A genetic algorithm approach to the product line design problem using the seller's return criterion: An extensive comparative computational study

Alexouda, Georgia; Paparrizos, Konstantinos

doi:10.1016/s0377-2217(00)00246-0

Cited by 50 publications

(27 citation statements)

References 27 publications

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“…Green and Krieger (1985) were among the first to model the product line design problem. Followup work includes McBride and Zufryden (1988) (using an integer programming method), Kohli and Sukumar (1990) (dynamic programming), Kalish (1993) andNair et al (1995) (heuristic for an extended Green and Krieger model), and Alexouda and Paparizos (2001) and Fligler et al (2006) (genetic algorithms). Chen and Hausman (2001) use choice-based conjoint analysis to model customer preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Rusmevichientong

Shen

Shmoys

2010

Operations Research

432

240

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The paper considers a stylized model of a dynamic assortment optimization problem, where given a limited capacity constraint, we must decide the assortment of products to offer to customers to maximize the profit. Our model is motivated by the problem faced by retailers of stocking products on a shelf with limited capacities and by the problem of placing a limited number of ads on a web page. We assume that each customer chooses to purchase the product (or to click on the ad) that maximizes her utility. We use the multinomial logit choice model to represent demand. However, we do not know the demand for each product. We can learn the demand distribution by offering different product assortments, observing resulting selections, and inferring the demand distribution from past selections and assortment decisions. We present an adaptive policy for joint parameter estimation and assortment optimization. To evaluate our proposed policy, we define a benchmark profit as the maximum expected profit that we can earn if we know the underlying demand distribution in advance. We show that the running average expected profit generated by our policy converges to the benchmark profit and establish its convergence rate. Numerical experiments based on sales data from an online retailer indicate that our policy performs well, generating over 90% of the optimal profit after less than two days of sales.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Rusmevichientong

Shen

Shmoys

2010

Operations Research

432

240

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“…Beam search, which was originally developed for artificial intelligence applications, has been used in scheduling since the late 1980s to give good approximate solutions to the early/tardy problem [34][35][36], job shop scheduling [37][38][39], project scheduling [40], assembly-line balancing [41,42], product line design [43][44][45] and numerous other applications. Since beam search limits the generation of the search tree to a predetermined number of the most promising branches at each level, it is particularly suited for the type of problem that we consider here, in which the branch-and-bound procedure becomes intractable for large problems, and we have a good evaluation function to guide the search.…”

Section: Solution Proceduresmentioning

confidence: 99%

Order acceptance with weighted tardiness

Slotnick

Morton

2007

Computers & Operations Research

129

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“…In addition, all optimization methods of the production line assume that the market is static. And the current companies do not want to introduce one or more new products (Alexouda and Paparrizos, 2001;Steiner and Hruschka, 2003;Tsafarakis et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Determining the Optimum Brands Diversity of Cheese Using PSO (Case Study: Mashhad)

Dadrasmoghadam¹,

Ghorbani²,

Karbasi³

et al. 2016

Industrial Engineering and Management Systems

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In the current study, factors affecting cheese brands products in grocery stores were evaluated with an emphasis on diversity. The sample data were collected from Noushad and Pegah Milk Industry in 2015 and data were extracted, reviewed, and analyzed from 435 grocery stores in Mashhad using seemingly unrelated regression model and particle swarm optimization algorithm. Results showed that optimum amount of Kalleh product diversity is higher than other competitors in the market, and Kalleh UF diversity is 100 to 250 grams, and Kalleh UF diversity with weight of 300 to 500 grams is more than other modes of diversity, and Kalleh brand must remove tin cheese from the market. Sabah Brand also should eliminate its glass and creamy diversity from market, UF diversity is mostly welcomed in market.

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A genetic algorithm approach to the product line design problem using the seller's return criterion: An extensive comparative computational study

Cited by 50 publications

References 27 publications

Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Order acceptance with weighted tardiness

Determining the Optimum Brands Diversity of Cheese Using PSO (Case Study: Mashhad)

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