A Pareto supplier selection algorithm for minimum the life cycle cost of complex product system

Du, Baigang; Guo, Sen; Huang, Xiaorong; Li, Yibing; Guo, Jun

doi:10.1016/j.eswa.2015.01.056

Cited by 48 publications

(19 citation statements)

References 44 publications

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“…The complexity of the decision and the large number of criteria involved increase the attractiveness of the Multi Criteria Decision Making (MCDM) approaches. LSP performance is a vital dimension in the evaluation process and many firms use LSPs' past performance records to select appropriate LSPs (Straight, 1999;Lai et al, 2002;Liu and Lyons, 2011;Rezaei et al, 2014;Du et al, 2015;Moghaddam 2015). However, using past performance records alone is insufficient for performing a comprehensive evaluation.…”

Section: Introductionmentioning

confidence: 99%

A novel technique for evaluating and selecting logistics service providers based on the logistics resource view

Alkhatib

Darlington

Yang

et al. 2015

Expert Systems with Applications

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The increasing importance of logistics outsourcing and availability of logistics services providers (LSPs) highlights the significance and complexity of the LSP evaluation and selection process.Most existing LSP evaluation and selection studies use historical performance data and assume independence among decision criteria. This paper proposes an integrated logistics outsourcing approach to evaluate and select LSPs based on their logistics resources and capabilities. This novel approach combines a fuzzy decision making trial, evaluation laboratory (FDEMATEL) and fuzzy techniques to order preferences by similarity to ideal solution (FTOPSIS) methods.The new multi-criteria decision making (MCDM) model addresses the impact relationships between decision criteria and ranks LSP alternatives against weighted resources and capabilities.The effectiveness of this approach is demonstrated through a real case study and a two-phase sensitivity analysis confirms its robustness.

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Section: Introductionmentioning

confidence: 99%

A novel technique for evaluating and selecting logistics service providers based on the logistics resource view

Alkhatib

Darlington

Yang

et al. 2015

Expert Systems with Applications

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“…Operator. The main purpose of crossover operator [58] is to generate better offspring by combining the genetic alleles of two selected parents from the population with probability Pc; the crossover position is generated randomly and the crossover progress can be shown in Figure 4 and it can be illustrated as follows. Firstly, randomly generate the crossover position (here are two intersections: 7-9-6-2-4, 2-1-8-9-3) for PtpC-1 and PtpC-2 and keep the middle serial number sequence unchanged.…”

Section: Crossovermentioning

confidence: 99%

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

Guo

Liu

et al. 2017

Mathematical Problems in Engineering

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The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

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“…In general, the traditional supplier selection criteria can be divided into two categories: qualitative and quantitative. Different qualitative criteria of suppliers such as transportation, purchasing and order costs, delivery time, product defect rate, and qualitative criteria such as product quality, warranties and claim policies, performance history, technical capability, geographical location, desire of business, and labor relations record are considered in the previous studies (Liao and Kao 2011;Ozkok and Tiryaki 2011;Arikan 2013;Toloo and Nalchigar 2011;Du et al 2015;Al Khaled and Hosseini 2015;Guneri et al 2011;Abdollahi et al 2015;Karsak and Dursun 2014;Kilincci and Onal 2011;You et al 2015;Kumar Kar 2014). Green supplier selection criteria have received a great deal of attentions from both academics and practitioners due to environmental standards and regulations imposed by governments to companies.…”

Section: Introductionmentioning

confidence: 99%

A hybrid ensemble and AHP approach for resilient supplier selection

Hosseini

Khaled²

2016

J Intell Manuf

125

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Suppliers play a crucial role in achieving the supply chain goals. In the context of risk management, suppliers are the most common source of external risks in modern supply chains. The recognition that continuity of supply chain flow under disruptive event is a critical issue has brought the attention of companies to the selection of resilient suppliers. In contrast to the extensive number of researches on traditional and green criteria of supplier selection, the criteria associated with resilient supplier selection are not well explored yet. This paper first seeks to explore the resilience criteria for supplier selection based on the notion of resilience capacities which can be divided into three categories: absorptive capacity, adaptive capacity, and restorative capacity. Absorptive capacity refers to the capability of system to withstand against disruptive event in prior or called as preparedness of supplier, while adaptive and restoration capacities imply the capability of supplier to adopt itself and restore from disruption or recoverability of supplier. We identified eight effective elements for resilience capacities which contribute to the resilience of suppliers. Advanced data mining approaches like predictive analytics models are used to predict the resilience value of each supplier. We applied ensemble methods by combining binomial logistics regression, classification and regression trees, and neural network to obtain better predictive performance than individual algorithm from the historical data to predict individual supplier's resiliency. This resilience value, obtained from ensemble B Seyedmohsen Hosseini

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A Pareto supplier selection algorithm for minimum the life cycle cost of complex product system

Cited by 48 publications

References 44 publications

A novel technique for evaluating and selecting logistics service providers based on the logistics resource view

A novel technique for evaluating and selecting logistics service providers based on the logistics resource view

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

A hybrid ensemble and AHP approach for resilient supplier selection

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