Location-inventory Problem Research Articles

Blood supply chain location-inventory problem considering incentive programs: comparison and analysis of NSGA-II, NRGA and electromagnetic algorithms

Blood supply chain location-inventory problem considering incentive programs: comparison and analysis of NSGA-II, NRGA and electromagnetic algorithms

Model and algorithm for the location-inventory problem in pallet pooling systems

Model and algorithm for the location-inventory problem in pallet pooling systems

Global multi-sourcing network design with inventory planning under uncertainty

ABSTRACT In the current era of turbulence, demand uncertainty and significant supply delays have become recurrent challenges for every global supply chain, which are accentuated by geopolitical issues, regional conflicts, natural disasters, etc. Companies are keen to decide when and where nearshoring and offshoring their supply locations and to adapt their inventory control policies to cope with significant perturbations. To deal with these challenges, we investigate the integration of inventory planning at the strategic level through a novel setting of the location-inventory problem under uncertainty. This problem is characterised by inbound multi-sourcing, order splitting among multiple suppliers, and optimised inventory decisions from sourcing to customers under uncertain demand and lead time. A two-stage stochastic optimisation model is proposed and solved using a Benders decomposition algorithm, supported by a sample average approximation approach. The results show that both lead time and demand uncertainties impact strategic and tactical decisions of the global supply network. Specifically, multi-sourcing and inbound order splitting policies have proven beneficial when the network faces a high lead time uncertainty. Based on an illustrative case of a global automobile spare parts supply network under different supply-side scenarios, managerial insights are gained on the benefit of onshore, nearshore, and offshore decisions. When facing a high lead time uncertainty, as in the pandemic case or any geopolitical episode, our findings suggest the necessity to rely on nearshoring for the supply side.

Integrating supplier selection decisions into an inventory location problem for designing the supply chain network

Integrating supplier selection decisions into an inventory location problem for designing the supply chain network

Optimal supply network with vendor managed inventory in a healthcare system with RFID

We consider a supply network for a medical implant manufacturer within the healthcare system, wherein the manufacturer oversees multi-hospital warehouses under a vendor-managed inventory (VMI) policy and utilizes RFID technology to reduce the network’s total costs and enhance customer service. The manufacturer must determine the warehouse locations, inventory policies, and RFID investments to minimize total costs. In this research, we develop a mixed-integer nonlinear program model to formulate the integrated decision problem, which combines the location-inventory problem with the allocation and assignment of RFID technology, including its investment level. A real-world problem is resolved and analyzed using the proposed approach.

A continuous approximation location-inventory model with exact inventory costs and nonlinear delivery lead time penalties

Most existing location-inventory models use exact locations (coordinates) for warehouses and customers but use simple approximations for inventory costs. A different approach is presented in this paper. While the locations are loosely modeled using a continuous approximation approach, a reorder point, order quantity inventory model with exact costs is used. Exact expressions for the transport time distribution and backorder time distribution (for Poisson demand) are combined with an exogenously given warehouse fulfillment time and form the delivery lead time. Customers perceive a long delivery lead time negatively. This creates a dynamic between the number of stocking locations used (which affects the transport time and cost) and the inventory policy (which affects the backorder time and inventory costs) because both influence the delivery lead time. Furthermore, we are able to model a nonlinear customer perception of the delivery lead time. Based on the presented model, the general characteristics and mechanisms of such a location-inventory problem are investigated. Among other things, we show for many probability distributions that the average backorder time per backorder is convex and that our model is quasi-convex, which allows for a wide range of optimization methods.

Joint location–inventory problem with economies and diseconomies of scale: A general model and an efficient column generation algorithm

This paper studies the location–inventory problem, which jointly optimizes the location, inventory, and allocation of distribution centers (DCs) to serve uncertain demand of multiple retailers. Problems of this kind have been extensively studied to carefully tradeoff various costs, including the fixed facility location cost and the variable facility operating cost Most existing works assumed the facility operating cost to be either a linear or a concave function of the volume of demand allocated. However, practical evidence suggests that when the volume of allocated demand exceeds a certain level, the marginal cost may increase due to overtime charge or facility congestion, leading to a complicated cost function. In this paper, we capture the facility operating cost as a general inverse S-shaped function of the volume of allocated demand, which is first concave due to economies of scale and then convex due to diseconomies of scale. We formulate this problem as a set-covering model and propose a column generation (CG) algorithm to solve its linear relaxation. The corresponding pricing problem has nice structural properties and can be solved efficiently by a branch-and-bound (B&B) method. The efficiency of the proposed algorithm is validated through extensive numerical studies, where two types of inverse S-shaped functions are considered to capture facility operating cost under various situations. Sensitivity analysis is also carried out to reveal managerial insights and analyze the delicate tradeoffs between several cost components.

An integrated approach for optimizing location-inventory and location-inventory-routing problem for perishable products

The grocery market, especially the perishable product market is under amazing developments in China, accounting for half of overall food expenditure. However, the supply chain of perishable products has encountered serious problems, including significant freight losses, high inventory and transportation costs, etc. To solve these problems, the current paper proposes an integrated approach for simultaneously optimizing the decisions involved in the supply chain, typically including location, inventory, and routing strategy. A three-echelon supply chain including suppliers, DCs and retailers is structured in this paper, with the impacts of whether allowing direct shipment from supplier to retailer being considered. Two typical problems are investigated: location-inventory problem (LIP) and location-inventory-routing problem (LIRP). Mixed integer nonlinear programming model for LIP is proposed while Mixed integer linear programming models for LIRP is proposed, with the objective of minimizing the overall costs of supply chain, which are composed of infrastructure construction costs, transportation costs, inventory costs, and freight losses. CPLEX is used for LIP model solution and a two-stage heuristic algorithm based on Simulated Annealing is designed to solve the LIRP model. The proposed mathematical models and solution method are then applied to a real supply chain network for fresh milk. The optimized results suggest that considering the direct shipment from supplier to retailer is beneficial in terms of reducing the overall supply chain costs by 31%, with 43% reductions in transportation costs, 69.4% reductions in freight losses, 99.4% increases in inventory costs. With the developments of e-retailers for perishable products, allowing the direct shipment from supplier to retailer will bring more benefits in the future.

Solving a multi-echelon inventory-location problem utilising an efficient Lagrangian relaxation-based heuristic

Solving a multi-echelon inventory-location problem utilising an efficient Lagrangian relaxation-based heuristic

A hybrid differential evolution algorithm for a location-inventory problem in a closed-loop supply chain with product recovery

Product recovery is an important business because of its great economic, social, and environmental benefits in practice. In this paper, a location-inventory problem (LIP) in a closed-loop supply chain (CLSC) is investigated to optimize facility location and inventory control decisions by considering product recovery. The objective is to optimize facility location and inventory control decisions to minimize the total cost of business operations in a closed-loop supply chain system. We formulate this problem as a mixed-integer nonlinear programming model and design a modified hybrid differential evolution algorithm (MHDE) to solve it efficiently. Finally, numerical results are presented to validate the performance of the new algorithm. The results show that MHDE is more efficient and effective than Lingo and other algorithms for the research problem under study. Managerial insights are also derived for business managers to improve their supply chain performance.

Optimizing a multi-echelon location-inventory problem with joint replenishment: A Lipschitz [formula omitted]-optimal approach using Lagrangian relaxation

This paper investigates a supply network design problem for cross-border e-commerce. The supply chain consists of suppliers, regional distribution centers, and distribution centers. Our goal is to determine simultaneously location decisions of regional distribution centers, and the inventory decisions for coordinated replenishment and delivery. We formulate this problem as a mixed-integer non-linear program, and develop a Lipschitz optimization algorithm and an iterative heuristic to solve it. Moreover, we propose a tight lower bound via constructing a dual problem. Computational results show that both the Lipschitz optimization method and the heuristic can produce a near-optimal solution with a gap below 1%. Finally, we analyze the influence of integrated decision making and capacity constraints. An interesting finding is that the capacity of regional distribution centers has a critical influence, while the capacity of distribution centers is trivial. Integrated decision making can achieve remarkable cost savings only if the capacity constraints of regional distribution centers are loose. Nevertheless, capacity constraints of distribution centers make almost no impact. This result can be a good guide for a company’s investment plan: one should give top priority to regional distribution centers, and give only low priority to distribution centers.

A meta heuristic approach for reliable capacitated facility joint inventory-location problem with round-trip transportation under imperfect information of disruption in a Fuzzy environment

In today’s systems and networks, disruption is inevitable. Designing a reliable system to overcome probable facility disruptions plays a crucial role in planning and management. This article proposes a reliable capacitated facility joint inventory-location problem where location-independent disruption may occur in facilities. The system tries to satisfy customer’s demands and considers penalty costs for unmet customer demand. The article aims to minimize total costs such as establishing inventory, uncovered demand’s penalty, and transportation costs. While many articles in this area only use exact methods to solve the problem, this article uses a metaheuristic algorithm, the red deer algorithm, and the exact methods. Various numerical examples have shown the outstanding performance of the red deer algorithm compared to exact methods. Sensitivity analyses show the impacts of various parameters on the objective function and the optimal facility layouts. Lastly, managerial insights will be proposed based on sensitivity analysis.

A Heuristic for the Two-Echelon Multi-Period Multi-Product Location–Inventory Problem with Partial Facility Closing and Reopening

In this paper, the two-echelon multi-period multi-product location–inventory problem with partial facility closing and reopening is studied. For each product and period, plants serve warehouses, which serve consolidation hubs, which service customers with independent, normally distributed demands. The schedule of construction, temporary partial closing, and reopening of modular capacities of facilities, the continuous-review inventory control policies at warehouses, the allocation of customer demands to hubs, and the allocation of hubs to warehouses are determined. The service levels for stockout at warehouses during lead time and the violation of warehouse and hub capacities are explicitly considered. The proposed mixed-integer non-linear program minimizes the weighted summation of the number of different facilities and logistical costs, so that the number of different facilities can be controlled. Since the proposed model is np-hard, the multi-start construction and tabu search improvement heuristic (MS-CTSIH) with two improvement strategies and the modified MS-CTSIH incorporating both strategies are proposed. The experiment shows that the two improvement strategies appear non-dominated, and the modified MS-CTSIH yields the best results. The comparison of the modified MS-CTSIH and a commercial solver on a small instance shows the efficiency and effectiveness of the modified MS-CTSIH. The sensitivity analyses of problem parameters are performed on a large instance.

A chance constrained closed-loop supply chain network design considering inventory-location problem

A chance constrained closed-loop supply chain network design considering inventory-location problem

A memetic algorithm for integrated location-inventory problem to optimize the total cost and customer service level

A memetic algorithm for integrated location-inventory problem to optimize the total cost and customer service level

A memetic algorithm for integrated location-inventory problem to optimise the total cost and customer service level

A memetic algorithm for integrated location-inventory problem to optimise the total cost and customer service level

A competitive stochastic bi-level inventory location problem

ABSTRACT This paper aims to address an inventory competitive facility location problem with the order quantity in a stochastic environment. In this problem, the facility location, customer design, and inventory control decisions have been integrated for two decision makers, i.e. the leader and the follower. To this end, a bi-level nonlinear nonconvex mixed integer mathematical model was proposed. Next, an alternative bi-level mixed-integer linear model was obtained. The enumeration branch-and-cut method in the field of bi-level linear programming was developed to come up with optimal or near-optimal solutions. To determine the branching choice and specialized cuts, a preference function was used. Computational results of some data tests were reported, and the results were compared with those of previous famous methods to show the efficiency of the algorithm. According to the computational results, the proposed algorithm could find effective solutions for solving such problems, which could compete with other well-known published algorithms.

Reliable closed-loop supply chain design problem under facility-type-dependent probabilistic disruptions

Closed-loop supply chains (CLSCs) have received considerable attention because of various economic and regulatory factors. A CLSC is characterized by more complicated network structures and higher uncertainties compared to traditional supply chain networks. Therefore, reliable CLSCs are being increasingly emphasized in academic circles due to the vast impacts of disruptions such as natural disasters and terrorist attacks. This paper studies a reliable location-inventory problem in a CLSC considering the mutual effects between failures of forward and reverse distribution centers (DCs) when they are co-located. The disruption probability of a co-located forward DC is different from that of a standalone forward DC, i.e., probabilistic disruptions are dependent on facility type. The problem is formulated as a nonconvex mixed-integer programming problem. A decomposition approach based on the outer approximation (DOA) algorithm is proposed to address the resulting model. The algorithm alternately solves relaxed master problems (mixed-integer linear programs, MILPs) and two nonlinear programming (NLPs) problems. Extensive numerical experiments are conducted to evaluate the performance of the proposed solution approach, after which managerial insights are explored.

A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms

This study proposes a scenario-based mixed-integer programming model to investigate the responsive-resilient inventory-location problem under uncertainty. The proposed model minimizes the total costs and makes decisions about the location, allocation, and inventory problems. The literature showed that simultaneous consideration of responsiveness and resilience measures has been ignored by the researchers. Hence, to fill this gap, this study considers responsiveness and resilience measures in the proposed model. Also, since the uncertainty exists in the nature of the research problem due to changes in the business environment, this paper applies the queuing theory and robust fuzzy stochastic optimization to cope with uncertainty. At first, the existed uncertainty in lead time and demand is tackled by employing the queuing theory, and some performance measures of the system are calculated. Then, the achieved results are incorporated into the fuzzy robust stochastic model. As the problem is an NP-Hard, this study develops several metaheuristic algorithms to solve the proposed model in a reasonable time. Then, the applicability of the proposed model and efficiency of the developed algorithms are shown by several numerical examples. Eventually, several sensitivity analyses are conducted on some important parameters of the model, and useful managerial insights are provided.

Collaborative Optimization for Location-Inventory Problem of Commercial Vehicle Distribution Network

This paper studies a bi-level programming model addressing the location-inventory problem which can determine the location of distribution centers and the inventory strategy of distribution centers and stores at the same time. In the location-inventory joint decision model, the order quantity is a continuous decision variable associating the upper and lower models and the remaining decision variables are 0-1 variables. In this paper, a branch and bound method is proposed and the SCIP solver is used to solve the bi-level mixed integer non-linear programming problem. The results show that collaborative optimization can save more costs than the one-by- one optimization method and that the optimal solution has better stability under the condition of demand fluctuation and satisfies the optimal allocation of important customers.