A New Heuristic for one Warehouse and N Retailers Problem (original) (raw)
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The vendor-managed inventory (VMI) is a common policy in supply chain management (SCM) to reduce bullwhip effects. Although different applications of VMI have been proposed in the literature, the multi-vendor multi-retailer single-warehouse (MV-MR-SW) case has not been investigated yet. This paper develops a constrained MV-MR-SW supply chain, in which both the space and the annual number of orders of the central warehouse are limited. The goal is to find the order quantities along with the number of shipments received by retailers and vendors such that the total inventory cost of the chain is minimized. Since the problem is formulated into an integer nonlinear programming model, the meta-heuristic algorithm of particle swarm optimization (PSO) is presented to find an approximate optimum solution of the problem. In the proposed PSO algorithm, a genetic algorithm (GA) with an improved operator, namely the boundary operator, is employed as a local searcher to turn it to a hybrid PSO. In addition, since no benchmark is available in the literature, the GA with the boundary operator is proposed as well to solve the problem and to verify the solution. After employing the Taguchi method to calibrate the parameters of both algorithms, their performances in solving some test problems are compared in terms of the solution quality.
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Supply chain is a systematic and strategic coordination of the traditional business function to across business function within supply chain for the purpose of improving the long-term performance of the business. The objective of the paper is to understand the saving concepts are used in operational path to achieve optimization in term of cost saving. Business nowadays going towards to minimizing the operational in order to compete with others The purpose is to achieve the minimization in inventory routing problem (IRP) with cost minimization consists inventory and transportation costs focus on the single warehouse with multiple customers. The operational costs can be reduced by solving the inventory management and transportation process for vehicle to replenish the inventory. In this paper, a mathematical model was developed and simulated by using an optimization software package to achieve the optimization. The findings show that the optimization can be achieved by reducing the movement of the vehicle To conclude, the bigger the vehicle's capacity, much capacity can add and less routes taken by the vehicle to supply the inventory to the customers.
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The Inventory Open Vehicle Routing Problem (IOVRP) involves decisions in inventory replenishment and vehicle routing of a system having multiple depots and multiple retailers. The research objective of this work is to develop practical replenishment decisions by applying meta-heuristics of an Ant Colony Algorithm. The routing solutions applying IOVRP and an Inventory Routing Problem (IRP) that are solved and compared to measure the methods’ performance. The result shows that the IOVRP gives 24.66% better solutions in term of total costs than the IRP. Additionally, sensitivity analysis of related factors, i.e., inventory holding costs, ordering cost and vehicle capacity, was performed on the percentage deviation of total costs. Based on the analysis of variance, there is an advantage of IOVRP over IRP when the problem involves small vehicle capacity, low ordering costs, and high holding costs.
A hybrid heuristic for an inventory routing problem
We consider an inventory routing problem in discrete time where a supplier has to serve a set of customers over a time horizon. A capacity constraint for the inventory is given for each customer and the service cannot cause any stock-out situation. Two different replenishment policies are considered, the order-up-to level and the maximum level policies. A single vehicle with a given capacity is available. The transportation cost is proportional to the distance traveled, whereas the inventory holding cost is proportional to the level of the inventory at the customers and at the supplier. The objective is the minimization of the sum of the inventory and transportation costs. We present a heuristic that combines a tabu search scheme with ad hoc designed mixed integer programming models. The effectiveness of the heuristic is proved over a set of benchmark instances for which the optimal solution is known.
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Manufacturers who resupply a large number of retailers on a periodic basis continually struggle with the question of how to formulate a replenishment strategy. This paper presents a comparative analysis of a series of heuristics for an inventory routing problem (IRP) that arises in a manufacturing supply chain. The IRP is formulated as a mixed integer program with the objective of maximizing the net benefits associated with making deliveries in a specific time period to a widely dispersed set of customers. It is assumed that inventory can accumulate at the customer sites, but that all demand must be met without backlogging. Because optimal solutions were not within reach of exact methods, a two-step procedure was developed that first estimates daily delivery quantities and then solves a vehicle routing problem for each day of the planning horizon. As part of the methodology, a linear program is used to determine which days it is necessary to make at least some deliveries to avoid stockouts.
OWNR problem with variable replenishment and inventory holding costs and quantity discounts
New Trends and Issues Proceedings on Humanities and Social Sciences, 2018
We consider an inventory/distribution system consisting of one warehouse and N retailers (OWNR). Warehouse replenishes its orders from external supplier and supplies all orders of retailers. The objective of this paper is to maximise supply chain benefits of the whole system. To achieve this objective SENYIGIT and AKKAN’s heuristic algorithm is used. We assume that demand is constant and deterministic; shortages are not allowed and lead times are negligible. Total system cost consists of order-quantity-dependent replenishment and inventory holding cost parameters, and quantity discounts are applicable. The main idea of new heuristic is to compare replenishment and inventory holding costs while manipulating the order quantity by order intervals of locations. While balance condition is searched by new version of SENYIGIT and AKKAN’s heuristic algorithm, quantity discounts and replenishment costs will reduce costs of whole systems. Capital limit constraint is also included to the m...