Solution of some engineering optimization problems

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" Solution of some engineering optimization problems "
Firas M. Tuffaha

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	55260
Doc. No	:	TL25214
Call number	:	‭3351065‬
Main Entry	:	Firas M. Tuffaha
Title & Author	:	Solution of some engineering optimization problems\ Firas M. Tuffaha
College	:	King Fahd University of Petroleum and Minerals (Saudi Arabia)
Date	:	2007
Degree	:	Ph.D.
student score	:	2007
Page No	:	129
Abstract	:	We consider two different engineering optimization problems; machining economics problem, and location allocation problem with single joint chance constraint. A classical example of machining economics problem is Multipass turning operation, where we have to find the optimal cutting parameters (cutting speed, feed rate, depth of cut) and the associated optimal number of passes that gives a minimum cost while meeting a set of technological constraints. We adopt the cost model presented by Shuaib et al. (1992) and show that the equality of cuts is the optimal policy. We introduce a solution procedure based on dividing the problem space into two sets, one main set governed by particle swarm optimization technique, and another subset controlled by any of the following developed methods: Scanning Method, Alternating Method, Feasible direction method and particle swarm optimization. Also we will consider the case in which the rough and finish cut values are fixed according to the machine resolution. We consider both the deterministic and stochastic discrete location allocation problem, where the single joint chance constraint is used for the later one. Assuming that locations are known, we will solve for the allocations by applying two methods, the Zoutendijk feasible direction method, and the successive linear approximation method. At the same time, we will introduce two linearization techniques inspired by the successive linear approximation to approximate the joint chance constraint by a set of linear approximations at a preselected set of points. The case where the demand distribution is described by discrete random variables will be considered. We present a heuristic inspired by the variable neighborhood framework to solve the discrete location allocation problem with single joint chance constraint. At the same time we give a new formulation for the deterministic discrete location allocation problem that transforms the problem from a nonlinear integer program into a linear integer program without adding any set of variables, only by adding an extra set of constraints. We consider the continuous location allocation problem with/without the single joint chance constraint. Motivated by the reformulation of the discrete location allocation version, we propose a discretization technique to solve the continuous case. In addition to this, we use particle swarm optimization heuristic to solve this problem.
Subject	:	Applied sciences; Machining economics; Multipass turning; Particle swarm optimization; Location allocation; Industrial engineering; Operations research; 0796:Operations research; 0546:Industrial engineering
Added Entry	:	King Fahd University of Petroleum and Minerals (Saudi Arabia)