Lead Time Uncertainty Research Articles

Planned lead times optimization for multi-level assembly systems under uncertainties

Planned lead times are crucial parameters in management of supply networks that continue to be more and more extended with multiple levels of inventory of components and uncertainties. The object of this study is the problem of determining planned lead times in multi-level assembly systems with stochastic lead times of different partners of supply chains. A general probabilistic model with a recursive procedure to calculate all the necessary distributions of probability is proposed. A Branch and Bound algorithm is developed for this model to determine planned order release dates for components at the last level of a BOM which minimize the sum of inventory holding and backlogging costs. Experimental results show the behavior of the proposed model and optimisation algorithm for different numbers of components at the last level of the BOM and for different numbers of levels and values of holding and backlogging costs. The model and algorithm can be used for assembly contracting in an assembly to order environment under lead time uncertainty.

Decision support to product configuration considering component replenishment uncertainty: A stochastic programming approach

Product configuration is to make decisions on component selections and combination to constitute a customized product under mass customization production. However, the uncertainties (such as component supplies) in product configuration setting are not considered in the existing product configurators. To handle the uncertainty in component replenishment lead-time, a new stochastic decision model is proposed in this paper using two-stage stochastic programming approach. Further, a pre-procuring strategy for component supply is employed to reduce total configuration costs and shorten the delivery date of customized products. The stochastic decision model for product configuration is solved by using Lagrangian relaxation algorithm. The effectiveness of the stochastic decision model is demonstrated through case studies from both computer configuration and ranger drilling machine configuration. Computational comparisons with a commercial solver (CPLEX) indicate that the proposed stochastic decision model provides competitive solution results.

A tri-level optimization model for inventory control with uncertain demand and lead time

We propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions. In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decision-making strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new tri-level optimization model almost always results in the lowest total cost in all parameter settings.

Avocados crossing borders: The problem of runaway objects and the solution of a shipping information pipeline for improving international trade

AbstractThis paper investigates the case of shipments of containers with avocados from farmers in Africa to grocery store shelves in the European Union. We find 3 predominant challenges to containerized shipping that effectively become trade barriers: international trade cost, lead time uncertainty, and security risks. We use activity theory to describe, understand, and analyze the shipping activity in the international trade ecosystem with focus on physical objects and their related information. We find that the shipment becomes problematic and can be characterized as a runaway object in the heterogeneous and multiple organizational setting of international trade. Our analysis of shipping reveals (1) inefficient collaboration across loosely coupled activity systems and (2) fragmented information infrastructures. We propose the solution of Shipping Information Pipeline, a shared information infrastructure, thus facilitating collaboration in containerized shipping and contributing to lowering trade barriers. Shipping Information Pipeline can significantly improve containerized shipping resulting in estimated potential benefits of up to 4.7% growth in global GDP.

Sustainable EOQ under Lead-Time Uncertainty and Multi-Modal Transport

In this paper, we examine a sustainable economic order quantity (S-EOQ) problem with a stochastic lead-time and multi-modal transportation options. With the S-EOQ, decisions of order quantities, as well as a reorder point could be influenced by various factors, including unit price, stock-out cost, lead-time variability and emission costs. For a better understanding, we present a mathematical model of the concomitant S-EOQ problem and, using numerical experiments, explore various scenarios to determine the effects of incorporating sustainability considerations into the traditional inventory model on operational decisions including the choice of transportation modal combination and the sourcing decisions

Coordination of pricing and leadtime quotation under leadtime uncertainty

In many service and make-to-order manufacturing firms, pricing and leadtime quotation are two of the most important decisions. We consider a firm that serves customers who are sensitive to both price and quoted leadtime, with pricing and leadtime decisions being delegated to the marketing and production departments, respectively. According to the power structure within the firm, we model the problem as a Nash game, production Stackelberg game, and marketing Stackelberg game. For each game, the unique equilibrium is derived. In addition, for each decentralized setting, we design a mechanism that can be used by the firm to coordinate the decisions of the production and marketing departments. It is observed that the coordination schemes in the different games are of the same form. In particular, we find that the detailed values of the coordinating transfer prices under different power structures are the same. Moreover, we demonstrate that under some mild conditions on the level of the potential demand, the marketing Stackelberg game Pareto-dominates the Nash game in terms of expected profits.

Analysis of the Optimal Threshold Policy of the E-Tailer with Mixture Strategy in E-Fulfillment

This study considers a supply chain consisting of a supplier and an e-tailer on the internet. The e-tailer replenishes products from the supplier for private inventory and sends drop shipping requests to him for delivering orders to customers when private inventory is insufficient or stock out, whereas the supplier provides drop shipping service with a limited ability for the e-tailer. This paper proposes an algorithm to simulate the scheduling sequences of the e-tailer with the optimal threshold policy and mixture strategy in every scheduling unit and obtains the optimal threshold of private inventory for the e-tailer to achieve average profit maximization. The impacts of mixture of demand and lead time uncertainty are examined. The influence of high priority demand variability on the optimal threshold policy in two complex scenarios are also considered in this study. These results have an important guiding significance for the e-tailer who adopts the mixture strategy in e-fulfillment under complex operating environments.

Multilevel Production Systems with Dependent Demand with Uncertainty of Lead Times

This study considers a multilevel assembly system with several components in each sublevel. It is assumed that actual lead time for all components is probabilistic; and periodic order quantity (POQ) policy for ordering is utilized. If at a certain level a job is not received at the expected time, a delay is incurred at the delivery of production at this level and this may result in backorders of the finished product. It is assumed in this case that a fixed percentage of the shortage is backlogged and other sales are lost. In the real situation, some but not all customers will wait for backlogged components during a period of shortage, such as for fashionable commodities or high-tech products with the short product life cycle. The objective of this study is to find the planned lead time and periodicity for the total components in order to minimize the expected fixed ordering, holding, and partial backlogging costs for the finished product. In this study, it is assumed that a percentage of components at each level are scrap. A general mathematical model is suggested and the method developed can be used for optimization planned lead time and periodicity for such an MRP system under lead time uncertainties.

Intelligent Approach to Inventory Control in Logistics under Uncertainty Conditions

The article presents a proposal for a combined application of fuzzy logic and genetic algorithms to control the procurement process in the enterprise. The approach presented in this paper draws particular attention to the impact of external random factors in the form of demand and lead time uncertainty. The model uses time-variable membership function parameters in a dynamic fashion to describe the modelled output fuzzy (sets) values. An additional element is the use of genetic algorithms for optimisation of fuzzy rule base in the proposed method. The approach presented in this paper was verified according to four criteria based on a computer simulation performed on the basis of the actual data from an enterprise.

Lead time uncertainty and supply chain coordination in lost sales inventory models

The effects of lead time uncertainty on optimal inventory policies in a two-echelon supply chain operating under continuous review inventory assumptions are examined. In this system, the buyer selects an inventory order quantity and reorder point to maximise its profits, while the seller selects an order quantity from its external supplier that is an integer multiple of the buyer's order quantity; unsatisfied demand is accounted for as lost sales and may also incur a penalty cost representing lost customer goodwill. Normally distributed demand per unit time is assumed and lead time can follow any discrete probability distribution, resulting in a mixture of Gaussians distribution for lead time demand. Examples where lead time is discrete and not well-approximated by a continuous distribution are examined, and use of the mixture of Gaussians approach versus a single normal distribution for lead time demand in these cases reduces supply chain costs by between 12% and 13%.

Robust inventory control under demand and lead time uncertainty

In this paper a general methodology is proposed based on robust optimization for an inventory control problem subject to uncertain demands and uncertain lead times. Several lead time uncertainty sets are proposed based on the budget uncertainty set, and a set based on the central limit theorem. Robust optimization models are developed for a periodic review, finite horizon inventory control problem subject to uncertain demands and uncertain lead times. We develop an approach based on Benders’ decomposition to compute optimal robust (i.e., best worst-case) policy parameters. The proposed approach does not assume distributional knowledge, makes no assumption regarding order crossovers, and is tractable in a practical sense. Comparing the new approach to an epigraph reformulation method, we demonstrate that the epigraph reformulation approach is overly conservative even when costs are stationary. The approach is benchmarked against the sample average approximation (SAA) method. Computational results indicate that the approach provides more stable and robust solutions compared to SAA in terms of standard deviation and worst-case solution, especially when the realized distribution is different than the sampled distribution.

Optimal Expediting Policies for a Serial Inventory System with Stochastic Lead Time

In recent supply chains, often operating multiple delivery modes such as standard freight shipping and air is an effective way of addressing both delivery lead time uncertainties and service rates. We propose a model on how to optimally operate multiple delivery modes. We consider a serial supply chain and an expediting option from intermediate installations to the downstream of the chain. The goods move stochastically among the installations and the system faces a stochastic demand. We identify systems that yield simple optimal policies, in which both regular ordering and expediting follow a variant of the base stock policy. Expediting allows the system to be leaner due to the reduced regular order amount. In addition, we provide managerial insights linking expediting, base stock levels, and expediting costs based on analytical and numerical results.

An Engineering Application of Earthquake Early Warning: ePAD-Based Decision Framework for Elevator Control

In a medium-to-large earthquake, there are often reports of people being trapped or injured in elevators. This study investigates using an earthquake early warning (EEW) system, which provides seconds to tens of seconds warning before seismic waves arrive at a site, to help people escape from the elevators before a strong shaking arrives. However, such an application remains as a major engineering challenge due to the uncertainty of the EEW information and the short lead time available. A recent study presented an earthquake probability-based automated decision-making (ePAD) framework to address these issues. This paper focuses on studying the influence of two commonly ignored factors, uncertainty of warning and lead time, on the decision of stopping the elevators and opening the doors when an EEW message is received. Application of the ePAD framework requires using the performance-based earthquake engineering methodology for elevator damage prediction, making decision based on a cost-benefit model and reducing computational time with a surrogate model. The authors’ results show that ePAD can provide rational decisions for elevator control based on EEW information under different amounts of lead time and uncertainty level of the warning.

Benefits of leadtime information and of its combination with demand forecast information

This paper studies both the benefits of leadtime information and the interrelationships among demand forecast information, leadtime uncertainty, and leadtime information, for improving inventory control performance. To this end, it explicitly incorporates both leadtime information and demand forecast information into an inventory model although they have conventionally been dealt with individually in the literature. Specifically, it considers a periodic-review inventory model with uncertain leadtimes, in which both leadtime information and demand forecast information are available. The leadtime information is modeled as the information of the arrival of outstanding orders and the demand forecast information as the Martingale Model of Forecast Evolution (MMFE).The paper analyzes the way to use both leadtime information and demand forecast information for inventory control operations. Specifically, under certain conditions, the optimality of the state-dependent base-stock policy is shown, and an effective heuristic policy is developed.It also derives some managerial insights for exploiting both leadtime information and demand forecast information in the case where leadtimes are uncertain. For instance, while leadtime information itself significantly improves inventory control performance, it also enhances the benefits of demand forecast information. Specifically, the benefits of demand forecast information may significantly decrease as leadtime uncertainty increases. However, the decrease of the benefits of demand forecast information may be alleviated with leadtime information.

Effects of inventory control on bullwhip in logistic systems under demand and lead time uncertainties

In this paper, we are interested on the inventory control problem of multi-stages supply chain characterized by finite capacities of resources and delays due to processing times. The objective is to define a control law which permits to satisfy the end-customer demands and for which supply chain requirements will be completely met. Our model includes two factors that commonly have an impact on the supply chain performances and cause the bullwhip effect: the variability of the customer demand and the uncertainty on the lead time. The developed approach is based on a saturated predictor-feedback structure. We examine then the bullwhip effect phenomenon, which is an important observation in supply chain management. The results demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by using a saturated command and a predictor-feedback structure.

An Optimization Model for Inventory System and the Algorithm for the Optimal Inventory Costs Based on Supply-Demand Balance

In order to investigate the inventory optimization of circulation enterprises, demand analysis was carried out firstly considering supply-demand balance. Then, it was assumed that the demand process complied with mutually independent compound Poisson process. Based on this assumption, an optimization model for inventory control of circulation enterprises was established with the goal of minimizing the average total costs in unit time of inventory system. In addition, the optimal computing algorithm for inventory costs was presented. Meanwhile, taking the agricultural enterprises in Aksu, Xinjiang, China, for example, the researchers conducted numerical simulation and sensitivity analysis. Through constantly adjusting and modifying the parameters values in model, the optimal stock and the optimal inventory costs were obtained. Therein, the numerical results showed that the uncertainty of lead time greatly influenced the optimal inventory strategy. Besides, it was demonstrated that the research results provided a valuable reference for the agricultural enterprises in terms of optimal management for inventory system.

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.

Robust design of (s, S) inventory policy parameters in supply chains with demand and lead time uncertainties

Demand and lead time uncertainties have significant effects on supply chain behaviour. In this paper, we present a single-product three-level multi-period supply chain with uncertain demands and lead times by using robust techniques to study the managerial insights of the supply chain inventory system under uncertainty. We formulate this problem as a robust mixed-integer linear program with minimised expected cost and total cost variation to determine the optimal (s, S) values of the inventory parameters. Several numerical studies are performed to investigate the supply chain behaviour. Useful guidelines for the design of a robust supply chain are also provided. Results show that the order variance and the expected cost in a supply chain significantly increase when the manufacturer’s review period is an integer ratio of the distributor’s and the retailer’s review periods.

Procurement Flexibility as a Tool for Supplier Selection in Disastrous Environments

Supply chain flexibility (SCF) is being viewed most important among the tools to counter and handle risks and uncertainties in today’s volatile markets. It may also be considered to plan strategies, in order to gain competitive edge over competitors. Recently extensive work is being reported in SCF focussing mainly on its multi dimensional aspects and implementation. However SCF is no free lunch and it may require huge investments to realise its potential benefits. ‘How much flexibility is required’, thus becomes the prime concern for firms willing to be flexible. This question is rarely discussed in the literature and in its scarcity, flexibility performance measurements remains untouched for risky and uncertain environments. Therefore measuring SCF is a prominent field where research needs to be focused. In this paper a mathematical model is proposed to evaluate supply chain flexibility at supplier buyer interface with an aim to attain robust supply in all possible scenarios. Further cost of procurement in view of flexibility is investigated in disastrous scenarios to support procurement decisions by managers. The paper contributes by analysing and evaluating SCF in environment of demand and lead time uncertainties and risks of major plant disruptions.

A State of the Art on Supply Planning and Inventory Control under Lead Time Uncertainty

In literature, most papers examine several stochastic demand processes where order lead times are constant. In reality, manufacturing firms use inventory management software, especially MRP, which ignored lead time uncertainty. It is true that in certain special cases, lead time uncertainty has essentially no effect and can be ignored. Nevertheless more often, lead time fluctuations strongly degrade tools performance and cause high production costs, just as demand uncertainty does. Seemingly, uncertainty has been neglected for a long time in favour of studying demand uncertainties. Industry agrees that it is overdue and there is a need to rectify this oversight. Nowadays, this gap in research activity begins to be filled in order to respond to companies having non-deterministic lead-times constraints. This paper reviews some of existing literature of supply planning tools under uncertainty of lead times. The extensive literature review that we compiled consists of citations from 1970 to 2012. A classification scheme for supply planning under uncertainty is defined.