For remote places having less-strong wind, single resources based renewable energy system (RES) with battery storage can sustainably and economically generate electrical energy. There is hardly any literature on optimal sizing of such RES for very low load demand situation. The objective of this study is to techno-economically optimize the system design of a Photovoltaic (PV)-battery storage RE...

In this research, we study the multi-skill resource-constrained project scheduling problem, where there are generalized precedence relations between project activities. Workforces are able to perform one or several skills, and their efficiency improves by repeating their skills. For this problem, a mathematical formulation has been proposed that aims to optimize project completion time, reworki...

Most companies operate to maximize profits and increase their market shares in competitive environments. Since the proper location of facilities conditions profits, facility problem (CFLP) has been extensively applied literature. This generally falls within class NP-hard problems, which are difficult solve. Therefore, choosing a solution method optimize is key factor. Even though CFLPs have con...

In this paper we experimentally assess, from both algorithmic and pragmatic perspectives, the adequacy of linguistic descriptions real data generated by two metaheuristics: simulated annealing genetic algorithm meta-heuristics. The type consider are fuzzy quantified statements (both Zadeh's type-1 type-2) involving three well-known quantification models (Zadeh's scalar Delgado's GD). We conduct...

Global optimisation of unknown noisy functions is a daunting task that seems to appear in domains ranging from games to control problems to meta-parameter optimisation for machine learning. We show how to incorporate heuristics to Stochastic Simultaneous Optimistic Optimization (STOSOO), a global optimisation algorithm that has very weak requirements from the function. In our case, heuristics c...

This paper describes a Benders decomposition algorithm capable of efficiently solving large-scale instances of the well-known multi-commodity capacitated network design problem with demand uncertainty. The problem is important because it models many real-world applications, including telecommunications, transportation, and logistics. This problem has been tackled in the literature with meta-heu...

Wireless sensor networks (WSNs) is one of the most important technologies in this century. As sensor nodes have limited energy resources, designing energy-efficient routing algorithms for WSNs has become the research focus. And because WSNs routing for maximizing the network lifetime is a NP-hard problem, many researchers try to optimize it with meta-heuristics. However, due to the uncertain va...

Cuckoo search (CS) is one of the most recent population-based meta-heuristics. CS algorithm is based on the cuckoo’s behaviour and the mechanism of Lévy flights. Unfortunately, the standard CS algorithm is proposed only for continuous optimisation problems. In this paper, we propose a discrete binary cuckoo search (BCS) algorithm in order to deal with binary optimisation problems. To get binary...

This paper presents several local search meta-heuristics for the problem of scheduling a single machine to minimise total weighted tardiness. A genetic algorithm for the static single machine total weighted tardiness problem is presented, and a multi-start version named metaGA is proposed. The obtained computational results permit to conclude about their efficiency and effectiveness. The resolu...

Hybrids of meta-heuristics have been shown to be more effective and adaptable than their parents in solving various combinatorial optimization problems. However, hybridized schemes are more tedious to implement due to their complexity. We address this problem by proposing the Meta-heuristics Development Framework (MDF). In addition to being a framework that promotes reuse to reduce developmenta...