Algoritmo de escalonamento de instância de máquina virtual na computação em nuvem

Pós-graduação em Ciência da Computação - IBILCE

Otimização de problemas multimodais usando meta-heurísticas evolutivas

Pós-graduação em Engenharia Elétrica - FEIS

O emprego de técnicas de qualidade de serviço em redes sem fio aplicado à automação predial

Pós-graduação em Engenharia Elétrica - FEIS

Multi-population genetic algorithm to solve the synchronized and integrated two-level lot sizing and scheduling problem

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A heuristic algorithm based on multi-assignment procedures for nurse scheduling

This paper tackles a Nurse Scheduling Problem which consists of generating work schedules for a set of nurses while considering their shift preferences and other requirements. The objective is to maximize the satisfaction of nurses' preferences and minimize the violation of soft constraints. This paper presents a new deterministic heuristic algorithm, called MAPA (multi-assignment problem-based algorithm), which is based on successive resolutions of the assignment problem. The algorithm has two phases: a constructive phase and an improvement phase. The constructive phase builds a full schedule by solving successive assignment problems, one for each day in the planning period. The improvement phase uses a couple of procedures that re-solve assignment problems to produce a better schedule. Given the deterministic nature of this algorithm, the same schedule is obtained each time that the algorithm is applied to the same problem instance. The performance of MAPA is benchmarked against published results for almost 250,000 instances from the NSPLib dataset. In most cases, particularly on large instances of the problem, the results produced by MAPA are better when compared to best-known solutions from the literature. The experiments reported here also show that the MAPA algorithm finds more feasible solutions compared with other algorithms in the literature, which suggest that this proposed approach is effective and robust. © 2013 Springer Science+Business Media New York.

Joint rolling-horizon scheduling of materials processing and lot-sizing with sequence-dependent setups

A lot sizing and scheduling problem from a foundry is considered in which key materials are produced and then transformed into many products on a single machine. A mixed integer programming (MIP) model is developed, taking into account sequence-dependent setup costs and times, and then adapted for rolling horizon use. A relax-and-fix (RF) solution heuristic is proposed and computationally tested against a high-performance MIP solver. Three variants of local search are also developed to improve the RF method and tested. Finally the solutions are compared with those currently practiced at the foundry.

A modified ant colony optimization algorithm modeled on tabu-search methods

An earlier model underlying the foraging strategy of a pachycodyla apicalis ant is modified. The proposed algorithm incorporates key features of the tabu-search method in the development of a relatively simple but robust global ant colony optimization algorithm. Numerical results are reported to validate and demonstrate the feasibility and effectiveness of the proposed algorithm in solving electromagnetic (EM) design problems.

Heuristics and meta-heuristics for lot sizing and scheduling in the soft drinks industry: A comparison study

This chapter studies a two-level production planning problem where, on each level, a lot sizing and scheduling problem with parallel machines, capacity constraints and sequence-dependent setup costs and times must be solved. The problem can be found in soft drink companies where the production process involves two interdependent levels with decisions concerning raw material storage and soft drink bottling. Models and solution approaches proposed so far are surveyed and conceptually compared. Two different approaches have been selected to perform a series of computational comparisons: an evolutionary technique comprising a genetic algorithm and its memetic version, and a decomposition and relaxation approach. © 2008 Springer-Verlag Berlin Heidelberg.

Evaluating genetic algorithms with different population structures on a lot sizing and scheduling problem

This paper studies the use of different population structures in a Genetic Algorithm (GA) applied to lot sizing and scheduling problems. The population approaches are divided into two types: single-population and multi-population. The first type has a non-structured single population. The multi-population type presents non-structured and structured populations organized in binary and ternary trees. Each population approach is tested on lot sizing and scheduling problems found in soft drink companies. These problems have two interdependent levels with decisions concerning raw material storage and soft drink bottling. The challenge is to simultaneously determine the lot sizing and scheduling of raw materials in tanks and products in lines. Computational results are reported allowing determining the better population structure for the set of problem instances evaluated. Copyright 2008 ACM.

Tabu search to solve the Synchronized and Integrated Two-Level Lot Sizing and Scheduling Problem

This paper proposes a tabu search approach to solve the Synchronized and Integrated Two-Level Lot Sizing and Scheduling Problem (SITLSP). It is a real-world problem, often found in soft drink companies, where the production process has two integrated levels with decisions concerning raw material storage and soft drink bottling. Lot sizing and scheduling of raw materials in tanks and products in bottling lines must be simultaneously determined. Real data provided by a soft drink company is used to make comparisons with a previous genetic algorithm. Computational results have demonstrated that tabu search outperformed genetic algorithm in all instances. Copyright 2011 ACM.

A priori reformulations for joint rolling-horizon scheduling of materials processing and lot-sizing problem

In many production processes, a key material is prepared and then transformed into different final products. The lot sizing decisions concern not only the production of final products, but also that of material preparation in order to take account of their sequence-dependent setup costs and times. The amount of research in recent years indicates the relevance of this problem in various industrial settings. In this paper, facility location reformulation and strengthening constraints are newly applied to a previous lot-sizing model in order to improve solution quality and computing time. Three alternative metaheuristics are used to fix the setup variables, resulting in much improved performance over previous research, especially regarding the use of the metaheuristics for larger instances. © 2013 Elsevier Ltd. All rights reserved.

A genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)