Supply Chain Simulation Research Articles

Risk-averse supply chain management via robust reinforcement learning

Classical reinforcement learning (RL) may suffer performance degradation when the environment deviates from training conditions, limiting its application in risk-averse supply chain management. This work explores using robust RL in supply chain operations to hedge against environment inconsistencies and changes. Two robust RL algorithms, Qˆ-learning and β-pessimistic Q-learning, are examined against conventional Q-learning and a baseline order-up-to inventory policy. Furthermore, this work extends RL applications from forward to closed-loop supply chains. Two case studies are conducted using a supply chain simulator developed with agent-based modeling. The results show that Q-learning can outperform the baseline policy under normal conditions, but notably degrades under environment deviations. By comparison, the robust RL models tend to make more conservative inventory decisions to avoid large shortage penalties. Specifically, fine-tuned β-pessimistic Q-learning can achieve good performance under normal conditions and maintain robustness against moderate environment inconsistencies, making it suitable for risk-averse decision-making.

Development of a Residual Biomass Supply Chain Simulation Model Using AnyLogistix: A Methodical Approach

Background: In the pursuit of sustainable energy sources, residual biomass has emerged as a promising renewable resource. However, efficiently managing residual biomass poses significant challenges, particularly in optimizing supply chain operations. Advanced modeling approaches are necessary to address these complexities. This study aims to develop a comprehensive methodological framework for creating simulation models tailored to agroforestry residual biomass supply chains. Methods: The study employs a hybrid simulation approach, integrating geographic information system mapping with a case study analysis. The simulation was conducted over a 365-day period, using the anyLogistix software (version 2.15.3.202209061204) to model various supply chain dynamics. The framework also accounts for financial, operational, customer satisfaction, and environmental metrics. Results: The simulation results showed a total expenditure of EUR 5,219,411.3, with transportation being the primary cost driver, involving 5678 trips and a peak capacity of 67.16 m3. CO2 emissions were measured at 487.7 kg/m3. The model performed as expected, highlighting the need for sustainable logistics strategies to reduce costs, lower losses, and improve productivity. Conclusions: This study presents one of the first detailed methodological frameworks for simulating agroforestry residual biomass supply chains. It provides valuable managerial insights into the financial, operational, and environmental aspects of supply chain management. The findings may stakeholders make informed decisions to enhance the sustainability of biomass utilization in energy production.

Determining the factor levels for a green supply chain using response surface methodology based discrete event simulation

PurposeThis study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO2 emissions and the maximization of the average service level, thereby enhancing overall supply chain performance.Design/methodology/approachResponse surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO2 emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables.FindingsThe high R2 values of 97.38% for CO2 and 97.28% for service level, along with adjusted R2 values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO2 encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval.Originality/valueThis study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.

Simulation and feasibility assessment of a green hydrogen supply chain: a case study in Oman.

The transition to sustainable energy is crucial for mitigating climate change impacts. This study addresses this imperative by simulating a green hydrogen supply chain tailored for residential cooking in Oman. The supply chain encompasses solar energy production, underground storage, pipeline transportation, and residential application, aiming to curtail greenhouse gas emissions and reduce the levelized cost of hydrogen (LCOH). The simulation results suggest leveraging a robust 7 GW solar plant. Oman achieves an impressive annual production of 9.78 TWh of green hydrogen, equivalent to 147,808 tonnes of H2, perfectly aligning with the ambitious goals of Oman Vision 2040. The overall LCOH for the green hydrogen supply chain is estimated at a highly competitive 6.826 USD/kg, demonstrating cost competitiveness when benchmarked against analogous studies. A sensitivity analysis highlights Oman's potential for cost-effective investments in green hydrogen infrastructure, propelling the nation towards a sustainable energy future. This study not only addresses the pressing issue of reducing carbon emissions in the residential sector but also serves as a model for other regions pursuing sustainable energy transitions. The developed simulation models are publicly accessible at https://hychain.co.uk , providing a valuable resource for further research and development in the field of green hydrogen supply chains.

Naval Antarctic Supply Chain Modeling and Simulation

In this research work, a discrete event simulation model (DES) is developed as a digital twin of the supply chain of Antarctic campaigns in a navy ship. This simulation aims to describe the system with all its details and complexity and, through this, support decision-making that identifies a supply chain configuration that significantly improves efficiency based on costs and operating times. For this, the Anylogistix simulation tool generates scenarios and visualizes results through charts and key performance indicators.

COVID-19 pandemic supply chain disruption and simulation of retail store resilience

COVID-19 pandemic supply chain disruption and simulation of retail store resilience

Novel Simulation Optimization Approach for Supply Chain Coordination and Management

The importance of supply chain management is paramount nowadays and this research seeks ways for better supply chain coordination. We present a roadmap for implementing true digital replicas of physical supply chains based on a disruptive simulation modeling approach. These digital replicas can enable the comparative evaluation of existing Just-In-Time (JIT) control policies for the first time without the unrealistic assumptions of existing studies in the literature. These realistic supply chain simulation models have the prospect of giving us better understanding of their behavior and of their complex dynamics. We provide guidelines for using the digital replicas and advanced optimization approaches to derive novel supply chain coordination policies pushing the field of JIT control beyond the state-of-the-art. An equally important contribution of this research is that it advances the field of hybrid discrete event-system dynamics (DES-SD) simulation with the prospect of pushing Systems Engineering to the next level. This research discusses theoretical foundations for mixing DES with SD simulation, and explores technical aspects and implementation details. The implications of this extent well beyond the supply chain field since both DES and SD have dozens of application areas including manufacturing, service systems and economics, social sciences, respectively. The paper is concluded with a discussion on the potential impact of the proposed approach including insights for practitioners, applicability of scientific results in industry and prerequisites for business process re-engineering.

The Practice of Healthy Elderly Service Innovation Model Based on Data Analytics

Abstract With the gradual deepening of China’s aging population, the status quo of “getting old before getting rich” has brought great challenges to the problem of elderly care. In this paper, we take the supply chain management of elderly services as the core and optimize the service cost and service quality through a two-layer planning model to obtain the best decision-making scheme. A particle Swarm Algorithm (PSO) is proposed to solve the model with intelligent evolution, and an incentive mechanism is established on the basis of the senior care supply chain, which maximizes its revenue by relying on the contract. Finally, by improving the SCORE model, data analysis and dynamic simulation of the senior care service supply chain can be carried out. The results show that the supply chain of senior care service reaches the optimization when the ratio of the capital input of community, institution and home is 0.2:0.2:0.6, and its equilibrium ratio stabilizes at about 96.0%:4%, which effectively alleviates the current pressure of senior care service. This study realizes the coordination of information and interests in the supply chain of community senior care service, which is of great practical significance for the innovation of healthy senior care service.

Supply Chain Simulation of Manufacturing Shirts Using System Dynamics for Sustainability

In supply chain management (SCM), goods and services flow from the raw materials stage to the end user with complexities and uncertainty at each stage. Computer modeling and simulation is a particularly useful method to examine supply chain operational issues because it can solve operational complexities that are challenging and time consuming to analyze. Manufacturing companies fear losing valuable time and assets during the manufacturing process; the inaccurate estimation of raw materials, human capital, or physical infrastructure not only leads to monetary loss for the manufacturing unit, but also has a detrimental effect on the environment. The purpose of this paper is to demonstrate that system dynamics modeling (SDM) in sustainable supply chain management (SSCM) can be applied to apparel manufacturing to optimize materials, labor, and equipment usage. Utilizing system dynamics (SD), the manufacturing unit can improve sustainability by reducing materials, labor, and equipment usage, which in turn reduces energy use. In our literature review, we did not identify any study addressing supply chain simulation of the manufacturing of shirts using SDM. We chose shirt manufacturing to demonstrate the model because of its relatively simple manufacturing process. In our study, we conclude that SDM simulation is an efficient way to optimize materials, labor, and equipment in apparel manufacturing. This leads to a more sustainable manufacturing process, as the model simulates different manufacturing supply chain scenarios in a risk-free environment, thereby minimizing waste and resources. Further, the outputs from the STELLA® model can be used as inputs into a subsequent life cycle assessment (LCA) model to determine the quantitative environmental impacts.

Surrogate-assisted evolutionary optimization for perishable inventory management in multi-echelon distribution systems

Simulation is widely used for analyzing supply chains with complex structures and stochastic nature. However, optimizing supply chain simulation models is usually computationally expensive. This study proposes a surrogate-assisted evolutionary optimization approach to optimize the inventory policies in multi-echelon distribution systems for perishable items under a limited number of evaluations. The random forest algorithm is used to build the surrogate model for a faster estimation of the performance of the inventory policies. A co-evolutionary differential evolution algorithm is proposed to simultaneously evolve the population through multiple searching strategies. The generated solutions are estimated by the low-cost surrogate model to select the promising solutions, which will be evaluated by the inventory model. Moreover, this study also integrates the surrogate model into two classic metaheuristic algorithms, particle swarm optimization and differential evolution. Also, a new performance indicator is proposed to examine the efficiency of the surrogate model in evolutionary computation. Two case studies are used to investigate the performance of the proposed algorithms. The experimental results show that both particle swarm optimization and differential evolution exhibit performance improvements exceeding 55% by using surrogate models under the limited number of function evaluations. Furthermore, the surrogate model reduces computational time for both algorithms by over 34% to achieve equivalent objective values. Finally, the proposed co-evolutionary differential evolution algorithm is compared with 12 algorithms, and the results show that the proposed algorithm consistently outperforms them. These findings confirm the usefulness of the surrogate model in evolutionary algorithms and the effectiveness of the proposed co-evolutionary strategy for solving perishable inventory problems.

Effective Risk Assessment and Management with IoT in Supply Chains

Supply chain risk management is a critical area in today’s globalized economy, and with the development of Internet of Things (IoT) technology, revolutionary changes are taking place in risk assessment and management methods. This paper aims to explore effective risk assessment and management with IoT in supply chains in China, emphasizing the crucial role of IoT technology in real-time data collection, predictive analytics, and traceability. We delve into the limitations of traditional risk assessment methods and how IoT enhances supply chain visibility and transparency. Furthermore, we speculate on the future applications of IoT technology in risk management, including autonomous vehicles, smart packaging, and supply chain simulations. This paper also focuses on the convergence of IoT with emerging technologies and the role of government and industry in shaping IoT adoption in supply chain risk management. Through this paper, we encourage organizations to harness IoT technology to enhance supply chain resilience and mitigate potential risks.

Enhancement the Performance of Multi-Level and Multi-Commodity in Supply Chain: A Simulation Approach

In today's global economy, effective supply chain management plays a pivotal role in the success of a business. The repercussions of a business strategy on the entire supply chain remain uncertain until it is implemented. Utilizing simulations offers the opportunity to gauge performance before implementing the strategy. The primary aim of employing supply chain simulation is to analyze the effects of different strategies on profit enhancement and cost reduction across all supply chain tiers. This research paper has formulated a discrete event simulation model using Arena software to evaluate and enhance the operational efficiency of the detergent supply chain. The problem involves multiple levels and commodities, encompassing four manufacturers, two intermediate storage warehouses, and four main distributors following an (s, S) inventory control approach. Shortages are permitted, leading to a partial loss or back-ordering of products. The overarching objective is to minimize the overall inventory costs within the system, accounting for holding costs at each tier, managing shortages, and the expense incurred due to lost sales. A range of scenarios are developed to set control parameters, with the evaluation of supply chain performance falling into two main categories: financial and operational considerations.

Game—DISASTER: Blockchain-Enabled Token Trading Game for Supply Chain Management

This paper describes the blockchain-enabled token trading game for supply chain management, a web-based supply chain simulation. The game uses blockchain technology’s concepts to create virtual markets for supplier capacity trading among retailers, in which participants take on the role of a retailer, have different valuations for products, and submit an order before knowing their demand; after demand realization, participants trade tokens (claims on the supplier’s capacity) among themselves using virtual markets to maximize their profits. The game provides participants with firsthand experience with how blockchain technology can be used in practice, thus serving as an interactive pedagogical tool. More generally, the game is intended to help supply chain management and blockchain technology students and executives understand the challenges of serving uncertain customer demand and the role of virtual markets in providing an effective remedy for reconciling supply and demand, thus improving supply chain performance. Supplemental Material: The supplemental material is available at https://doi.org/10.1287/ited.2023.0015 . The Teaching Note and PowerPoint presentation are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

Residual Agroforestry Biomass Supply Chain Simulation Insights and Directions: A Systematic Literature Review

Residual biomass is a reliable source of energy and hence requires effective supply chain management for optimal performance and sustainability. While there are various studies on this recent trend, a comprehensive review of the literature on simulation-based modeling of the supply chain for residual agroforestry biomass is lacking. This study aims to present a systematic review of relevant literature surrounding residual agroforestry supply chain simulation insights and directions. The systematic literature review was carried out in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 standards and intends to answer the research questions based on (1) Key Performance Indicators (KPI); (2) Simulation techniques; and (3) Efficient supply chain. A search of the Science Direct, SCOPUS, and UA EBSCO databases was conducted using the appropriate keywords combination. The databases were searched, and a total of 1617 papers were appraised automatically. Subsequently, the titles, keywords, and abstracts of 172 papers were examined. Following the full-text analysis, 20 papers in addition to 27 articles taken from other sources matched the requirements for study inclusion. The publications accessed reveals that simulation-based techniques will optimize the supply chain for residual biomass when applied.

A Hybrid Modelling Framework for E-Commerce Supply Chain Simulation: Complex Adaptive Systems Perspective

A Hybrid Modelling Framework for E-Commerce Supply Chain Simulation: Complex Adaptive Systems Perspective

Supply chain logistics with quantum and classical annealing algorithms

Noisy intermediate-scale quantum (NISQ) hardware is almost universally incompatible with full-scale optimization problems of practical importance which can have many variables and unwieldy objective functions. As a consequence, there is a growing body of literature that tests quantum algorithms on miniaturized versions of problems that arise in an operations research setting. Rather than taking this approach, we investigate a problem of substantial commercial value, multi-truck vehicle routing for supply chain logistics, at the scale used by a corporation in their operations. Such a problem is too complex to be fully embedded on any near-term quantum hardware or simulator; we avoid confronting this challenge by taking a hybrid workflow approach: we iteratively assign routes for trucks by generating a new binary optimization problem instance one truck at a time. Each instance has sim 2500 quadratic binary variables, putting it in a range that is feasible for NISQ quantum computing, especially quantum annealing hardware. We test our methods using simulated annealing and the D-Wave Hybrid solver as a place-holder in wait of quantum hardware developments. After feeding the vehicle routes suggested by these runs into a highly realistic classical supply chain simulation, we find excellent performance for the full supply chain. Our work gives a set of techniques that can be adopted in contexts beyond vehicle routing to apply NISQ devices in a hybrid fashion to large-scale problems of commercial interest.

Environment and economic analysis of reverse supply chain scenarios for remanufacturing using discrete-event simulation approach.

The study covers the concepts involved in reverse supply chain modeling using the case of a manufacturing company. The purpose of this study is to build a sustainable reverse supply chain model for resource conservation through remanufacturing of stator shafts by using a discrete-event simulation approach. The simulation studies in the reverse supply chain have taken up cases of either plastic or electronic waste remanufacturing, while very limited studies deal with simulation of sustainable reverse supply chains using a manufacturing industry case study from international customers. In this study, reverse supply chain using simulation study in manufacturing sector is carried out using Arena Rockwell simulation software. The simulation model is built using discrete-event simulation for returns from customers of two developed countries, i.e., Germany and the USA to Chennai, India. The study emphasizes full container load and less than container load modes of shipment scenarios and multiple return cases. The comparative analysis suggests that the value-added and non-value-added time of the reverse supply chain is slightly greater in the less container load scenario. The wait time per entity in remanufacturing processes similar for both shipment scenarios varies significantly based on return cases. The cost and carbon emission associated with transportation, in the reverse supply chain inclusive of social carbon cost, have also been estimated. Therefore, the study proposes a possible sustainable reverse supply chain framework that could be adopted by different manufacturing industries and yield opportunities for performance improvement.

Data-driven change control design for product and supply chain synchronous evolution systems under cyber-attacks

A product and supply chain synchronous evolution system (PSCSES) is a kind of complex distributed cyber-physical system (CPS), which contains several unmodeled dynamics and uncertainties, and the system structure and parameters are vulnerable to changes due to drastic demand fluctuations and unexpected network events. Thus, a product design change is an important way to maintain the stability of the synchronous evolution system to meet customers' demands and ensure economic efficiency. In this paper, the emergency change control problem of a distributed CPS-based PSCSES under DoS attacks is studied. First, an emergency change compensation mechanism in which a predictor based on recurrent neural networks is proposed to compensate for the lost inventory data caused by cyber-attacks. Second, the $H_{\infty}$ consensus control problem is transformed into a multiplayer zero-sum graph game using game theory, and a Q-learning-based $H_{\infty}$ consensus control protocol is designed, such that sufficient conditions that guarantee the consensus of the system can be derived. The bullwhip effects caused by the customers' demand and design change are analyzed thereby. Third, using the strategy iteration algorithm, the online solution is resolved for the controller design under the unknown model. Finally, the effectiveness of the proposed method is verified through the simulation of a collaborative production supply chain.

Supply-chain simulations for shaping lockdown policies

ABSTRACT To prevent the spread of COVID-19, many governments have imposed lockdowns. This practice has resulted in economic stagnation across broad areas because of the shock of the lockdown propagated to other regions through supply chains. Using supply-chain data for 1.6 million firms in Japan, this study examines how the economic effects of lockdowns in multiple regions interact with each other. Our major findings are twofold. First, when multiple regions coordinate the timing of their lockdowns, their economic losses are smaller than when they do so asynchronously. Second, the benefit of synchronous lockdown in multiple regions is larger when they are connected through a larger number of supply-chain links. Our results suggest a need for policy coordination across regions and countries when lockdowns are imposed.

Evaluating Food Loss Reduction Measures of Perishable Foods by Supply Chain Simulation and Game Theoretical Analysis

Evaluating Food Loss Reduction Measures of Perishable Foods by Supply Chain Simulation and Game Theoretical Analysis