Modeling Supply Networks and Business Cycles as Unstable Transport Phenomena

Physical concepts developed to describe instabilities in traffic flows can be generalized in a way that allows one to understand the well-known instability of supply chains (the so-called ``bullwhip effect''). That is, small variations in the consumption rate can cause large variations in the production rate of companies generating the requested product. Interestingly, the resulting oscillations have characteristic frequencies which are considerably lower than the variations in the consumption rate. This suggests that instabilities of supply chains may be the reason for the existence of business cycles. At the same time, we establish some link to queuing theory and between micro- and macroeconomics.Comment: For related work see http://www.helbing.or

Stock Management in Hospital Pharmacy using Chance-Constrained Model Predictive Control

One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals.Junta de Andalucía P12-TIC-240

Deterministic and stochastic optimal inventory control with logistic stock-dependent demand rate

It has been suggested by many supply chain practitioners that in certain cases inventory can have a stimulating effect on the demand. In mathematical terms this amounts to the demand being a function of the inventory level alone. In this work we propose a logistic growth model for the inventory dependent demand rate and solve first the continuous time deterministic optimal control problem of maximising the present value of the total net profit over an infinite horizon. It is shown that under a strict condition there is a unique optimal stock level which the inventory planner should maintain in order to satisfy demand. The stochastic version of the optimal control problem is considered next. A bang-bang type of optimal control problem is formulated and the associated Hamilton-Jacobi-Bellman equation is solved. The inventory level that signifies a switch in the ordering strategy is worked out in the stochastic case. Copyright © 2014 Inderscience Enterprises Ltd

Food supply chain network robustness : a literature review and research agenda

Today’s business environment is characterized by challenges of strong global competition where companies tend to achieve leanness and maximum responsiveness. However, lean supply chain networks (SCNs) become more vulnerable to all kind of disruptions. Food SCNs have to become robust, i.e. they should be able to continue to function in the event of disruption as well as in normal business environment. Current literature provides no explicit clarification related to robustness issue in food SCN context. This paper explores the meaning of SCN robustness and highlights further research direction

Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation for Inventory Management Under Uncertainty

This study proposes an Ensemble Differential Evolution with Simula-tion-Based Hybridization and Self-Adaptation (EDESH-SA) approach for inven-tory management (IM) under uncertainty. In this study, DE with multiple runs is combined with a simulation-based hybridization method that includes a self-adaptive mechanism that dynamically alters mutation and crossover rates based on the success or failure of each iteration. Due to its adaptability, the algorithm is able to handle the complexity and uncertainty present in IM. Utilizing Monte Carlo Simulation (MCS), the continuous review (CR) inventory strategy is ex-amined while accounting for stochasticity and various demand scenarios. This simulation-based approach enables a realistic assessment of the proposed algo-rithm's applicability in resolving the challenges faced by IM in practical settings. The empirical findings demonstrate the potential of the proposed method to im-prove the financial performance of IM and optimize large search spaces. The study makes use of performance testing with the Ackley function and Sensitivity Analysis with Perturbations to investigate how changes in variables affect the objective value. This analysis provides valuable insights into the behavior and robustness of the algorithm.Comment: 15 pages, 6 figures, AsiaSIM 2023 (Springer

Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

The bullwhip effect describes the tendency for the variance of orders in supply chains to increase as one moves upstream from consumer demand. We report on a set of laboratory experiments with a serial supply chain that tests behavioral causes of this phenomenon, in particular the possible influence of coordination risk. Coordination risk exists when individuals' decisions contribute to a collective outcome and the decision rules followed by each individual are not known with certainty, for example, where managers cannot be sure how their supply chain partners will behave. We conjecture that the existence of coordination risk may contribute to bullwhip behavior. We test this conjecture by controlling for environmental factors that lead to coordination risk and find these controls lead to a significant reduction in order oscillations and amplification. Next, we investigate a managerial intervention to reduce the bullwhip effect, inspired by our conjecture that coordination risk contributes to bullwhip behavior. Although the intervention, holding additional on-hand inventory, does not change the existence of coordination risk, it reduces order oscillation and amplification by providing a buffer against the endogenous risk of coordination failure. We conclude that the magnitude of the bullwhip can be mitigated, but that its behavioral causes appear robust.National Science Foundation (U.S.) (Grant SES-0214337)Mary Jean and Frank P. Smeal College of Business Administration (Center for Supply Chain Research)Sloan School of Management (Project on Innovation in Markets and Organizations

Performance analysis of manufacturing networks : surplus-based control

In the modern market, keeping high competition in brands and varieties in type of products is the way for survival of manufacturing industries. Therefore production control methods with capabilities of quick responses to rapid changes in the demand and efficient distribution of the raw material throughout the network are of importance among leading manufacturers. Nowadays, the production control problem has been widely studied and a lot of valuable approaches including queuing theory, Petri nets, dynamic programming, linear programming, hybrid systems were proposed and some of them are implemented. Though up to this moment many methods have been developed, the factory performance remains a challenging problem for further research. Motivated by this problem we study the performance of several manufacturing networks operated by surplus-based control. In the surplus-based control, decisions are made based on the demand tracking error, which is the difference between the cumulative demand and the cumulative output of the network. The studied networks are a single machine, a manufacturing line, a multi-product manufacturing line, a re-entrant machine and a re-entrant line. The performance analysis is based on the performance factors such as demand tracking errors and inventory levels. Specifically, given the presence of unknown but bounded production speed perturbations as well as demand rate fluctuations, we investigate how close the cumulative production output of a manufacturing network follows its cumulative production demand under a surplus-based control policy. The research is subdivided into theoretical analysis, simulation-based analysis and experimental analysis parts. Theoretical analysis By means of analytical tools, the relation between the production demand tracking accuracy and the inventory levels of the networks is investigated. In order to find this relation, classical tools from control theory are used. Models of production flow processes are formulated by means of difference as well as differential equations. In order to analyze their performance, optimal control theory and Lyapunov theory approaches are exploited. Simulation based analysis By means of simulation tools, the theoretical results on performance are evaluated by time-based simulation models. Thus, all theoretical results are illustrated and confirmed by computer simulation. Also two comparative studies are conducted. The first comparative study is realized in order to test the theoretical results on more accurate models, which are event-based. The results are shown to be in agreement with the theory. The second comparative study is on time-based models, where the behavior of a line, a single re-entrant machine and a re-entrant line is tested under three commonly used surplus-based production policies. The performance of each network is evaluated and the results are presented. Experimental analysis An experimental prototype is invented, designed and developed for education and research purposes. The prototype is a hardware tool that serves as a liquid-based emulator of manufacturing network processes. In its core, the liquid-based emulator consists of several electrical pumps and liquid reservoirs. The electrical pumps emulate manufacturing machine behavior, while the liquid reservoirs serve as the intermediate product storages, also called buffers. In the platform, pumps and tanks can be interconnected in a flexible manner. In that way the prototype permits an easy and intuitive way of studying manufacturing control techniques and performance of several network topologies. A detailed system description is provided. Several network configurations and experimentations are presented and discussed