A coordinated location-inventory problem in closed-loop supply chain

Abstract

This paper considers a coordinated location-inventory model under uncertain demands for a closed loop supply chain comprising of one plant, forward and reverse distribution centers, and retailers. The inventory of new and returned products is managed at forward and reverse distribution centers respectively through a periodic review policy. The proposed model determines the location of forward and reverse distribution centers and the associated capacities, the review intervals of the inventory policy at distribution centers, and the assignments of retailers to the distribution centers. We model six different coordination strategies. All the models are formulated as nonlinear integer programs with chance constraints and transformed to conic quadratic mixed-integer programs that can be efficiently solved by CPLEX. An outer approximation based solution algorithm is developed to solve the conic quadratic mixed-integer program. The benefit of different types of coordination strategies is shown through extensive computational testing.

Combined Location-Inventory Optimization of Deteriorating Products Supply Chain Based on CQMIP under Stochastic Environment

“…AslNajafi et al [12] considered a dynamic closed-loop locationinventory problem and designed a hybrid metaheuristic algorithm based on multiobjective particle swarm optimization (MOPSO) and nondominated sorting genetic algorithm-II (NSGA-II) to solve the problem. Zhang and Unnikrishnan [13] presented a location-inventory model with uncertain demands, which is based on integer nonlinear programming and can be transformed to conic quadratic mixed-integer programming, and used CPLEX software solve this problem. Diabat et al [14] presented a joint location-inventory model based on uncertain demands and lead times, the use of which can determine not only the location and number of distribution centers but also the size, and adopted a hybrid algorithm to solve the presented model; this algorithm arose from simulated annealing and direct search.…”

A Capacitated Location-Inventory Model with Demand Selection

“…Recently, there is an extensive body of research on the supply chain network design, such as supply chain network design with multiechelon inventory [5,6], multicommodity supply chain network design [7,8], multisourcing supply chain network design [9][10][11], multiobjective supply chain network design [12,13], reliable supply chain network design [14][15][16], closed-loop supply chain network design [17,18], and green supply chain network design [19][20][21][22]. We refer the readers to Shen [23], Melo et al [24], Farahani et al [25], and Govindan et al [26] for reviews of related research.…”

A Multi Echelon Location-Inventory Model with Lateral Transshipment

“…Also, they extended their research by considering demand and lead time dependencies in the model and again conic programming with compact formulation was used as solution approach (Shahabi et al, 2014). Zhang and Unnikrishnan (2016) developed a closed loop supply chain with distribution centers in forward and reverse network under periodic review policy. The model is reformulated by first 1 METRIC: Multi Echelon Technique for Recoverable Item Control linearization of the product variables and then converting the chance constraint using conic programming.…”