Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments

In the current work we introduce a novel estimation of distribution algorithm to tackle a hard combinatorial optimization problem, namely the single-machine scheduling problem, with uncertain delivery times. The majority of the existing research coping with optimization problems in uncertain environment aims at finding a single sufficiently robust solution so that random noise and unpredictable circumstances would have the least possible detrimental effect on the quality of the solution. The measures of robustness are usually based on various kinds of empirically designed averaging techniques. In contrast to the previous work, our algorithm aims at finding a collection of robust schedules that allow for a more informative decision making. The notion of robustness is measured quantitatively in terms of the classical mathematical notion of a norm on a vector space. We provide a theoretical insight into the relationship between the properties of the probability distribution over the uncertain delivery times and the robustness quality of the schedules produced by the algorithm after a polynomial runtime in terms of approximation ratios

Analisis dan penilaian prestasi lengah lepas tangan menggunakan protokol pencetusan sesi (SIP) bagi sistem terintegrasi UMTS-WLAN

Teknologi rangkaian tanpa vvayar 4G merupakan penggabungan beberapa teknologi rangkaian capaian yang berbeza seperti rangkaian Universal Mobile Telecommunication System (UMTS) dan Rangkaian Kawasan Setempat Tanpa Wayar (WLAN). Rangkaian 4G menyokong mobiliti tanpa kelim {seamless) dalam menjanjikan perhubungan dan perkhidmatan yang terbaik kepada pelanggan. Protokol Pencetusan Sesi (SIP) yang berada pada lapisan aplikasi telah diramalkan sebagai calon terbaik bagi menguruskan mobiliti di dalam rangkaian 4G. Rangkaian 4G yang menawarkan aplikasi multimedia dalam perkhidmatannya mesti mempunyai lengah lepas tangan yang rendah bagi mencapai objektif penubuhannya. Tujuan utama disertasi ini adalah untuk menilai lengah lepas tangan bagi sistem terintegrasi UMTSWLAN yang menggunakan SIP sebagai protokol pengisyaratan. Model simulasi menggunakan MATLAB dibangunkan untuk menilai prestasi lengah lepas tangan tersebut. Model simulasi menggambarkan pergerakan hos mobil ke rangkaian UMTS dan WLAN. Lengah lepas tangan yang berlaku diukur berdasarkan model analitik. Prestasi lengah lepas tangan dinilai berdasarkan perubahan kadar ralat kerangka (FER), kadar ketibaan sesi SIP dan halaju hos mobil (MIT) semasa MH bergerak ke rangkaian UMTS dan WLAN. Keputusan simulasi menunjukkan bahawa lengah lepas tangan meningkat dengan penambahan FER dan kadar ketibaan sesi SIP. Halaju kebolehgerakan pengguna memberi kesan terhadap nilai lengah lepas tangan. Keputusan juga menunjukkan lengah lepas tangan minimum yang berlaku sewaktu MH bergerak ke rangkaian UMTS adalah 1.9565 saat dengan lebar jalur saluran 128kbps dan ke rangkaian WLAN adalah sekitar 0.8651 saat dengan lebar jalur saluran 11 Mbps. Berdasarkan nilai ini, lengah lepas tangan semasa MH bergerak ke rangkaian UMTS atau WLAN adalah tidak boleh diterima untuk penjurusan multimedia. Di dalam kajian ini didapati capaian tanpa wayar GPRS menyumbang lengah terbesar daripada keseluruhan lengah lepas tangan ke rangkaian UMTS

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested