A hybrid approach to supplier selection and order allocation using Z-number-based data envelopment analysis and machine learning

This paper investigates a multi-objective supplier selection and order allocation problem under quantity discounts in a fuzzy environment. Prior research on supplier selection and order allocation with quantity discounts mainly considered partial fuzziness of the decision problem; a situation where both the objectives of the decision maker and the constraints are fuzzy has not been studied up to now. This paper closes this gap by integrating both aspects into a single model. First, a combination of fuzzy preference programming and interval-based TOPSIS is proposed for evaluating suppliers. Secondly, based on the scores obtained in the first step, a fuzzy multi-objective linear programming model is developed. Subsequently, a new solution procedure for solving the fuzzy multi-objective linear programming model is presented. The procedure first transforms fuzzy constraints and coefficients into deterministic coefficients, and then three different fuzzy programming approaches, namely interactive fuzzy multi-objective linear programming, and the weighted additive as well as the weighted max-min method are implemented. Finally, the performance of each method is evaluated by computing the distance between each solution and the preferred solution.

Multi-objective game theory model and fuzzy programing approach for sustainable watershed management

A multi-objective nonlinear planning model of biomass power generation for supporting subsidy policies optimization

Global path planning and multi-objective path control for unmanned surface vehicle based on modified particle swarm optimization (PSO) algorithm

A proposed method for third-party reverse logistics partner selection and order allocation in the cellphone industry

This research develops a fuzzy, multi-objective, multi-product and multi-period mathematical model for sustainable supplier selection and order allocation in the automotive industry. The problem has been investigated in previous studies, but order allocation in fuzzy environments has attracted less attention. To fill this gap, this research integrates sustainable supplier selection and order allocation with inflation, risk and fuzzy uncertainties. The most important criteria of sustainable supplier selection are developed to select the suppliers. The supplier selection process is conducted using an analytical hierarchy process. The order allocation process determines the optimum purchasing quantity of items from each supplier in each period. To achieve this, six objective functions, total cost, economic score, environmental score, social score, inflation rate and risk level, and the related constraints are considered in the model. An approach to a solution and sensitivity analysis are also provided. The results identify the best suppliers and optimum order allocation.

In spite of the increasing awareness apparent in the previous studies regarding the evaluation suppliers considering sustainability aspects, there are limitations on the incorporation of sustainable performance in terms of traditional, green and social aspects in supplier selection and order allocation. This paper presents the development of an integrated fuzzy TOPSIS-possibilistic multi objectives model to (1) solve a two-stage sustainable supplier selection problem; and (2) allocate the optimal flow of products quantity that should be ordered from suppliers towards the minimization of expected costs, environmental impact and travel time and maximization of social impact. Suppliers’ sustainable performance was based on traditional, green and social criteria, and quantified by using fuzzy TOPSIS and then integrated into the possibilistic multi objective model. The latter helps decision makers in having an order allocation plan that considers sustainability aspect. Furthermore, the multi-objective optimization model was re-developed as a possibilistic multi-objective optimization model (PMOOM) to handle the dynamic nature in some of the input data. Next, the LP-metrics method was employed to derive Pareto solutions out of the PMOOM. The quality of the obtained Pareto solutions was evaluated using the global criterion approach aiming to help decision makers in selecting the final Pareto solution. The applicability of the developed integrated fuzzy TOPSIS-possibilistic multi-objective approach was proven with sensitivity analysis on a case study of a meat supply chain.

A hybrid approach using data envelopment analysis and case-based reasoning for housing refurbishment contractors selection and performance improvement

In previous studies on the order allocation of the supply chain, suppliers involved in order allocation are expected to accept orders passively. However, in the actual order allocation process of logistics service supply chain (LSSC), functional logistics service providers (FLSPs) are strategic. They will pre-estimate the order allocation results to decide whether or not to participate in order allocation. Besides, FLSPs will compete for orders by bidding strategy when there are more than one FLSP in order allocation. Therefore, it is necessary to introduce the pre-estimate behaviour and competitive-bidding strategy of FLSPs into the study of order allocation in LSSC. In this article, the pre-estimate behaviour and competitive-bidding strategy are considered and the bidding range of each FLSP is obtained. It is assumed that the logistics service integrator (LSI) allocates the order sequentially to FLSPs from the lowest price to highest price. Then, a multi-objective dynamic programming model with the objectives of the cost of LSI and the order satisfaction of FLSPs is built. Numerical analysis is followed to discuss the effects of some parameters on the order allocation results. Research shows that the quote of a FLSP only depends on its own cost and the highest industry cost but irrelevant to the industry lowest cost when considering competitive-bidding strategy of FLSPs; besides, too low or too high in industry cost affects the performance of order allocation; furthermore, pre-estimate behaviour and competitive-bidding strategy of FLSPs can help reduce the order allocation cost of LSI and improve the performance of LSSC. In the end, an example of Tianjin Baoyun Logistics Company is used to introduce the order allocation process of logistics service when Baoyun considers pre-estimate behaviour and competitive-bidding strategy of FLSPs, which helps to illustrate the application of model conclusions.

Redundancy-reliability allocation problems in multi-stage series-parallel systems under uncertain environments are addressed in this study. First, a multi-objective programming model is formulated for simultaneously maximizing system reliability and minimizing system total cost. Due to the nature of uncertainty in the problem, the fuzzy set theory and technique are used to convert the deterministic multi-objective programming model into a fuzzy nonlinear programming problem. A heuristic method is developed to get a set of satisfactory solutions for the fuzzy nonlinear programming problem. Then, a modified data envelopment analysis (DEA) model, is applied for completely ranking those satisfactory solutions considering some criteria of satisfactory, reliability, cost, volume and weight. A case study that is related to the electronic control unit installed on aircraft engine over-speed protection system is used to implement the developed approach. Results suggest that the developed fuzzy programming and DEA approach can effectively resolve the fuzzy and uncertain problem when design goals and constraints are not clearly confirmed at the initial conceptual design phase.   Key words: Fuzzy programming, data envelopment analysis, reliability allocation.

Recently, the Chinese government has implemented stringent water requirements based on the concept of ‘Basing four aspects on water resources’. However, existing research has inadequately addressed the constraints of water resources on population, city boundaries, land, and production, failing to adequately analyze the interplay between water resource limitations and urban development. Recognizing the interconnectedness between urban water use and economic development, a multi-objective model becomes crucial for optimizing urban water resources. This study establishes a nonlinear multi-objective water resources joint optimization model, aligning with the “Basing four aspects on water resources” requirement to maximize urban GDP and minimize total water use. A genetic algorithm (NSGA-II Algorithm) is applied to solve this complex nonlinear multi-objective model and obtain the Pareto solution set, addressing information loss inherent in the traditional water quota method. The model was tested in Wujiang District, an area located in China’s Jiangsu Province that has been rapidly urbanizing over the past few decades, and yielded 50 non-inferior water resource optimization schemes. The results reveal that the Pareto solution set visually illustrates the competition among objectives and comprehensively displays the interplay between water and urban development. The model takes a holistic approach to consider the relationships between water resources and urban population, land use, and industries, clearly presenting their intricate interdependencies. This study serves as a valuable reference for the rational optimization of water resources in urban development.

The concept of supply chain integration (SCI) has been widely set out in the academic literature in recent years. The advantages associated with the integrated approach have been articulated, as have possible approaches to planning for SCI. However, there is a dearth of literature in the area of SCI implementation. This chapter describes a piece of action research that aims to identify some of the critical success factors and inhibitors to success in relation to SCI. The action research was carried out in a complex hospital environment. Implementing anything that is new is typically met with resistance. Resistance to change is a natural response and the only way to get buy-in is to impress. That usually means presenting something of benefit and interesting. SCI in this regard requires that innovation is present in both concept and output. Innovative ideas, approaches and re-invention is a constant requirement for operational and strategic efficiencies. Similarly in SCI new and challenging ways must be incorporated into the implementation process.

Data envelopment analysis (DEA) is popularly used to evaluate relative efficiency among public or private firms. Most DEA models are established by individually maximizing each firm's efficiency according to its advantageous expectation by a ratio. Some scholars have pointed out the interesting relationship between the multiobjective linear programming (MOLP) problem and the DEA problem. They also introduced the common weight approach to DEA based on MOLP. This paper proposes a new linear programming problem for computing the efficiency of a decision-making unit (DMU). The proposed model differs from traditional and existing multiobjective DEA models in that its objective function is the difference between inputs and outputs instead of the outputs/inputs ratio. Then an MOLP problem, based on the introduced linear programming problem, is formulated for the computation of common weights for all DMUs. To be precise, the modified Chebychev distance and the ideal point of MOLP are used to generate common weights. The dual problem of this model is also investigated. Finally, this study presents an actual case study analysing R&D efficiency of 10 TFT-LCD companies in Taiwan to illustrate this new approach. Our model demonstrates better performance than the traditional DEA model as well as some of the most important existing multiobjective DEA models.

Data envelopment analysis (DEA) model has been widely applied for estimating efficiency scores of decision making units (DMUs) and is especially used in many applications in transportation. In this paper, a novel common weight credibility DEA (CWCDEA) model is proposed to evaluate DMUs considering uncertain inputs and outputs. To develop a credibility DEA model, a credibility counterpart constraint is suggested for each constraint of DEA model. Then, the weights generated by the credibility DEA (CDEA) model are considered as ideal solution in a multi-objective DEA model. To solve the multi-objective DEA model, a goal programming model is proposed. The goal programming model minimized deviations from the ideal solutions and found the common weights of inputs and outputs. Using the common weights generated by goal programming model, the final efficiency scores for decision making are calculated. The usefulness and applicability of the proposed approach have been shown using a data set in the airline industry.

The research on efficiency valuations has used two distinct approaches. One is the nonparametric approach known as data envelopment analysis (DEA), the other is the parametric approach based on regression analysis or its extension such as constrained canonical correlation analysis (CCCA). Interestingly, a recent study has employed a hybrid approach that cross-fertilizes DEA and CCCA to compensate for the drawbacks of the two methods and capture their positive aspects. This approach first applies DEA to select efficient units and then utilizes CCCA to identify a smooth efficient frontier with the selected efficient units only. We extend it to incorporate a categorical variable that reflects an environmental effect on efficiency performance. The need for considering a categorical variable arises in practice for an equitable efficiency valuation, as illustrated by managerial performance evaluation of the branches of a fast-food company, where the location of branches such as commercial or noncommercial area significantly affects their performance. We demonstrate various possible ways to handle such a categorical variable in the framework of a hybrid approach and characterize each of the methods. Based on this study, we suggest one method that simultaneously utilizes an extension of DEA, referred to as DEA with categorical variable, and CCCA employing a dummy variable, as in multiple regressions with dummy variables. Through an application to the branches of a fast-food company, we show the efficacy of the suggested method in terms of penalizing the advantageous location effect and compensating for the disadvantageous location effect. We also provide some discussions on the limitations underlying the hybrid approach in order to guide a proper use of this approach to the other potential applications.