Solving uncapacitated multilevel lot-sizing problems using a particle swarm optimization with flexible inertial weight

AbstractThe multilevel lot-sizing (MLLS) problem is a key production planning problem in materials requirements planning (MRP) system. The MLLS problem deals with determining the production lot-sizes of various items appearing in the product structure over a given finite planning horizon to minimize the production cost, the inventory carrying cost, the back ordering cost and etc. This paper proposed a particle swarm optimization (PSO) algorithm for solving the uncapacitated MLLS problem with assembly structure. All the mathematical operators in our algorithm are redefined and the inertial weight parameter can be either a negative real number or a positive one. The feasibility and effectiveness of our algorithm are investigated by comparing the experimental results with those of a genetic algorithm (GA)

Lot-Sizing Problem for a Multi-Item Multi-level Capacitated Batch Production System with Setup Carryover, Emission Control and Backlogging using a Dynamic Program and Decomposition Heuristic

Wagner and Whitin (1958) develop an algorithm to solve the dynamic Economic Lot-Sizing Problem (ELSP), which is widely applied in inventory control, production planning, and capacity planning. The original algorithm runs in O(T^2) time, where T is the number of periods of the problem instance. Afterward few linear-time algorithms have been developed to solve the Wagner-Whitin (WW) lot-sizing problem; examples include the ELSP and equivalent Single Machine Batch-Sizing Problem (SMBSP). This dissertation revisits the algorithms for ELSPs and SMBSPs under WW cost structure, presents a new efficient linear-time algorithm, and compares the developed algorithm against comparable ones in the literature. The developed algorithm employs both lists and stacks data structure, which is completely a different approach than the rest of the algorithms for ELSPs and SMBSPs. Analysis of the developed algorithm shows that it executes fewer number of basic actions throughout the algorithm and hence it improves the CPU time by a maximum of 51.40% for ELSPs and 29.03% for SMBSPs. It can be concluded that the new algorithm is faster than existing algorithms for both ELSPs and SMBSPs. Lot-sizing decisions are crucial because these decisions help the manufacturer determine the quantity and time to produce an item with a minimum cost. The efficiency and productivity of a system is completely dependent upon the right choice of lot-sizes. Therefore, developing and improving solution procedures for lot-sizing problems is key. This dissertation addresses the classical Multi-Level Capacitated Lot-Sizing Problem (MLCLSP) and an extension of the MLCLSP with a Setup Carryover, Backlogging and Emission control. An item Dantzig Wolfe (DW) decomposition technique with an embedded Column Generation (CG) procedure is used to solve the problem. The original problem is decomposed into a master problem and a number of subproblems, which are solved using dynamic programming approach. Since the subproblems are solved independently, the solution of the subproblems often becomes infeasible for the master problem. A multi-step iterative Capacity Allocation (CA) heuristic is used to tackle this infeasibility. A Linear Programming (LP) based improvement procedure is used to refine the solutions obtained from the heuristic method. A comparative study of the proposed heuristic for the first problem (MLCLSP) is conducted and the results demonstrate that the proposed heuristic provide less optimality gap in comparison with that obtained in the literature. The Setup Carryover Assignment Problem (SCAP), which consists of determining the setup carryover plan of multiple items for a given lot-size over a finite planning horizon is modelled as a problem of finding Maximum Weighted Independent Set (MWIS) in a chain of cliques. The SCAP is formulated using a clique constraint and it is proved that the incidence matrix of the SCAP has totally unimodular structure and the LP relaxation of the proposed SCAP formulation always provides integer optimum solution. Moreover, an alternative proof that the relaxed ILP guarantees integer solution is presented in this dissertation. Thus, the SCAP and the special case of the MWIS in a chain of cliques are solvable in polynomial time

Adaptive genetic algorithm based on fuzzy reasoning for the multilevel capacitated lot-sizing problem with energy consumption in synchronizer production

The multilevel capacitated lot-sizing problem (MLCLSP) is a vital theoretical problem of production planning in discrete manufacturing. An improved algorithm based on the genetic algorithm (GA) is proposed to solve the MLCLSP. Based on the solution results, the distribution of energy consumption in a synchronous production case is analyzed. In the related literature, the GA has become a much-discussed topic in solving these kinds of problems. Although the standard GA can make up for the defects of the traditional algorithm, it will lead to the problems of unstable solution results and easy local convergence. For these reasons, this research presents an adaptive genetic algorithm based on fuzzy theory (fuzzy-GA) to solve the MLCLSP. Firstly, the solving process of the MLCLSP with the fuzzy-GA is described in detail, where algorithms for key technologies such as the capacity constraint algorithm and the algorithm of solving fitness value are developed. Secondly, the auto-encoding of decision variables for MLCLSPs is studied; within this, the decision variables of whether to produce or not are encoded into a hierarchical structure based on the bill of material; combined with external demand, the decision variables of lot-sizing are constructed. Thirdly, the adaptive optimization process of parameters of the GA for the MLCLSP based on fuzzy theory is expounded, in which membership function, fuzzy rule, and defuzzification of the MLCLSP is mainly presented. Experimental studies using the processed dataset collected from a synchronizer manufacturer have demonstrated the merits of the proposed approach, in which the energy consumption distribution of the optimized production plan is given. The optimal lot-sizing is closer to the average value of the optimal value compared with the standard GA, which indicates that the proposed fuzzy-GA approach has better convergence and stability

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Resolución del problema del tamaño de lote multinivel capacitado aplicando optimización por enjambre de partículas con búsqueda local

Un problema que se presentan en los sistemas de manufactura de las empresas, especialmente pequeñas y medianas empresas, es que la programación de la producción está basada bajo modelos de arrastre de la demanda deterministas, es decir, el proceso de la planificación de los tamaños de lotes de insumos o componentes para la fabricación de productos finales que poseen jerarquías multiniveles y restricciones de capacidad como horas hombres, números de maquinas entre otras, son un problema que se pueden convertir en oportunidades de mejora debido a que se pueden equilibran los costos de ordenamiento de un lote y los costos de inventarios por productos que permita obtener una reducción en los costos totales y así mejorar la productividad de las empresas. Este trabajo presenta una metodología para la solución del problema del tamaño de lote multinivel capacitado, basado en una técnica metaheurísticas llamada optimización por enjambre de partículas o PSO por sus siglas en ingles (Particle Swarm Optimization), la cual se ha demostrado que tiene un buen desempeño dentro de las familia de metaheurísticas, así que se propone el desarrollo de este tema agregando el concepto una búsqueda local que permita generar óptimos locales y permita mejorar las soluciones encontradas por las partículas, denominando a está técnica como la utilización de una hiperheurística.Incluye bibliografía, anexo

A decentralized metaheuristic approach applied to the FMS scheduling problem

La programación de FMS ha sido uno de los temas más populares para los investigadores. Se han entregado varios enfoques para programar los FMS, incluidas las técnicas de simulación y los métodos analíticos. Las metaheurísticas descentralizadas pueden verse como una forma en que la población se divide en varias subpoblaciones, con el objetivo de reducir el tiempo de ejecución y el número de evaluaciones, debido a la separación del espacio de búsqueda. La descentralización es una ruta de investigación prominente en la programación, por lo que el costo de la computación se puede reducir y las soluciones se pueden encontrar más rápido, sin penalizar la función objetivo. En este proyecto, se propone una metaheurística descentralizada en el contexto de un problema de programación flexible del sistema de fabricación. La principal contribución de este proyecto es analizar otros tipos de división del espacio de búsqueda, particularmente aquellos asociados con el diseño físico del FMS. El desempeño del enfoque descentralizado se validará con los puntos de referencia de programación de FMS.FMS scheduling has been one of the most popular topics for researchers. A number of approaches have been delivered to schedule FMSs including simulation techniques and analytical methods. Decentralized metaheuristics can be seen as a way where the population is divided into several subpopulations, aiming to reduce the run time and the numbers of evaluation, due to the separation of the search space. Decentralization is a prominent research path in scheduling so the computing cost can be reduced and solutions can be found faster, without penalizing the objective function. In this project, a decentralized metaheuristic is proposed in the context of a flexible manufacturing system scheduling problem. The main contribution of this project is to analyze other types of search space division, particularly those associated with the physical layout of the FMS. The performance of the decentralized approach will be validated with FMS scheduling benchmarks.Ingeniero (a) IndustrialPregrad

Genetic Algorithm for Solving the Integrated Production-Distribution-Direct Transportation Planning

This paper proposes a model of integrated production, distribution and transportation planning for 4-echelon supply chain system that consists of a manufacturer using a continuous production process, a distribution center, distributors and retailers. By means of time-dependent demand at all retailers and direct transportation from one echelon to its successive echelons, the purpose of this paper is to determine production/replenishment and transportation policies at manufacturer, distribution center, distributors and retailers in order to minimize annually total system cost. Due to the proposed model is classified as a mixed integer non-linear programming so it is almost impossible to solve the model using the exact optimization methods and a lot of time is needed when the enumeration methods is applied to solve only a small scale problem. In this paper, we apply the genetic algorithm for solving the model. Using integer encoding for constructing the chromosome, the best solution is going to be searched. Compared with enumeration method, the difference of the result is only 0.0594% with the consumption time is only 0.5609% time that enumeration methods need

MRP IV: Planificación de requerimientos de materiales cuarta generación. Integración de la planificación de la producción y del transporte de aprovisionamiento

Tesis por compendioEl sistema de planificación de requerimientos de materiales o MRP (Material Requirement Planning), desarrollado por Orlicky en 1975, sigue siendo en nuestros días y, a pesar de sus deficiencias identificadas, el sistema de planificación de la producción más utilizado por las empresas industriales. Las evoluciones del MRP se vieron reflejadas en el sistema MRPII (Manufacturing Resource Planning), que considera restricciones de capacidad productiva, MRPIII (Money Resource Planning), que introduce la función de finanzas; y la evolución comercial del mismo en el ERP (Enterprise Resource Planning), que incorpora modularmente todas las funciones de la empresa en un único sistema de decisión, cuyo núcleo central es el MRP. Los desarrollos posteriores de los sistemas ERP han incorporado las nuevas tecnologías de la información y comunicaciones. Asimismo, éstos se han adaptado al contexto económico actual caracterizado por la globalización de los negocios y la deslocalización de los proveedores desarrollando otras funciones como la gestión de la cadena de suministro o del transporte, entre otros. Por otro lado, existen muchos trabajos en la literatura académica que han intentado resolver algunas de las debilidades del MRP tales como la optimización de los resultados, la consideración de la incertidumbre en determinados parámetros, el inflado de los tiempos de entrega, etc. Sin embargo, tanto en el ámbito comercial como en el científico, el MRP y sus variantes se centran en el requerimiento de los materiales y en la planificación de las capacidades de producción, lo que es su desventaja principal en aquellas cadenas de suministro donde existe una gran deslocalización de los proveedores de materias primas y componentes. En estos entornos, la planificación del transporte adquiere un protagonismo fundamental, puesto que los elevados costes y las restricciones logísticas suelen hacer subóptimos e incluso infactibles los planes de producción propuestos, siendo la re-planificación manual una práctica habitual en las empresas. Esta tesis doctoral propone un modelo denominado MRPIV, que considera de forma integrada las decisiones de la planificación de materiales, capacidades de recursos de producción y el transporte, con las restricciones propias de este último, tales como diferentes modos de recogida (milk-run, camión completo, rutas) en la cadena de suministro con el objetivo de evitar la suboptimización de estos planes que en la actualidad se generan usualmente de forma secuencial e independiente. El modelo propuesto se ha validado en una cadena de suministro del sector del automóvil confirmando la reducción de costes totales y una planificación más eficiente del transporte de los camiones necesarios para efectuar el aprovisionamiento.Díaz-Madroñero Boluda, FM. (2015). MRP IV: Planificación de requerimientos de materiales cuarta generación. Integración de la planificación de la producción y del transporte de aprovisionamiento [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48524TESISCompendi