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"This paper addresses a new robust multi objective multi period model for supply chain planning under uncertainty considering quantity discounts. The proposed model maximizes the current proht of the distributor by making a balance between the total costs of the supply chain and the distributor company s revenues of selling products and also maximizes the company s expected profit by introducing brands and taking the risk of loss on it. Considering uncertainty in the purchasing cost, selling fees, and demand fluctuations, the new robust multi objective mixed integer programming model is solved as a single objective mixed integer programming model by utilizing the LP metrics method. By settling regulatory penalty parameters and considering different economic scenarios, the robustness and effectiveness of the developed model are verified with the data from BEH PAKHSH Company, a commodities distributor in Iran. The outcomes show that the proposed model is a promising approach to run an efficient supply chain."

"Integrasi sistem merupakan salah satu kunci penting dalam mengoptimalkan performa sistem supply chain secara komprehensif. Sistem terintegrasi sendiri dapat digambarkan sebagai sistem yang mengatur rangkaian proses yang melibatkan aktivitas dari hulu producer hingga hilir end customer . Selama horizon waktu tersebut, aktivitas supply chain terus berjalan ditiap entitasnya. Selama waktu tersebut pula, salah satu hal penting lainnya yang perlu diperhatikan adalah unsur time value of money. Ini pula yang mendasari pentingnya perhitungan nilai future value, dimana nilai ini juga berkontribusi terhadap total biaya yang dikeluarkan. Fokus dari penelitian ini adalah mengembangkan model optimasi sistem supply chain tiga tingkat multi entitas dengan melibatkan unsur perhitungan future value FV dalam fungsi tujuannya. Adapun variable yang menjadi perhatian utama yaitu jumlah barang/produk yang diproduksi dan distribusikan oleh produser kepada distributor, serta jumlah produk yang didistribusikan oleh distributor kepada retailer. Terdapat dua fungsi tujuan yang diharapkan dapat dicapai dari penelitian ini, yaitu meminimalkan total biaya yang dikeluarkan dalam sistem supply chain dan meningkatkan servis level pengiriman produk kepada customer. Penelitian ini menggunakan pendekatan genetic algorithm algoritma genetik untuk optimasi persamaan supply chain tiga tingkat. Adapun model algoritma yang digunakan adalah Multi Objective Genetic Algorithm MOGA dan Non Dominated Sorting Genetic Algorithm NSGAII. Hasil yang diperoleh menunjukkan konfigurasi optimal untuk jumlah produk yang diproduksi dan dikirim ditiap periodenya, sehingga total biaya yang diperoleh dan outstanding service level dapat diminimalkan.

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Integrated system are the critical key in optimizing performance of supply chain system comprehensively. Integrated system can described as regulator in arranging process flow end to end. In the certain horizon time, supply chain activity are still going and through each entity involved. Actually, the other point that need to be consider are time value of money perspective. This consideration take more specific factor that called 'future value' calculation. However, it also contribute to the total cost spend in supply chain system.

The purpose of this research are to develop and solve supply chain three echelon optimization equation. Decision variable consist of quantity of product create and distributed from producer to distributor and quantity of product delivered from distributor to retailer. There are two objective function are presented by this model, first minimization of total cost in supply chain system and second minimization of delivery tardiness delivery surplus of product in supply chain system.Genetic algorithm GA approach is applied to solve the equation model and particularly separated to MOGA and NSGAII method.

The result shown optimal configuration of product quantity delivered in each period, that impact to minimal total cost and improve service level."

"Salah satu masalah yang dihadapi dalam Supply Chain Management adalah pencarian jalur. Jalur terbaik tidak hanya tergantung pada jarak, tetapi juga variabel lain, seperti: kualitas perusahaan yang terlibat, kualitas produk yang dikirimkan, dan nilai lain yang dipengaruhi oleh pengukuran kualitas. Umumnya, Ant Colony Optimization bisa mencari jalur terbaik yang hanya memiliki satu jalur objektif. Tapi akan sulit untuk diadopsi, karena dalam kasus nyata, jalur supply memiliki banyak jalur dan tujuan (khususnya pasokan minyak kelapa sawit berbasis bioenergi). Tujuan dari penelitian ini adalah untuk meningkatkan Ant Colony Optimization dalam menyelesaikan masalah jalur supply dengan menggunakan Fuzzy Ant Colony Optimization. Tujuan pengembangan Fuzzy Ant Colony Optimization dijelaskan disini, yaitu digunakan untuk mencari jalur supply terbaik.

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AbstractOne of problem faced in supply chain management is path searching. The best path depend not only on distance, but also other variables, such as: the quality of involved companies, quality of delivered product, and other value resulted by quality measurement. Commonly, the ant colony optimization could search the best path that has only one objective path. But it would be difficult to be adopted, because in the real case, the supply path has multi path and objectives (especially in palm oil based bioenergy supply). The objective of this paper is to improve the ant colony optimization for solving multi objectives based supply path problem by using fuzzy ant colony optimization. The developed multi objectives fuzzy ant colony optimization design was explained here, that it was used to search the best supply path."

"ABSTRACTTraditional approaches in constructing response surface models typically ignore model uncertainty. If the relationship between the input factors and output characteristics of a process is very complex, traditional model building approaches may have limited effectiveness. In this paper, we propose a multi model ensemble and then implement this ensemble model to optimize the process performance. To form a multi model ensemble, we need to determine the weights of the different models, that is, values indicating relative importance among the models. To determine the weights, a hybrid weighting method is proposed, in which both global and local weighting methods are taken into account. Based on the hybrid weights of different models, a multi model ensemble is built and optimized. An example is illustrated to verify the effectiveness of the proposed approach. The results show that the proposed model can achieve more accurate predictive capability and that a better process improvement is reached."

"This paper focuses on the robust resource constrained project scheduling problem (RCPSP) with discrete time/resource trade offs, in which activity duration and resource are uncertain variables. Combining flexible RCPSP (FRCPSP) with robustness, a discrete mathematical model is developed and resource leveling problem objective is considered to describe the flexible resource allocation comprehensively. In addition, surrogate measures are also introduced, providing an accurate estimate of the schedule robustness. Priority based heuristic methods and resource assignment heuristic are employed to generate and modify the priorities of selected activities, meanwhile obtain the different executing modes. Furthermore, each surrogate measure is compared according to the scheduling performance through the computational experiments. The practicability of proposed multi objective mathematical model and the efficiency of algorithm are verified by a numerical example. Finally, the performance analysis is also presented by the robustness assessment, and the results prove that the proposed approach is more effective than the traditional one."

"ABSTRACTIn this paper, we develop a four stage procedure for evaluating and improving a suppliers volume flexibility. In the first stage, we use historical demand data to develop a model for forecasting their future quantities. In the next stage, we develop an algorithm for smoothing the forecasted future demands. We show that applying this algorithm not only reduces production fluctuations and damages, but also improves the flexibility of the supplier and hence the overall supply chain. We investigate the economical conditions of applying this algorithm. In the third stage, by considering the forecasted future demands, we develop a mathematical single-period flexibility measure. In the fourth stage, the developed measure is extended to a multi period model for applying in the multi period supply collaborations and especially in the VMI systems. Furthermore, we consider weight coefficients for taking into account the different importance of flexibility from the buyers perspective over the time horizon. By applying in a real case study about an oil refinery, we verify the developed model and investigate the effects of its parameters through the sensitivity analysis."

"Pada tahap awal desain kapal, optimasi dimensi utama memiliki dampak yang signifikan dalam menentukan kinerja kapal dan total cost of ownership. Penelitian ini berfokus pada pendekatan multi-objective optimization (MOP) dengan surrogate model untuk tahap awal desain kapal. Penelitian ini menerapkan pendekatan ensemble dari 3 surrogate model: PR (Polynomial Regression), Kriging, dan BPNN-PSO (Backpropagation Neural Networks – Particle Swarm Optimizer) dan adaptive switching metamodeling (ASM) framework pada MOP. Framework ini didapatkan dari taksonomi surrogate model berdasarkan bagaimana fungsi objective dan constraint dimodelkan secara independen atau agregat. Hasil akurasi surrogate model menunjukkan ensemble surrogate model mempunyai performa terbaik dengan Mean Absolute Error (MAE) 10.75 dan R2 0.98. Kemudian, hasil optimization menunjukkan kombinasi Kriging dengan ASM memberikan performa terbaik dengan nilai Inverted Generational Distance (IGD) paling kecil dan hypervolume paling besar dibandingkan kombinasi lainnya. Di sisi lain, framework dengan fungsi objective dan constraint dioptimalkan secara independen (framework M1-2), mendapatkan performa IGD yang paling baik untuk ensemble maupun individual surrogate model. Varian solusi desain dari kombinasi Kriging dan ASM framework memberikan nilai objective kebutuhan daya 60% lebih kecil dan berat baja 7% lebih kecil (dengan waktu desain 300 kali lebih cepat), jika dibandingkan dengan hasil desain oleh desainer kapal.

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In the early stages of ship design, optimization of main ship dimensions significantly impacts ship performance and the total cost of ownership. This research focuses on the Multi-Objective Optimization (MOP) approach with the surrogate model for the early stages of ship design. This study applies an ensemble approach of 3 surrogate models: PR (Polynomial Regression), Kriging, and BPNN-PSO (Backpropagation Neural Networks - Particle Swarm Optimizer) and Adaptive Switching Metamodeling (ASM) framework on MOP. This framework is obtained from the surrogate model taxonomy based on how the objective and constraint functions are modeled independently or in aggregate. The results of the surrogate model accuracy show that the ensemble surrogate model has the best performance with a Mean Absolute Error (MAE) of 10.75 and R2 of 0.98. Then the optimization results show that the combination of Kriging with the ASM framework has the best performance with the smallest IGD value and the largest hypervolume compared to other combinations. Meanwhile, frameworks with objective and constraint functions optimized independently (framework M1-2) have the best IGD performance for both ensemble and individual surrogate models. The design solution variant of the Kriging and ASM framework has objective values of 60% less effective power and 7% less steel weight requirements (with design time 300 times faster), when compared to the original design by the expert/ship designer."

"ABSTRAKPermasalahan jaringan rantai pasok atau supply chain network pada umumnya hanya memperhatikan model optimasi biaya dengan menentukan konfigurasi supply chain yang optimal dalam lingkungan bisnis yang tidak pasti. Sekarang ini, muncul banyak tekanan dari masyarakat untuk merancang supply chain network yang berkelanjutan untuk mengurangi emisi karbon yang dihasilkan dari kegiatan supply chain dengan biaya yang proporsional. Studi ini membahas perancangan supply chain network yang berkelanjutan dengan mempertimbangkan skema perdagangan karbon dan ­trade-credit dari pemasok untuk memaksimalkan total profit supply chain. Permasalahan yang dibahas tidak hanya tentang pengambilan keputusan mengenai jumlah, lokasi, dan kapasitas dari fasilitas supply chain, aliran produk antar fasilitas di dalam network, dan harga jual produk, tetapi juga mengenai jumlah pemesanan material kepada pemasok yang optimal berdasarkan pada skema trade-credit yang berbeda-beda. Sebuah model robust fuzzy programming yang dikembangkan dari integrasi model robust optimization dan fuzzy programming digunakan untuk menangani ketidakpastian pada permintaan pelanggan dan komponen biaya. Sebuah studi kasus pada perusahaan baja Taiwan dilakukan untuk menunjukkan kinerja dan efisiensi model yang diusulkan. Hasil pengujian menunjukkan bahwa model yang diusulkan dapat meningkatkan total keuntungan supply chain sebesar 3 persen dan mengurangi waktu perhitungan sebesar 76% dibandingkan dengan model scenario-based robust stochastic programming. Temuan kami juga menunjukkan bahwa konfigurasi supply chain network yang optimal dipengaruhi oleh skenario perdagangan karbon yang berbeda dan pemilihan pemasok juga dipengaruhi oleh skema trade-credit

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ABSTRACTClassical supply chain networks mainly concern on the economic optimization model by determining the optimal supply chain configuration under the chaotic business environment. There has been an immense pressure from the society to design more sustainable supply chain networks for reducing the carbon emissions generated from supply chain activities with reasonable cost. This study addresses the sustainable supply chain network design problem considering carbon trading policy and trade-credit from suppliers to optimize the total supply chain profit in both physical market and carbon market. The problem entails decisions regarding not only the number, location, and capacity of facilities, the product flow among entities in the network, and the product-selling price but also the optimal economic order quantity to suppliers under different trade-credit schemes. A robust fuzzy programming model based on the integration of robust optimization and fuzzy programming is applied to tackle the uncertainties in demand and relevant costs. A case study in Taiwan steel firm was conducted to demonstrate the efficacy and efficiency of the proposed model. Results show that the proposed model improves the total supply chain profit, including the profits from physical and carbon markets, by approximately 3 percent and reduces the computational time by 76% compared to the scenario-based robust stochastic programming. Our findings also show that the optimal configuration of supply chain network is sensitive to different scenario of carbon trade and selection of supplier is affected by the trade-credit policy."

"Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes.Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises.This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters."

"This work encapsulates the essential developments in this field into a single resource, as well as to set an agenda for further development in the field. This brief focuses on the demand flexibility in supply chains with fragmented results distributed throughout the literature. These results have strong implications for managing real-world complex operations planning problems.This book exploits dimensions of demand flexibility in supply chains and characterizes the best fit between demand properties and operations capabilities and constraints. The origins and seminal works are traced in integrated demand and operations planning and an in-depth documentation is provided for the current state of the art. Systems with inherent costs and constraints that must respond to some set of demands at a minimum cost are examined. Crucial unanswered questions are explored and the high-value research directions are highlighted for both practice and for the development of new and interesting optimization models and algorithms."