scholarly journals A Nonlinear Integer Programming Model for Integrated Location, Inventory, and Routing Decisions in a Closed-Loop Supply Chain

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Hao Guo ◽  
Congdong Li ◽  
Ying Zhang ◽  
Chunnan Zhang ◽  
Yu Wang
Keyword(s):  
Supply Chain ◽  
Integer Programming ◽  
Facility Location ◽  
Vehicle Routing ◽  
Closed Loop ◽  
Programming Model ◽  
Nonlinear Integer Programming ◽  
Inventory Routing ◽  
Closed Loop Supply Chain ◽  
Integer Programming Model

Facility location, inventory management, and vehicle routing are three important decisions in supply chain management, and location-inventory-routing problems consider them jointly to improve the performance and efficiency of today’s supply chain networks. In this paper, we study a location-inventory-routing problem to minimize the total cost in a closed-loop supply chain that has forward and reverse logistics flows. First, we formulate this problem as a nonlinear integer programming model to optimize facility location, inventory control, and vehicle routing decisions simultaneously in such a system. Second, we develop a novel heuristic approach that incorporates simulated annealing into adaptive genetic algorithm to solve the model efficiently. Last, numerical analysis is presented to validate our solution approach, and it also provides meaningful managerial insight into how to improve the closed-loop supply chain under study.

The Uncertain Programming of the Closed-Loop Supply Chain with Remanufacturing System

2012 ◽  
Vol 190-191 ◽  
pp. 218-221 ◽  
Author(s):  
Yu Juan Chen ◽  
Dong Bo Liu ◽  
Hong Wei Mao ◽  
Zi Qiang Zhang
Keyword(s):  
Supply Chain ◽  
Hybrid System ◽  
Closed Loop ◽  
Programming Model ◽  
Uncertain Programming ◽  
Fuzzy Simulation ◽  
Closed Loop Supply Chain ◽  
Proposed Model ◽  
Uncertain Model ◽  
Intelligent Optimization Algorithm

This paper addresses an integrated uncertain programming model for a closed-loop supply chain with manufacturing/remanufacturing hybrid system. The hybrid system is studied under the grey fuzzy uncertainty and grey uncertainty. The hybrid intelligent optimization algorithm integrating the grey fuzzy simulation, neural network and genetic algorithm can optimize the uncertain model. One numerical example is given to illustrate the effectiveness of the proposed model and algorithm.

scholarly journals Green closed-loop supply chain network design: a novel bi-objective chance-constraint approach

2021 ◽  
Vol 55 (2) ◽  
pp. 811-840
Author(s):  
Amin Reza Kalantari Khalil Abad ◽  
Seyed Hamid Reza Pasandideh
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Closed Loop ◽  
Programming Model ◽  
Expected Value ◽  
Chance Constraint ◽  
Chance Constrained Programming ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Closed Loop Supply Chain

In this paper, a novel chance-constrained programming model has been proposed for handling uncertainties in green closed loop supply chain network design. In addition to locating the facilities and establishing a flow between them, the model also determines the transportation mode between facilities. The objective functions are applied to minimize the expected value and variance of the total cost CO2 released is also controlled by providing a novel chance-constraint including a stochastic upper bound of emission capacity. To solve the mathematical model using the General Algebraic Modeling System (GAMS) software, four multi-objective decision-making (MODM) methods were applied. The proposed methodology was subjected to various numerical experiments. The solutions provided by different methods were compared in terms of the expected value of cost, variance of cost, and CPU time using Pareto-based analysis and optimality-based analysis. In Pareto-based analysis, a set of preferable solutions were presented using the Pareto front; then optimality-based optimization was chosen as the best method by using a Simple Additive Weighting (SAW) method. Experimental experiments and sensitivity analysis demonstrated that the performance of the goal attainment method was 13% and 24% better that of global criteria and goal programming methods, respectively.

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