Charged System Search and Magnetic Charged System Search Algorithms for Construction Site Layout Planning Optimization

Abstract

IntroductionConstruction site layout planning can be defined as an effort to place different temporary facilities in available site locations such that multiple objectives are satisfied as much as possible. The site layout can have a considerable effect on the constructions operations and should cover several involved constraints [1]. In construction projects, besides the required resources like materials, machinery, manpower and the construction project time and budget, available spaces are vital for placing the facilities and construction requirements transportations [2]. Due to the limitations of the construction site space as a resource, its management is necessary [3]. With the extension of high-rise building construction and construction activities besides the lack of available spaces in construction sites, proper utilization of this resource has been highlighted because of its significant positive influences on direct cost, safety, and security of the site which consequently effects on the total cost and schedule of the project. Therefore, it can be considered as the heart of productivity in construction projects [4][5] [6]. In a construction project, contractors usually have the responsibility of an efficient site layout planning. This is considered as a complex task because of the numerous factors involved in such facilities, site surroundings, constraints and etc. [7]. An efficient construction site layout planning has to deal with many conflicting objectives and is a decision-making procedure [8]. Not paying attention to site layout planning will lead to inefficiency of the project, additional cost for material handling, reworking, further cost for setting up facility in dynamic sites, wasting time and creation of an unsafe site [9][4]. In recent decades, site layout planning problem has attracted researchers' attention and many researches were conducted on this issue.Site layout planning problem is an NP-Hard problem from the viewpoint of complexity [10]. This means there is no recognized algorithm that is capable of finding the optimum solution in polynomial time [11]. By passing of time and science development, trial and error or applying experts' opinions have been replaced by optimization methods and researchers have proposed a wide range of techniques and methods to

Optimization algorithms for construction site layout planning: a systematic literature review

“…In the selected articles, metaheuristic algorithms are widely used to solve the CSLP problems because of four major reasons: (1) considering combinatorial optimization problems, a global optimal solution is impossible (Ning et al , 2019; Said and El-Rayes, 2013a) or cannot be found at a reasonable cost (e.g. computational time) (Benjaoran and Peansupap, 2019; Farmakis and Chassiakos, 2018; Kaveh et al , 2018a; Razavialavi and Abourizk, 2017a; Li et al , 2015; Xu and Song, 2015; Kalmár et al , 2014); (2) more objectives and constraints should be considered for congested construction sites and large-scale projects (Kumar and Cheng, 2015; Kaveh et al , 2018b; Abdelmegid et al , 2015; Xu and Li, 2012; Said and El-Rayes, 2011); (3) the metaheuristic algorithms are not problem-specific (Li et al , 2019; Khalafallah and Hyari, 2018), and the derivatives of objective functions are not required (Kaveh and Vazirinia, 2019; Xu et al , 2016b; Gholizadeh et al , 2010; Zhou et al , 2009); and (4) in complex mathematical models (e.g. bi-level models), it is difficult to use exact algorithms to find all feasible solutions (Song et al , 2016, 2018a; Li et al , 2015).…”

“…With the same number of facilities and locations, the representation in (a) has been utilized in both exact (Huang and Wong, 2015; Wong et al , 2010) and metaheuristic algorithms (Gholizadeh et al , 2010; Xu and Li, 2012). When considering dummy facilities in (b), only metaheuristic algorithms are explored (Li et al , 2019; Kaveh et al , 2018a; Song et al , 2017; Akanmu et al , 2016). In other words, the discussions on the use of exact algorithms to solve the CSLP problems with dummy facilities are limited (Huang and Wong, 2019; Huang et al , 2010; Yi et al , 2018).…”

“…Non-rotatable (Figure 9a) and rotatable facilities (Figure 9b) are also discussed. Heuristic (Pem and Malyusz, 2016; Andayesh and Sadeghpour, 2013) and metaheuristic algorithms (Oral et al , 2018; Kaveh et al , 2018b; Kaveh et al , 2018a; Khalafallah and Hyari, 2018; Abune'meh et al , 2016; Xu and Song, 2015) have been utilized in the layouts of non-rotatable facilities. Based on the reviewed articles, only metaheuristic algorithms (Abotaleb et al , 2016; Yahya and Saka, 2014; Zhou et al , 2009) are selected and utilized for rotatable facilities.…”

Tilt Integral Derivative Controller Optimized by Battle Royale Optimization for Wind Generator Connected to Grid

“…Among the various algorithms several meta heuristic algorithms are used for solving complicated problems [29]. These algorithms inspired by nature in most cases such as Particle Swarm Optimization [30], Whale Optimization algorithm [31], Antlion Optimizer [32], Bat algorithm [33], Ant Colony optimization [34], Magnetic Charges System Search [35], Grey Wolf Optimizer [36], Harmony Search [37], League Championship algorithm [38], Dragonfly algorithm [39] etc. have been presented in literature.…”

Improved magnetic charged system search optimization algorithm with application to satellite formation flying

“…Considering the good performances of MCSS, this algorithm has been chosen to be further developed in this work. Until now, MCSS has mainly been used for structural design optimization (Kaveh & Zolghadr, 2014;Kaveh et al, 2018), although there is also another work (Kumar et al, 2016) that applies successfully the MCSS to a more common topic in artificial intelligence, which is the clustering optimization (a very useful technique used for example in machine learning and pattern recognition); in particular, in (Kaveh et al, , 2015 an improved version of MCSS has already been proposed by the authors, but the reported improvements are only related to the part of the algorithm dealing with the harmony search, whereas the improvements proposed in the present paper concern the whole structure of the algorithm. Indeed, this paper deals with the development of an Improved version of the Magnetic Charged System Search algorithm, called IMCSS, and its applications in space trajectory planning, reagrading in particular satellite formation flying manoeuvres.…”