PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem

Subramanian, P.; Ramkumar, N.; Narendran, T.T.; Ganesh, K.

doi:10.1016/j.asoc.2012.10.004

Cited by 57 publications

(23 citation statements)

References 36 publications

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“…SA has some advantages and disadvantages compared to other meta-heuristics, such as GA and particle swarm solution (PSO). The advantages of this algorithm (e.g., easier implementation, convergence attributes and utilizing hill climbing) make it popular from meta-heuristic algorithms in the recent decade (Subramanian et al, 2013).…”

Section: Simulated Annealingmentioning

confidence: 99%

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Yousefi¹,

Tavakkoli–Moghaddam²,

Oliaei³

et al. 2017

10.5267/j.ijiec

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A vehicle routing problem with time windows (VRPTW) is an important problem with many real applications in a transportation problem. The optimum set of routes with the minimum distance and vehicles used is determined to deliver goods from a central depot, using a vehicle with capacity constraint. In the real cases, there are other objective functions that should be considered. This paper considers not only the minimum distance and the number of vehicles used as the objective function, the customer's satisfaction with the priority of customers is also considered. Additionally, it presents a new model for a bi-objective VRPTW solved by a revised multi-choice goal programming approach, in which the decision maker determines optimistic aspiration levels for each objective function. Two meta-heuristic methods, namely simulated annealing (SA) and genetic algorithm (GA), are proposed to solve large-sized problems. Moreover, the experimental design is used to tune the parameters of the proposed algorithms. The presented model is verified by a real-world case study and a number of test problems. The computational results verify the efficiency of the proposed SA and GA.

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Section: Simulated Annealingmentioning

confidence: 99%

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Yousefi¹,

Tavakkoli–Moghaddam²,

Oliaei³

et al. 2017

10.5267/j.ijiec

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“…Subramanian et al [28] considered a closed loop supply chain and addressed the issues of designing the network and of optimizing the distribution. They considered four variants of the problems and modeled it as an integer linear programming.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated Multi-Echelon Model for a Sustainable Closed Loop Supply Chain Network Design

Aravendan¹,

Panneerselvam²

2014

IIM

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The integration of entire supply and value chain into a closed loop network is gaining more importance in recent times in order to ensure a business to be economically and environmentally sustainable with the changing trends in business and social environments, growing environmental consciousness in the society and government legislations to protect the environment as well as the business. In this context, this paper considers a multi-echelon closed loop supply chain network design with forward and reverse logistics components. An attempt has been made to develop a mixed integer non-linear programming model for this problem with different costs so that the sum of the total cost is minimized subject to different constraints pertaining to capacities of the entities of the system, demands of first customers and second customers. A generalized model is presented and then its application is illustrated using an example problem by solving the model using LINGO14. This model forms as a tool to compare future meta-heuristics to check the closeness of their solutions with corresponding optimal solutions.

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“…Due to increasing adoption of RL processes, integration of distribution channel in the supply chain network becomes important. Subramanian, Ramkumar, Narendran and Ganesh (2013) focused on integration with four variants of RL network with dedicated warehouse delivery locations. They found that for different scenarios, a single product, single period will perform well with constant demand and uncertain returns.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal inventory policy in a closed loop supply chain system with multiple periods

Arumugam

Natarajan

Sapabathy

et al. 2017

JIEM

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Abstract:Purpose: This paper aims to model and optimize the closed loop supply chain for maximizing the profit by considering the fixed order quantity inventory policy in various sites at multiple periods.Design/methodology/approach: In forward supply chain, a standard inventory policy can be followed when the product moves from manufacturer, distributer, retailer and customer but the inventory in the reverse supply chain of the product with the similar standard policy is very difficult to manage. This model investigates the standard policy of fixed order quantity by considering the three major types of return-recovery pair such as commercial returns, end-ofuse returns, end -of-life returns and their inventory positioning at multiple periods. The model is configured as mixed integer linear programming and solved by IBM ILOG CPLEX OPL studio. Findings:To find the performance of the model a numerical example is considered for a product with three Parts (A which of 2nos, B and C) for 12 multiple periods. The results of the analysis show that the manufacturer can know how much should to be manufacture in multiple periods based on Variations of the demand by adopting the FOQ inventory policy at different sites considering its capacity constraints. In addition, it is important how much of parts should be purchased from the supplier at the given 12 periods.-237-Journal of Industrial Engineering and Management -https://doi.org/10.3926/jiem.2205 Originality/value: A sensitivity analysis is performed to validate the proposed model two parts.First part of the analysis will focus on the inventory of product and parts and second part of analysis focus on profit of the company. The analysis which provides some insights in to the structure of the model.

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PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem

Cited by 57 publications

References 36 publications

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

An Integrated Multi-Echelon Model for a Sustainable Closed Loop Supply Chain Network Design

Optimal inventory policy in a closed loop supply chain system with multiple periods

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