An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities

The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, a genetic algorithm, which consists of a boundary search heuristic (BSH), a linear program, and a dual simplex method, is developed for an UA-FLP. To test the performance of the proposed technique, several test problems taken from the literature are used in the analysis. The results show that the proposed heuristic performs well with respect to solution quality and computational time.

PENERAPAN PROGRAM LINIER MENGGUNAKAN METODE DUAL SIMPLEKS DAN METODE QUICK SIMPLEKS UNTUK MEMINIMUMKAN BIAYA (STUDI KASUS: KELOMPOK WANITA TANI (KWT) SENTOSA SANTUL)

The Sentosa Santul Women Farmers Group (KWT) is a group of women farmers in Dusun Santul, Kampar Utara District an is engaged in the field of food crops is chili. The Sentosa Santul Women Farmers group (KWT) uses 4 types of fertilizers for chili plant fertilization, namely hydro complex fertilizer, phonska, NPK Zamrud and goat manure.The KWT wants the minimum fertilizer cost but the nutrients in the plants are met. The method used in this research is the dual simplex method and the quick simplex method. The purpose of this study is to determine the minimum costs that must be incurred by the Womens Farmer Group (KWT) for fertilization using the dual simplex method and the quick simplex method to obtain an optimum and feasible solution. For the dual simplex method, the optimum and feasible solution were obtained using the Gauss Jordanelimination. While the quick simplex method, the solution is illustrated using a matrix to reduce the number of iterations needed to achieve the optimal solution. Based on the research result, it is found that the quick simplex method is more efficient than the dual simplex method. This can be seen from the number of iterations carried out. Dual simplex method iteration there are two iterations and quick simplex one iteration. The dual simplex method and the quick simplex method produce the same value.

A Modification of Quadratic Programming Algorithm

In the existing methods for solving Quadratic Programming Problems having linearly factorized objective function and linear constraints, all the linear factors of the objective function are supposed to be positive for all feasible solutions. Here, a modification of the existing methods is proposed and it has been proved that the modified method can be applied to find the optimal solution of the problem even if all the linear factors of the objective function are not necessarily positive for all feasible solutions. Moreover, the proposed method can be applied to find the optimal solution of the problem even if the basic solution at any stage is not feasible. If the initial basic solution is feasible, we use simplex method to find the optimal solution. If the basic solution at any stage is not feasible, we use dual simplex method to find the optimal solution. Numerical examples are given to illustrate the method and the results are compared with the results obtained by other methods.

A dual simplex method for grey linear programming problems based on duality results

PurposeFor extending the common definitions and concepts of grey system theory to the optimization subject, a dual problem is proposed for the primal grey linear programming problem.Design/methodology/approachThe authors discuss the solution concepts of primal and dual of grey linear programming problems without converting them to classical linear programming problems. A numerical example is provided to illustrate the theory developed.FindingsBy using arithmetic operations between interval grey numbers, the authors prove the complementary slackness theorem for grey linear programming problem and the associated dual problem.Originality/valueComplementary slackness theorem for grey linear programming is first presented and proven. After that, a dual simplex method in grey environment is introduced and then some useful concepts are presented.

Self-Regulating Artificial-Free Linear Programming Solver Using a Jump and Simplex Method

An enthusiastic artificial-free linear programming method based on a sequence of jumps and the simplex method is proposed in this paper. It performs in three phases. Starting with phase 1, it guarantees the existence of a feasible point by relaxing all non-acute constraints. With this initial starting feasible point, in phase 2, it sequentially jumps to the improved objective feasible points. The last phase reinstates the rest of the non-acute constraints and uses the dual simplex method to find the optimal point. The computation results show that this method is more efficient than the standard simplex method and the artificial-free simplex algorithm based on the non-acute constraint relaxation for 41 netlib problems and 280 simulated linear programs.

A Note on Branch and Bound Algorithm for Integer Linear Programming

In branch and bound algorithm for integer linear programming the usual approach is incorporating dual simplex method to achieve feasibility for each sub-problem. Although one can also employ the phase 1 simplex method but the simplicity and easy implementation of the dual simplex method bounds the users to use it. In this paper a new technique for handling sub-problems in branch and bound method has been presented, which is an efficient alternative of dual simplex method.