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Summary: The application of renewable energy sources in electrical energy generation is becoming widespread due to the decrease of installation costs and the increase of environmental concerns. Hybrid power generation systems are advantageous to meet the load demand, but optimal sizing is the main concern for having a cost‐effective system based on given load demand and techno‐economic indicators. This paper proposes a deterministic algorithm and utilizes genetic and artificial bee colony (ABC) optimization algorithms for optimal sizing of PV/battery and PV/WT/battery hybrid systems with minimum levelized cost of electricity (LCOE) constraint for two locations, Nigde and Bozcaada, in Turkey. The loss of power supply probability (LPSP) is used to build a reliable system and to make sure that the system produces required energy. Experimental results showed that optimal sizing of each location is different due to different wind and solar characteristics of locations. PV/battery model is more suitable for Nigde location with 1.22% LPSP and 0.1514 [$/kWh] LCOE, while PV/WT/battery model is more cost‐efficient for Bozcaada location with 1.952% LPSP and 0.0872 [$/kWh] LCOE. Time performances of the algorithms are also investigated. It has been seen that the ABC algorithm has better performance and less execution time. This study demonstrated that heuristic algorithms are more applicable than deterministic algorithms, due to fast discovery of optimal solutions for hybrid renewable energy systems. [ABSTRACT FROM AUTHOR]

This paper considers the energy consumption minimization in permutation flow shop (PFS) scheduling problem. The energy consumption of eachmachine is decomposed into two parts: useful part which completes the operation at current stage and wasted part which is consumed during idle period. The objective considered here is to minimize the total wasted energy consumption which is a weighted summation of the idle time of each machine. To solve this new problem, a branch-and-bound algorithm is developed. Two lower bounds are proposed, and an initial upper bound by using a variant of NEH heuristic algorithm is applied. Compared with themakespan minimization criterion, thismodel deduces more energy-saving solutions. Experimental results also validate the efficiency of the proposed algorithm for problems with job number not larger than 15. [ABSTRACT FROM AUTHOR]