Design of supply chain networks with multi-phased discount price and service level: formulation, complexity, and algorithm

Chen, Kuei‐Hsien; Wong, Jui-Tsung; Su, Chwen-Tzeng

doi:10.1080/02522667.2009.10699880

Cited by 2 publications

(11 citation statements)

References 21 publications

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“…Several objectives reported in the literature include minimizing the total supply chain network cost. The most common component of total supply chain network cost is transportation cost (Shankar et al, 2013a(Shankar et al, , 2013bKuo and Han, 2011;Che, 2012;Wang, 2009;Barr on et al, 2012;Kadadevaramath et al, 2012;Kuo and Huang, 2009;Moncayo-Martínez and Recio, 2014;Bashiri and Tabrizi, 2010;Zhang et al, 2012;Chen et al, 2009aChen et al, , 2009bValidi et al, 2013;Kumar et al, 2010;Chandra et al, 2013). This includes the cost of the movement of vehicles, wages of vehicle operators and hauling charges.…”

Section: Flow Constraintmentioning

confidence: 99%

“…Another important aspect of DND is inventory carrying cost. Several authors have also used inventory carrying cost as an important component for minimizing the total supply chain cost (Chen et al, 2009a(Chen et al, , 2009bKuo and Han, 2011;Moncayo-Martı¨nez and Zhang, 2013;Bashiri and Tabrizi, 2010). The inventory carrying cost involves storage cost, insurance cost, cost of stuck capital, pilferage cost and obsolescence cost.…”

Section: Flow Constraintmentioning

confidence: 99%

“…Thus, while designing a distribution network, one may have to take inventory carrying cost into account, as it is also dependent on the cost of storage, which is location dependent. In DND, one also encounters allocation-related objective function components such as demand allocation (Kuo and Huang, 2009) and time dependent components, such as delivery time (Moncayo-Martínez and Recio, 2014) and manufacturing time (Chen et al, 2009a(Chen et al, , 2009b. A generic statement for DND by minimizing the total supply chain can be depicted as follows:…”

Section: Flow Constraintmentioning

confidence: 99%

“…The inventory management problem is also constrained by a number of constraints similar to DND, such as capacity (Panicker et al, 2013;Chen et al, 2009aChen et al, , 2009bValidi et al, 2013;Shankar et al, 2013aShankar et al, , 2013bKuo and Huang, 2009;Shankar et al, 2013aShankar et al, , 2013b, demand (Panicker et al, 2013;Validi et al, 2013;Shankar et al, 2013aShankar et al, , 2013bChan and Kumar, 2009;Shankar et al, 2013aShankar et al, , 2013b, flow (Chen et al, 2009a(Chen et al, , 2009bShankar et al, 2013aShankar et al, , 2013bShankar et al, 2013aShankar et al, , 2013b and inventory balance (Chen et al, 2009a(Chen et al, , 2009bSadeghi et al, 2013;Bashiri and Tabrizi, 2010). However, in addition to these already discussed constraints in Section 6.1, financial constraints are also evident in SI literature on SCM (Taleizadeh et al, 2013).…”

Section: Inventory Management Constraintsmentioning

confidence: 99%

“…In the resource allocation problem, one very critical issue is the time taken by the allocated manufacturing facility or any other process taking place in the facility. At times, it is possible that a manufacturing facility may need more time in making the product, under time constraints, and in such situations, processing time constraints are imposed (Tang et al, 2013;Kannan et al, 2009;Chen et al, 2009aChen et al, , 2009b. Delivery time constraints are also evident in the literature (Tang et al, 2013).…”

Section: Inventory Management Constraintsmentioning

confidence: 99%

See 4 more Smart Citations

Swarm intelligence approaches in supply chain management: potentials, challenges and future research directions

Soni

Jain

Chan

et al. 2019

SCM

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Purpose It is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM. Design/methodology/approach A structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms. Findings The paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers. Originality/value The paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

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Section: Flow Constraintmentioning

confidence: 99%

Section: Flow Constraintmentioning

confidence: 99%

Section: Flow Constraintmentioning

confidence: 99%

Section: Inventory Management Constraintsmentioning

confidence: 99%

Section: Inventory Management Constraintsmentioning

confidence: 99%

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Swarm intelligence approaches in supply chain management: potentials, challenges and future research directions

Soni

Jain

Chan

et al. 2019

SCM

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Application of smart electronic systems, firm characteristics and efficient energy consumption – a case of public universities in Uganda

Isingoma-Wakaisuka

Ibanda

Naluwooza

et al. 2020

IJSE

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PurposeThe purpose of the study is to examine the relationship between the application of smart electronic systems, firm characteristics and efficient energy consumption: a case of public universities in Uganda.Design/methodology/approachThe study adopted both quantitative and qualitative approach as well as descriptive cross-sectional survey design tantamounting to an experimental-observation approach. A sample of four public academic universities were explored using primary data. A semi-structured questionnaire together with an evaluation form and a tested experimental kit (from one of the leading electronics centres in Uganda) was used to examine the consumption rates of different electronic appliances of less than 30 Amps. Further, a Pearson product moment correlation (r) analysis was also used to determine the direction of a relationship among the variables together with a linear relationship (regression) to predict a linear association of one or more variables. Recommendations were also given.FindingsSmart electronic systems make a significant determining factor to both firm characteristics (age, number of students, administrative staff and support staff) as well as efficient energy consumption. Nonetheless, there is no significant difference of efficient energy consumption as far as firm characteristics are concerned.Research limitations/implicationsResults support the contributions of the theory of technology and acceptance model by affirming that a number of factors influence the usefulness and ease of use of the smart electronic systems, which in turn influence energy consumption.Practical implicationsUniversities' management should endeavour to install smart electronic systems. But still, government should try to lower taxes on smart electronic systems and genuine agents should be named for easy and affordable access of the users, universities inclusive.Originality/valueThe study contributes towards a theoretical position by affirming the usefulness of technology acceptance model for efficient energy consumption in public universities.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2019-0083

show abstract

Design of supply chain networks with multi-phased discount price and service level: formulation, complexity, and algorithm

Cited by 2 publications

References 21 publications

Swarm intelligence approaches in supply chain management: potentials, challenges and future research directions

Swarm intelligence approaches in supply chain management: potentials, challenges and future research directions

Application of smart electronic systems, firm characteristics and efficient energy consumption – a case of public universities in Uganda

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