Unit Inventory Cost Research Articles

Research on Distribution and Inventory Cooperation of Agricultural Means Supply Chain

This paper constructed a biobjective model based on the total cost and time satisfaction to provide a desirable solution to the distribution and inventory cooperation of agricultural means supply chain. The model simulated how the distribution center and retailers collaborate to meet the needs of the order customer in the random lead time and out-of-stock loss costs. By the features of the model, the biobjective genetic algorithm was improved based on elitism selection, aiming to improve the quality of noninferior solution in biobjective model. Finally, the influence degree of the lead time of delivery, unit inventory cost, and unit transport cost on the total cost of the system was quantified through the analysis of examples and sensitivity of model parameters. This research has provided valuable new insights into the distribution and inventory coordination of supply chain.

Decision-Making Optimization of Risk-Seeking Retailer Managed Inventory Model in a Water Supply Chain

Water retailer managed inventory is a classical and inevitable inventory management mode in present economic society. Stochastic models can more clearly explain demand uncertainty and are closely related to water supply chains. Risk preferences are widely valued in behavioral operation management. Related to the risk preferences in inventory management, the research on risk aversion is dominant, while risk-seeking is insufficient. Based on the model assumptions, the risk-seeking retailer’s optimal decision-making inventory model with stochastic demand in a water supply chain is studied. The risk-seeking retailer’s optimal inventory quantity, optimal inventory cost, supplier profit, retailer profit, and the profit of the entire water supply chain are derived. The validity of the equations is proved. The sensitivity analysis of the risk-seeking retailer’s optimal inventory decision-making is carried out. The risk level effects on the five dimensions, the retail price, wholesale price, unit shortage cost, unit inventory cost, and unit residual value, are displayed through numerical simulation. The optimal inventory quantity and optimal inventory cost of the risk-seeking retailer are obtained.

Integrated inventory and production policy for manufacturing with perishable raw materials

This research investigates an integrated inventory and production scheduling problem (IIPSP) in a manufacturer that deals with the perishable goods. The objective is to find an optimal schedule to minimize the sum of inventory cost and production cost. Both single-plant problem and multi-plant problem are investigated in this paper. For the single-plant problem, we prove that it is optimal to arrange the processing of raw materials in descending order of the value of the product of consumption rate and unit inventory cost. For the more complex multi-plant problem, we first prove that it is NP-hard, and then, we propose a hybrid intelligent algorithm to solve it. The experiments show that the proposed algorithm is superior to several other algorithms in both effectiveness and efficiency.

Joint Optimization of Ship Sailing Speed and Container Handling Rate for a Near-sea Liner Service Route

This paper jointly designs the optimal ship sailing speeds on shipping legs and the optimal handling rates at ports of a near-sea liner service route operated by a container liner shipping company. A mixed integer linear programming model is developed, which can be solved directly by the commercial solvers to give a global optimal solution. The model is further applied to a real-life case. Results demonstrate that both bunker price and unit inventory cost affect the optimal ship sailing speed and the optimal container handling rate.

Production, Replenishment and Inventory Policies for Perishable Products in a Two-Echelon Distribution Network

The research on production, delivery and inventory strategies for perishable products in a two-echelon distribution network integrates the production routing problem (PRP) and two-echelon vehicle routing problem (2E-VRP), which mainly considers the inventory and delivery sustainability of perishable products. The problem investigated in this study is an extension of the basic problems, and it simultaneously optimizes production, replenishment, inventory, and routing decisions for perishable products that will deteriorate over the planning horizon. Additionally, the lead time has been considered in the replenishment echelon, and the unit inventory cost varying with the inventory time is considered in the inventory management. Based on a newly designed model, different inventory strategies are discussed in this study: old first (OF) and fresh first (FF) strategies both for the first echelon and second echelon, for which four propositions to model them are proposed. Then, four valid inequalities, including logical inequalities, a ( ℓ , S , W W ) inequality, and a replenishment-related inequality, are proposed to construct a branch-and-cut algorithm. The computational experiments are conducted to test the efficiency of valid inequalities, branch-and-cut, and policies. Experimental results show that the valid inequalities can effectively increase the relaxed lower bound by 4.80% on average and the branch-and-cut algorithm can significantly reduce the computational time by 58.18% on average when compared to CPLEX in small and medium-sized cases. For the selection of strategy combinations, OF–FF is suggested to be used in priority.

Pre-positioning inventory and service outsourcing of relief material supply chain

Service outsourcing is very common in a commercial supply chain, and in humanitarian relief area, the transportation service is usually outsourced. To practice relief supply more effectively, it seems essential to enlarge outsourcing from shipping to more areas, and private enterprises could play a vital role. This paper examines the optimal pre-disaster order quantity of a certain relief commodity, based on a two-stage coordinated approach. Our findings show that the delay cost, shortage penalty cost, risk of supply shortage, salvage value, expected perishable rate, unit inventory cost and reactive price have significant impacts on the optimal amount of propositioned inventory. Moreover, the outsourcing strategies differ by types of relief commodities. For perishable supplies, proactive or reactive outsourcing would improve the benefits of buyer and supplier simultaneously. As for imperishable supplies, it is better to combine proactive insourcing approach and reactive outsourcing strategy. In view of some supplies whose monitoring cost is high, the insourcing approach is much better than the outsourcing approach.

Buy online and pick up in-store: Design of the service area

Retailers increasingly adopt the buy-online-pick-up-in-store (BOPS) mode of order fulfillment. We study BOPS in this paper by developing a theoretical model in which a physical retailer (store) adopting BOPS uses a recommended service area to fulfill orders from both determined (online) and casual (offline) customers in one order cycle. We obtain three major findings: (i) The ratio of unit inventory cost to BOPS customers’ arrival rate to the store is the key factor that determines the size of the BOPS service area. (ii) From the point of view of product type, we provide the retailer with practical guidelines for judging whether a certain type of product should be allowed for BOPS or not. (iii) When orders can be cancelled, a moderate cancellation policy (MCP) is more beneficial to the retailer than a liberal/strict cancellation policy (LCP/SCP). Furthermore, compared with the reserve-online-pick-up-and-pay-in-store (ROPS) mode, BOPS is less profitable under LCP/SCP, but they have the same profitability under MCP.

A stochastic multi-stage fixed charge transportation problem: Worst-case analysis of the rolling horizon approach

We introduce a stochastic multi-stage fixed charge transportation problem, in which a producer has to satisfy an uncertain demand within a deadline. At each time period, a fixed transportation cost can be paid to buy a transportation capacity. If the transportation capacity is used, the supplier also pays an uncertain unit transportation cost. A unit inventory cost is charged for the unsatisfied demand. The aim is to determine the transportation capacities to buy and the quantity to send at each time period in order to minimize the expected total cost. We prove that this problem is NP-hard, we propose a multi-stage stochastic optimization model formulation, and we determine optimal policies for particular cases, with deterministic unit transportation costs or demand and zero fixed costs. Furthermore, we provide the worst–case analysis of the rolling horizon approach, a classical heuristic approach for solving multi-stage stochastic programming models, applied to this NP-hard problem and to polynomially solvable particular cases. Worst–case results show that the rolling horizon approach can be very suboptimal. We also provide experimental results.

Liner ship route schedule design with port time windows

This paper examines a practical tactical liner ship route schedule design problem, which is the determination of the arrival and departure time at each port of call on the ship route. When designing the schedule, the availability of each port in a week, i.e., port time window, is incorporated. As a result, the designed schedule can be applied in practice without or with only minimum revisions. This problem is formulated as a mixed-integer nonlinear nonconvex optimization model. In view of the problem structure, an efficient holistic solution approach is proposed to obtain global optimal solution. The proposed solution method is applied to a trans-Atlantic ship route. The results demonstrate that the port time windows, port handling efficiency, bunker price and unit inventory cost all affect the total cost of a ship route, the optimal number of ships to deploy, and the optimal schedule.

Genetic algorithm for multi-level assembly systems under stochastic lead times

The aim of this paper is to propose tools to adapt and parameterize the Material Requirement Planning (MRP) method under lead time uncertainty. We study multi-level assembly systems with one type of finished products and several types of components. We consider that each component has a fixed unit inventory cost and the finished product has a backlogging cost per unit of time. The lead times of components are discrete random variables, and the costumer's demand of the finished product is known. A general mathematical model for supply planning of multi-level assembly systems is presented. A Genetic Algorithm (GA) method is proposed to minimize the sum of the average inventory holding cost for components and the average backlogging and inventory holding costs for the finished product.

Mathematical Model for Supply Planning of Multi-level Assembly Systems with Stochastic Lead Times

The deal for industrial companies is to satisfy their customers with the best quality, the most reliable lead times, and the lowest costs. One of the keys to achieve it is in the inventory control in the Supply Chain. Moreover, it is mandatory to possess necessary components in order to produce the requested products by the due date. But, as the components lead time is an unpredictable parameter, it is difficult to take it into account correctly. The paper deals with this question considering that each component has a fixed unit inventory cost; the finished product has an inventory cost and a backlogging cost per unit of time. Then, a general mathematical model for supply planning of multilevel assembly system is presented to calculate the expected value of the total cost which equals to the sum of the inventory holding costs for the components, the backlogging and the inventory holding costs for the finished product.

Analysis of Direct Shipping Policies in an Inventory-Routing Problem with Discrete Shipping Times

We consider the problem in which several products have to be shipped from a supplier to a set of retailers over an infinite time horizon. Each product is made available at the supplier and absorbed by each retailer at given constant production and consumption rates. For each product, the unit inventory cost at the supplier is equal to the unit inventory cost at the retailers. Shipments can be performed in each discrete time instant by a fleet of vehicles of given transportation capacity, and routing is allowed. The aim is to determine shipping policies that minimize the sum of transportation cost and inventory cost both at the supplier and at the retailers. We study direct shipping policies, i.e., policies in which each retailer is served independently. We show the worst-case performance of the best single, best double, best triple, best frequency-based, and optimal direct shipping policies. In each of these policies each link is optimized independently. We prove that, in the worst case, the ratio between the cost of the optimal direct shipping policy and the optimal cost of the problem in which routing is allowed—i.e., the worst-case performance bound of this policy—is not greater than two whenever the unit volume on each link is not lower than 1/4 of the vehicle capacity. Moreover, if the unit volume is not lower than the capacity, the bound is about 1.21. These bounds are tight and obtained by applying frequency-based policies that make use of at most three different shipping frequencies and cannot be improved by allowing more frequencies or using time-based policies on each link. Computational results show that the best among the frequency-based direct shipping policies used to obtain the worst-case results gives an average percent increase error, with respect to a lower bound on the optimal cost of the problem in which routing is allowed, of 6.44% on a large set of randomly generated problem instances.

On the asymptote of the optimal routing policy for two service stations

It has been previously proved that the optimal routing control of a two-station Markovian network with linear cost is described by a monotone switching curve. With the discounted cost objective function, it is proven in this paper that the optimal switching curve has a finite asymptotic limit when c/sub 1/ not=c/sub 2/, where c/sub i/ is the unit inventory cost at station i. Whereas, for the case with c/sub 1/=c/sub 2/, as well as the case with the long-run average objective function, the switching curve does not have a finite asymptote. >