Abstract:How to allocate goods in shop shelves makes great influence to sales amount. Searching best fit allocation of goods to shelves is a kind of combinatorial problem. This becomes a problem of integer programming and utilizing genetic algorithm may be an effective method. Reviewing past researches, there are few researches made on this. Formerly, we have presented a papers concerning optimization in allocating goods to shop shelves utilizing genetic algorithm. In those papers, the problem that goods were not allow… Show more
“…= (5,2,7,11,9,6,3,1,14,12,19,16,18,17,13,10,15,20) This coincides with the result of optimal solution by the calculation of all considerable cases, therefore it coincides with a theoretical optimal solution. We take up simple problem and we can confirm the effectiveness of GA approach.…”
Section: Numerical Examplesupporting
confidence: 85%
“…A sample set of initial population is exhibited in Table 8. ( 50,26,16,49,13,12,43,8,40,36,21,53,35,60,7,31,39,22 ) 2 =…”
“…Set Benefit as , , ( = 1, ⋯ , )( = 1, ⋯ , )( = 1, ⋯ , )wheregoods is placed at shelf position of shelf . The sales period is the same with above stated(1).…”
How to allocate goods in shop shelves makes great influence to sales amount. Searching best fit allocation of goods to shelves is a kind of combinatorial problem. This becomes a problem of integer programming and utilizing genetic algorithm may be an effective method. Reviewing past researches, there are few researches made on this. Formerly, we have presented papers concerning optimization in allocating goods to shop shelves utilizing genetic algorithm. In those papers, the problem that goods were not allowed to allocate in multiple shelves and the problem that goods were allowed to allocate in multiple shelves were pursued. In this paper, we examine the problem that does not allow goods to be allocated in multiple shelves and introduce the concept of sales profits and sales probabilities. Expansion of shelf is executed. Optimization in allocating goods to shop shelves is investigated. An application to the convenience store with POS sales data of cup noodles is executed. Utilizing genetic algorithm, optimum solution is pursued and verified by a numerical example. Comparison with other past papers was executed. Various patterns of problems must be examined hereafter.
“…= (5,2,7,11,9,6,3,1,14,12,19,16,18,17,13,10,15,20) This coincides with the result of optimal solution by the calculation of all considerable cases, therefore it coincides with a theoretical optimal solution. We take up simple problem and we can confirm the effectiveness of GA approach.…”
Section: Numerical Examplesupporting
confidence: 85%
“…A sample set of initial population is exhibited in Table 8. ( 50,26,16,49,13,12,43,8,40,36,21,53,35,60,7,31,39,22 ) 2 =…”
“…Set Benefit as , , ( = 1, ⋯ , )( = 1, ⋯ , )( = 1, ⋯ , )wheregoods is placed at shelf position of shelf . The sales period is the same with above stated(1).…”
How to allocate goods in shop shelves makes great influence to sales amount. Searching best fit allocation of goods to shelves is a kind of combinatorial problem. This becomes a problem of integer programming and utilizing genetic algorithm may be an effective method. Reviewing past researches, there are few researches made on this. Formerly, we have presented papers concerning optimization in allocating goods to shop shelves utilizing genetic algorithm. In those papers, the problem that goods were not allowed to allocate in multiple shelves and the problem that goods were allowed to allocate in multiple shelves were pursued. In this paper, we examine the problem that does not allow goods to be allocated in multiple shelves and introduce the concept of sales profits and sales probabilities. Expansion of shelf is executed. Optimization in allocating goods to shop shelves is investigated. An application to the convenience store with POS sales data of cup noodles is executed. Utilizing genetic algorithm, optimum solution is pursued and verified by a numerical example. Comparison with other past papers was executed. Various patterns of problems must be examined hereafter.
“…Formerly, we have presented a paper concerning optimization in allocating goods to shop shelves utilizing genetic algorithm [11]. In those papers, the problem that goods were not allowed to allocate in multiple shelves and the problem that goods were allowed to allocate in multiple shelves were pursued.…”
How to allocate goods in shop shelves makes great influence to sales amount. Searching best fit allocation of goods to shelves is a kind of combinatorial problem. This becomes a problem of integer programming and utilizing genetic algorithm may be an effective method. Reviewing past researches, there are few researches made on this. Formerly, we have presented a paper concerning optimization in allocating goods to shop shelves utilizing genetic algorithm. In those papers, the problem that goods were not allowed to allocate in multiple shelves and the problem that goods were allowed to allocate in multiple shelves were pursued. In this paper, we examine the problem that allows goods to be allocated in multiple shelves and introduce the concept of sales profits and sales probabilities. Optimization in allocating goods to shop shelves is investigated. Expansion of shelf is executed. Utilizing genetic algorithm, optimum solution is pursued and verified by a numerical example. Various patterns of problems must be examined hereafter.
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