Optimal and Heuristic Lead-Time Quotation For an Integrated Steel Mill With a Minimum Batch Size

This paper presents a model of lead-time policies for a production system, such as an integrated steel mill, in which the bottleneck process requires a minimum batch size. An accurate understanding of internal lead-time quotations is necessary for making good customer delivery-date promises, which must take into account processing time, queueing time and time for arrival of the requisite volume of orders to complete the minimum batch size requirement. The problem is modeled as a stochastic dynamic program with a large state space. A computational study demonstrates that lead time for an arriving order should generally be a decreasing function of the amount of that product already on order (and waiting for minimum batch size to accumulate), which leads to a very fast and accurate heuristic. The computational study also provides insights into the relationship between lead-time quotation, arrival rate, and the sensitivity of customers to the length of delivery promises

Lead-Time Quotation When Customers are Sensitive to Reputation

Firms consider a variety of factors when making lead-time promises, including current shop status and the size of the incoming order. The profit-maximising model presented in this paper is the first to include reputation effects explicitly in a lend-time optimisation model. Reputation is considered to be the lasting effect on the market of a firm\u27s delivery performance over time, and so it affects the future as well as the current profits. The model is complicated, and a counter-example demonstrates that qualitative monotonicity results are not obtainable. A computational study explores the relationships between shop status, order size, reputation, market characteristics and the lead-time decision. Regression analysis sheds light on these relationships and suggests three heuristics, which provide near-optimal solutions with relatively short running times

Exploiting market size in service systems

W e study a profit-maximizing firm providing a service to price and delay sensitive customers. We are interested in analyzing the scale economies inherent in such a system. In particular, we study how the firm's pricing and capacity decisions change as the scale, measured by the potential market for the service, increases. These decisions turn out to depend intricately on the form of the delay costs seen by the customers; we characterize these decisions up to the dominant order in the scale for both convex and concave delay costs. We show that when serving customers on a first-come, first-served basis, if the customers' delay costs are strictly convex, the firm can increase its utilization and extract profits beyond what it can do when customers' delay costs are linear. However, with concave delay costs, the firm is forced to decrease its utilization and makes less profit than in the linear case. While studying concave delay costs, we demonstrate that these decisions depend on the scheduling policy employed as well. We show that employing the last-come, first-served rule in the concave case results in utilization and profit similar to the linear case, regardless of the actual form of the delay costs

Convexity Properties and Comparative Statics for M/M/S Queues with Balking and Reneging

We use sample path arguments to derive convexity properties of an M/M/S queue with impatient customers that balk and renege. First, assuming that the balking probability and reneging rate are increasing and concave in the total number of customers in the system (head-count), we prove that the expected head-count is convex decreasing in the capacity (service rate). Second, with linear reneging and balking, we show that the expected lost sales rate is convex decreasing in the capacity. Finally, we employ a sample-path sub-modularity approach to comparative statics. That is, we employ sample path arguments to show how the optimal capacity changes as we vary the parameters of customer demand and impatience. We find that the optimal capacity increases in the demand rate and decreases with the balking probability, but is not monotone in the reneging rate. This means, surprisingly, that failure to account for customersâ reneging may result in over-investment in capacity. Finally, we show that a seemingly minor change in system structure, customer commitment during service, produces qualitatively different convexity properties and comparative statics.Operations Management Working Papers Serie

A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers

In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation

Joint lead time and price quotation : dynamic or static?

Intuitively, quoting dynamic lead time and price to customers based on real-time system state provides more efficient capacity utilization and increases revenue compared with quoting static lead time and price. However, dynamic quotation may require higher operational costs for the firm and it is often inconvenient to customers. This study aims to compare dynamic and static lead time and price quotations under fixed capacity and different potential demand rates. We hypothesize that there exists a potential demand rate under which the additional costs of dynamic quotation and the additional profit from dynamic quotation are equal. Thus static quotation may yield better performance under certain potential demand rates. We use an M/M/1 queuing model to model the supply system of a firm and formulate profit maximization models in an average reward criterion under both static and dynamic lead time and price quotations. Numerical analyses are presented to illustrate performances of both static and dynamic lead time and price quotation and thus find the threshold potential demand rate. Besides, we study performance of two different kinds of dynamic lead time quotation and find that when firm can decide their price, performance of dynamic lead time quotation is good enough and when firm cannot decide their price, the dynamic lead time quotation is good only when lead time sensitive factor is small and potential demand rate is big

A Survey of the Inventory Control-Detailed Scheduling Problem

10960179Includes bibliographical references (p. [217]-251) and index.Jonathan Golovin, editor

Priority Service Pricing with Heterogeneous Customers: Impact of Delay Cost Distribution

This is the peer reviewed version of the following article: Cao, P., Wang, Y. and Xie, J. (2019), Priority Service Pricing with Heterogeneous Customers: Impact of Delay Cost Distribution. Prod Oper Manag, 28: 2854-2876., which has been published in final form at https://doi.org/10.1111/poms.13086. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.National Natural Science Foundation of Chin