A genetic algorithm for supermarket location problem

2015 ◽  
Vol 35 (1) ◽  
pp. 122-127 ◽  
Author(s):  
Mohammed Alnahhal ◽  
Bernd Noche
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Location Problem ◽  
Programming Model ◽  
Fixed Costs ◽  
Assembly Lines ◽  
Content Type ◽  
Mixed Model Assembly Lines ◽  
Mutation Operators ◽  
First Time

Purpose – This purpose of this paper is to investigate the location problem of supermarkets, feeding by material the mixed model assembly lines using tow trains. It determines the number and the locations of these supermarkets to minimize transportation and inventory fixed costs of the system. Design/methodology/approach – This is done using integer programming model and real genetic algorithm (RGA) in which custom chromosomes representation, two custom mating and two custom mutation operators were proposed. Findings – The performance of RGA is very good since it gives results that are very close or identical to the optimal ones in reasonable CPU time. Research limitations/implications – The study is applicable only if a group of supermarkets feed the same assembly line. Originality/value – For the first time in supermarket location problem, limitation on availability of some areas for possible supermarkets ' locations and capacity of the supermarkets were taken into consideration.

Sequencing Mixed-Model Assembly Lines to Minimize the Variation of Parts Consumption by Hybrid Genetic Algorithms

2012 ◽  
Vol 566 ◽  
pp. 253-256
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Initial Population ◽  
Assembly Lines ◽  
Mixed Model Assembly Lines ◽  
Mutation Operators ◽  
Sequencing Problems ◽  
Crossover And Mutation ◽  
Mixed Model Assembly

This paper is concerned about the sequencing problems in mixed-model assembly lines. The optimization objective is to minimizing the variation of parts consumption. The mathematical models are put forward. Since the problem is NP-hard, a hybrid genetic algorithm is newly-designed for solving the models. In this algorithm, the new method of forming the initial population is presented, the hybrid crossover and mutation operators are adopted, and moreover, the adaptive probability values for performing the crossover and mutation operations are used. The optimization performance is compared between the hybrid genetic algorithm and a genetic algorithm proposed in early published literature. The computational results show that satisfactory solutions can be obtained by the hybrid genetic algorithm and it performs better in terms of solution’s quality.

Optimally scheduling and loading tow trains of in-plant milk-run delivery for mixed-model assembly lines

2020 ◽  
Vol 40 (3) ◽  
pp. 511-530
Author(s):  
Binghai Zhou ◽  
Zhexin Zhu
Keyword(s):  
Mixed Model ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Clonal Selection Algorithm ◽  
Assembly Lines ◽  
Content Type ◽  
Mixed Model Assembly Lines ◽  
Milk Run ◽  
Delivery Scheduling ◽  
Mixed Model Assembly

Purpose This paper aims to investigate the scheduling and loading problems of tow trains for mixed-model assembly lines (MMALs). An in-plant milk-run delivery model has been formulated to minimize total line-side inventory for all stations over the planning horizon by specifying the departure time, parts quantity of each delivery and the destination station. Design/methodology/approach An immune clonal selection algorithm (ICSA) combined with neighborhood search (NS) and simulated annealing (SA) operators, which is called the NSICSA algorithm, is developed, possessing the global search ability of ICSA, the ability of SA for escaping local optimum and the deep search ability of NS to get better solutions. Findings The modifications have overcome the deficiency of insufficient local search and deepened the search depth of the original metaheuristic. Meanwhile, good approximate solutions are obtained in small-, medium- and large-scale instances. Furthermore, inventory peaks are in control according to computational results, proving the effectiveness of the mathematical model. Research limitations/implications This study works out only if there is no breakdown of tow trains. The current work contributes to the in-plant milk-run delivery scheduling for MMALs, and it can be modified to deal with similar part feeding problems. Originality/value The capacity limit of line-side inventory for workstations as well as no stock-outs rules are taken into account, and the scheduling and loading problems are solved satisfactorily for the part distribution of MMALs.

A Modified Genetic Algorithm for Sequencing Problem in Mixed Model Assembly Lines

2013 ◽  
Vol 655-657 ◽  
pp. 1675-1681
Author(s):  
Shu Xu ◽  
Fu Ming Li
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Assembly Lines ◽  
Sequencing Problem ◽  
Mixed Model Assembly Lines ◽  
Modified Genetic Algorithm ◽  
Good Ability ◽  
Sequencing Problems ◽  
Dependent Setup Times ◽  
Mixed Model Assembly

On the base of summarizing and contrasting the objectives of sequencing problem in mixed model assembly lines (MMAL) , and in consideration of the influence sequence-dependent setup times , a objective is proposed to minimize the total unfinished works and idle times over all jobs and stations . And the corresponding model is presented. To solve this model, a modified genetic algorithm is proposed to determine suitable sequences. Comparing with the Lingo 9 software, the proposed GA turns out to have a good ability to solve the sequencing problems.

Balancing mixed-model assembly lines with sequence-dependent tasks via hybrid genetic algorithm

2015 ◽  
Vol 65 (1) ◽  
pp. 83-107 ◽  
Author(s):  
Qiuhua Tang ◽  
Yanli Liang ◽  
Liping Zhang ◽  
Christodoulos A. Floudas ◽  
Xiaojun Cao
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Assembly Lines ◽  
Mixed Model Assembly Lines ◽  
Sequence Dependent ◽  
Dependent Tasks ◽  
Mixed Model Assembly

A genetic algorithm approach for balancing two-sided assembly lines with setups

2019 ◽  
Vol 39 (5) ◽  
pp. 827-839 ◽  
Author(s):  
Yilmaz Delice
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Complex Structure ◽  
Real Life ◽  
Setup Time ◽  
Assembly Lines ◽  
Content Type ◽  
Sequence Dependent ◽  
Time Required ◽  
First Time

Purpose This paper aims to discuss the sequence-dependent forward setup time (FST) and backward setup time (BST) consideration for the first time in two-sided assembly lines. Sequence-dependent FST and BST values must be considered to compute all of the operational times of each station. Thus, more realistic results can be obtained for real-life situations with this new two-sided assembly line balancing (ALB) problem with setups consideration. The goal is to obtain the most suitable solution with the least number of mated stations and total stations. Design/methodology/approach The complex structure it possesses has led to the use of certain assumptions in most of the studies in the ALB literature. In many of them, setup times have been neglected or considered superficially. In the real-life assembly process, potential setup configurations may exist between each successive task and between each successive cycle. When two tasks are in the same cycle, the setup time required (forward setup) may be different from the setup time required if the same two tasks are in consecutive cycles (backward setup). Findings Algorithm steps have been studied in detail on a sample solution. Using the proposed algorithm, the literature test problems are solved and the algorithm efficiency is revealed. The results of the experiments revealed that the proposed approach finds promising results. Originality/value The sequence-dependent FST and BST consideration is applied in a two-sided assembly line approach for the first time. A genetic algorithm (GA)-based algorithm with ten different heuristic rules was used in this proposed model.

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