Development of a hybrid metaheuristic for the efficient solution of strategic supply chain management problems: application to the energy sector

Supply chain management (SCM) addresses the strategic, tactical, and operational decision making that optimizes the supply chain performance. The strategic level defines the supply chain configuration: the selection of suppliers, transportation routes, manufacturing facilities, production levels, technologies. The tactical level plans and schedules the supply chain to meet actual demand. The operational level executes plans. Tactical and operational level decision-making functions are distributed across the supply chain. To increase or optimize performance, supply-chain functions must be perfectly coordinated. But the cycles of the enterprise and the market make this difficult: raw material does not arrive on time, production facilities fail, workers are ill, customers change or cancel orders, therefore, causing deviations from the plan. In some cases, these situations may be dealt with locally. In other cases, the problem cannot be ”locally contained” and modifications across many functions are required. Consequently, the supply chain management system must coordinate the revision of plans or schedules. The ability to better understand an algorithm is important to focus on the following variables: tactical and operational levels of the supply chain so that the timely dissemination of information, accurate coordination of decisions, and management of actions among people and systems is achieved ultimately determines the efficient, coordinated achievement of enterprise goal

Contribution to the development of efficient algorithms for solving complex single-objective and multi-objective optimization models

L’optimització en enginyeria de processos és un àrea molt estesa que ha anat evolucionant al llarg del temps i ha passat de ser una metodologia d'interès purament acadèmic a una tecnologia que té, i que contínua tenint, gran impacte en la indústria. En aquesta tesi ens hem centrat en el desenvolupament mètodes basats en dues eines típiques d'optimització: programació matemàtica i metaheurístiques. Els objectius d'aquesta tesi són: el primer és desenvolupar una metaheuristica híbrida per a l'optimització del disseny de cadenes de subministrament, d'un sol objectiu (cost o benefici), on tots els paràmetres són coneguts a priori; el segon és desenvolupar un algorisme efectiu per a reducció d'objectius facilitant la resolució de problemes multi-objectiu; i finalment s'han implementat una sèrie de millores en el mètode de la restricció èpsilon per millorar l'eficiència en la resolució de problemes multi-objectiu. Tots els algorismes presentats han estat comparats i avaluats amb els mètodes establerts per la literatura.La optimización en ingeniería de procesos es un área muy extensa que ha ido evolucionando a lo largo del tiempo y ha pasado de ser una metodología de interés puramente académico a una tecnología que tiene, y que continua teniendo, gran impacto en la industria. En esta tesis nos hemos centrado en el desarrollo de métodos basados en dos herramientas típicas de optimización: programación matemática y metaheurísticas. Los objetivos de esta tesis son: el primero es desarrollar una metaheuristica híbrida para la optimización del diseño de cadenas de suministro, de un solo objetivo (coste o beneficio), donde todos los parámetros son conocidos a priori; el segundo es desarrollar un algoritmo efectivo para la reducción de objetivos facilitando la resolución de problemas multi-objetivo; y finalmente se han implementado una serie de mejoras en el método de la restricción epsilon para mejorar la eficiencia en la resolución de problemas multi-objetivo. Todos los algoritmos presentados han sido comparados y evaluados con los métodos establecidos por la literatura.Optimization has become a major area in process systems engineering. It has evolved from a methodology of academic interest into a technology that has and continues to make significant impact in industry. In this thesis we have focused on development of tools based on two standard optimization methods: mathematical programming and metaheuristics. The objectives of this thesis are: firstly, the development of a hybrid metaheuristic for optimizing the design of supply chains, single objective (cost or benefit), where all parameters are known previously; secondly, the development of an effective algorithm for objective reduction facilitating the resolution of multi-objective problems; and finally, we improved the epsilon-constraint algorithm in multi-objective optimization. All the algorithms presented have been assessed with the methods established in the literature

Development of a hybrid metaheuristic for the efficient solution of strategic supply chain management problems: application to the energy sector

Supply chain management (SCM) addresses the strategic, tactical, and operational decision making that optimizes the supply chain performance. The strategic level defines the supply chain configuration: the selection of suppliers, transportation routes, manufacturing facilities, production levels, technologies. The tactical level plans and schedules the supply chain to meet actual demand. The operational level executes plans. Tactical and operational level decision-making functions are distributed across the supply chain. To increase or optimize performance, supply-chain functions must be perfectly coordinated. But the cycles of the enterprise and the market make this difficult: raw material does not arrive on time, production facilities fail, workers are ill, customers change or cancel orders, therefore, causing deviations from the plan. In some cases, these situations may be dealt with locally. In other cases, the problem cannot be ”locally contained” and modifications across many functions are required. Consequently, the supply chain management system must coordinate the revision of plans or schedules. The ability to better understand an algorithm is important to focus on the following variables: tactical and operational levels of the supply chain so that the timely dissemination of information, accurate coordination of decisions, and management of actions among people and systems is achieved ultimately determines the efficient, coordinated achievement of enterprise goal

A biased-randomized discrete-event algorithm for the hybrid flow shop problem with time dependencies and priority constraints

Based on a real-world application in the semiconductor industry, this article models and discusses a hybrid flow shop problem with time dependencies and priority constraints. The analyzed problem considers a production where a large number of heterogeneous jobs are processed by a number of machines. The route that each job has to follow depends upon its type, and, in addition, some machines require that a number of jobs are combined in batches before starting their processing. The hybrid flow model is also subject to a global priority rule and a “same setup” rule. The primary goal of this study was to find a solution set (permutation of jobs) that minimizes the production makespan. While simulation models are frequently employed to model these time-dependent flow shop systems, an optimization component is needed in order to generate high-quality solution sets. In this study, a novel algorithm is proposed to deal with the complexity of the underlying system. Our algorithm combines biased-randomization techniques with a discrete-event heuristic, which allows us to model dependencies caused by batching and different paths of jobs efficiently in a near-natural way. As shown in a series of numerical experiments, the proposed simulation-optimization algorithm can find solutions that significantly outperform those provided by employing state-of-the-art simulation software.Peer ReviewedPostprint (published version

Combining heuristics with simulation and fuzzy logic to solve a flexible-size location routing problem under uncertainty

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033). In addition, it has received the support of the Doctoral School at the Universitat Oberta de Catalunya (Spain) and the Universidad de La Sabana (INGPhD-12-2020).Peer ReviewedPostprint (published version

Fuzzy simheuristics: solving optimization problems under stochastic and uncertainty scenarios

Simheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements. This paper introduces the concept of fuzzy simheuristics, which extends the simheuristics approach by making use of fuzzy techniques, thus allowing us to tackle optimization problems under a more general scenario, which includes uncertainty elements of both stochastic and non-stochastic nature. After reviewing the related work, the paper discusses, in detail, how the optimization, simulation, and fuzzy components can be efficiently integrated. In order to illustrate the potential of fuzzy simheuristics, we consider the team orienteering problem (TOP) under an uncertainty scenario, and perform a series of computational experiments. The obtained results show that our proposed approach is not only able to generate competitive solutions for the deterministic version of the TOP, but, more importantly, it can effectively solve more realistic TOP versions, including stochastic and other uncertainty elements

A biased-randomized learnheuristic for solving the team orienteering problem with dynamic rewards

In this paper we discuss the team orienteering problem (TOP) with dynamic inputs. In the static version of the TOP, a fixed reward is obtained after visiting each node. Hence, given a limited fleet of vehicles and a threshold time, the goal is to design the set of routes that maximize the total reward collected. While this static version can be efficiently tackled using a biased-randomized heuristic (BR-H), dealing with the dynamic version requires extending the BR-H into a learnheuristic (BR-LH). With that purpose, a 'learning' (white-box) mechanism is incorporated to the heuristic in order to consider the variations in the observed rewards, which follow an unknown (black-box) pattern. In particular, we assume that: (i) each node in the network has a 'base' or standard reward value; and (ii) depending on the node's position inside its route, the actual reward value might differ from the base one according to the aforementioned unknown pattern. As new observations of this black-box pattern are obtained, the white-box mechanism generates better estimates for the actual rewards after each new decision. Accordingly, better solutions can be generated by using this predictive mechanism. Some numerical experiments contribute to illustrate these concepts