Bütünleşik tedarik zinciri çizelgeleme modelleri: Bir literatür taraması

Research on integration of supply chain and scheduling is relatively recent, and number of studies on this topic is increasing. This study provides a comprehensive literature survey about Integrated Supply Chain Scheduling (ISCS) models to help identify deficiencies in this area. For this purpose, it is thought that this study will contribute in terms of guiding researchers working in this field. In this study, existing literature on ISCS problems are reviewed and summarized by introducing the new classification scheme. The studies were categorized by considering the features such as the number of customers (single or multiple), product lifespan (limited or unlimited), order sizes (equal or general), vehicle characteristics (limited/sufficient and homogeneous/heterogeneous), machine configurations and number of objective function (single or multi objective). In addition, properties of mathematical models applied for problems and solution approaches are also discussed.Bütünleşik Tedarik Zinciri Çizelgeleme (BTZÇ) üzerine yapılan araştırmalar nispeten yenidir ve bu konu üzerine yapılan çalışma sayısı artmaktadır. Bu çalışma, bu alandaki eksiklikleri tespit etmeye yardımcı olmak için BTZÇ modelleri hakkında kapsamlı bir literatür araştırması sunmaktadır. Bu amaçla, bu çalışmanın bu alanda çalışan araştırmacılara rehberlik etmesi açısından katkı sağlayacağı düşünülmektedir. Bu çalışmada, BTZÇ problemleri üzerine mevcut literatür gözden geçirilmiş ve yeni sınıflandırma şeması tanıtılarak çalışmalar özetlenmiştir. Çalışmalar; tek veya çoklu müşteri sayısı, sipariş büyüklüğü tipi (eşit veya genel), ürün ömrü (sınırlı veya sınırsız), araç karakteristikleri (sınırlı/yeterli ve homojen/heterojen), makine konfigürasyonları ve amaç fonksiyonu sayısı (tek veya çok amaçlı) gibi özellikler dikkate alınarak kategorize edildi. Ayrıca problemler için uygulanan matematiksel modellerin özellikleri ve çözüm yaklaşımları da tartışılmıştır

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

An Algorithm for Load Planning of Renewable Powered Machinery with Variable Operation Time

Postprin

Coordinated Production and Delivery Operations With Parallel Machines and Multiple Vehicles

This paper investigated a coordinated optimization problem of production and delivery operations with parallel machines and multiple vehicles so that a more cost-effective and sustainable supply chain performance can be achieved. We propose an effective hybrid metaheuristic solution framework to deal with this problem, by which the investigated problem is decomposed into 3 sub-problems namely, vehicle assignment, parallel machine scheduling and traveling salesman sub-problem. This framework is established for handling the 3 sub-problems in a coordinated manner so as to simplify the optimization process and to reduce the computational complexity. To evaluate the effectiveness of the methodology, this paper integrates a genetic algorithm, the longest processing time heuristic and a tabu search under this framework to solve the investigated problem. Extensive numerical experiments have been conducted and experimental results show that the proposed solution framework can handle the investigated problem efficiently and effectively

Robust optimization for energy transactions in multi-microgrids under uncertainty

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid

Integrated production-distribution scheduling with energy considerations for efficient food supply chains

Abstract Quantitative approaches for the integration of production and distribution planning are attracting the interest of scholars and companies in recent years. They can significantly improve supply chain performance and sustainability. In this paper, we propose an optimization model for the integrated scheduling of production and distribution activities, with reference to a real-life company in the food sector. The model takes into consideration changeover times and perishability, and aims to jointly minimize energy, storage and distribution costs. Its applicability is shown through a set of computational experiments, carried out on instances generated from historical data. Two different rescheduling strategies, where the first one reproduces the current behaviour of the firm, are compared. The results show that the current practices of the company can be improved and the model is a valid tool for supporting operational business decisions

Economic health-aware LPV-MPC based on system reliability assessment for water transport network

This paper proposes a health-aware control approach for drinking water transport networks. This approach is based on an economic model predictive control (MPC) that considers an additional goal with the aim of extending the components and system reliability. The components and system reliability are incorporated into the MPC model using a Linear Parameter Varying (LPV) modeling approach. The MPC controller uses additionally an economic objective function that determines the optimal filling/emptying sequence of the tanks considering that electricity price varies between day and night and that the demand also follows a 24-h repetitive pattern. The proposed LPV-MPC control approach allows considering the model nonlinearities by embedding them in the parameters. The values of these varying parameters are updated at each iteration taking into account the new values of the scheduling variables. In this way, the optimization problem associated with the MPC problem is solved by means of Quadratic Programming (QP) to avoid the use of nonlinear programming. This iterative approach reduces the computational load compared to the solution of a nonlinear optimization problem. A case study based on the Barcelona water transport network is used for assessing the proposed approach performance.Peer ReviewedPostprint (published version

Techno-economic assessment of energy storage systems in multi-energy microgrids utilizing decomposition methodology

Renewable resources and energy storage systems integrated into microgrids are crucial in attaining sustainable energy consumption and energy cost savings. This study conducts an in-depth analysis of diverse storage systems within multi-energy microgrids, including natural gas and electricity subsystems, with a comprehensive focus on techno-economic considerations. To achieve this objective, a methodology is developed, comprising an optimization model that facilitates the determination of optimal storage system locations within microgrids. The model considers various factors, such as operating and emission costs of both gas and electricity subsystems, and incorporates a sensitivity analysis to calculate the investment and maintenance costs associated with the storage systems. Due to the incorporation of voltage and current relations in the electricity subsystem as well as gas pressure and flow considerations in the natural gas subsystem, the developed model is classified as a mixed-integer nonlinear programming model. To address the inherent complexity in solving, a decomposition approach based on Outer Approximation/Equality Relaxation/Augmented Penalty is developed. This study offers scientific insights into the costs of energy storage systems, potential operational cost savings, and technical considerations of microgrid operation. The results of the developed decomposition approach demonstrate significant advantages, including reduced solving time and a decreased number of iterations

Distributionally Robust Hydrogen Optimization with Ensured Security and Multi-Energy Couplings

Power-to-gas (P2G) can convert excessive renewable energy into hydrogen via electrolysis, which can then be transported by natural gas systems to bypass constrained electricity systems. However, the injection of hydrogen could impact gas quality since gas composition fundamentally changes, adversely effecting the combustion, safety and lifespan of appliances. This paper develops a new gas quality management scheme for hydrogen injection into natural gas systems produced from excessive wind power. It introduces four gas quality indices for the integrated electricity and gas system (IEGS) measuring gas quality, considering the coordinated operation of tightly coupled infrastructures. To maintain gas quality under an acceptable range, the gas mixture of nitrogen and liquid petroleum gas with hydrogen is adopted to address the gas quality violation caused by hydrogen injection. A distributionally robust optimization (DRO) modelled by Kullback-Leibler (KL) divergence-based ambiguity set is applied to flexibly control the robustness to capture wind uncertainty. Case studies demonstrate that wind power is maximally utilized and gas mixture is effectively managed, thus improving both gas quality and performance of IEGS. The work can benefit system operators with i) ensured gas quality under hydrogen injection ii) low system operation cost and iii) high renewable energy penetratio

기후 변동성을 고려한 재생 에너지 기반 마이크로 그리드의 기술 경제성 분석 및 에너지 관리 기법 개발

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부, 2023. 2. 이종민.Micro-grids based on renewable energy resources have become a pivotal technology to address the growth of global climate crisis. While renewable energy is essential for the micro-grids, it has an intermittent nature and strong uncertainty, thus the climatic variability is a key issue for the micro-grids. Nevertheless, previous micro-grid's techno-economic analyses have rarely taken account of climatic variability, and there have been few studies related to sizing and energy management of a multi-stack micro-grid. We exploit big data driven analysis and mixed-integer stochastic energy management to resolve these issues. Utilizing climate data from 13,488 regions in 218 countries, climatic variability in techno-economic analysis is investigated. After reprocessing the data via uniform manifold approximation and projection, the dimensionally reduced data are clustered using hierarchical density-based spatial clustering of applications with noise algorithm, and optimal sizes of clusters micro-grids are compared to each other clusters according to climate patterns. The effects of climate on the sizes and costs of micro-grids are revealed based on the climate sensitivity analyses, which emphasizes the need to take climatic fluctuations into account when designing micro-grids. To decide structures and sizes of stacks, we propose mixed-integer stochastic programming that is appropriate for energy management of a multi-stack micro-grid under climate uncertainty. Validation of the proposed method's performance is followed by verification of the climatic influences on design of a multi-stack micro-grid through each illustrative example. In conclusion, it is indicated that climatic variability takes a significant role in micro-grids based on renewable energy. The contributions of this thesis can be written as follows: First, the correlation analysis through unsupervised clustering is carried out to verify that climatic variability is a factor that determine the design of techno-economical micro-grids. Mitigating their noise and clustering them via UMAP and HDBSCAN algorithm, climate data from 13,844 cities in 218 nations are used to the correlation analysis. Second, the strategies to install and operate a micro-grid during long project's lifespan are suggested according to regional climatic features. In third, a mixed-integer stochastic programming is developed to control a multi-stack micro-grid's energy distributions. Finally, it is verified that the climatic effects are noticeable in design of a multi-stack micro-grid.전세계적인 기후 위기의 증가를 대처하기 위해서 재생가능한 에너지원을 기반으로 하는 마이크로 그리드 (micro-grid) 는 중심 기술이 되고 있다. 재생 에너지는 마이크로 그리드에 필수적이지만 간헐적인 특성과 강한 불확실성을 가지고 있어 기후 변동성이 마이크로 그리드의 핵심 문제이다. 그럼에도 불구하고, 기존의 마이크로 그리드의 기술 경제성 분석들은 기후 변동성을 거의 고려하지 않았으며, 다중 스택 (multi-stack) 마이크로 그리드의 에너지 크기 조정 및 에너지 관리와 관련된 연구는 거의 없다. 우리는 이러한 문제를 해결하기 위해 빅 데이터 기반 분석과 혼합 정수 확률론적 기반의 (mixed-integer stochastic) 에너지 관리를 활용하였다. 218개국 13,488개 지역의 기후 데이터를 활용하여 기술 경제 분석의 기후 변동성을 조사하였다. 균일한 매니폴드 근사 및 투영 (uniform manifold approximation and projection) 을 통해 데이터를 전처리한 후 노이즈를 사용한 계층적 밀도 기반 공간 클러스터링 (hierarchical density-based spatial clustering of applications with noise) 알고리즘을 사용하여 차원 축소된 데이터를 클러스터링하고, 기후 패턴에 따라서 클러스터의 마이크로 그리드의 최적 크기를 서로 비교하였다. 기후 민감도 분석으로 마이크로 그리드의 규모와 비용에 기후가 미치는 영향을 밝혀냈으며, 이는 마이크로 그리드의 설계 시 기후 변동을 고려할 필요성을 강조한다. 다중 스택 마이크로 그리드의 구조와 스택의 크기를 결정하기 위해서 우리는 기후 불확정성의 존재하에서 다중 스택 마이크로 그리드의 에너지 관리에 적합한 혼합 정수 확률 프로그래밍 (mixed-integer stochastic programming ) 를 제안하였다. 각각의 예시 문제를 통해서 제안된 방법이 유효한 것을 확인한 이후에 다중 스택 마이크로 그리드의 설계에 기후가 영향을 미치는 것을 입증하였다. 결과적으로, 이는 재생 에너지 기반의 마이크로 그리드에서 기후 변동성이 중요한 역할을 하는 것을 시사한다. 본 학위논문이 제시하는 분석 및 방법의 특징은 다음과 같이 요약할 수 있다. 우선, 기후 변동성이 기술 경제적인 마이크로 그리드의 설계의 결정 요인 중 하나라는 것을 확인하기 위해서 비지도학습 클러스터링 (unsupervised clustering) 을 이용한 관계성 분석을 시행하였다. 균일한 매니폴드 근사 및 투영과 노이즈를 사용한 계층적 밀도 기반 공간 클러스터링 알고리즘을 사용하여 218개국가의 13,844개 지역의 기후 데이터의 노이즈를 완화시키고 클러스터링을 진행하였다. 다음으로, 지역적인 기후 특징을 바탕으로 마이크로 그리드의 설치와 장기적인 운영을 위한 전략을 제안하였다. 세번째로는, 다중 스택 마이크로 그리드의 에너지 분배를 제어하기 위해서 혼합 정수 확률 프로그래밍 방법론을 개발하였다. 마지막으로, 다중 스택 마이크로 그리드 설계에서 기후 영향이 두드러짐을 확인했다.1. Introduction 1 1.1 Motivation and previous work 1 1.2 Statement of contributions 5 1.3 Outline of the thesis 7 2. Background and preliminaries 8 2.1 Uniform manifold approximation and projection 8 2.2 Hierarchical density-based spatial clustering of applications with noise 9 2.3 Equipments models used in micro-grid 9 2.3.1 PV module 10 2.3.2 Wind turbine 11 2.3.3 Electrolyzer 11 2.3.4 Fuel cell 12 2.3.5 Energy storage - battery and hydrogen tank 12 2.4 Net present cost 13 2.5 Stochastic model predictive control 16 2.5.1 Stochastic tube model predictive control 17 3. Techno-economic analysis of micro-grid system design through climate region clustering 19 3.1 Introduction 19 3.2 Methods 22 3.2.1 Climatic feature extraction by UMAP 22 3.2.2 Clustering climate groups by HDBSCAN 24 3.2.3 Problem formulation 29 3.3 Result and discussion 33 3.3.1 Feature extraction and clustering of regional climate 33 3.3.2 Optimization of micro-grid considering climate variations 43 3.3.3 Sensitivity analysis on the optimal unit sizes to climate variability 59 4. Energy management and design of multi-stack micro-grid under climatic uncertainty 66 4.1 Introduction 66 4.2 Method 70 4.2.1 Objective function 70 4.2.2 Irradiance and load demands 71 4.2.3 Mixed-integer stochastic programming for a multi-stack micro-grid 74 4.3 Result and discussion 80 4.3.1 Size decision of a multi-stack micro-grid under climate uncertainty 80 5. Concluding remarks 86 5.1 Summary of the contributions 87 5.2 Future works 88 Bibliography 91박