Recent Results on Approximate Optimization Methods for the Unit Commitment Problem

International audienceThis work provides an account of recently proposed methods to address the Unit Commitment (UC) problem. In the UC problem, the goal is to schedule a subset of a given group of electrical power generating units and also to determine their production output in order to meet energy demands at minimum cost. In addition, the solution must satisfy a set of technological and operational constraints. Here, computational results are reported for the most effective methodologies. Amongst the problems chosen to report the computational results are the most frequently used benchmark problems, due to Kazarlis, Bakirtzis and Petridis. In the problems considered, the units, which can be up to 100, have to be scheduled for 24-hour period

Pendekatan Biased Random Key Genetic Algorithm dengan Multiple-Parent untuk Kasus Capacitated Closed Vehicle Routing Problem With Time Windows

Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) is a Vehicle Routing Problem (VRP) which considers truck capacity and distributor’s working hours constraints. Since CCVRPTW is a NP-Hard problem, designing an effective and efficient algorithm to solve the problem becomes an important task. In this research, a Biased Random Key Genetic Algorithm (BRKGA) with multiple parent is designed to address the CCVRPTW. The proposed algorithm is then coded in MATLAB and applied to solve an optimization problem for distributing soft drink. The performance of the algorithm is compared to a heuristic that has been used to solve the same problem. The result shows that: (1) the proposed BRKGA with multiple parent outperforms the heuristic in terms of the obtained total distribution cost, (2) the proposed algorithm further improves the performance of the standard BRKGA, and (3) Obtaining the third parent from the non-elite class population yields a better result compared to if it is taken from the whole population

Perancangan Biased Random Key Genetic Algorithm dengan Multiple Populations untuk Menyelesaikan Capacitated Vehicle Routing Problem with Time Windows

This research deals with a variation of Vehicle Routing Problem (VRP) by accommodating capacity and time constraints, also known as Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). Soft drink distribution from depot to a number of outlets is an example of CTWVRP where every vehicle used to meet all demand from outlets must not exceed the capacity of the truck while the distribution process activity is restricted by the service hours at the distribution company. The main problem of this research is therefore how to determine the route of the truck such that the total transportation cost is minimized without violating the constraints. The CVRPTW is a Non Polynomial Hard (NP-Hard) Problem, therefore an efficient algorithm is needed to solve this problem effectively in a reasonable computation time. This research proposes a Biased Random Key Genetic Algorithm (BRKGA) with multiple populations which is coded in MATLAB for addressing the soft drink distribution. The performance of the proposed algorithm is then compared to a heuristic procedure that is previously used for dealing with the same problem. The result shows that the BRKGA with multiple population yields a lower total transportation cost compared to that of resulted from the heuristic. In addition, the use of multiple populations could further improve the performance of the basic BRKGA

Penyelesaian Capacitated Closed Vehicle Routing Problem With Time Windows (Ccvrptw) Menggunakan Brkga Dengan Local Search

Determining the shortest route sequence for distributing productfrom depot to a number of outlets to minimize the total distribution cost is the main problem to deal with in Vehicle Routing Problem (VRP). Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) is a variant of VRPthat accommodates the truck capacity and the working hours at the distribution. As CCVRPTW falls into NP-hard problem, it requires an efficient and effective algorithm to find the optimal solution. This research is aimed at designing Biased Random Key Genetic Algorithm (BRKGA) combined with a local search to solve CCVRPTW. The proposed algorithm is then coded in MATLAB. Using extensive numerical tests, the best setting of the algorithm parameters is obtained. The performance of the algorithm is then compared to a heuristic for solving a soft drink distribution problem. The result shows that BRKGA hybridized with a local search returns a lower total distribution cost compared to that of resulting from the heuristic. in addition, it is demonstrated that the proposed algorithm could further improve the performance of the standard BRKGA