Muhamad Zameri Mat Saman

Sustainable supplier selection and order lot-sizing: An integrated multi-objective decision-making process

Within supply chains activities, selecting appropriate suppliers based on the sustainability criteria (economic, environmental and social) can help companies move toward sustainable development. Although several studies have recently been accomplished to incorporate sustainability criteria into supplier selection problem, much less attention has been devoted to developing a comprehensive mathematical model that allocates the optimal quantities of orders to suppliers considering lot-sizing problems. In this research, we propose an integrated approach of rule-based weighted fuzzy method, fuzzy analytical hierarchy process and multi-objective mathematical programming for sustainable supplier selection and order allocation combined with multi-period multi-product lot-sizing problem. The mathematical programming model consists of four objective functions which are minimising total cost, maximising total social score, maximising total environmental score and maximising total economic qualitative score. The proposed model is developed based on the parameters achieved through the preprocessing of suppliers’ social, environmental and economic data by a rule-based weighted fuzzy approach and fuzzy analytical hierarchy process. The proficiency and applicability of the proposed approach is illustrated by a case study of packaging films in food industry. Considering sustainability criteria in the proposed model reveals that a higher value of sustainable purchasing is achievable in comparison with a single-objective cost-based model.

A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments

The Multiple Criteria Decision Making (MCDM) utility determining approaches and fuzzy sets are considered to be new development approaches, which have been recently presented, extended, and used by some scholars in area of decision making. There is a lack of research regarding to systematic literature review and classification of study about these approaches. Therefore; in the present study, the attempt is made to present a systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including Step-wise Weight Assessment Ratio Analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) and fuzzy extensions which discussed in recent years. Regarding this, some major databases including Web of Science, Scopus and Google Scholar have been nominated and systematic and meta-analysis method which called “PRISMA” has been proposed. In addition, the selected articles were classified based on authors, the year of publication, journals and conferences names, the technique and method used, research objectives, research gap and problem, solution and modeling, and finally results and findings. The results of this study can assist decision-makers in handling information such as stakeholders’ preferences, interconnected or contradictory criteria and uncertain environments. In addition, findings of this study help to practitioners and academic for adopting the new MCDM utility techniques such as WASPAS and SWARA in different application areas and presenting insight into literature.

Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms.

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

Sustainability evaluation using fuzzy inference methods

Sustainable development comprises three aspects: environmental, economic and social. These elements can be extended to include a number of sustainability parameters including pollution, cost and energy, among others. As the number of criteria increases, the complexity of a sustainability evaluation also increases. Furthermore, the mixture of quantitative and qualitative data regarding sustainability parameters complicates this evaluation. In this paper, a comprehensive method for evaluating sustainability is proposed to guide engineers in making better decisions. The fuzzy inference approach was adopted because it can simultaneously handle qualitative and quantitative data. These data are converted into numerical values and are expressed as a sustainability indicator. A case study involving the choice of the best electrical power generation option is presented. The result demonstrates that this fuzzy sustainability evaluation method is capable of decreasing the complexity involved in decision-making.

An integrated approach for sustainable supplier selection using fuzzy logic and fuzzy AHP

Supplier selection is one of the important processes in supply chain management. Regarding the emergence of sustainability issues in recent decades, companies have incorporated these issues in conventional supplier selection in order to meet governmental legislations and market demands. These issues have been noticed by various researchers. However, there are limited research activities which considered all aspects of sustainability for supplier selection problem as an integrated assessment. In this paper, an integrated approach of Fuzzy Analytical Hierarchy Process and fuzzy logic has been proposed in order to solve sustainable supplier selection problem. Fuzzy analytical hierarchy process has been used to calculate the weight of sustainable criteria and sub criteria. Then, fuzzy logic was utilized in order to assess the suppliers based on the weights acquired by Fuzzy analytical hierarchy process. Finally, a case study of petroleum industry has been carried out in order to show the validity of proposed approach.

Proposed analysis of performance measurement for a production system

Hitherto, very few performance measures have been constructed for selecting the right production system. Before a new, advanced manufacturing system (e.g. Just In Time and Flexible Manufacturing System) is implemented in a company, it is of paramount importance that extensive analysis should be done to ensure that the ladder is lying against the right wall. Currently, the selection of a production system is mostly centred on perceptions or mere judgments from experience. Hence, a quantitative form of selection would be more convincing, as it will show and compare the degree of advantage between each manufacturing system in numbers and percentages. This paper discusses the authors’ attempt to formulate a performance measure that could quantitatively analyse and select the best production system for a company. A questionnaire survey has been carried out in a multinational company in Johore, Malaysia and the results are used to formulate the performance measure. Reliability tests on the instrument and correlation tests on the six identified manufacturing outputs were performed. Tests of significance were also done on the outputs used.

Supplier Selection: A Hybrid Approach Using ELECTRE and Fuzzy Clustering

Vendor selection is a strategic issue in supply-chain management for any organization to identify the right supplier. Such selection in most cases is based on the analysis of some specific criteria. Most of the researches so far concentrate on multi-criteria decision making (MCDM) analysis. However, it incurs a huge computational complexity when a large number of suppliers are considered. So, data mining approaches would be required to convert raw data into useful information and knowledge. Hence, a new hybrid model of MCDM and data mining approaches was proposed in this research to address the supplier selection problem. In this paper, Fuzzy C-Means (FCM) clustering as a data mining model has been used to cluster suppliers into groups. Then, Elimination and Choice Expressing Reality (ELECTRE) method has been employed to rank the suppliers. The efficiency of this method was revealed by conducting a case study in an automotive industry.

Critical Success Factors of Green Design Implementation for Malaysia Automotive Industry

As manufactured vehicles have become more global, the competitive pressures from multi-national companies have increased substantially. Based on that, it is clearly shows that, the important of understanding the implementation of green indicators concept. Hence, the principal aim of this paper is to highlight the establishment of Green Design concept for Malaysia automotive industry by establishing a set of ‘Critical Success Factors’ that will be applied at the design stage. Survey methodology was employed to collect data. Data were obtained from 104 automotives companies and related suppliers in Malaysia with 29% response rate. This paper presents findings of Confirmatory Factor Analysis (CFA) results on green design concept for Malaysia automotive industry.

EDAS: Software for End-of-Life Disassembly Analysis

In recent years, many countries have developed legislations which are aimed to force manufacturers to recycle their products at the end of their life. However, before end-of-life products can be recycled, end-of-life disassembly needs to be in place. It entails large amounts of capital expenditure and time. Besides this, product designers also do not have experience in disassembling and recycling to determine the impact of various design aspects on difficulty at the disassembly stage. Therefore, there is a need for a tool to analyse the disassemblability and recyclability. This paper proposes software named as EDAS to fulfil those needs.

Integration of morphological analysis theory and artificial neural network approach for sustainable product design: a case study of portable vacuum cleaner

A need for incorporating sustainability requirements during product development phase so as to ensure green initiatives is the vital focus of todays industries. However, proposed approaches are lacking in terms of sustainability aspects and difficulty in selecting the most sustainable product assembly model at the end of the methodologies. Besides, useful tools such as life-cycle assessment (LCA), streamlined LCA and environmental matrix for assessing the environmental impacts associated with a product have been incorporated in order to fulfil those limitations but the variation in price and complexity makes it difficult to match the goal, scope and budget of the product design. Therefore, this study presents an integrated morphological analysis theory and artificial neural network approach for producing products in sustainable manner that caters to environment, economic and social aspects. As a result from the case study, the most sustainable of new portable vacuum cleaner models can be systematically selected.