Um algoritmo genético para programação de projectos em redes de actividades com complementaridade de recursos

Neste artigo abordamos a questão da alocação óptima dos recursos, mais especificamente, a análise da complementaridade dos recursos (recurso principal ou recurso-P e do recurso de suporte ou recurso-S) às actividades de um projecto. Neste artigo apresenta-se um algoritmo genético, baseado num alfabeto de chaves aleatórias, e analisa-se o procedimento para criar soluções a partir de um cromossoma.We address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource or P-resource and supportive resource or S-resource) to activities in a project. In this paper we present a Genetic Algorithm, based in a random keys alphabet, and we analyse the procedure to build solutions from a chromosome

Sequencing activities in a project network considering resource complementarity

Project management is a methodology widely used in organizations that believe in innovation and choose to organize their resources around projects. This paper presents new results and developments of a model that address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource and supportive resource) in a project. The concept of complementarity, which has been discussed based on an economic view, can be incorporated into the engineering domain as an enhancement of the efficacy of a “primary” resource (P-resource) by adding to it another “supportive” resource (S-resource). No replacement takes place. The gain achieved from such action is manifested in improved performance; e.g., shorter duration or improved quality, because of the enhanced performance of the P-resource. But such gain is usually achieved at an increased cost; namely the cost of the support resource(s). We developed a conceptual system capable of determining the ideal timing, and the ideal mixture of resources allocated to the activities of a project, such that the project is completed on time, if not earlier, with minimal cost. We present new computational results of a Genetic Algorithm, based in a random keys alphabet, with an optimized process that allowed reaching better results. The sequence of activities and the resource combinations for each pair activity/resources were obtained, respecting network constraints, showing the flexibility of the solution considering resources distribution and early resources release

Sequencing activities in a project network using resource complementarity model

The methodology of project management has been widespread in organizations of different functions and sizes. In this context, we address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource and supportive resource) in a project. We develop a conceptual system capable of determining the ideal timing, and the ideal mixture of resources allocated to the activities of a project, such that the project is completed on time, if not earlier, with minimal cost. In this paper we present new computational results of a Genetic Algorithm, based in a random keys alphabet to optimize the process to reach better results

A genetic algorithm for project scheduling in activity networks under resource complementarity

We address the issue of optimal resource allocation, and more specifically, the analysis of complementarity of resources (primary resource or P-resource and supportive resource or S-resource) to activities in a project. The concept of complementarity can be incorporated into the engineering domain as an enhancement of the efficacy of a "primary" resource (P-resource) by adding to it other "supportive" resources (S-resources). We developed a Genetic Algorithm capable of determining the ideal mixture of resources allocated to the activities of a project, such that the project is completed with minimal cost. This problem has a circularity issue that greatly increases its complexity. In this paper we present a constructive algorithm to build solutions from a chromosome that will be integrated in a Genetic Algorithm, which we illustrate by application to a small instance of the problem. The Genetic Algorithm is based on a random keys chromosome that is very easy to implement and allows using conventional genetic operators for combinatorial optimization problems. A project is formed by a set of activities. Each activity uses a specific set of resources, and it is also necessary to guarantee that there is no overlap in the time it takes to process activities in the same resource

Modelo de otimização do espaço livre de armazenagem num silo de cereais

Este trabalho baseia-se num caso de estudo real de planeamento de operações de armazenagem num silo rural de cereais, e enquadra-se nos problemas de planeamento e programação de armazéns. Os programadores deparam-se diariamente com o problema de arranjar a melhor solução de transferência entre células de armazenagem, tentando maximizar o número de células vazias, por forma a ter maior capacidade para receber novos lotes, respeitando as restrições de receção e expedição, e as restrições de capacidade das linhas de transporte. Foi desenvolvido um modelo matemático de programação linear inteira mista e uma aplicação em Excel, com recurso ao VBA, para a sua implementação. Esta implementação abrangeu todo o processo relativo à atividade em causa, isto é, vai desde a recolha de dados, seu tratamento e análise, até à solução final de distribuição dos vários produtos pelas várias células. Os resultados obtidos mostram que o modelo otimiza o número de células vazias, tendo em conta os produtos que estão armazenados mais os que estão para ser rececionados e expedidos, em tempo computacional inferior a 60 segundos, constituindo, assim, uma importante mais valia para a empresa em causa.This work is based on a real case study for planning storage operations in a rural grain silo, and fits the problems of planning and programming of warehouses. Programmers are faced daily with the problem of finding the best solution for transfers between storage cells, trying to maximize the number of empty cells in order to have greater capacity to receive new lots, subject to receiving, dispatching, and transportation lines capacity constraints. We developed a mixed integer linear programming model and an Excel/VBA application for its implementation. This implementation included the entire process, ranging from data collection, its treatment and analysis to the final solution for the distribution of various products by various cells. The results show that the model optimizes the number of empty cells, in computation time less than 60 seconds, and thereby constitutes a significant added value to the company concerned

RFID based warehouse management system. a case study of rok industries

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African Institutions of Science and TechnologyIn the supply chain and logistics industry, the precision of the data assets inventory plays a crucial role in warehouse activities. The operations like storage locations arrangement, inventory management, and maintaining the flow of incoming and outcoming goods lead to the success of the warehouse. Nowadays, most people prefer online shopping all over the world because it is faster than local trade. Thus, there's massive information in the supply chain and logistics sector to explore in order to improve operations of the warehouse such as receiving, ordering, shipping, storage assignment to facilitate the automation in the warehouse. As result, this research is proposed to improve warehouse activities. The RFID-based warehouse management system automates storage and inventory management without any human intervention. In order to accomplish the objectives of the study, a software program was developed with sets of rules, and an algorithm to optimize the inventory operations with the help of RFID technology. The predefined rules help in giving priorities to some of the selected products to store and retrieve in the indicated location. The long-range RFID reader is used in this project. The reader is attached to the entrance of the gate of the warehouse to facilitate the reading of incoming goods. Once the goods arrive in the warehouse, the storage location function will assign to each one the storage location and update status in the database. A handheld reader attached to the forklift facilitates inventory management and communicates with the application via a wireless network and finally store data in the database for future use. After developing this system, a test was conducted for testing the feasibility and applicability of the system. The output showed that the inventory management operation was made strides, and the correctness of inventory location increased from 72.8% to 99%. The cycle time moreover decreases from 60 minutes to 20 minutes which is down to 28.79%

Sequencing and Routing in a Large Warehouse with High Degree of Product Rotation

The paper deals with a sequencing and routing problem originated by a real-world application context. The problem consists in defining the best sequence of locations to visit within a warehouse for the storage and/or retrieval of a given set of items during a specified time horizon, where the storage/retrieval location of an item is given. Picking and put away of items are simultaneously addressed, by also considering some specific requirements given by the layout design and operating policies which are typical in the kind of warehouses under study. Specifically, the considered sequencing policy prescribes that storage locations must be replenished or emptied one at a time by following a specified order of precedence. Moreover, two fleet of vehicles are used to perform retrieving and storing operations, whose routing is restricted to disjoint areas of the warehouse. We model the problem as a constrained multicommodity flow problem on a space-time network, and we propose a Mixed-Integer Linear Programming formulation, whose primary goal is to minimize the time traveled by the vehicles during the time horizon. Since large-size realistic instances are hardly solvable within the time limit commonly imposed in the considered application context, a matheuristic approach based on a time horizon decomposition is proposed. Finally, we provide an extensive experimental analysis aiming at identifying suitable parameter settings for the proposed approach, and testing the matheuristic on particularly hard realistic scenarios. The computational experiments show the efficacy and the efficiency of the proposed approach

Serial batch processing machine scheduling: a cement industry case study

Dissertação de mestrado em Engenharia de SistemasThis work arises in the Cement Industry in the process of scheduling the clients to the warehouse and assignment to docking bays. The goal is to solve the scheduling and assignment problem, to improve both company’s service levels and the efficiency of its resources. After the real problem analysis, it was possible to conclude that it could be solved as a batching machine scheduling problem, where the jobs are the clients to be schedule, and the machine is the warehouse. The problem can be described as max 1 | rj,s-batch | Cmax . A Mixed Integer Linear Programming (MILP) model was proposed. However, as the number of jobs increased it started having computational difficulties. To overcome the problems of the MILP model two heuristics were proposed. The first one is a Constructive Algorithm (CA) that creates a first solution for the problem. The second heuristic is a metaheuristic algorithm, based on Simulated Annealing procedures, that starts with the initial solution of the CA and through three possible moves starts constructing the neighboring solutions space. After constructing the neighboring solutions space, it returns the best solution found. The computational tests proved that both the MILP model and the heuristics can ensure both feasible and optimum solutions. However, the MILP model consumes more computational resources. For some larger instances and giving a maximum limit of computational time of 8 hours, the MILP model cannot reach the optimality, nor the good results obtained by the heuristics, for those larger instances. The machine scheduling is a good approach for scheduling the trucks to the warehouse. Since it is also an innovative approach for the problem, considering the literature studied, maybe this work will inspire others to work on this idea or, at least, serve as a basis for future researches.Este trabalho tem como cenário a Indústria Cimenteira no processo de agendamento de clientes para atendimento no armazém e atribuição de pontos de carga. O objetivo é resolver o problema de agendamento visando otimizar tanto os níveis de serviço da empresa bem como a eficiência dos seus recursos. Depois da análise detalhada do problema real foi possível concluir que este podia ser resolvido como um problema de processamento em lotes em máquina única, onde as tarefas a agendar seriam os clientes e a máquina o armazém. O problema pode então ser descrito como 1 | rj,s-batch | Cmax . Um modelo de Programação Linear Inteira Mista (PLIM) foi proposto. Contudo, à medida que o número de tarefas aumentava, o modelo começava a ter dificuldades computacionais na obtenção de solução ótima. Para ultrapassar essas dificuldades, foram desenhadas e propostas duas heurísticas. A primeira é um Algoritmo Construtivo (AC) capaz de retornar uma solução inicial. A segunda, uma meta-heurística, baseada na abordagem do Simulated Annealing, que trabalha a solução inicial gerada pelo AC, através de três movimentos possíveis, e gera uma vizinhança de soluções. Depois, procura e retorna a melhor solução possível dessa vizinhança. Os testes computacionais provaram que tanto o modelo de PLIM como as heurísticas são capazes de retornar tanto soluções possíveis como ótimas. Contudo, o modelo de PLIM consome muitos mais recursos computacionais do que as heurísticas. Para instâncias de tamanho superior, dado um tempo de computação máximo de 8 horas, o PLIM, não conseguindo atingir a solução ótima, nem sequer consegue atingir soluções tão boas como as das heurísticas. A abordagem de agendamento em máquinas, utilizada neste trabalho, mostrou-se ser uma boa abordagem para o agendamento de clientes no armazém. Para além disso, esta é uma abordagem inovadora, tendo em conta a literatura estudada, e, talvez possa inspirar outros autores a trabalhar nesta ideia ou então servir de base para pesquisas futuras

Scheduling the truckload operations in automatic warehouses

This work presents a scheduling problem that arises in an automatic storage/retrieval warehouse system AS/RS involving the scheduling of the truck load operations. The truck loading operations are modelled as job shop problem with recirculation. The loads are considered as jobs, the pallets of a load are seen as the job’s operations. The forklifts are the machines. The minimization of the makespan allows minimizing the idle time of the warehouse’s equipments. A procedure based on genetic algorithms is presented to sequence the pallets of a set of loads that are prepared simultaneously. The genetic algorithm includes specific knowledge of the problem to improve its efficiency. This work presents interesting computational results for the minimization of the makespan