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Scientia Iranica A (2018) 25(3), 1094{1104

Sharif University of TechnologyScientia Iranica

Transactions A: Civil Engineeringhttp://scientiairanica.sharif.edu

A hybrid WOA-CBO algorithm for construction sitelayout planning problem

A. Kaveha; and M. Rastegar Moghaddamb

a. Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak,Tehran, P.O. Box 16846-13114, Iran.

b. School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, P.O. Box 16846-13114, Iran.

Received 26 July 2016; received in revised form 7 December 2016; accepted 24 April 2017

KEYWORDSOptimization;Site layout problem;Meta-heuristicalgorithms;WOA algorithm;WOA-CBO algorithm.

Abstract. The Whale Optimization Algorithm (WOA) is a recently developed swarm-based optimization algorithm inspired by the hunting behavior of humpback whales. Thisstudy attempts to enhance the original formulation of the WOA by hybridizing it with someconcepts of the Colliding Bodies Optimization (CBO) in order to improve solution accuracy,reliability, and convergence speed. The new method, called WOA-CBO algorithm, isapplied to construction site layout planning problem. To show eciency and performanceof the WOA and WOA-CBO in construction site layout problems, three case studies areselected. The rst case is a discrete and equal area facility layout problem that everyfacility can assign to any location; the second case is an unequal area version of discretefacility layout problem with more constraints; and the last case is a continuous model ofconstruction site layouts. These cases are studied by WOA, CBO, and WOA-CBO, andthe results are compared with each other. 2018 Sharif University of Technology. All rights reserved.

1. Introduction

Construction Site Layout Planning (CSLP) has beenfound to be a critical task in construction planning byexperts and this task is emphasized on implementingearly in the planning phase [1,2].

An appropriate layout of facilities leads to highproductivity and project success by minimizing thetravel cost, decreasing time and eort spent on materialhandling, and improving safety, especially for hugeconstruction sites [3,4]. Despite these benets, ownersand bidding contractors are unwilling to spend moneyfor eective site layout planning and management.This is because of the competitive bidding structureof the construction industry. Therefore, especially for

*. Corresponding author. Tel.: 021-44202710E-mail address: alikaveh@iust.ac.ir (A. Kaveh)

doi: 10.24200/sci.2017.4212

projects with limited budgets, managers prefer to usetheir previous experience, ad-hoc rules, and rst-come-rst-serve approach, which leads to ineciency andproductivity losses [2,5].

The purpose of designing a construction site lay-out is to assign a set of specic temporary facilities suchas job oce, labor residence, warehouse, and batchplants to the appropriate location, while satisfying aset of design requirements and maximize design qualityin terms of design preferences such as minimizing thetotal travel cost between facilities [2,6].

Construction plant site can be modeled into twotypes, namely, discrete and continuous. In the rsttype (discrete), managers identify feasible places forlocating facilities; then, these places are divided intorectangular blocks each of which can be assigned to afacility. When costs associated with the ow betweenfacilities are linear with respect to distance traveled andquantity of ow, this type of problem can be formulatedas Quadratic Assignment Problem (QAP) [7,8]. The

A. Kaveh and M. Rastegar Moghaddam/Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 1094{1104 1095

QAP is a classic combinatorial optimization problemand is well known for its various applications [9]. QAPis known as a non-polynomial hard (NP-hard) problemand due to combinatorial complexity, it cannot besolved exhaustively for reasonably sized layout prob-lems [6]. As an instance, for n facilities, the numberof feasible congurations is n! with larger growth thanen. This is a huge number, even for a small n. For 10facilities, the number of possible alternatives is alreadywell over 3,628,000 or for 15 facilities, it is a 12-digitnumber. In real problems, a project with n = 15 isknown as a small project [10]. In the second type(continuous), facilities can be located in every availablespaces unlike the previous model and no predeterminedlocations are considered. This leads to less limitationsin nding proper locations for facilities. In spite ofthe above-mentioned exibility, the searching processof this approach is more complicated; therefore, robustmethods are required for this type [11].

The aforementioned complexity has motivated re-searchers to apply various heuristic and meta-heuristicalgorithms in order to obtain optimal or near optimalsolutions to construction site layout problems.

Li and Love [12] applied the Genetic Algorithm(GA) to nd the optimal solution in a site-levelunequal-area facility layout problem; also, Cheung etal. [13] used a GA model for optimizing a site pre-castyard. Osman et al. [14] proposed a hybrid cad-basedalgorithm using genetic algorithm in order to optimizethe location of the temporary facilities on a site. El-rayes et al. [15] employed multi-objective version of GAfor solving a continuous and multi-objective site layoutproblem.

Many researchers have utilized Particle SwarmOptimization (PSO) in construction site layouteld [2,16-18]. For instance, Zang and Wang [17]proposed a PSO model for an un-equal area staticCSLP problem under single objective function. Xu andLi [18] considered a Multi-Objective Particle SwarmOptimization algorithm (MOPSO) to solve a dynamicsite layout problem in fuzzy random environment. Lienand Cheng [16] proposed a hybrid Particle-Bee Algo-rithm (PBA) for construction site layout optimizationunder single objective function to locate facilities inpredetermined locations.

Lam et al. [19], Gharaie et al. [20], and Calisand Yuksel [21] also employed another optimizationalgorithm. They used ant colony to solve a staticsite layout problem. Ning et al. [22] employed Max-Min Ant System (MMAS), which was one of thestandard variants of the Ant Colony Optimization(ACO) algorithms, to solve a dynamic construction sitelayout planning. Mixed integer programming [23],Tabu search [24], harmony search [4], and collidingbodies optimization [25] are other methods that areused in this eld.

In this study, WOA and WOA-CBO algorithmsare proposed to solve construction site layout problems.Whale optimization algorithm was developed by Mir-jalili and Lewis [26]. WOA-CBO algorithm is an en-hanced version of WOA that is introduced in this paperfor the rst time. Three case studies are conducted toshow the eciency and performance of the WOA andWOA-CBO in both models of construction site layoutproblem and the obtained results are compared.

2. Meta-heuristic algorithms

In this study, we have used two new meta-heuristicalgorithms in construction site layout problems; WhaleOptimization Algorithm (WOA) and the hybrid Al-gorithm (WOA-CBO). In this section, rst, whaleoptimization algorithm is explained and then, a newversion of whale optimization algorithm is introducedthat is called WOA-CBO algorithm.

2.1. Whale Optimization Algorithm (WOA)Whale optimization algorithm is a novel meta-heuristicalgorithm, which mimics the social behavior of hump-back whales. This meta-heuristic algorithm has beendeveloped by Mirjalili and Lewis [26]. In this al-gorithm, the spiral bubble-net feeding maneuver ismathematically modeled to perform optimization. Itshould be mentioned that bubble-net feeding is aunique behavior that can only be observed in hump-back whales [26]. In order to update the positionof the whales during optimization, two behaviors areidentied, namely, the shrinking encircling mechanismand the spiral bubble-net feeding maneuver. Since theposition of the optimum design in the search space isnot known, the basic WOA algorithm assumes thatthe current best candidate solution is the optimum oris close to the optimum, and the other search agentswill update their positions towards the best searchagent [26]. The WOA procedure is extracted from [26]and is briey explained in the following.

The WOA starts with a set of random popula-tions. At each iteration, search agents update theirpositions according to A vector's value. Updatingmechanism is detailed in following. This processcontinues until terminating criterion is satised.

The WOA algorithm has two phases, namely,exploitation and exploration. This algorithm smoothlytransits between exploration and exploitation phases.The transition is done due to variation of A vector'svalue. A vector's value decreases during iterations;half of iterations are assigned to exploration phase,when jAj 1, and the other half are dedicated toexploitation, when jAj < 1. Here, the sign jj indicatesthe absolute value. The vector A is computed asfollows:~A = 2~a:~r ~a; (1)

1096 A. Kaveh and M. Rastegar Moghaddam/Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 1094{1104

where ~a linearly decreases from 2 to 0 over the courseof iterations and r is a random vector in [0,1].

2.1.1. Bubble-net attacking method (exploitationphase)

In order to model the bubble-net behavior of humpbackwhales mathematically, two approaches are considered,namely, shrinking encircling mechanism and spiralupdating position. Since the humpback whales swimaround the prey within a shrinking circle and along aspiral-shape path simultaneously, WOA assumes thatthere is a probability of 50% to choose between thesetwo behaviors. Shrinking encircling mechanism ismodeled in the following formulae:

~C = 2:~r; (2)

~D =~C: ~Xbest ~X ; (3)