Vendor selection problem: a multi-criteria approach based on strategic decisions

Many factors in the current scenario have influenced manufacturing organisations to have a competitive edge by concentrating on entire supply chains. Sourcing decisions are one of the strategic decisions because they enable companies to reduce costs and improve profit figures. The main task in sourcing is vendor selection. Recent challenges such as shortened product life cycle, just-in-time environment, and the importance of strategic partnerships in upstream chains always influence companies to prioritise vendor selection. In addition, outsourced parts and components account for a significant contribution in the cost of finished goods. Thus evaluating and selecting the right vendor is the key to business. Vendors are selected merely on the basis of cost factors in the traditional approach. However, companies eventually have understood that their approach which emphasises costs as the sole criterion is inefficient and needs to be changed. To deal with the complex process of vendor evaluation, multiple criteria decision-making techniques have evolved. This study presents the integrated approach of multiple multi criteria decision making (MCDM) techniques such as fuzzy logic, strength-weakness-opportunity-threat (SWOT) analysis, and data envelopment analysis. The efficacy of the proposed approach is evident from the case study of an automotive component manufacturer involving 20 vendors, comprising of pre-qualification by fuzzy SWOT and final selection by DEA.     

Bi-objective partial flexible job shop scheduling problem: NSGA-II,NRGA, MOGA and PAES approaches

This paper deals with a problem of partial flexible job shop with the objective of minimising makespan and minimising total operation costs. This problem is a kind of flexible job shop problem that is known to be NP-hard. Hence four multi-objective, Pareto-based, meta-heuristic optimisation methods, namely non-dominated sorting genetic algorithm (NSGA-II), non-dominated ranked genetic algorithm (NRGA), multi-objective genetic algorithm (MOGA) and Pareto archive evolutionary strategy (PAES) are proposed to solve the problem with the aim of finding approximations of optimal Pareto front. A new solution representation is introduced with the aim of solving the addressed problem. For the purpose of performance evaluation of our proposed algorithms, we generate some instances and use some benchmarks which have been applied in the literature. Also a comprehensive computational and statistical analysis is conducted in order to analyse the performance of the applied algorithms in five metrics including non-dominated solution, diversification, mean ideal distance, quality metric and data envelopment analysis are presented. Data envelopment analysis is a well-known method for efficiently evaluating the effectiveness of multi-criteria decision making. In this study we proposed this method of assessment of the non-dominated solutions. The results indicate that in general NRGA and PAES have had a better performance in comparison with the other two algorithms.     

A joint pricing,lot-sizing,and supplier selection model

In this paper, we integrate the three strategies that are important to most firms, namely pricing, lot-sizing and supplier selection. Combining the three objectives of total profit, inconsistency, and deficiency with a set of constraints, we formulate this integrated problem as a multi-objective nonlinear programming model, proposing a genetic algorithm (NSGA-II) that provides decision-makers with a number of Pareto-optimal solutions, one of which can be selected on the basis of the higher-level information. We analyse the trade-off between the different Pareto-optimal solutions and discuss the results of that analysis. We then evaluate the performance of NSGA-II compared with SPEA2 in solving the model, which shows NSGA-II performs better. Finally, concluding remarks and suggestions for future research are provided.     

Recent advances in research on reconfigurable machine tools: a literature review

A recent trend in research on reconfigurable manufacturing systems is the development of reconfigurable machine tools (RMTs). A RMT can be used as a group of machines through change of its configuration to satisfy different manufacturing requirements. A literature review is provided in this paper to demonstrate the state-of-the-art advances and challenges on research and development of RMTs from the perspectives of architecture design, configuration design and optimisation, and system integration and control. In architecture design, semi-open and open architectures based on modular design approach are often selected to allow different modules of the machine to be added and removed. In configuration design and optimisation, operations of reconfiguration processes are analysed and optimised to achieve variety of configurations with the minimum reconfiguration effort. In system integration and control, transfers of motion, energy and data among different modules of the RMT are carried out. The challenges in research on RMTs are also discussed.     

In search for classification and selection of spare parts suitable for additive manufacturing: a literature review

This paper reviews the literature on additive manufacturing (AM) technologies and equipment, and spare parts classification criteria to propose a systematic process for selecting spare parts which are suitable for AM. This systematic process identifies criteria that can be used to select spare parts that are suitable for AM. The review found that there is limited research that addresses identifying processes for spare parts selection for AM, even though companies have identified this to be a key challenge in adopting AM. Seven areas for future research are identified relating to the methodology of spare parts selection for AM, processes for cross-functional integration in selecting spare parts for AM, broadening the spare parts portfolio that is suitable for AM (by considering usage of AM in conjunction with conventional technologies), and potential impact of AM on product modularity and integrality.     

A study on credit scoring modeling with different feature selection and machine learning approaches

A bit hurdle for financial institutions is to decide potential candidates to give a line of credit identifying the right people without any credit risk. For such a crucial decision, past demographic and financial data of debtors is important to build an automated artificial intelligence credit score prediction model based on machine learning classifier. In addition, for building robust and accurate machine learning models, important input predictors (debtor's information) must be selected. The present computational work focuses on building a credit scoring prediction model. A publicly available German credit data is incorporated in this study. An improvement in the credit scoring prediction has been shown with the use of different feature selection techniques (such as Information-gain, Gain-Ratio and Chi-Square) and machine learning classifiers (Bayesian, Naïve Bayes, Random Forest, Decision Tree (C5.0) and SVM (support Vector Machine)). Further, a comparative analysis is performed between different machine learning classifiers and between different feature selection techniques. Different evaluation metrics are considered for analyzing performance of the models (such as accuracy, F-measure, false positive rate, false negative rate and training time). After analysis, a best combination of machine learning classifier and feature selection technique are identified. In this study, a combination of random forest (RF) and Chi-Square (CS) is found good, among other combinations, with respect to good performance accuracy, F-measure and low false positive and false negative rates. However, training time for this particular combination was found to be slightly higher. Result of C5.0 with chi-square was comparable with the best one. This study provides an opportunity to financial institutions to build an automated model for better credit scoring.     

Review on ranking and selection: A new perspective

In this paper, we briefly review the development of ranking and selection (R&S) in the past 70 years, especially the theoretical achievements and practical applications in the past 20 years. Different from the frequentist and Bayesian classifications adopted by Kim and Nelson (2006b) and Chick (2006) in their review articles, we categorize existing R&S procedures into fixed-precision and fixed-budget procedures, as in Hunter and Nelson (2017). We show that these two categories of procedures essentially differ in the underlying methodological formulations, i.e., they are built on hypothesis testing and dynamic programming, respectively. In light of this variation, we review in detail some well-known procedures in the literature and show how they fit into these two formulations. In addition, we discuss the use of R&S procedures in solving various practical problems and propose what we think are the important research questions in the field.     

A fast branch,bound and remember algorithm for disassembly line balancing problem

In recent years, the interests of disassembly line have increased owing to economic reasons and the increase of environmental awareness. Effective line can provide many advantages in terms of economic aspect and it facilitates competition the companies with others. This study contributes to the relevant literature by a branch, bound and remember algorithm for disassembly line balancing problem with AND/OR precedence. The proposed exact solution method employs the memory-based dominance rule to eliminate the reduplicated sub-problems by storing all the searched sub-problems and to utilise cyclic best-first search strategy to obtain high-quality complete solutions fast. In this paper, minimising the number of stations is taken as the performance measure. The proposed methodology is tested on a set of 260 instances and compared with the mathematical model using CPLEX solver and five well-known metaheuristics. Computational results show that the proposed method is capable of obtaining the optimal solutions for all the tested instances with less than 0.1?seconds on average. Additionally, comparative study demonstrates that the proposed method is the state-of-the-art algorithm and outperforms the CPLEX solver and metaheuristics in terms of both solution quality and search speed aspects.     

A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However, research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi-criteria analysis, challenges and motivations. Therefore, implementing blockchain in multi-criteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore, the uncertainty and supply chain performance lead to improved users’ confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance, while development performance can be under development.     

A review on method entities in the academic literature: extraction,evaluation, and application

Scientometrics - In scientific research, the method is an indispensable means to solve scientific problems and a critical research object. With the advancement of sciences, many scientific methods...     

A bi-objective parallel machine problem with eligibility,release dates and delivery times of the jobs

The scheduling of parallel machines is a well-known problem in many companies. Nevertheless, not always all the jobs can be manufactured in any machine and the eligibility appears. Based on a real-life problem, we present a model which has m parallel machines with different level of quality from the highest level for the first machine till the lowest level for the last machine. The set of jobs to be scheduled on these m parallel machines are also distributed among these m levels: one job from a level can be manufactured in a machine of the same or higher level but a penalty, depending on the level, appears when a job is manufactured in a machine different from the highest level i.e. different from the first machine. Besides, there are release dates and delivery times associated to each job. The tackled problem is bi-objective with the criteria: minimisation of the final date – i.e. the maximum for all the jobs of their completion time plus the delivery time – and the minimisation of the total penalty generated by the jobs. In a first step, we analyse the sub-problem of minimisation of the final date on a single machine for jobs with release dates and delivery times. Four heuristics and an improvement algorithm are proposed and compared on didactic examples and on a large set of instances. In a second step an algorithm is proposed to approximate the set of efficient solutions and the Pareto front of the bi-objective problem. This algorithm contains two phases: the first is a depth search phase and the second is a backtracking phase. The procedure is illustrated in detail on an instance with 20 jobs and 3 machines. Then extensive numerical experiments are realised on two different sets of instances, with 20, 30 and 50 jobs, 3 or 4 machines and various values of penalties. Except for the case of 50 jobs, the results are compared with the exact Pareto front.     

A logistics provider evaluation and selection methodology based on AHP,DEA and linear programming integration

Saaty's AHP is helpful in evaluating alternatives thanks to its effective procedure to determine the relative weights of several comparison criteria. Combining the results of expert interviews, AHP can be very useful for a company in choosing a third party logistics service provider (3PL). However, in the traditional AHP procedure, several results may be rejected when the consistency ratio (CR) of the respondent exceeds a certain threshold. As a consequence, AHP interviews may be repeated several times with a consequent waste of time. In many industrial domains, a faster way to choose a supplier would thus be appreciated. In this paper we propose a mathematical method that combines AHP, DEA and linear programming in order to support the multi-criteria evaluation of third party logistics service providers. The proposed model aims to overcome the limitation of the AHP method, merging experts’ indications with objective judgments which originate from historical data analysis. Suppliers' past performance is thus used to correct eventual errors resulting from the acceptance of interviews where the consistency ratio is high. The proposed model has been validated on the real case of an international logistics service provider.     

A note on DEA models in technology selection: an improvement of Karsak and Ahiska's approach

This paper introduces a compact form for the maximum value of the non-Archimedean in Data Envelopment Analysis (DEA) models applied for the technology selection, without the need to solve a linear programming (LP). Using this method the computational performance the common weight multi-criteria decision-making (MCDM) DEA model proposed by Karsak and Ahiska (International Journal of Production Research, 2005, 43(8), 1537–1554) is improved. This improvement is significant when computational issues and complexity analysis are a concern.     

Systematic literature review on city logistics: overview,classification and analysis

City logistics is one of the significant branches of supply chain management, dealing with the logistics and transportation activities in urban areas. This research area has recently appropriated an exponential growth of publications. This paper presents a bibliometric analysis along with a systematic literature review to organise the results of surveying more than 370 papers and research works published since 2010. We identify the top contributing research topics. The most common keywords used in the city logistics literature are referred to in order to propose six research categories identifying the main innovative research perspectives.     

Service-oriented manufacturing: A literature review and future research directions

This study presents a systematic review of the literature on service-oriented manufacturing (SOM). Specifically, we focus on the impact of SOM on firm operating decisions, which distinguishes this work from previous reviews. This study proposes a classification framework for SOM research based on product flow, from its design to its final disposal. Although SOM has been studied for many years, most related research remains conceptual. Our criterion for choosing papers is that they must be relevant to practical problems. This review aims to provide readers a guide that will facilitate their search for papers in their field of interest. More importantly, we hope that this review can provide insightful managerial implications for SOM.     

A review on composite materials and process parameters optimisation for the fused deposition modelling process

Fused deposition modelling is the most significant technique in additive manufacturing (AM) that refers to the process where successive layers of material are deposited in a computer-controlled environment to create a three-dimensional object. The main limitations of using fused deposition modelling (FDM) process in the industrial applications are the narrow range of available materials and parts fabricated by FDM are used only as demonstration or conceptual parts rather than as functional parts. Recently, researchers have studied many ways in order to increase the range of materials available for the FDM process which resulted in the increase in the scope of FDM in various manufacturing sectors. Most of the research are focussed on the composite materials such as metal matrix composites, ceramic composites, natural fibre-reinforced composites and polymer matrix composites. This article intends to review the research carried out so far in developing samples using different composite materials and optimising their process parameters for FDM in order to improve different mechanical properties and other desired properties of the FDM components.     

Electric vehicle scheduling and optimal charging problem: complexity,exact and heuristic approaches

This paper deals with the Electric Vehicle (EV) Scheduling and Optimal Charging Problem. More precisely, given a fleet of EVs and Combustion Engine Vehicles (CVs), a set of tours to be processed by vehicles and a charging infrastructure, the problem aims to optimise the assignment of vehicles to tours and minimise the charging cost of EVs while considering several operational constraints mainly related to chargers, electricity grid and EVs driving range. We prove that the Electric Vehicle Scheduling and Charging Problem (EVSCP) is NP-hard in the ordinary sense. We provide a mixed-integer linear programming formulation to model the EVSCP and use CPLEX to solve small and medium instances. To solve large instances, we propose two heuristics: a Sequential Heuristic (SH) and a Global Heuristic (GH). The SH considers the EVs sequentially. To each EV, it assigns a set of tours and guarantees the feasibility of a charging schedule. Then, it generates an optimal charging schedule for this EV. However, the GH computes, in the first step, a feasible assignment of tours to all EVs. In the second step, it applies a global Min-Cost-Flow-based charging algorithm to minimise the charging cost of the EVs fleet. To evaluate the efficiency of our solving approaches, computational results on a large set of real and randomly generated test instances are reported and compared.     

Supplier quality development: A review of literature and industry practices

Managing quality of suppliers is of prime importance in supply chains to minimise costs arising due to poor-quality products and services. Supplier quality development (SQD) is a strategic quality development activity for increasing quality, reliability and efficiency of suppliers. This paper reports results of review on the academic literature and industrial practices on SQD in order to find main concepts, steps, prevalent methodologies, tools and techniques, and best practices. We reviewed 177 papers from years 1980–2013 including 22 company manuals for conducting this study. The proposed review assists readers in developing a better understanding of SQD, involved processes and their key elements, tools and techniques used, challenges in supplier quality management, best practices from academics and industry, and open research problems in SQD that require detailed investigation.     

Quantitative approaches for the integration of production and distribution planning in the supply chain: a systematic literature review

Production and distribution are the two primary internal elements of the supply chain. This paper presents a systematic literature review (SLR) of quantitative approaches for the integration of production and distribution planning (IPDP) in the supply chain. We provide a classification framework with eight dimensions and give a brief overview of the quantitative approaches such as modelling approaches and solution approaches for the IPDP problem in the supply chain. The SLR has been carried out using two basic search databases Scopus and Web of Science. In all, we identify relevant articles in the period from 2000 to 2019. We also highlight certain research opportunities, suggestions, and research gaps for possible future research by assessing the current knowledge on the quantitative approaches for IPDP problems in the supply chain.     

A review of multiaxial fatigue of weldments: experimental results, design code and critical plane approaches

A survey of biaxial (bending or tension and torsion) constant amplitude fatigue of welded connections is presented. Re-analysis of 233 experimental results from eight different studies has been performed based on hot spot stresses and three potential damage parameters: maximum principal stress range; maximum shear stress range; and a modified critical plane model for welds. Of the three methods, the critical plane model was most successful in resolving the data to a single S – N line. The design curve for all toe failures based on the critical plane model was FAT 97 with a slope of 3. By excluding butt welds and including only fillet welds that failed at the weld toe, the design curve was increased to FAT 114 with a slope of 3. However, observed scatter was 70–100% larger than that observed in uniaxial loaded specimens analysed using the hot spot approach.