Assessing the Remanufacturability of Office Furiniture: A Multi-Criteria Decision Making Approach

While the average life cycle of consumer goods is continuously decreasing, the amount of used product at their end-of-life (EOL) is accumulating fast at and at the same pace. Most EOL products end up in landfills, and many of which are not biodegradable. These two challenges have necessitated renewed global interest in product EOL management strategies by manufacturers, third party companies, consumers and governments. Remanufacturing is one of the EOL strategies which is highly environmental-friendly. Additionally, remanufacturing is seen as one of the highly profitable re-use business strategies. The selling price of remanufactured products is usually about 50—80% of a new one, making remanufacturing a win—win solution, saving both money and preserving the environment as well as raising the bottom-line of enterprises. Through the literature review of remanufacturing, we realize many researchers in this area have focused on a few product categories such as automotive, electrical and electronic equipment as well as ink cartridge, thus accelerating innovations for the remanufacture of these product categories. There is therefore, a need to explore the remanufaturability of other products, especially the ones with high market potential growth as well as profit margin. Furniture industry is the one that fits the description and is the focus of this thesis. The goal of this exploratory research is to present the first framework of its kind that aims at assessing the remanufacturability of office furniture. The proposed evaluation model considers three aspects of the assessment problem: economic, social and environmental to obtain a holistic view of remanufacturability of office furniture. We apply the fuzzy TOPSIS methodology to deal with incomplete and often subjective information during the evaluation. Furthermore, we validate our evaluation model using published research data for a multi-criteria allocation decision making (MCDM) problem. Through the model validation, we show that the proposed evaluation model has the capability to solve MCDM problems. Lastly, a case study which involves three pieces of office furniture is used to illustrate the function of the proposed model

Optimizing The Transportation of Petroleum Products in A Possible Multi-Level Supply Chain

The goal of many supply chain optimization problems is to minimize the costs of the entire supply chain network. However, since environmental protection is one of the main concerns, the green supply chain network has been seriously considered as a solution to this concern in order to minimize its effects on nature. This article refers to the modeling and solution of a green supply chain network for the transportation of petroleum products in order to reduce the annual costs, considering the environmental effects. In this article, the cost elements of the supply chain such as the transportation costs of each petroleum product, operating costs, the cost of purchasing crude oil products and the fixed costs of building oil centers as well as the components of the environmental effects of the supply chain such as the amount of gas emissions and volatile organic particles produced by transportation options in the supply chain. considered green. Considering these two components (cost and environmental impact), we have proposed a multi-objective supply chain model. In this facility model, oil centers have limited capacity and at each level of the chain, there are several types of transportation options with different costs. To solve the problem, we have used two multi-objective particle swarm optimization algorithms and genetic multi-objective optimization algorithm with non-dominant sorting II with a priority-based decoding to encode the chromosome. Finally, we have used TOPSIS method to compare these two algorithms

Evaluation of Pull Production Control Strategies Under Uncertainty: An Integrated Fuzzy Ahp-Topsis Approach

Purpose: Just-In-Time (JIT) production has continuously been considered by industrial practitioners and researchers as a leading strategy for the yet popular Lean production. Pull Production Control Policies (PPCPs) are the major enablers of JIT that locally control the level of inventory by authorizing the production in each station. Aiming to improve the PPCPs, three authorization mechanisms: Kanban, constant-work-in-process (ConWIP), and a hybrid system, are evaluated by considering uncertainty. Design/methodology/approach: Multi-Criteria Decision Making (MCDM) methods are successful in evaluating alternatives with respect to several objectives. The proposed approach of this study applies the fuzzy set theory together with an integrated Analytical Hierarchy Process (AHP) and a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. Findings: The study finds that hybrid Kanban-ConWIP pull production control policies have a better performance in controlling the studied multi-layer multi-stage manufacturing and assembly system. Practical implications: To examine the approach a real case from automobile electro-mechanical part production industry is studied. The production system consists of multiple levels of manufacturing, feeding a multi-stage assembly line with stochastic processing times to satisfy the changing demand. Originality/value: This study proposes the integrated Kanban-ConWIP hybrid pull control policies and implements several alternatives on a multi-stage and multi-layer manufacturing and assembly production system. An integrated Fuzzy AHP TOPSIS method is developed to evaluate the alternatives with respect to several JIT criteriaPeer Reviewe

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

A Multi-Criteria Decision Making Approach to Feedstock Selection

Selection of the appropriate feedstock for biodiesel production, taking into consideration several potentially conflicting quantitative and qualitative criteria, is a complex multiple-criteria decision making (MCDM) problem that requires an extensive evaluation process of a group of decision makers (DMs). In this paper, as the MCDM method, fuzzy Analytic Hierarchy Process (F-AHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) methods are integrated to evaluate plant based feedstock alternatives for biodiesel production in Turkey. The F-AHP method is used to determine the importance weights of criteria, and the F-TOPSIS method is implemented to evaluate and rank feedstock alternatives with respect to a set of qualitative and quantitative benefit criteria. More specifically, in this paper, plant based feedstocks in Turkey: Sunflower, peanut, cottonseed, canola, safflower, soybean, and poppy seed are evaluated and ranked by decision makers (DMs) with respect to several benefit criteria: Price adequacy, suitability of the plant to the climate and environment, benefits of the plant after processing (the sediment), suitability of the feedstock for technological processing, and yield efficiency, implementing the integrated fuzzy AHP-TOPSIS method

A systematic review of decision-making in remanufacturing

Potential benefits have made remanufacturing attractive over the last decade. Nevertheless, the complexity and uncertainties associated with the process of managing returned products make remanufacturing challenging. Since this process involves enormous decision-making practices, various methods/techniques have been developed. This review is to specify the current challenges and opportunities for decision-making in remanufacturing. To achieve this, we perform a systematic review over decision-making in remanufacturing by classifying decisions into different managerial levels and areas. Adopting a systematic approach which provides a repeatable, transparent and scientific process, 241 key articles have been identified following a multi-stage review process. Our review indicates that most studies focuses on strategic-level(48%) and tactical-level (34%)with only 5% focusing on operational-level and the rest on two levels(13%). Regarding decision-making methods, most studies propose mathematical models (60%) followed by analytical models (31%). Furthermore, only 36% of the studies address uncertainties in which stochastic approach is mostly applied. A total of 21 knowledge gaps are highlighted to direct future research work

Cognitive Robotic Disassembly Sequencing For Electromechanical End-Of-Life Products Via Decision-Maker-Centered Heuristic Optimization Algorithm

End-of-life (EOL) disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and models to solve related problems. End-of-life disassembly focuses on regaining the value added into products which are considered to have completed their useful lives due to a variety of reasons such as lack of technical functionality and/or lack of demand. Disassembly is known to possess unique characteristics due to possible changes in the EOL product structure and hence, cannot be considered as the reverse of assembly operations. With the same logic, obtaining a near-optimal/optimal disassembly sequence requires intelligent decision making during the disassembly when the sequence need to be regenerated to accommodate these unforeseeable changes. That is, if one or more components which were included in the original bill-of-material (BOM) of the product is missing and/or if one or more joint types are different than the ones that are listed in the original BOM, the sequencer needs to be able to adapt and generate a new and accurate alternative for disassembly. These considerations require disassembly sequencing to be solved by highly adaptive methodologies justifying the utilization of image detection technologies for online real-time disassembly. These methodologies should also be capable of handling efficient search techniques which would provide equally reliable but faster solutions compared to their exhaustive search counterparts. Therefore, EOL disassembly sequencing literature offers a variety of heuristics techniques such as Genetic Algorithm (GA), Tabu Search (TS), Ant Colony Optimization (ACO), Simulated Annealing (SA) and Neural Networks (NN). As with any data driven technique, the performance of the proposed methodologies is heavily reliant on the accuracy and the flexibility of the algorithms and their abilities to accommodate several special considerations such as preserving the precedence relationships during disassembly while obtaining near-optimal or optimal solutions. This research proposes three approaches to the EOL disassembly sequencing problem. The first approach builds on previous disassembly sequencing research and proposes a Tabu Search based methodology to solve the problem. The objectives of this proposed algorithm are to minimize: (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the results of the previous Genetic Algorithm with the proposed Tabu Search Algorithm is also included. Following this, the disassembly sequencing problem is further investigated by introducing an automated disassembly framework for end-of-life electronic products. This proposed model is able to incorporate decision makers’ (DMs’) preferences into the problem environment for efficient material and component recovery. The proposed disassembly sequencing approach is composed of two steps. The first step involves the detection of objects and deals with the identification of precedence relationships among components. This stage utilizes the BOMs of the EOL products as the primary data source. The second step identifies the most appropriate disassembly operation alternative for each component. This is often a challenging task requiring expert opinion since the decision is based on several factors such as the purpose of disassembly, the disassembly method to be used, and the component availability in the product. Given that there are several factors to be considered, the problem is modeled using a multi-criteria decision making (MCDM) method. In this regard, an Analytic Hierarchy Process (AHP) model is created to incorporate DMs’ verbal expressions into the decision problem while validating the consistency of findings. These results are then fed into a metaheuristic algorithm to obtain the optimum or near-optimum disassembly sequence. In this step, a metaheuristic technique, Simulated Annealing (SA) algorithm, is used. In order to test the robustness of the proposed Simulated Annealing algorithm an experiment is designed using an Orthogonal Array (OA) and a comparison with an exhaustive search is conducted. In addition to testing the robustness of SA, a third approach is simultaneously proposed to include multiple stations using task allocation. Task allocation is utilized to find the optimum or near-optimum solution to distribute the tasks over all the available stations using SA. The research concludes with proposing a serverless architecture to solve the resource allocation problem. The architecture also supports non-conventional solutions and machine learning which aligns with the problems investigated in this research. Numerical examples are provided to demonstrate the functionality of the proposed approaches

Decision Support System for Managing Reverse Supply Chain

Reverse logistics are becoming more and more important in the overall Industry area because of the environment and business factors. Planning and implementing a suitable reverse logistics network could bring more profit, customer satisfaction, and an excellent social picture for companies. But, most of the logistics networks are not equipped to handle the return products in reverse channels. Reverse logistics processes and plans rely heavily on reversing the supply chain so that companies can correctly identify and categorize returned products for disposition, an area that offers many opportunities for additional revenue. The science of reverse logistics includes return policy administration, product recall protocols, repairs processing, product repackaging, parts management, recycling, product disposition management, maximizing liquidation values and much more. The focus of this project is to develop a reverse logistics management system/ tools (RLMS). The proposed tools are demonstrated in the following order. First, we identify the risks involved in the reverse supply chain. Survey tool is used to collect data and information required for analysis. The methodologies that are used to identify key risks are the six sigma tools, namely Define, Measure, Analyse, Improve and Control (DMAIC), SWOT analysis, cause and effect, and Risk Mapping. An improved decision-making method using fuzzy set theory for converting linguistic data into numeric risk ratings has been attempted. In this study, the concept of ‘Left and Right dominance approach’(Chen and Liu, 2001) and Method of ‘In center of centroids’ (Thoran et al., 2012a,b) for generalized trapezoidal fuzzy numbers has been used to quantify the ‘degree of risk’ in terms of crisp ratings. After the analysis, the key risks are identified are categorized, and an action requirement plan suggested for providing guidelines for the managers to manage the risk successfully in the context of reverse logistics. Next, from risk assessment findings, information technology risk presents the highest risk impact on the performance of the reverse logistics, especially lack of use of a decision support system (DSS). We propose a novel multi-attribute decision (MADM) support tool that can categorizes return products and make the best alternative selection of recovery and disposal option using carefully considered criteria using MADM decision making methodologies such as fuzzy MOORA and VIKOR. The project can be applied to all types of industries. Once the returned products are collected and categorized at the retailers/ Points of return (PoR), an optimized network is required to determine the number of reprocessing centres to be opened and the optimized optimum material flow between retailers, reprocessing, recycling and disposal centers at minimum costs. The research develops a mixed integer linear programming model for two scenarios, namely considering direct shipping from retailer/ PoR to the respective reprocessing centers and considering the use of centralized return centers (CRC). The models are solved using LINGO 15 software and excel solver tools respectively. The advantage of the implementation of our solution is that it will help improve performance and reduce time. This benefits the company by having a reduction in their cost due to uncertainties and also contributes to better customer satisfaction. Implementation of these tools at ABZ computer distributing company demonstrates how the reverse logistics management tools can used in order to be beneficial to the organization. The tool is designed to be easily implemented at minimal cost and serves as a valuable tool for personnel faced with significant and costly decisions regarding risk assessment, decision making and network optimization in the reverse supply chain practices