An Integrated Model for Lot Sizing with Supplier Selection Considering Quantity Discounts, Expiry Dates, and Budget Availability

In this paper, a dynamic multi-product multi-period lot sizing with supplier selection problem (DLSSP) with quantity discount, expiry dates, and budget availability is presented. Demand of products for each period are independent and known. The cost consists of ordering, purchasing, transportation, expiry, holding, and interest charge. The objective is to find the optimal order quantity of all items in each period to minimize inventory cost. A mixed integer nonlinear model programming (MINLP) is first developed to model the problem. Since model is hard to solve using exact method, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is applied, in which design parameters are set using Taguchi method. Computational results demonstrate the applicability of the proposed model and comparing the results show efficiency of both algorithms as well. The results show that, while both algorithms have statistically similar performances, GA is the better algorithm in all problems

Multiproduct Multiperiod Newsvendor Problem with Dynamic Market Efforts

We study a multiperiod multiproduct production planning problem where the production capacity and the marketing effort on demand are both considered. The accumulative impact of marketing effort on demand is captured by the Nerlove and Arrow (N-A) advertising model. The problem is formulated as a discrete-time, finite-horizon dynamic optimization problem, which can be viewed as an extension to the classic newsvendor problem by integrating with the N-A model. A Lagrangian relaxation based solution approach is developed to solve the problem, in which the subgradient algorithm is used to find an upper bound of the solution and a feasibility heuristic algorithm is proposed to search for a feasible lower bound. Twelve kinds of instances with different problem size involving up to 50 products and 15 planning periods are randomly generated and used to test the Lagrangian heuristic algorithm. Computational results show that the proposed approach can obtain near optimal solutions for all the instances in very short CPU time, which is less than 90 seconds even for the largest instance

Development of a novel lot sizing model with variable lead time in supply chain environment

Supply chain management (SCM) addresses the management of materials and information across the entire chain from suppliers to producers, distributors, retailers, and customer. The theory of supply chain management suggests that lead time reduction is a pioneer to the use of market mediation to reduce transaction uncertainty in the chain, which can be conceptualized as the primary goal of supply chain management. In the past few decades, scholars gave ample attention about the impact of inventory on SCM. This paper relates to the development of a lot sizing model for a single component multiple delivery system with variable demand and lead time of a multinational transformer company. Two models and the modification were developed on the basis of the following assumptions. For first model distribution of demand is normal, distribution of procurement lead time is exponential and the quantity is coming in a single lot. For second model distribution of demand is normal distribution of ‘procurement’ and ‘administrative delay’ lead time is exponential and the quantity is coming in a single lot. Modification of the first model has been done by taking the effect of multiple deliveries in the models and correcting the Re-order point as obtained from the previous models. The results were observed by the second model and analysis has been done for different parametric conditions. The effect of multiple deliveries is also taken into account. The optimum re-order point and economic ordering quantity with various different inputs have been discussed

Dynamic Pricing for Managing Product Selling on Fruit Supply Chain Management

Recently fresh fruit sector is grown not only due to increasing of demand that spirited by healthy lifestyle but also requirement of quality food should be eaten daily. Its complexity make many research considered fruit in certain supply chain, called as Fruit Supply Chain (FSC). In FSC, customers tend to purchase products with a longer remaining lifetime and avoid the ones which give aging signal. Customer willingness to pay decreases once the product start to be deteriorated, which may cause slower demand for aging fruits. Consequently, retailers should enable discounted price for aging fruits products to retain or improve demand rate. Hence, a solution of this is creating price that dynamically following the condition of goods. This research establishes pricing scheme, which is dynamic pricing to FSC. Main purpose of this research is explaining how to maximize supply chain profit by applying dynamic pricing. Remind that there is deterioration that does exist on FSC product and its customer preferences, dynamic pricing will be close to the real life particularly applied by FSC players. A set of mathematical model is optimized on this research. It addresses dynamic pricing for FSC players to achieve better profitability. The result proves that dynamic pricing is urgent to be done. In order to avoid unsold product due to became deteriorated, FSC players can separate selling period into three periods, which are forward buying period, normal price period, and markdown price period. Moreover, there are several parameters involved on optimization has different impact on FSC profitability, where it should be thoroughly focused on by FSC players collaboratively

판매촉진을 도입한 수요 불확실성 재고관리 모형

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 문일경.As the globalization of markets accelerates competition among companies, sales promotion, which refers to short-term incentives promoting sales of products or services, plays a prominent role. Although there are various types of sales promotions, such as price reduction, buy-x-get-y-free, and trade-in program, the common purpose is to induce the purchase of customers by offering benefits. This successful strategy has caught the attention of researchers, including operations management and supply chain management. Thus, various studies have been conducted to examine strategies for ongoing operations and to demonstrate the effects of the sales promotion, which are based on the strategic level. However, research at the tactical or operational level has been conducted insufficiently. This dissertation examines the inventory models considering (i) markdown sale, (ii) buy one get one free (BOGO), and (iii) trade-in program. First, the newsvendor model is considered. By introducing the decision variable, which represents the start time of markdown sale, the retailer can obtain the optimal combination of the start time of a markdown sale and an order quantity. Under certain conditions in a decentralized system, however, the start time of a markdown sale where the retailer obtains the highest profit is the least profitable for the manufacturer. To avoid irrational ordering behavior by a retailer against a manufacturer, a revenue-sharing contract is proposed. Second, the mobile application, ``My Own Refrigerator'', is considered in the inventory model. It enables customers to store BOGO products in their virtual storage for later use. That is, customers can drop by the store to pick up the extra freebies in the future. The promotion involves a high degree of uncertainty regarding the revisiting date because customers who buy the product do not need to take both products on the day of purchase. To deal with this uncertainty, we propose a robust multiperiod inventory model by addressing the approximation of a multistage stochastic optimization model. Third, the trade-in program is considered. It is one of the sales promotions that companies collect used old-generation products from customers and provide them with new-generation products at a discount price. It also helps to acquire the additional products which are required for the refurbishment service. A multiperiod stochastic inventory model based on the closed-loop supply chain system is proposed by incorporating the trade-in program and refurbishment service simultaneously. The stochastic optimization model is approximated to the robust counterpart, which features a deterministic second-order cone program.시장의 세계화에 따른 기업 간의 경쟁이 가속화됨에 따라, 단기 인센티브를 통해 고객의 제품 또는 서비스 구매를 유도하는 판매촉진의 역할이 중요해졌다. 가격 인하, 행사상품 증정, 트레이드인프로그램과 같은 다양한 유형의 판매촉진 전략이 존재하지만, 공통된 주요 목적은 기업이 고객에게 혜택을 제공하여 고객의 수요를 증대시키는 것이다. 판매촉진의 성공적인 전략은 경영과학 또는 공급망관리 분야를 포함한 관련 학계의 관심을 이끌었다. 지속적인 운영을 위한 전략을 검토하고 전략적 수준 계획을 기반으로 하는 판매 촉진의 효과를 입증하기 위한 다양한 연구가 수행되었습니다. 하지만 운영 수준의 소매업체 입장에서의 연구는 미흡한 실정이다. 본 논문에서는 (i) 마크 다운 (ii) buy one get one free (BOGO), 및 (iii) 트레이드인프로그램을 고려한 재고관리모형을 다룬다. 먼저, 신문가판원 모형에 마크 다운 시작 시점을 나타내는 결정 변수를 도입하여 최적의 마크 다운 시작 시점과 주문량의 조합을 제공하는 모형을 제안한다. 분산 시스템의 특정 조건에서는 소매업자가 가장 높은 이익을 얻는 시점이 제조업자에게 낮은 수익성을 야기할 수 있다. 따라서 본 연구는 제조업자에 대한 소매업자의 비합리적 주문을 막기 위한 이익분배계약을 제안한다. 이익분배계약을 통한 중앙집권화 시스템은 분산 시스템에서 얻은 이익에 비해 소매업자와 제조업자의 이익을 향상시킴을 수치실험을 통해 확인하였다. 둘째, 모바일 어플리케이션 ``나만의 냉장고''를 고려한 재고모형을 고려한다. 이 앱을 통해 BOGO 행사제품을 구매한 고객은 증정품을 구매 당일 날 가져가지 않고 미래에 재방문하여 수령할 수 있는 혜택을 받는다. 하지만 소매업자 입장에서는 고객이 증정품을 언제 수령해 갈 지에 대한 불확실성이 존재하며 이는 기존의 재고관리 운영방식에는 한계점이 있음을 시사한다. 본 연구에서는 고객의 재방문에 대한 불확실성을 고려한 복수기간 추계계획 재고모형을 수립하며 이를 효율적으로 계산하기 위한 강건최적화 모형으로 근사화하였다. 셋째, 리퍼서비스와 트레이드인프로그램을 고려한 폐회로 공급망 시스템 기반의 복수기간 재고관리모형을 제안한다. 신세대 제품, 리퍼서비스 및 트레이드인프로그램에 대한 세 가지 유형의 불확실한 수요에 대한 상관관계를 반영함에 따라 복수기간 추계계획 재고모형이 수립된다. 복수기간 추계계획 재고모형의 계산이 어렵다는 한계를 극복하고자 강건최적화 모형으로 근사화하였다.Chapter 1 Introduction 1 1.1 Sales promotion 1 1.2 Inventory management 3 1.3 Research motivations 6 1.4 Research contents and contributions 8 1.5 Outline of the dissertation 10 Chapter 2 Optimal Start Time of a Markdown Sale Under a Two-Echelon Inventory System 11 2.1 Introduction and literature review 11 2.2 Problem description 17 2.3 Analysis of the decentralized system 21 2.3.1 Newsvendor model for a retailer 21 2.3.2 Solution procedure for an optimal combination of the start time of the markdown sale and the order quantity 25 2.3.3 Profi t function of a manufacturer 25 2.3.4 Numerical experiments of the decentralized system 27 2.4 Analysis of a centralized system 35 2.4.1 Revenue-sharing contract 35 2.4.2 Numerical experiments of the centralized system 38 2.5 Summary 40 2.5.1 Managerial insights 41 Chapter 3 Robust Multiperiod Inventory Model with a New Type of Buy One Get One Promotion: "My Own Refrigerator" 43 3.1 Introduction and literature review 43 3.2 Problem description 51 3.2.1 Demand modeling 52 3.2.2 Sequences of the ordering decision 54 3.3 Mathematical formulation of the IMMOR 56 3.3.1 Mathematical formulation of the IMMOR under the deterministic demand 58 3.3.2 Mathematical formulation of the IMMOR under the stochastic demand 58 3.3.3 Distributionally robust optimization approach for the IMMOR 60 3.4 Computational experiments 76 3.4.1 Experiment 1: tractability of the RIMMOR 77 3.4.2 Experiment 2: robustness of the RIMMOR 78 3.4.3 Experiment 3: e ect of duration of the expiry date under the different customers' revisiting propensities 78 3.5 Summary 83 3.5.1 Managerial insights 83 Chapter 4 Robust Multiperiod Inventory Model Considering Refurbishment Service and Trade-in Program 85 4.1 Introduction 85 4.2 Literature review 91 4.2.1 Effects of the trade-in program and strategic-level decisions for the trade-in program 91 4.2.2 Inventory or lot-sizing model in a closed-loop supply chain system 94 4.2.3 Distinctive features of this research 97 4.3 Problem description 100 4.3.1 Demand modeling 103 4.3.2 Decision of the inventory manager 105 4.4 Mathematical formulation 108 4.4.1 Mathematical formulation of the IMRSTIP under the deterministic demand model 108 4.4.2 Mathematical formulation of the IMRSTIP under the stochastic demand model 110 4.4.3 Distributionally robust optimization approach for the IMRSTIP 111 4.5 Computational experiments 125 4.5.1 Demand process 125 4.5.2 Experiment 1: tractability of the RIMRSTIP 128 4.5.3 Experiment 2: approximation error from the expected value given perfect information 129 4.5.4 Experiment 3: protection against realized uncertain factors 130 4.5.5 Experiment 4: di erences between modeling demands from VARMA and ARMA 131 4.5.6 Experiments 5 and 6: comparisons of backlogged refurbishment service with or without trade-in program 133 4.6 Summary 136 Chapter 5 Conclusions 138 5.1 Summary 138 5.2 Future research 140 Bibliography 142 Chapter A 160 A.1 160 A.2 163 A.3 163 A.4 164 A.5 165 A.6 166 Chapter B 168 B.1 168 B.2 171 B.3 172 Chapter C 174 C.1 174 C.2 174 국문초록 179Docto

Bilişim paylaşımı ile gerçek zamanlı üretim planlama ve kontrol sistemi tasarımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Dijital teknolojilerin yaygınlaşması ve hayatın her alanına girmesi ihtiyaçların bireysel kapsamda ele alınmasını sağlamış, rekabeti kişiye özgü çözüm ve ürün üretme boyutuna taşımıştır. Buna bağlı olarak, üretim sistemlerinin gelişimi de çeşitliliği artırmaya ve yönetmeye yönelik olarak devam etmektedir. Bu gelişim ve dönüşüm süreci temel taşlarından birisi kitlesel özelleştime (mass - customization) olan dördüncü sanayi devrimi (Endüstri 4.0) olarak adlandırılmıştır. Dünyanın Endüstri 4.0'a ayak uydurabilmesi için üretim ortamında çeşitliliği ve çeşitliliğe bağlı olarak meydana gelecek değişkenliği yönetebilmesi gerekmektedir. Üretim ortamında, değişkenliğin yönetilebilmesi için geliştirilen yöntemler değişkenlikleri stok tutarak veya zaman toleransları ile çalışarak yönetmektedirler. Bu durum verimliliğin azalmasına ve birim başına düşen sabit maliyetin artmasına neden olmaktadır. Çalışmada, klasik yaklaşımların olumsuz yönlerinen arındırılmış bir üretim planlama yaklaşımı ve modeli önerilmiştir. Önerilen modelin değişkenliklerden etkilenmemesi için model değişken olan miktar parametresi yerine, değişkenliklerden daha az etkilenecek olan zaman parametresi üzerine kurulmuştur. Modelde stok seviyesi yerine stoğun tükenmesine kalan süreye dikkat edilmekte, çizelgeleme sürecinde de üretimin tamamlanmasına kalan süreye ve termin tarihine göre önceliklendirme yapılmaktadır. Model zaman hedeflerine bağlı çalığtığından gerçek zamanlı bir modeldir. Üretim modeli nin gerçek zamanlı olması değişkenliklerden, miktar tabanlı yaklaşıma göre, çok az etkilenmesini sağlamıştır. Yapılan kıyaslama çalışmalarıyla gerçek zamanlı planlama sisteminin üretim ortamındaki değişkenliklerden etkilenmediği ve emniyet stoksuz ortamda, gecikmeleri azaltarak üretimin tamamlanmasını sağladığı ortaya konmuştur. Üstelik bu çıktılar O(n) zaman karmaşıklığına sahip, kısa sürede, sonlanan algoritmalarla elde edilmiştir. Modelin uygulanması algoritmik olarak kolay olsa da, gerçek zamanlı olduğundan, gerçek zamanlı olarak belirlenen işlem döngüsü içerisinde güncel stok ve üretim verisine ihtiyaç duyulmaktadır. Bu veriler Endüstri 4.0 teknolojileriyle elde edilebilen veriler olduğundan, gerçek zamanlı üretim modeli modern üretim sistemlerinde uygulanabilir bir modeldir. Modelin üretim sistemine katkısı, sistemi aynı anda hem itme hem de çekme sistemi gibi çalıştırabilmesidir. Bu sayede üretim sistemi iki biçimde de çalışabilmektedir. Verimli olan stretejiye dinamik olarak geçmek de stok maliyetinin %90'dan fazla azalmasını sağlamıştır.Spread of digital technology in every slice of life provides that the needs have been addressed within the individual scope and also it increases competition to the level of both individual solution and personal production. Accordingly, the development of production systems continues to enhance for managing the diversity. One of the milestones of this development and transformation process is mass customization called the fourth industrial revolution, Industry4.0. Enterprises should be able to overcome with the diversity and variability due to diversity in the production environment in order to keep pace with Industry 4.0. The methods improved in attempt to cope with variability in the production, are keeping inventory or working with time tolerances. In this case, efficiency decreases and overhead cost per unit increases in. A novel production planning approach and a model which is eliminated from negative aspect of conventional methods has been proposed, in this study. The proposed model is based on a time parameter less affected by the variances rather than the quantity in order to avoid being influenced by the changes. The remaining time to stock-out instead of inventory level is taken into account in this model, and prioritization is proceed according to the time remaining to complete the production and due date in the scheduling process. Thus, the model based on a time parameter is a real-time model. Being real-time provides, the model, to be affected from variances less than quantity based methods. It is presented that the real-time model is not affected by the variances in the manufacturing environment, and provides completing manufacturing process with less delays by using no safety stock. Besides, an algorithm having O(n) time complexity provides this result. Though the application of model is easy as algorithmically, the model, being real-time, requires the live inventory and production data within the determined time cycle. Because the data can be gained by the cyber-physical technologies of Industry 4.0, real-time model can be applied to modern production systems. The contribution of this model to production systems is that the model assimilates manufacturing systems as pull or push system at the same time. Selecting the productive strategy dynamically enables the decrease of more than 90% inventory cost