URL: http://scialert.net/abstract/?doi=jas.2010.1380.1388International audienceWe present in this study some manufacturing systems scheduling constraints that we adapt to the operating theatre scheduling. The scheduling that should comply with all operating constraints, related to quality, to patients and numerous resources information can be considered as a two-stage hybrid flow shop problem without buffer. We confirmed the existence of the constraints after a large study with operating theatre managers at several hospitals. For resolution, we use two methods: Local search for constraint satisfaction in the initial feasible solution and Tabu search with restricted neighborhood system for Makespan Optimization. In one hand, we use yearly data from an existing multidisciplinary which has 3 operating rooms and 9 beds and releases 10 surgeries per day. In the other hand, we schedule others examples with different variables. The amelioration rates of both makespan and constraints conflict were interessting according to program execution time

The design of energy systems is very important in order to reduce operating costs and guarantee the reliability of a system. This paper proposes a new algorithm to solve the design problem of optimal multi-objective redundancy of series-parallel power systems. The chosen algorithm is based on the hybridization of two metaheuristics, which are the bat algorithm (BA) and the generalized evolutionary walk algorithm (GEWA), also called BAG (bat algorithm with generalized flight). The approach is combined with the Ushakov method, the universal moment generating function (UMGF), to evaluate the reliability of the multi-state series-parallel system. The multi-objective design aims to minimize the design cost, and to maximize the reliability and the performance of the electric power generation system from solar and gas generators by taking into account the reliability indices. Power subsystem devices are labeled according to their reliabilities, costs and performances. Reliability hangs on an operational system, and implies likewise satisfying customer demand, so it depends on the amassed batch curve. Two different design allocation problems, commonly found in power systems planning, are solved to show the performance of the algorithm. The first is a bi-objective formulation that corresponds to the minimization of system investment cost and maximization of system availability. In the second, the multi-objective formulation seeks to maximize system availability, minimize system investment cost, and maximize the capacity of the system.

In electrical power engineering, elements such as reliability analysis, modeling, and optimization for complex systems are of the utmost importance. Although there exist myriad studies regarding reliability optimization with conventional methods, researchers are still seeking to find more efficient and accurate methods to address the issue of the redundancy allocation problem. To that effect, an ideal power energy management approach is put forward for the operation of a hybrid microgrid system with different kinds of productions. In the present study, we suggest three algorithms in order to optimize the series-parallel power energy system: the Firefly (FA), Bat (BA), and Interior Search (ISA) algorithms. Moreover, the reliability estimate of the system is solved with the Ushakov algorithm (UMGF). The components may completely fail, which decreases their performance rate. Furthermore, the optimization results are achieved using objective functions that include the total cost of the system, emission gases (NO_X, SO₂, and CO₂) of the power production from fuel cells, diesel generators, and gas turbines, and take into consideration the dependability indices. Devices used in power subsystems are characterized based on their dependabilities, performances, capital costs, and maintenance costs. Reliability hinges on a functioning system, which naturally entails meeting customer demand; as a result, it is influenced by the accumulated batch curve. This method provides an idea with regards to the economic cost optimization of microgrid systems. Finally, we present the results of numeric simulations.

In the last years, a set of bio-inspired metaheuristics has proved their efficiencies in combinational and continues optimization areas. This paper intends to hybrid a discrete version of Bat Algorithm (BA) with Generalized Evolutionary Walk Algorithm (GEWA) to solve the mono-processors two stages Hybrid Flow Shop scheduling. The authors compare the modified bat algorithm with the original one, with Particle Swarm Optimization (PSO) and with others results taken from literature. Computational results on a standard two-stage hybrid flow shop benchmark of 70 cases, and about 1700 instances, indicate that the proposed algorithm finds the best makespan (Cmax) in a good processing time comparing to the original bat algorithm and other algorithms.

This article intends to resolve the evolving environmental economic power dispatching problem (EED) using an enhanced version of the bat algorithm (BA) which is the Bat Algorithm with Generalized Fly (BAG). A good solution based on the Evolutionary Boundary Constraint Handling Scheme rather than the well-known absorbing technique and a good choice of the bi-objective function are provided to maintain the advantages of such algorithms on this problem. In the first stage, an individual economic power dispatch problem is considered by minimizing the fuel cost and taking into account the maximum pollutant emission. In the second stage and after weighting soft constraints satisfaction maximization and hard constraints abuse penalties, the proposed approach of the bi-objective environmental and economic load dispatch was built on a pareto function. The approach was tested on a thermal power plant with 10 generators and an IEEE30 power system of 6 generators. The results on the two datasets compared to those of other methods show that the proposed technique yields better cost and pollutant emissions.