Designing a realistic ICT closed loop supply chain network with integrated decisions under uncertain demand and lead time

Vahdani, Behnam; Ahmadzadeh, Elham

doi:10.1016/j.knosys.2019.05.003

Cited by 19 publications

(10 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These encompassed mathematical models (Wang Han et al, 2019), game theory (Shekarian et al, 2021), stochastic methods (Baptista et al, 2018), MILP (Mixed-Integer Linear Programming) in conjunction with other techniques (Cardoso et al, 2016;Biçe & Batun, 2021;Jindal & Sangwan, 2014), as well as MINLP (Mixed-Integer Nonlinear Programming) combined with other procedures (Polo et al, 2019). Furthermore, in subsequent developments, heuristic approaches and metaheuristic algorithms are utilized for CLSC/RL/RSC risk management, aiming to achieve near-optimal results (Vahdani & Ahmadzadeh, 2019). Gooran et al (2018) proposed a GA (Genetic Algorithm) approach along with Monte Carlo simulation, while Asl-Najafi et al (2015) designed a method that addresses inventory risk by combining MOPSO (Multi-Objective Particle Swarm Optimization) with the Non-Dominated Sorting Genetic Algorithm (NSGA).…”

Section: Methods and Approaches For Rsc Risk Managementmentioning

confidence: 99%

“…These approaches are limited in their ability to adapt to changes over multiple periods. Metaheuristic approaches such as GA (Genetic Algorithm), NSGA II (Nondominated Sorting Genetic Algorithm II), and MOPSO (Multi-objective Particle Swarm Optimization) is used to design optimization models for RSC risk management while considering uncertainty (Jabbarzadeh et al, 2018;Vahdani & Ahmadzadeh, 2019). This research aims to develop a comprehensive optimization model for RSC management, considering multi-purpose and multi-period aspects, as well as supply and demand uncertainties and reservations associated with the quantity and quality of returned goods.…”

Section: Figure 1 Keyword Reference Network Visualizationmentioning

confidence: 99%

See 1 more Smart Citation

Risk Management in Reverse Supply Chain for Sustainable Agri-food Industry: A Systematic Literature Review and Future Research Agenda

Ummi,

Marimin,

Noor

et al. 2023

OSCM: An Int. Journal

View full text Add to dashboard Cite

In implementing a Reverse Supply Chain (RSC) for food waste products, several risks must be borne by producers or third parties. This research aims to identify risks in RSC operations, analyze existing methods and approaches, and improve a further framework model for managing the risk of RSC for the sustainability of the agricultural food industry. Using the PRISMA protocol, data was collected from a systematic review and synthesis of 78 articles published between 2012 and 2021. The result showed that although several research have been carried out on RSC risk, there is still very little research on risk management in the agri-food industry. It also found the potential aspects for future research in managing RSC risk in the agri-food industry, include process risk by considering product damage, supply-demand uncertainty, quantity and quality product return uncertainty, transportation and technology, and financial and environmental risk. According to the literature, MILP and integration with other approaches are frequently used in decision-making to manage the risk of RSC. Exploration of future research requires developing RSC risk management in the integrated agri-food industry. Furthermore, it is important to optimize the risk management of the RSC by considering the complexity of the risk and designing the sustainability of the RSC network in the agri-food industry. Focus is presently on the Hybrid Intelligent Decision Support System (HIDSS) approach for the development of a concept is the most appropriate new concept to reduce various types of risks in the RSC of the agri-food industry.

show abstract

Section: Methods and Approaches For Rsc Risk Managementmentioning

confidence: 99%

Section: Figure 1 Keyword Reference Network Visualizationmentioning

confidence: 99%

Risk Management in Reverse Supply Chain for Sustainable Agri-food Industry: A Systematic Literature Review and Future Research Agenda

Ummi,

Marimin,

Noor

et al. 2023

OSCM: An Int. Journal

View full text Add to dashboard Cite

show abstract

“…This approach can prevent a distinct impact on economic performance via changes in quality. In [35], a mixed-integer nonlinear programming (MILP) model was proposed to integrate pricing with facility location and inventory control decisions in a CLSCN in the information and communications technology (ICT) industry. This model maximizes the total profit obtained by selling the new ICT products or collecting the used ICT products.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel algorithm to allocate customers and retailers in a closed-loop supply chain under probabilistic demand

Keramatlou¹,

Javadian²,

Didehkhani³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this paper, a closed-loop supply chain (CLSC) is modeled to obtain the best location of retailers and allocate them to other utilities. The structure of CLSC includes production centers, retailers’ centers, probabilistic customers, collection, and disposal centers. In this research, two strategies are considered to find the best location for retailers by focusing on 1- the type of expected movement 2- expected coverage (distance and time) for minimizing the costs and maximizing the profit by considering the probabilistic customer and uncertainly demand. First of all, the expected distances between customers and retailers are calculated per movement method. These values are compared with the Maximum expected coverage distance of retailers, which is displayed in algorithm 1 heuristically, and the minimum value is picked. Also, to allocate customers to retailers, considering the customer's movement methods and comparing it with Maximum expected coverage time, which is presented in Algorithm 2 heuristically, the minimum value is chosen to this end, a bi-objective nonlinear programming model is proposed. This model concurrently compares Strategies 1 and 2 to select the best competitor. Based on the chosen strategy, the best allocation is determined by employing two heuristic algorithms, and the locations of the best retailers are determined. As the proposed model is NP-hard, a meta-heuristics (non-dominated sorting genetic) algorithm is employed for the solution process. Afterward, the effectiveness of the proposed model is validated and confirmed, and the obtained results are analyzed. For this purpose, a numerical example is given and solved through the optimization software.

show abstract

“…However, the uncertain behavior of demand and lead time leads to a failure to satisfy customers’ needs. Previous studies have separately discussed the parameters of demand probability and lead-time probability without establishing and extracting the parameters of the demand-during-lead-time probability distribution, such as the mean of demand during lead time μ L and the standard deviation of demand during lead time σ L (Axsäter and Viswanathan, 2012; Braglia et al , 2019; Sayid Albana et al , 2018; Vahdani and Ahmadzadeh, 2019).…”

Section: Problem Descriptionmentioning

confidence: 99%

Supply chain management and market responsiveness: a simulation study

Aljanabi

Ghafour

2020

JBIM

View full text Add to dashboard Cite

Purpose This study aims to provide a practical solution to the relationship between supply chain (SC) integration and market responsiveness (MR). A method is proposed to integrate SC and MR parameters, namely, product supply and demand in the context of low-value commodities (e.g. cement). Design/methodology/approach Simulation and forecasting approaches are adopted to develop a potential procedure for addressing demand during lead time. To establish inventory measurements (safety stock and reorder level) and increase MR and the satisfaction of customer’s needs, this study considers a downstream SC including manufacturers, depots and central distribution centers that satisfies an unbounded number of customers, which, in turn, transport the cement from the industrialist. Findings The demand during lead time is shown to follow a gamma distribution, a rare probability distribution that has not been considered in previous studies. Moreover, inventory measurements, such as the safety stock, depending on the safety factor under a certain service level (SL), which enables the SC to handle different responsiveness levels in accordance with customer requests. In addition, the quantities of the safety stock and reorder point represent an optimal value at each position to avoid over- or understocking. The role of SC characteristics in MR has largely been ignored in existing research. Originality/value This study applies SC flexibility analyzes to overcome the obstacles of analytical methods, especially when the production process involves probabilistic variables such as product availability and demand. The use of an efficient method for analyzing the forecasting results is an unprecedented idea that is proven efficacious in investigating non-dominated solutions. This approach provides near-optimal solutions to the trade-off between different levels of demand and the SC responsiveness (SLs) with minimal experimentation times.

show abstract

Designing a realistic ICT closed loop supply chain network with integrated decisions under uncertain demand and lead time

Cited by 19 publications

References 51 publications

Risk Management in Reverse Supply Chain for Sustainable Agri-food Industry: A Systematic Literature Review and Future Research Agenda

Risk Management in Reverse Supply Chain for Sustainable Agri-food Industry: A Systematic Literature Review and Future Research Agenda

A novel algorithm to allocate customers and retailers in a closed-loop supply chain under probabilistic demand

Supply chain management and market responsiveness: a simulation study

Contact Info

Product

Resources

About