135 research outputs found
้ฃๅใกใใฅใผใฎ่ฒฉๅฃฒไฟ้ฒๅนๆใ่ๆ ฎใใๅๅ้ณๅๆฑบๅฎใขใใซ
่ฒฉๅฃฒๆฉไผใญในใๅคฑใใๅๅ้ณๅใจ้กงๅฎขใๅผใใคใใ่ฒฉๅฃฒไฟ้ฒ่จ็ปใฎๆฑบๅฎใฏ, ๅฐๅฃฒๅบใๅฉ็ใไผธใฐใใใใซ้ๅธธใซ้่ฆใชๅ้กใงใใ. ้ๅธธ, ใใฎๅ้กใฏPOS ใใผใฟใๅบใซๅฐๅฃฒๅบใฎๅฉๆฝคๆๅคงๅๅ้กใจใใฆๅฎๅผๅใใใ. ใใใ, POS ใใผใฟใใใงใฏๅๅใใฉใฎใใใช้ฃๅใกใใฅใผใซไฝฟใใใใใๅใใใชใใชใฉ, ๅๅๆฟ็ญ, ่ฒฉๅฃฒๆฟ็ญใซใใใคใใฎๅ้ก็นใๆๆใใใฆใใ. ไธๆนใง, ๆฌ็ ็ฉถใงๅฏพ่ฑกใจใใใใผใฟใซใฏ, 194 ไธๅธฏใฎใขใใฟใผใฎ1 ๅนด้ใฎๆ, ๆผ, ๅคใฎ้ฃๅใกใใฅใผใจไฝฟ็จๆๆใ่จ้ฒใใใฆใใ. ใใฃใฆ, ๅฏพ่ฑกใใผใฟใๅฉ็จใใใฐ, ใขใใฟใผใๅๅใใฉใฎใใใช้ฃๅใกใใฅใผใซไฝฟ็จใใใใ็ฅใใใจใใงใ, ้ฃๅใกใใฅใผใฎๆๆกใซๅบใฅใๅๅ้ณๅใจ่ฒฉๅฃฒไฟ้ฒ่จ็ปใๆฑบๅฎใงใใ. ๆฌ็ ็ฉถใงใฏ, ้ฃๅใกใใฅใผใฎ่ฒฉๅฃฒไฟ้ฒๅนๆใ่ๆ
ฎใ, ้กงๅฎขใฎๅฃฒๅ ดๆบ่ถณๅบฆๆๅคงๅๅ้กใจใใฆ, ๅฐๅฃฒๅบใฎๅๅ้ณๅใจ่ฒฉๅฃฒไฟ้ฒ้ฃๅใกใใฅใผใๆฑบๅฎใใๆ้ฉๅใขใใซใๆๆกใใ. ใใใฆ, ๅ็ทๅฝข้
ใฎ็ทๅฝขๅ, ้็ทๅฝข้ขๆฐใฎๅบๅ็ทๅฝข่ฟไผผใๅฉ็จใใฆ, ๅ้กใ0-1 ๆททๅๆดๆฐ็ทๅฝข่จ็ปๅ้กใซๅธฐ็ใ, ๆ้ฉๅใฝใซใใผใๅฉ็จใใฆๆฑ่งฃใใ. ่จ็ฎใฎ็ตๆ, ๅๅ้ณๅใจ่ฒฉๅฃฒไฟ้ฒ้ฃๅใกใใฅใผใซใฏ, ๅญฃ็ฏๆงใๅนณๆฅใจไผๆฅใฎ้ใใ็พใ, ใใใใใใใคใใฎๆ็ใช็ฅ่ฆใๅพใใใจใใงใใ
Online Advertising Assignment Problems Considering Realistic Constraints
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ฐ์
๊ณตํ๊ณผ, 2020. 8. ๋ฌธ์ผ๊ฒฝ.With a drastic increase in online communities, many companies have been paying attention to online advertising. The main advantages of online advertising are traceability, cost-effectiveness, reachability, and interactivity. The benefits facilitate the continuous popularity of online advertising. For Internet-based companies, a well-constructed online advertisement assignment increases their revenue. Hence, the managers need to develop their decision-making processes for assigning online advertisements on their website so that their revenue is maximized.
In this dissertation, we consider online advertising assignment problems considering realistic constraints. There are three types of online advertising assignment problems: (i) Display ads problem in adversarial order, (ii) Display ads problem in probabilistic order, and (iii) Online banner advertisement scheduling for advertising effectiveness. Unlike previous assignment problems, the problems are pragmatic approaches that reflect realistic constraints and advertising effectiveness. Moreover, the algorithms the dissertation designs offer important insights into the online advertisement assignment problem.
We give a brief explanation of the fundamental methodologies to solve the online advertising assignment problems in Chapter 1. At the end of this chapter, the contributions and outline of the dissertation are also presented. In Chapter 2, we propose the display ads problem in adversarial order. Deterministic algorithms with worst-case guarantees are designed, and the competitive ratios of them are presented. Upper bounds for the problem are also proved. We investigate the display ads problem in probabilistic order in Chapter 3. This chapter presents stochastic online algorithms with scenario-based stochastic programming and Benders decomposition for two probabilistic order models. In Chapter 4, an online banner advertisement scheduling model for advertising effectiveness is designed. We also present the solution methodologies used to obtain valid lower and upper bounds of the model efficiently. Chapter 5 offers conclusions and suggestion for future studies.
The approaches to solving the problems are meaningful in both academic and industrial areas. We validate these approaches can solve the problems efficiently and effectively by conducting computational experiments. The models and solution methodologies are expected to be convenient and beneficial when managers at Internet-based companies place online advertisements on their websites.์จ๋ผ์ธ ์ปค๋ฎค๋ํฐ์ ๊ธ๊ฒฉํ ์ฑ์ฅ์ ๋ฐ๋ผ, ๋ง์ ํ์ฌ๋ค์ด ์จ๋ผ์ธ ๊ด๊ณ ์ ๊ด์ฌ์ ๊ธฐ์ธ์ด๊ณ ์๋ค. ์จ๋ผ์ธ ๊ด๊ณ ์ ์ฅ์ ์ผ๋ก๋ ์ถ์ ๊ฐ๋ฅ์ฑ, ๋น์ฉ ํจ๊ณผ์ฑ, ๋๋ฌ ๊ฐ๋ฅ์ฑ, ์ํธ์์ฉ์ฑ ๋ฑ์ด ์๋ค. ์จ๋ผ์ธ์ ๊ธฐ๋ฐ์ ๋๋ ํ์ฌ๋ค์ ์ ์ง์ฌ์ง ์จ๋ผ์ธ ๊ด๊ณ ํ ๋น๊ฒฐ์ ์ ๊ด์ฌ์ ๋๊ณ ์๊ณ , ์ด๋ ๊ด๊ณ ์์ต๊ณผ ์ฐ๊ด๋ ์ ์๋ค. ๋ฐ๋ผ์ ์จ๋ผ์ธ ๊ด๊ณ ๊ด๋ฆฌ์๋ ์์ต์ ๊ทน๋ํ ํ ์ ์๋ ์จ๋ผ์ธ ๊ด๊ณ ํ ๋น ์์ฌ ๊ฒฐ์ ํ๋ก์ธ์ค๋ฅผ ๊ฐ๋ฐํ์ฌ์ผ ํ๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ํ์ค์ ์ธ ์ ์ฝ์ ๊ณ ๋ คํ ์จ๋ผ์ธ ๊ด๊ณ ํ ๋น ๋ฌธ์ ๋ค์ ์ ์ํ๋ค. ๋ณธ ๋
ผ๋ฌธ์์ ๋ค๋ฃจ๋ ๋ฌธ์ ๋ (1) adversarial ์์๋ก ์งํํ๋ ๋์คํ๋ ์ด ์ ๋๋ฌธ์ , (2) probabilistic ์์๋ก ์งํํ๋ ๋์คํ๋ ์ด ์ ๋๋ฌธ์ ๊ทธ๋ฆฌ๊ณ (3) ๊ด๊ณ ํจ๊ณผ๋ฅผ ์ํ ์จ๋ผ์ธ ๋ฐฐ๋ ๊ด๊ณ ์ผ์ ๊ณํ์ด๋ค. ์ด์ ์ ์ ์๋์๋ ๊ด๊ณ ํ ๋น ๋ฌธ์ ๋ค๊ณผ ๋ฌ๋ฆฌ, ๋ณธ ๋
ผ๋ฌธ์์ ์ ์ํ ๋ฌธ์ ๋ค์ ํ์ค์ ์ธ ์ ์ฝ๊ณผ ๊ด๊ณ ํจ๊ณผ๋ฅผ ๋ฐ์ํ๋ ์ค์ฉ์ ์ธ ์ ๊ทผ ๋ฐฉ์์ด๋ค. ๋ํ ์ ์ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์จ๋ผ์ธ ๊ด๊ณ ํ ๋น ๋ฌธ์ ์ ์ด์๊ด๋ฆฌ์ ๋ํ ํต์ฐฐ๋ ฅ์ ์ ๊ณตํ๋ค.
1์ฅ์์๋ ์จ๋ผ์ธ ๊ด๊ณ ํ ๋น ๋ฌธ์ ์ ๋ํ ๋ฌธ์ ํด๊ฒฐ ๋ฐฉ๋ฒ๋ก ์ ๋ํด ๊ฐ๋จํ ์๊ฐํ๋ค. ๋๋ถ์ด ์ฐ๊ตฌ์ ๊ธฐ์ฌ์ ๊ฐ์๋ ์ ๊ณต๋๋ค. 2์ฅ์์๋ adversarial ์์๋ก ์งํํ๋ ๋์คํ๋ ์ด ์ ๋๋ฌธ์ ๋ฅผ ์ ์ํ๋ค. worst-case๋ฅผ ๋ณด์ฅํ๋ ๊ฒฐ์ ๋ก ์ ์๊ณ ๋ฆฌ์ฆ์ ์ค๊ณํ๊ณ , ์ด๋ค์ competitive ratio๋ฅผ ์ฆ๋ช
ํ๋ค. ๋๋ถ์ด ๋ฌธ์ ์ ์ํ๋ ์
์ฆ๋๋ค. 3์ฅ์์๋ probabilistic ์์๋ก ์งํํ๋ ๋์คํ๋ ์ด ์ ๋๋ฌธ์ ๋ฅผ ์ ์ํ๋ค. ์๋๋ฆฌ์ค ๊ธฐ๋ฐ์ ํ๋ฅ ๋ก ์ ์จ๋ผ์ธ ์๊ณ ๋ฆฌ์ฆ๊ณผ Benders ๋ถํด๋ฐฉ๋ฒ์ ํผํฉํ ์ถ๊ณ ์จ๋ผ์ธ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. 4์ฅ์์๋ ๊ด๊ณ ํจ๊ณผ๋ฅผ ์ํ ์จ๋ผ์ธ ๋ฐฐ๋ ๊ด๊ณ ์ผ์ ๊ณํ์ ์ค๊ณํ๋ค. ๋ํ, ๋ชจ๋ธ์ ์ ํจํ ์ํ๊ณผ ํํ์ ํจ์จ์ ์ผ๋ก ์ป๋ ๋ฐ ์ฌ์ฉ๋๋ ๋ฌธ์ ํด๊ฒฐ ๋ฐฉ๋ฒ๋ก ์ ์ ์ํ๋ค. 5์ฅ์์๋ ๋ณธ ๋
ผ๋ฌธ์ ๊ฒฐ๋ก ๊ณผ ํฅํ ์ฐ๊ตฌ๋ฅผ ์ํ ๋ฐฉํฅ์ ์ ๊ณตํ๋ค.
๋ณธ ๋
ผ๋ฌธ์์ ์ ์ํ๋ ๋ฌธ์ ํด๊ฒฐ ๋ฐฉ๋ฒ๋ก ์ ํ์ ๋ฐ ์ฐ์
๋ถ์ผ ๋ชจ๋ ์๋ฏธ๊ฐ ์๋ค. ์์น ์คํ์ ํตํด ๋ฌธ์ ํด๊ฒฐ ์ ๊ทผ ๋ฐฉ์์ด ๋ฌธ์ ๋ฅผ ํจ์จ์ ์ด๊ณ ํจ๊ณผ์ ์ผ๋ก ํด๊ฒฐํ ์ ์์์ ๋ณด์ธ๋ค. ์ด๋ ์จ๋ผ์ธ ๊ด๊ณ ๊ด๋ฆฌ์๊ฐ ๋ณธ ๋
ผ๋ฌธ์์ ์ ์ํ๋ ๋ฌธ์ ์ ๋ฌธ์ ํด๊ฒฐ ๋ฐฉ๋ฒ๋ก ์ ํตํด ์จ๋ผ์ธ ๊ด๊ณ ํ ๋น๊ด๋ จ ์์ฌ๊ฒฐ์ ์ ์งํํ๋ ๋ฐ ์์ด ๋์์ด ๋ ๊ฒ์ผ๋ก ๊ธฐ๋ํ๋ค.Chapter 1 Introduction 1
1.1 Display Ads Problem 3
1.1.1 Online Algorithm 4
1.2 Online Banner Advertisement Scheduling Problem 5
1.3 Research Motivations and Contributions 6
1.4 Outline of the Dissertation 9
Chapter 2 Online Advertising Assignment Problem in Adversarial Order 12
2.1 Problem Description and Literature Review 12
2.2 Display Ads Problem in Adversarial Order 15
2.3 Deterministic Algorithms for Adversarial Order 17
2.4 Upper Bounds of Deterministic Algorithms for Adversarial Order 22
2.5 Summary 28
Chapter 3 Online Advertising Assignment Problem in Probabilistic Order 30
3.1 Problem Description and Literature Review 30
3.2 Display Ads Problem in Probabilistic Order 33
3.3 Stochastic Online Algorithms for Probabilistic Order 34
3.3.1 Two-Stage Stochastic Programming 35
3.3.2 Known IID model 37
3.3.3 Random permutation model 41
3.3.4 Stochastic approach using primal-dual algorithm 45
3.4 Computational Experiments 48
3.4.1 Results for known IID model 55
3.4.2 Results for random permutation model 57
3.4.3 Managerial insights for Algorithm 3.1 59
3.5 Summary 60
Chapter 4 Online Banner Advertisement Scheduling for Advertising Effectiveness 61
4.1 Problem Description and Literature Review 61
4.2 Mathematical Model 68
4.2.1 Objective function 68
4.2.2 Notations and formulation 72
4.3 Solution Methodologies 74
4.3.1 Heuristic approach to finding valid lower and upper bounds 75
4.3.2 Hybrid tabu search 79
4.4 Computational Experiments 80
4.4.1 Results for problems with small data sets 82
4.4.2 Results for problems with large data sets 84
4.4.3 Results for problems with standard data 86
4.4.4 Managerial insights for the results 90
4.5 Summary 92
Chapter 5 Conclusions and Future Research 93
Appendices 97
A Initial Sequence of the Hybrid Tabu Search 98
B Procedure of the Hybrid Tabu Search 99
C Small Example of the Hybrid Tabu Search 101
D Linearization Technique of Bilinear Form in R2 104
Bibliography 106Docto
Minimizing food waste in grocery store operations: literature review and research agenda
Research on grocery waste in food retailing has recently attracted particular interest. Investigations in this area are relevant to address the problems of wasted resources and ethical concerns, as well as economic aspects from the retailerโs perspective. Reasons for food waste in retail are already well-studied empirically, and based on this, proposals for reduction are discussed. However, comprehensive approaches for preventing food waste in store operations using analytics and modeling methods are scarce. No work has yet systematized related research in this domain. As a result, there is neither any up-to-date literature review nor any agenda for future research. We contribute with the first structured literature review of analytics and modeling methods dealing with food waste prevention in retail store operations. This work identifies cross-cutting store-related planning areas to mitigate food waste, namely (1) assortment and shelf space planning, (2) replenishment policies, and (3) dynamic pricing policies. We introduce a common classification scheme of literature with regard to the depth of food waste integration and the characteristics of these planning problems. This builds our foundation to review analytics and modeling approaches. Current literature considers food waste mainly as a side effect in costing and often ignores product age dependent demand by customers. Furthermore, approaches are not integrated across planning areas. Future lines of research point to the most promising open questions in this field
Constrained Assortment Optimization under the Cross-Nested Logit Model
We study the assortment optimization problem under general linear
constraints, where the customer choice behavior is captured by the Cross-Nested
Logit model. In this problem, there is a set of products organized into
multiple subsets (or nests), where each product can belong to more than one
nest. The aim is to find an assortment to offer to customers so that the
expected revenue is maximized. We show that, under the Cross-Nested Logit
model, the assortment problem is NP-hard, even without any constraints. To
tackle the assortment optimization problem, we develop a new discretization
mechanism to approximate the problem by a linear fractional program with a
performance guarantee of , for any accuracy
level . We then show that optimal solutions to the approximate
problem can be obtained by solving mixed-integer linear programs. We further
show that our discretization approach can also be applied to solve a joint
assortment optimization and pricing problem, as well as an assortment problem
under a mixture of Cross-Nested Logit models to account for multiple classes of
customers. Our empirical results on a large number of randomly generated test
instances demonstrate that, under a performance guarantee of 90%, the
percentage gaps between the objective values obtained from our approximation
methods and the optimal expected revenues are no larger than 1.2%
A Facility Layout Design Methodology for Retail Environments
Based on an overall consideration of the principles and characteristics in designing a retail area layout, this research is the first work to integrate aisle structure design, block layout, which is specific department placement, and intra-block departmental layout, which is detailed fixture layout design, as a whole process. The main difference between previous research and this proposed research is the formulation of mathematical models that can be specified applied in the retail sector. Unlike manufacturing, in retail environments, the design objective is profit maximization. This is accomplished by maximizing the area exposure, optimizing the adjacency preference of all departments, and adjusting the intra-block layout and evaluating the effectiveness of layout design
Demand Estimation at Manufacturer-Retailer Duo: A Macro-Micro Approach
This dissertation is divided into two phases. The main objective of this phase is to use Bayesian MCMC technique, to attain (1) estimates, (2) predictions and (3) posterior probability of sales greater than certain amount for sampled regions and any random region selected from the population or sample. These regions are served by a single product manufacturer who is considered to be similar to newsvendor. The optimal estimates, predictions and posterior probabilities are obtained in presence of advertising expenditure set by the manufacturer, past historical sales data that contains both censored and exact observations and finally stochastic regional effects that cannot be quantified but are believed to strongly influence future demand. Knowledge of these optimal values is useful in eliminating stock-out and excess inventory holding situations while increasing the profitability across the entire supply chain.
Subsequently, the second phase, examines the impact of Cournot and Stackelberg games in a supply-chain on shelf space allocation and pricing decisions. In particular, we consider two scenarios: (1) two manufacturers competing for shelf space allocation at a single retailer, and (2) two manufacturers competing for shelf space allocation at two competing retailers, whose pricing decisions influence their demand which in turn influences their shelf-space allocation. We obtain the optimal pricing and shelf-space allocation in these two scenarios by optimizing the profit functions for each of the players in the game. Our numerical results indicate that (1) Cournot games to be the most profitable along the whole supply chain whereas Stackelberg games and mixed games turn out to be least profitable, and (2) higher the shelf space elasticity, lower the wholesale price of the product; conversely, lower the retail price of the product, greater the shelf space allocated for that product
Stochastic Optimization Models for Perishable Products
For many years, researchers have focused on developing optimization models to design and manage supply chains. These models have helped companies in different industries to minimize costs, maximize performance while balancing their social and environmental impacts. There is an increasing interest in developing models which optimize supply chain decisions of perishable products. This is mainly because many of the products we use today are perishable, managing their inventory is challenging due to their short shelf life, and out-dated products become waste. Therefore, these supply chain decisions impact profitability and sustainability of companies and the quality of the environment. Perishable products wastage is inevitable when demand is not known beforehand. A number of models in the literature use simulation and probabilistic models to capture supply chain uncertainties. However, when demand distribution cannot be described using standard distributions, probabilistic models are not effective. In this case, using stochastic optimization methods is preferred over obtaining approximate inventory management policies through simulation.
This dissertation proposes models to help businesses and non-prot organizations make inventory replenishment, pricing and transportation decisions that improve the performance of their system. These models focus on perishable products which either deteriorate over time or have a fixed shelf life. The demand and/or supply for these products and/or, the remaining shelf life are stochastic. Stochastic optimization models, including a two-stage stochastic mixed integer linear program, a two-stage stochastic mixed integer non linear program, and a chance constraint program are proposed to capture uncertainties. The objective is to minimize the total replenishment costs which impact prots and service rate. These models are motivated by applications in the vaccine distribution supply chain, and other supply chains used to distribute perishable products.
This dissertation also focuses on developing solution algorithms to solve the proposed optimization models. The computational complexity of these models motivated the development of extensions to standard models used to solve stochastic optimization problems. These algorithms use sample average approximation (SAA) to represent uncertainty. The algorithms proposed are extensions of the stochastic Benders decomposition algorithm, the L-shaped method (LS). These extensions use Gomory mixed integer cuts, mixed-integer rounding cuts, and piecewise linear relaxation of bilinear terms. These extensions lead to the development of linear approximations of the models developed. Computational results reveal that the solution approach presented here outperforms the
standard LS method.
Finally, this dissertation develops case studies using real-life data from the Demographic Health Surveys in Niger and Bangladesh to build predictive models to meet requirements for various childhood immunization vaccines. The results of this study provide support tools for policymakers to design vaccine distribution networks
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