disassembly line balancing problem

In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that requires the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures. Since the complexity of SDDLBP increases with the number of parts of the product, an efficient methodology based on artificial bee colony (ABC) is proposed to solve the SDDLBP. ABC is an optimization technique which is inspired by the behavior of honey bees. The performance of the proposed algorithm was tested against six other algorithms. The results show that the proposed ABC algorithm performs well and is superior to the other six algorithms in terms of the objective values performance. (C) 2013 Elsevier Ltd. All rights reserved

for sequence-dependent disassembly line balancing problem

For environmentally conscious and sustainable manufacturing, manufacturers need to incorporate product recovery by designing manufacturing systems to include reverse manufacturing by considering both assembly and disassembly systems. Just as the assembly line is considered the most efficient way to assemble a product, the disassembly line is seen to be the most efficient way to disassemble a product. While having some similarities to assembly, disassembly is not the reverse of the assembly process. The challenge lies in the fact that it possesses unique characteristics. In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) that is concerned with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent part removal time increments. SDDLBP is not a trivial problem since it is proven to be NP-complete. Further complications occur by considering multiple objectives including environmental and economic goals that are often contradictory. Therefore, it is essential that an efficient methodology be developed. A new approach based on the particle swarm optimization algorithm with a neighborhood-based mutation operator is proposed to solve the SDDLBP. Case scenarios are considered, and comparisons with ant colony optimization, river formation dynamics, and tabu search approaches are provided to demonstrate the superior functionality of the proposed algorithm

current state and future trends

Fatigue causes cracking or breakage in a material due to repeated loads; it causes the material to become unusable. Therefore, knowing the fatigue life of materials is crucial for the implementation of designs, economy and human life. Soft computing methodologies, a subset of artificial intelligence emerging to simulate human intelligence, deal with approximate models and seek solutions to complex real-life problems relying on both computational power of machines and the high accuracy of the algorithms. In this study, soft computing methods adapted for estimating/predicting the fatigue life of engineering structures and materials are investigated. For this purpose, 95 articles published between 1995 and 2020 have been examined in detail. With this review, it is aimed to reveal the efficiency of soft computing methods and contribute to their development. Recommendations have been made to draw attention to these methodologies which are expected to be used in many areas in the future.C1 [Kalayci, Can B.; Karagoz, Sevcan] Pamukkale Univ, Dept Ind Engn, Fac Engn, Denizli, Turkey.[Karakas, Ozler] Pamukkale Univ, Dept Mech Engn, Fac Engn, TR-20160 Denizli, Turkey

optimization: A case study of Istanbul Stock Exchange

While investors used to create their portfolios according to traditional portfolio theory in the past, today modern portfolio approach is widely preferred. The basis of the modern portfolio theory was suggested by Harry Markowitz with the mean variance model. A greater number of securities in a portfolio is difficult to manage and has an increased transaction cost. Therefore, the number of securities in the portfolio should be restricted. The problem of portfolio optimization with cardinality constraints is NP-Hard. Meta-heuristic methods are generally preferred to solve since problems in this class are difficult to be solved with exact solution algorithms within acceptable times. In this study, a particle swarm optimization algorithm has been adapted to solve the portfolio optimization problem and applied to Istanbul Stock Exchange. The experiments show that while in low risk levels it is required to invest into more number of assets in order to converge unconstrained efficient frontier, as risk level increases the number of assets to be held is decreased

A variable neighbourhood search algorithm for disassembly lines

Purpose - The purpose of this paper is to efficiently solve disassembly line balancing problem (DLBP) and the sequence-dependent disassembly line balancing problem (SDDLBP) which are both known to be NP-complete.Design/methodology/approach - This manuscript utilizes a well-proven metaheuristics solution methodology, namely, variable neighborhood search (VNS), to address the problem.Findings - DLBPs are analyzed using the numerical instances from the literature to show the efficiency of the proposed approach. The proposed algorithm showed superior performance compared to other techniques provided in the literature in terms of robustness to reach better solutions.Practical implications - Since disassembly is the most critical step in end-of-life product treatment, every step toward improving disassembly line balancing brings us closer to cost savings and compelling practicality.Originality/value - This paper is the first adaptation of VNS algorithm for solving DLBP and SDDLBP

mean-variance portfolio optimization

Portfolio optimization is the process of determining the best combination of securities and proportions with the aim of having less risk and obtaining more profit in an investment. Utilizing covariance as a risk measure, mean-variance portfolio optimization model has brought a revolutionary approach to quantitative finance. Since then, along with the advancements in computational power and algorithmic enhancements, a lot of efforts have been made on improving this model by considering real-life conditions and solving model variants with various methodologies tested on various data and performance measures. A comprehensive literature review of recent and novel papers is crucial to establish a pattern of the past, and to pave the way on future directions. In this paper, a total of 175 papers published in the last two decades are selected within the scope of operations research community and reviewed in detail. Thus, a comprehensive survey on the deterministic models and applications suggested for mean-variance portfolio optimization in which several variants of this model as well as additional real-life constraints are studied. The review classifies the approaches according to exact and approximate attempts and analyzes the proposed algorithms based on various data and performance indicators in depth. Areas of future research are outlined. (C) 2019 Elsevier Ltd. All rights reserved.C1 [Kalayci, Can B.; Ertenlice, Okkes; Akbay, Mehmet Anil] Pamukkale Univ, Fac Engn, Dept Ind Engn, TR-20160 Kinikli, Denizli, Turkey

algorithm

Traveling Salesman Problem (TSP) is the problem of finding a minimum distance tour of cities beginning and ending at the same city and that each city are visited only once. As the number of cities increases, it is difficult to find an optimal solution by exact methods in a reasonable duration. Therefore, in recent five decades many heuristic solution methods inspired of nature and biology have been developed. In this paper, a new metaheuristic method inspired of the by-passing the obstacle strategy of blind mole rats living in their individual tunnel systems under the soil is designed for solving TSP. The method is called as Blind Mole-rat Algorithm. The proposed algorithm is tested on different size of symmetric TSP problems and the results are compared to the best known results. Initial test results are promising although proposed metaheuristic is not yet competitive enough among other algorithms in the literature

portfolio optimization

One of the most studied variant of portfolio optimization problems is with cardinality constraints that transform classical mean-variance model from a convex quadratic programming problem into a mixed integer quadratic programming problem which brings the problem to the class of NP-Complete problems. Therefore, the computational complexity is significantly increased since cardinality constraints have a direct influence on the portfolio size. In order to overcome arising computational difficulties, for solving this problem, researchers have focused on investigating efficient solution algorithms such as metaheuristic algorithms since exact techniques may be inadequate to find an optimal solution in a reasonable time and are computationally ineffective when applied to large-scale problems. In this paper, our purpose is to present an efficient solution approach based on an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for solving cardinality constrained portfolio optimization problem. Computational results confirm the effectiveness of the solution methodology. (C) 2017 Elsevier Ltd. All rights reserved

artificial bee colony algorithm

This paper presents a fuzzy extension of the disassembly line balancing problem (DLBP) with fuzzy task processing times since uncertainty is the main character of real-world disassembly systems. The processing times of tasks are formulated by triangular fuzzy membership functions. The balance measure function is modified according to fuzzy characteristics of the disassembly line. A hybrid discrete artificial bee colony algorithm is proposed to solve the problem whose performance is studied over a well-known test problem taken from open literature and over a new data set introduced in this study. Furthermore, the influence of the fuzziness on the computational complexity of HDABC is evaluated and the solution quality of the proposed algorithm is compared against discrete and traditional versions of the artificial bee colony algorithm. Computational comparisons demonstrate the superiority of the proposed algorithm. (C) 2014 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved