Strategies for scheduling jobs in an identical parallel machine production environment

CAPESCNP

Incorporating Memory and Learning Mechanisms Into Meta-RaPS

Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics. Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning mechanisms into Meta-RaPS, which is currently classified as a memoryless metaheuristic, can help the algorithm produce higher quality results. The proposed Meta-RaPS versions were created by taking different perspectives of learning. The first approach taken is Estimation of Distribution Algorithms (EDA), a stochastic learning technique that creates a probability distribution for each decision variable to generate new solutions. The second Meta-RaPS version was developed by utilizing a machine learning algorithm, Q Learning, which has been successfully applied to optimization problems whose output is a sequence of actions. In the third Meta-RaPS version, Path Relinking (PR) was implemented as a post-optimization method in which the new algorithm learns the good attributes by memorizing best solutions, and follows them to reach better solutions. The fourth proposed version of Meta-RaPS presented another form of learning with its ability to adaptively tune parameters. The efficiency of these approaches motivated us to redesign Meta-RaPS by removing the improvement phase and adding a more sophisticated Path Relinking method. The new Meta-RaPS could solve even the largest problems in much less time while keeping up the quality of its solutions. To evaluate their performance, all introduced versions were tested using the 0-1 Multidimensional Knapsack Problem (MKP). After comparing the proposed algorithms, Meta-RaPS PR and Meta-RaPS Q Learning appeared to be the algorithms with the best and worst performance, respectively. On the other hand, they could all show superior performance than other approaches to the 0-1 MKP in the literature

Design and analysis of algorithms for solving a class of routing shop scheduling problems

Ph.DDOCTOR OF PHILOSOPH

Exact and Heuristic Hybrid Approaches for Scheduling and Clustering Problems

This thesis deals with the design of exact and heuristic algorithms for scheduling and clustering combinatorial optimization problems. All the works are linked by the fact that all the presented methods arebasically hybrid algorithms, that mix techniques used in the world of combinatorial optimization. The algorithms are all efficient in practice, but the one presented in Chapter 4, that has mostly theoretical interest. Chapter 2 presents practical solution algorithms based on an ILP model for an energy scheduling combinatorial problem that arises in a smart building context. Chapter 3 presents a new cutting stock problem and introduce a mathematical formulation and a heuristic solution approach based on a heuristic column generation scheme. Chapter 4 provides an exact exponential algorithm, whose importance is only theoretical so far, for a classical scheduling problem: the Single Machine Total Tardiness Problem. The relevant aspect is that the designed algorithm has the best worst case complexity for the problem, that has been studied for several decades. Furthermore, such result is based on a new technique, called Branch and Merge, that avoids the solution of several equivalent sub-problems in a branching algorithm that requires polynomial space. As a consequence, such technique embeds in a branching algorithm ideas coming from other traditional computer science techniques such as dynamic programming and memorization, but keeping the space requirement polynomial. Chapter 5 provides an exact approach based on semidefinite programming and a matheuristic approach based on a quadratic solver for a fractional clustering combinatorial optimization problem, called Max-Mean Dispersion Problem. The matheuristic approach has the peculiarity of using a non-linear MIP solver. The proposed exact approach uses a general semidefinite programming relaxation and it is likely to be extended to other combinatorial problems with a fractional formulation. Chapter 6 proposes practical solution methods for a real world clustering problem arising in a smart city context. The solution algorithm is based on the solution of a Set Cover model via a commercial ILP solver. As a conclusion, the main contribution of this thesis is given by several approaches of practical or theoretical interest, for two classes of important combinatorial problems: clustering and scheduling. All the practical methods presented in the thesis are validated by extensive computational experiments, that compare the proposed methods with the ones available in the state of the art

The Vehicle Routing Problem with Release and Due Dates

A novel extension of the classical vehicle routing and scheduling problems is introduced that integrates aspects of machine scheduling into vehicle routing. Associated with each customer order is a release date that defines the earliest time that the order is available to leave the depot for delivery and a due date that indicates the time by which the order should ideally be delivered to the customer. The objective is to minimize a convex combination of the operational costs and customer service level, represented by the total distance traveled and the total weighted tardiness of delivery, respectively. A path-relinking algorithm (PRA) is proposed to address the problem, and a variety of benchmark instances are generated to evaluate its performance. The PRA exploits the efficiency and aggressive improvement of neighborhood search but relies on a new path-relinking procedure and advanced population management strategies to navigate the search space effectively. To provide a comparator algorithm to the PRA, we embed the neighborhood search into a standard iterated local search algorithm (ILS). Extensive computational experiments on the benchmark instances show that the newly defined features have a significant and varied impact on the problem, and the performance of the PRA dominates that of the ILS algorithm. The online supplement is available at https://doi.org/10.1287/ijoc.2017.0756 . </jats:p

An efficient discrete artificial bee colony algorithm for the blocking flow shop problem with total flowtime minimization

This paper presents a high performing Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with flow time criterion. To develop the proposed algorithm, we considered four strategies for the food source phase and two strategies for each of the three remaining phases (employed bees, onlookers and scouts). One of the strategies tested in the food source phase and one implemented in the employed bees phase are new. Both have been proved to be very effective for the problem at hand. The initialization scheme named HPF2(¿, µ) in particular, which is used to construct the initial food sources, is shown in the computational evaluation to be one of the main procedures that allow the DABC_RCT to obtain good solutions for this problem. To find the best configuration of the algorithm, we used design of experiments (DOE). This technique has been used extensively in the literature to calibrate the parameters of the algorithms but not to select its configuration. Comparing it with other algorithms proposed for this problem in the literature demonstrates the effectiveness and superiority of the DABC_RCTPeer ReviewedPostprint (author’s final draft

A general Framework for Utilizing Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A concise overview

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem. The algorithms are evaluated based on their ability to improve resource utilization, minimize energy consumption, reduce environmental impact, and promote socially responsible production practices. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives

Evaluación de funciones de utilidad de GRASP en la programación de producción para minimizar la tardanza total ponderada en una máquina

This paper considers the total weighted tardiness minimization in a single machineenvironment (1|| wj Tj ) a scheduling problem which has been proved to be NP-Hard. The solution approach uses the Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic known for the quality of the solutions it can generate and the selective ability of its utility function during the construction phase. This work proposes and analyses three different utility functions for the problem in question. A statistical study showed significant differences between the mean values obtained from the proposed utility functions. The computational experiments were carried out using problems instances found in the OR-LIBRARY, and the outcome of these experiments were competitive solutions compared to the best known values of the instances involved. This work also shows the ease of developing GRASP methods for solving scheduling problems in a simple spreadsheet software such as MS Excel.Este artículo aborda la minimización de la tardanza total ponderada en un entorno de producción (1|| wj Tj ) que es conocido en complejidad como de tipo NP-hard. El enfoque de solución propuesto utiliza la metaheurística Greedy Randomized Adaptive Search Procedure (GRASP), la cual es reconocida por la correlación existente entre la calidad de las soluciones y la capacidad discriminante de la función de utilidad empleada en su fase constructiva. Este trabajo propone y analiza tres diferentes funciones de utilidad para este problema en particular. El desempeño de estas funciones se evaluó mediante un estudio estadístico que evidenció diferencias significativas en los valores medios de tardanza total ponderada, explicadas por el factor función de utilidad. La fase experimental se desarrolló usando instancias de la librería OR-LIBRARY y permitió obtener soluciones competitivas en calidad con respecto a los mejores valores conocidos para las instancias de este problema. Este trabajo ilustra la potencialidad de uso de métodos GRASP implementados en una hoja de cálculo normal para hallar soluciones a problemas de programación de la producción