18-19), 4247-4271. doi:10.1080/00207540701450013Hartmann, S., & Sprecher, A. (1996). A note on «hierarchical models for multi-project planning and scheduling». European Journal of Operational Research, 94(2), 377-383. doi:10.1016/0377-2217(95)00158-1Christofides, N., Alvarez-Valdes, R., & Tamarit, J. M. (1987). Project scheduling with resource constraints: A branch and bound approach. European Journal of Operational Research, 29(3), 262-273. doi:10.1016/0377-2217(87)90240-2Zhu, G., Bard, J. F., & Yu, G. (2006). A Branch-and-Cut Procedure for the Multimode Resource-Constrained Project-Scheduling Problem. INFORMS Journal on Computing, 18(3), 377-390. doi:10.1287/ijoc.1040.0121Kolisch, R., & Hartmann, S. (1999). Heuristic Algorithms for the Resource-Constrained Project Scheduling Problem: Classification and Computational Analysis. International Series in Operations Research & Management Science, 147-178. doi:10.1007/978-1-4615-5533-9_7Józefowska, J., Mika, M., Różycki, R., Waligóra, G., & Węglarz, J. (2001). Annals of Operations Research, 102(1/4), 137-155. doi:10.1023/a:1010954031930Bouleimen, K., & Lecocq, H. (2003). A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. European Journal of Operational Research, 149(2), 268-281. doi:10.1016/s0377-2217(02)00761-0Alcaraz, J., Maroto, C., & Ruiz, R. (2003). Solving the Multi-Mode Resource-Constrained Project Scheduling Problem with genetic algorithms. Journal of the Operational Research Society, 54(6), 614-626. doi:10.1057/palgrave.jors.2601563Zhang, H., Tam, C. M., & Li, H. (2006). Multimode Project Scheduling Based on Particle Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 21(2), 93-103. doi:10.1111/j.1467-8667.2005.00420.xJarboui, B., Damak, N., Siarry, P., & Rebai, A. (2008). A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems. Applied Mathematics and Computation, 195(1), 299-308. doi:10.1016/j.amc.2007.04.096Li, H., & Zhang, H. (2013). Ant colony optimization-based multi-mode scheduling under renewable and nonrenewable resource constraints. Automation in Construction, 35, 431-438. doi:10.1016/j.autcon.2013.05.030Lova, A., Tormos, P., Cervantes, M., & Barber, F. (2009). An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes. International Journal of Production Economics, 117(2), 302-316. doi:10.1016/j.ijpe.2008.11.002Peteghem, V. V., & Vanhoucke, M. (2010). A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. European Journal of Operational Research, 201(2), 409-418. doi:10.1016/j.ejor.2009.03.034Węglarz, J., Józefowska, J., Mika, M., & Waligóra, G. (2011). Project scheduling with finite or infinite number of activity processing modes – A survey. European Journal of Operational Research, 208(3), 177-205. doi:10.1016/j.ejor.2010.03.037Kolisch, R., & Hartmann, S. (2006). Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research, 174(1), 23-37. doi:10.1016/j.ejor.2005.01.065Debels, D., De Reyck, B., Leus, R., & Vanhoucke, M. (2006). A hybrid scatter search/electromagnetism meta-heuristic for project scheduling. European Journal of Operational Research, 169(2), 638-653. doi:10.1016/j.ejor.2004.08.020Paraskevopoulos, D. C., Tarantilis, C. D., & Ioannou, G. (2012). Solving project scheduling problems with resource constraints via an event list-based evolutionary algorithm. Expert Systems with Applications, 39(4), 3983-3994. doi:10.1016/j.eswa.2011.09.062Drexl, A. (1991). Scheduling of Project Networks by Job Assignment. Management Science, 37(12), 1590-1602. doi:10.1287/mnsc.37.12.1590BOCTOR, F. F. (1996). Resource-constrained project scheduling by simulated annealing. International Journal of Production Research, 34(8), 2335-2351. doi:10.1080/0020754960890502

A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem

[EN] This article focuses on obtaining sustainable and energy-efficient solutions for limited resource programming problems. To this end, a model for integrating makespan and energy consumption objectives in multi-mode resource-constrained project scheduling problems (MRCPSP-ENERGY) is proposed. In addition, a metaheuristic approach for the efficient resolution of these problems is developed. In order to assess the appropriateness of theses proposals, the well-known Project Scheduling Problem Library is extended (called PSPLIB-ENERGY) to include energy consumption to each Resource-Constrained Project Scheduling Problem instance through a realistic mathematical model. This extension provides an alternative to the current trend of numerous research works about optimization and the manufacturing field, which require the inclusion of components to reduce the environmental impact on the decision-making process. PSPLIB-ENERGY is available at http://gps.webs.upv.es/psplib-energy/.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Spanish Government under the research projects TIN2013-46511-C2-1 and TIN2016-80856-R.Morillo-Torres, D.; Barber, F.; Salido, MA. (2017). A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 1(1):1-13. https://doi.org/10.1177/0954405417711734S1131

CBO and CSS Algorithms for Resource Allocation and Time-Cost Trade-Off

Resource allocation project scheduling problem (RCPSP) has been one of the challenging subjects among researchers in the last decades. Though several methods have been adopted to solve this problem, however, new metahuristics are available to solve this problem for finding better solution with less computational time. In this paper two new metahuristic algorithms are applied for solving this problem known as charged system search (CSS) and colliding body optimization (CBO). The results show that both of these algorithms find reasonable solutions, however CBO could find the result in a less computational time having a better quality. Two case studies are conducted to evaluate the performance and applicability of the proposed algorithms

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

Effective and efficient estimation of distribution algorithms for permutation and scheduling problems.

Estimation of Distribution Algorithm (EDA) is a branch of evolutionary computation that learn a probabilistic model of good solutions. Probabilistic models are used to represent relationships between solution variables which may give useful, human-understandable insights into real-world problems. Also, developing an effective PM has been shown to significantly reduce function evaluations needed to reach good solutions. This is also useful for real-world problems because their representations are often complex needing more computation to arrive at good solutions. In particular, many real-world problems are naturally represented as permutations and have expensive evaluation functions. EDAs can, however, be computationally expensive when models are too complex. There has therefore been much recent work on developing suitable EDAs for permutation representation. EDAs can now produce state-of-the-art performance on some permutation benchmark problems. However, models are still complex and computationally expensive making them hard to apply to real-world problems. This study investigates some limitations of EDAs in solving permutation and scheduling problems. The focus of this thesis is on addressing redundancies in the Random Key representation, preserving diversity in EDA, simplifying the complexity attributed to the use of multiple local improvement procedures and transferring knowledge from solving a benchmark project scheduling problem to a similar real-world problem. In this thesis, we achieve state-of-the-art performance on the Permutation Flowshop Scheduling Problem benchmarks as well as significantly reducing both the computational effort required to build the probabilistic model and the number of function evaluations. We also achieve competitive results on project scheduling benchmarks. Methods adapted for solving a real-world project scheduling problem presents significant improvements

Solving the nuclear dismantling project scheduling problem by combining mixed-integer and constraint programming techniques and metaheuristics

Scheduling of megaprojects is very challenging because of typical characteristics, such as expected long project durations, many activities with multiple modes, scarce resources, and investment decisions. Furthermore, each megaproject has additional specific characteristics to be considered. Since the number of nuclear dismantling projects is expected to increase considerably worldwide in the coming decades, we use this type of megaproject as an application case in this paper. Therefore, we consider the specific characteristics of constrained renewable and non-renewable resources, multiple modes, precedence relations with and without no-wait condition, and a cost minimisation objective. To reliably plan at minimum costs considering all relevant characteristics, scheduling methods can be applied. But the extensive literature review conducted did not reveal a scheduling method considering the special characteristics of nuclear dismantling projects. Consequently, we introduce a novel scheduling problem referred to as the nuclear dismantling project scheduling problem. Furthermore, we developed and implemented an effective metaheuristic to obtain feasible schedules for projects with about 300 activities. We tested our approach with real-life data of three different nuclear dismantling projects in Germany. On average, it took less than a second to find an initial feasible solution for our samples. This solution could be further improved using metaheuristic procedures and exact optimisation techniques such as mixed-integer programming and constraint programming. The computational study shows that utilising exact optimisation techniques is beneficial compared to standard metaheuristics. The main result is the development of an initial solution finding procedure and an adaptive large neighbourhood search with iterative destroy and recreate operations that is competitive with state-of-the-art methods of related problems. The described problem and findings can be transferred to other megaprojects

Eficiencia energética en la programación de tareas con recursos restringidos

In the field of operations research, the set of scheduling problems of activities is considered as one of the most relevant ones due to its great applicability and complexity. Within the broad variety of problems in this set, it is remarkable the Resource-Constrained Project Scheduling Problem (RCPSP), since it is regarded as the most important-base problem in this area and it has been the object of study in countless research projects. Basically, this problem consists of a project split into sets of activities that are related to each other by means of precedence-constraints, and require an amount of each limited resource, to be performed. The objective, then, is to allocate in the most efficient way those resources to the activities in order to optimize a scoring function such as the makespan. Similar in importance is the multimodal-version of the RCPSP, called MRCPSP, in which for each activity there exists multiple execution modes that involve a different combination of limited resources, giving rise to a different execution time. In the literature, it has been addressed widely these two problems with both exact methods and approximation methods, being these latter the most successful. These research works have focused mainly on obtaining economic advantages such as costs and project time minimization. However, with the accelerating globalization and the fast countries' growing economies, the race for power resources have increased sharply. In fact, the importance of taking into account the energy consumption on modeling has become so important that it is now considered as important as other performance measures such as productivity and costs. Hence, the main goal of this Ph.D. dissertation is to develop a new RCPSP and MRCPSP approach based on the energetic efficiency, which is aimed at searching for sustainable solutions in terms of time and energy consumption. To this end, it has been proposed an extension of the RCPSP, named MRCPSP-ENERGY, which considers besides the traditional resources of the RCPSP, a variable energetic consumption that generates different execution modes for the activities. This proposal includes a new optimization criterion based on the energetic efficiency of a project, which considers simultaneously the minimization of both the total duration and the energy consumption of such project. Moreover, in order to assess the solution methods for the MRCPSP-ENERGY, the standard library mostly used for this purpose has been extended and a new one has been proposed, called PSPLIB-ENERGY. In order to solve the proposed problem, firstly, the most successful metaheuristics methods, which address the RCPSP, were analyzed. Secondly, it was shown that these methods lead to redundant solutions, hindering the search. Therefore, an evolutive method was proposed, whose main contribution is the development of a new mutation operator that reduces the number of redundant solutions. Similarly, in the multimodal case, it was determined that the most widespread searching methods are also focused on the activity list representation and therefore they yield redundant solutions. As a solution alternative for the MRCPSP-ENERGY, it was shown that such search can be carried out by focusing on the mode list representation, as different mode lists also reach diverse solutions, giving rise to a less number of redundant solutions. Keeping in mind this finds, it was proposed a new evolutive method for solving the MRCPSP-ENERGY, which unifies both searching methods such that the search is conducted with two optimization phases. Based on the obtained results given by the PSPLIB-ENERGY library, the proposed method proved to be able to reach highly efficient solutions.En la investigación operativa, el conjunto de problemas de secuenciación de actividades es considerado como uno de los más relevantes debido a su gran aplicabilidad y complejidad. Dentro de la amplia variedad de problemas en este conjunto, destaca el problema de programación de tareas con recursos restringidos (RCPSP por su sigla en inglés), pues es considerado como el problema base más importante en esta área y ha sido objeto de estudio de numerosas investigaciones. Básicamente, consiste de un proyecto subdividido en un conjunto de actividades que se encuentran relacionadas mediante restricciones de precedencia y requieren, para ser ejecutadas, una cantidad de cada tipo de recurso cuya disponibilidad máxima se encuentra limitada. El objetivo es asignar los recursos a las actividades de la manera más eficiente posible para optimizar una medida de desempeño, por ejemplo, la duración total del proyecto. Igualmente importante es la versión multi-modal del RCPSP, llamada MRCPSP, en la que para cada actividad existen múltiples modos de ejecución que involucran una combinación diferente de recursos limitados, dando origen a un tiempo de ejecución distinto. En la literatura se han abordado ampliamente estos dos problemas tanto con métodos exactos como de aproximación, siendo estos últimos los más exitosos. Estos trabajos se han centrado principalmente en la obtención de beneficios económicos, como la minimización de los costes o la obtención de la mínima duración del proyecto. Sin embargo, con la aceleración de la globalización y el rápido desarrollo de los países, la competencia por recursos energéticos ha aumentado drásticamente. Incluso, la importancia de tener en cuenta el consumo de energía en los modelos ha crecido de tal manera que, ahora es considerado con la misma relevancia que otras medidas de desempeño como la productividad y los costes. Así, el objetivo principal de esta tesis es desarrollar un nuevo enfoque del RCPSP y del MRCPSP, basado en la eficiencia energética, la cual busca soluciones sostenibles en términos de tiempo y de consumo energético. Para este fin, se ha propuesto una extensión del RCPSP denominada MRCPSP-ENERGY, la cual considera, además de los recursos tradicionales del RCPSP, un consumo de energía variable que da origen a distintos modos de ejecución de las actividades. Esta propuesta incluye un nuevo criterio de optimización basado en la eficiencia energética del proyecto, que tiene en cuenta de manera simultánea la minimización de la duración del proyecto y el consumo total de energía. Adicionalmente, con el objetivo de evaluar los métodos de solución para el MRCPSP-ENERGY, se ha ampliado la librería estándar de prueba más extendida para el RCPSP y se ha propuesto una nueva librería, denominada PSPLIB-ENERGY. Para encontrar solución al problema propuesto, primero se analizaron los mejores métodos metaheurísticos que abordan el RCPSP. Luego, se identificó que estos métodos conducen a soluciones redundantes, entorpeciendo la búsqueda. Por tanto, se propuso un método evolutivo cuya principal aportación es el desarrollo de un nuevo operador de mutación que disminuye la generación de soluciones redundantes. Similarmente, en el caso multi-modal se detectó que los principales métodos de búsqueda también se centran en la representación de lista de actividades y por tanto generan soluciones redundantes. Como alternativa de solución para el MRCPSP-ENERGY, se mostró que la búsqueda puede realizarse enfocándose en la lista de modos, ya que diferentes listas de modos también pueden alcanzar soluciones distintas, generando un menor número de soluciones redundantes. Teniendo en cuenta estos hallazgos, se propuso un nuevo método evolutivo para resolver el MRCPSP-ENERGY, que unifica ambos métodos de búsqueda para realizarla en dos fases de optimización. Basándose en los resultados obtenidos en la PSPLIB-ENERGY, se concluye que el mEn la investigació operativa, el conjunt de problemes de seqüenciació d'activitats és considerat com un dels més rellevants a causa de la seua gran aplicabilitat i complexitat. Dins de l'àmplia varietat de problemes en este conjunt, destaca el problema de programació de tasques amb recursos restringits (RCPSP per la seua sigla en anglés) , perquè és considerat com el problema base més important en esta àrea i ha sigut objecte d'estudi de nombroses investigacions. Bàsicament, consistix d'un projecte subdividit en un conjunt d'activitats que es troben relacionades per mitjà de restriccions de precedència i requerixen, per a ser executades, una quantitat de cada tipus de recurs la disponibilitat màxima de la qual es troba limitada. L'objectiu és assignar els recursos a les activitats de la manera més eficient possible per a optimitzar una mesura d'exercici, per exemple, la duració total del projecte. Igualment important és la versió multi- modal del RCPSP, crida MRCPSP, en la que per a cada activitat hi ha múltiples modes d'execució que involucren una combinació diferent de recursos limitats, donant origen a un temps d'execució distint. En la literatura s'han abordat àmpliament estos dos problemes tant amb mètodes exactes com d'aproximació, sent estos últims els més reeixits. Estos treballs s'han centrat principalment en l'obtenció de beneficis econòmics, com la minimització dels costos o l'obtenció de la mínima duració del projecte. No obstant això, amb l'acceleració de la globalització i el ràpid desenrotllament dels països, la competència per recursos energètics ha augmentat dràsticament. Inclús, la importància de tindre en compte el consum d'energia en els models ha crescut de tal manera que, ara és considerat amb la mateixa rellevància que altres mesures d'exercici com la productivitat i els costos. Així, l'objectiu principal d'esta tesi és desenrotllar un nou enfocament del RCPSP i del MRCPSP, basat en l'eficiència energètica, la qual busca solucions sostenibles en termes de temps i de consum energètic. Per a este fi, s'ha proposat una extensió del RCPSP denominada MRCPSP- ENERGY, la qual considera, a més dels recursos tradicionals del RCPSP, un consum d'energia variable que dóna origen a distints modes d'execució de les activitats. Esta proposta inclou un nou criteri d'optimització basat en l'eficiència energètica del projecte, que té en compte de manera simultània la minimització de la duració del projecte i el consum total d'energia. Addicionalment, amb l'objectiu d'avaluar els mètodes de solució per al MRCPSP-ENERGY, s'ha ampliat la llibreria estàndard de prova més estesa per al RCPSP i s'ha proposat una nova llibreria, denominada PSPLIB-ENERGY. Per a trobar solució al problema proposat, primer es van analitzar els millors mètodes metaheurísticos que aborden el RCPSP. Després, es va identificar que estos mètodes conduïxen a solucions redundants, entorpint la busca. Per tant, es va proposar un mètode evolutiu la principal aportació del qual és el desenrotllament d'un nou operador de mutació que disminuïx la generació de solucions redundants. Semblantment, en el cas multi- modal es va detectar que els principals mètodes de busca també se centren en la representació de llista d'activitats i per tant generen solucions redundants. Com a alternativa de solució per al MRCPSP-ENERGY, es va mostrar que la busca pot realitzar-se enfocant-se en la llista de modes, ja que diferents llistes de modes també poden aconseguir solucions distintes, generant un menor nombre de solucions redundants. Tenint en compte estes troballes, es va proposar un nou mètode evolutiu per a resoldre el MRCPSP-ENERGY, que unifica ambdós mètodes de busca per a realitzar-la en dos fases d'optimització. Basant-se en els resultats obtinguts en la PSPLIB-ENERGY, es conclou que el mètode proposat és capaç d'aconseguir solucions altament eficients.Morillo Torres, D. (2017). Eficiencia energética en la programación de tareas con recursos restringidos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90654TESI

Proactive-reactive, robust scheduling and capacity planning of deconstruction projects under uncertainty

A project planning and decision support model is developed and applied to identify and reduce risk and uncertainty in deconstruction project planning. It allows calculating building inventories based on sensor information and construction standards and it computes robust project plans for different scenarios with multiple modes, constrained renewable resources and locations. A reactive and flexible planning element is proposed in the case of schedule infeasibility during project execution

Optimization Models for Multiple Resource Planning

Multiple resource planning is a very crucial undertaking for most organizations. Apart from reducing operational complexity, multiple resource planning facilitates efficient allocation of resources which reduces costs by minimizing the cost of tardiness and the cost for additional capacity. The current research investigates multiple resource loading problems (MRLP). MRLPs are very prevalent in today's organizational environments and are particularly critical for organizations that handle concurrent, time-intensive, and multiple-resource projects. Using data obtained from the Ministry of Administrative Development, Labor and Social Affairs (ADLSA), an MRLP is proposed. The problem utilizes data regarding staff, time, equipment, and finance to ensure efficient resource allocation among competing projects. In particular, the thesis proposes a novel model and solution approach for the MRLP. Computational experiments are then performed on the model. The results show that the model performs well, even in higher instances. The positive results attest to the effectiveness of the proposed MRLP proble