Fixed costs and shared resources allocation in two-stage network DEA

Zhu, Weiwei; Zhang, Qian; Wang, Haiqing

doi:10.1007/s10479-017-2599-8

Cited by 38 publications

(12 citation statements)

References 10 publications

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“…Data envelopment analysis (DEA) is a mathematical programming method used to evaluate the relative efficiency of DMUs with multiple inputs and outputs, first proposed by Charnes [40], [41]. DEA can deal with complex systems with multiple inputs and multiple outputs and has been rapidly developed in theory and application [42].…”

Section: Two-stage Deamentioning

confidence: 99%

Multiobjective Ecological Strategy Optimization for Two-Stage Disassembly Line Balancing With Constrained-Resource

2020

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Effective disassembly of obsolete products is critical for remanufacturing, recycling and recycling. However, existing disassembly studies have few or no resource constraints, such as limited numbers of disassembly operators and tools. Two-stage disassembly model is used to improve the flexibility to adapt to remanufacturing market demand. Based on existing references, this paper establishes a multiobjective two-stage disassembly model based on two-stage data envelopment analysis (two-stage DEA). The resource-constructed two-stage DEA model in this paper fully considers the dynamic configuration information of the output factors in each stage. To avoid the problem where the overall efficiency is optimal, the research constructs a resource constrained efficiency two-stage disassembly model. First, with the goal of minimum disassembly time, economy, energy consumption and environment, this study proposes eNSGA-II. By using mixed mutation and ecological evolution strategies, a well-distributed noninferior solution set is obtained. Second, the group of 16 disassembly schemes is used as decision-making units (DMUs) of the two-stage DEA. A comparison with Chen's model rankings shows that the model in this paper performs better. DMU 6 has the highest efficiency ranking, which is the best disassembly solution. Finally, the effectiveness of the proposed algorithm is verified by examples. Compared with NSGA-II and SPEA-II, eNSGA-II shows better performance and effectiveness. INDEX TERMS Disassembly, eNSGA-II, remanufacturing, resource constrained, two-stage DEA.

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Section: Two-stage Deamentioning

confidence: 99%

Multiobjective Ecological Strategy Optimization for Two-Stage Disassembly Line Balancing With Constrained-Resource

2020

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“…Moreover, centralized resource allocation based on the improved step‐by‐step efficiency proposed by Fang (2015) can be expressed as a specific modeling in the transformation possibility set defined by Korhonen and Syrjänen (2004). Recent studies on resource allocation that adopt DEA method are numerous in terms of both theoretical extension and practical applications such as network‐based resource allocation (Li et al., 2019; Ang et al., 2020), fixed cost allocation with competition or cooperation relationships (Zhu et al., 2019; Li et al., 2020), and resource allocation based on cost (revenue) efficiency (Fang and Li, 2015; Ray, 2016). Some of the most prevalent applications of DEA‐based resource allocation are carbon emission allocation in environmental conservation (Ma et al., 2018; Zhou et al., 2018), regional highway transportation systems (Wu et al., 2018), higher education institutions (Wang, 2019), and public healthcare sectors (Yang, 2017b).…”

Section: Introductionmentioning

confidence: 99%

DEA‐based centralized resource allocation with network flows

Tao

Xiong

2020

Int Trans Operational Res

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This study investigates the centralized resource allocation problem among individual units using network flow theory in a data envelopment analysis (DEA) framework. Existing research on DEA-based resource allocation is primarily concerned with the amount of allocated resources to individual units, and only a few studies have considered resource flows when resources are allocated. To identify the internal structure of the resource allocation process, network structure is introduced and network flow theory is used to search for the optimal resource allocation scheme. Moreover, resource allocation cost is considered in network flows and a resource allocation gain (RAG) function is formulated to provide a quantitative analysis of the trade-off between production gains from resource allocation and resource allocation cost in the resource allocation process. In addition, several characteristics of the RAG function are also discussed. Lastly, the proposed method is demonstrated through an expository example with several random data and an application of the centralized resource reallocation to bank systems.

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“…Yu et al [38] proposed a fixed cost allocation method based on the two-stage network DEA and the cross-efficiency to allocate the fixed cost adequately. Zhu et al [39] discussed the fixed cost allocation problem by using a two-stage network DEA method and proposed three procedures to obtain a fair cost allocation plan based on different objectives in reality. Ding et al [40] first presented additive two-stage models to evaluate the performance for each DMU when allocating the fixed cost, and via introducing the concepts of satisfaction degree and fairness degree, an approach was proposed to obtain an optimal allocation plan.…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Allocation in a Chinese Province-Level Region Based on Two-Stage Network Structures

et al. 2019

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With the increasingly severe global environment and climate change, the growing social attention toward the environmental problems has prompted local governments to make policy adjustments. The formulation of the carbon emission right allocation scheme is important for policy-makers. Many researchers have studied the problem of carbon emission right allocation by using data envelopment analysis (DEA) models. However, the existing literature using traditional models consider each Decision-Making Unit (DMU) as a “black box” without taking the internal structure into account, but in fact, it is more accurate for formulating the scheme when considering the inner operation of DMUs. This paper investigates the allocation plan of carbon emission right among each province in China from 2007–2016 based on a two-stage DEA model. The results indicate that, first, there is no space for carbon emission in the north, northeast, and northwest from 2007–2016, while in the southern regions, it always exists. In addition, the carbon emission permits of the southern and eastern regions are increasing, but in the southwestern regions, the carbon emission space barely fluctuated during this decade. Second, the potential of carbon emission reduction of each region tends to be stable after 2014, and in the north and northwest, it fluctuated greatly from 2007–2016. Besides, the northwest region has had the potential of emission reduction since 2010, while it also exists in the northern region after 2014.

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Fixed costs and shared resources allocation in two-stage network DEA

Cited by 38 publications

References 10 publications

Multiobjective Ecological Strategy Optimization for Two-Stage Disassembly Line Balancing With Constrained-Resource

Multiobjective Ecological Strategy Optimization for Two-Stage Disassembly Line Balancing With Constrained-Resource

DEA‐based centralized resource allocation with network flows

Carbon Emission Allocation in a Chinese Province-Level Region Based on Two-Stage Network Structures

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