An investigation into modeling and simulation approaches for sustainable operations management

Modeling and simulation (M&S) studies have been widely used in industry to gain insights into existing or proposed systems of interest. The majority of these studies focus on productivity-related measures to evaluate systems' performance. This paradigm, however, needs to be shifted to cope with the advent of sustainability, as it is increasingly becoming an important issue in the managerial and the organizational agendas. The application of M&S to evaluate the often-competing metrics associated with sustainable operations management (SOM) is likely to be a challenge. The aim of this review is to investigate the underlying characteristics of SOM that lend towards modeling of production and service systems, and further to present an informed discussion on the suitability of specific modeling techniques in meeting the competing metrics for SOM. The triple bottom line, which is a widely used concept in sustainability and includes environmental, social, and economic aspects, is used as a benchmark for assessing this. Findings from our research suggest that a hybrid (combined) M&S approach could be an appropriate method for SOM analysis; however, it has its challenges.This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

Hybrid simulation modeling for regional food systems

In response to concerns regarding the serious environmental and social issues associated with conventional food distribution systems, consumer demand for regionally-produced food is growing. Regional food hubs are playing a critical role in meeting this growing demand. Food hubs aggregate, distribute, and market regionally-produced food, with a goal of promoting and supporting environmental and social sustainability. They provide an alternative distribution channel through which small and mid-sized producers can access wholesale markets, and they improve consumer access to regional food at competitive prices. Despite the benefits they provide, food hubs struggle to maintain profitability, and they face many challenges to growth and success. In particular, they are often unable to achieve the logistical and operational efficiencies that characterize conventional large-scale food distribution. This is partly due to a lack of implementation of efficiency-enhancing conventional supply chain practices in food hub operations. One possible method of improving food hub efficiency targets their inbound logistics operations. This thesis studies the inbound operations of a regional food hub in Iowa, with a focus on the scheduling of producers’ deliveries to the food hub. This thesis proposes a hybrid simulation modeling framework to show how the advantages of both discrete event simulation (DES) and agent-based simulation (ABS) can be leveraged to address socio-technical problems in regional food supply chains. The usefulness of this hybrid methodology is demonstrated through the development of an empirically-based hybrid simulation model of the inbound logistics operations of a food hub in Iowa. ABS was used to model the decision-making process of producers for scheduling their deliveries. DES was used to model the inbound operations of the food hub, including the receiving and storing of the goods brought by the producers. Four different versions of the hybrid simulation model are used to examine the effectiveness of various policies in encouraging producers to schedule their deliveries, as well as the impacts of producer scheduling on food hub efficiency and effectiveness. Experimental results suggest that different incentives vary in their degree of effectiveness, and increasing the percentage of producers who schedule their deliveries is unlikely to improve overall system operations by itself – in order for all participants to benefit, the food hub manager must also adjust the hub’s inbound operations to account for producers who refuse to schedule. This hybrid model will help guide policy recommendations to food hub managers to make their inbound operations more efficient and effective

A Review Of Hybrid Simulation In Healthcare

Hybrid Simulation (HS) has been applied to healthcare systems, but there is still limited literature and an opportunity to develop research. This review explores applications of HS in healthcare, to outline research gaps and foster new research in HS to solve complex real healthcare problems. The twelve application papers found through a systematic literature search covered nearly all hybrid combinations. Discrete Event (DES) and System Dynamics (SD) were found to be the most popular combination, and AnyLogic, the most used HS tool. We found that none of the papers we reviewed used the SD-ABS approach, which raises questions about the need and challenges associated with certain combinations. HS in healthcare applications, for the most part, are published in conference proceedings. We discuss opportunities for research and, in particular, the potential for HS application in problems related to communicable disease and healthcare services planning

A distributed simulation methodological framework for OR/MS applications

Distributed Simulation (DS) allows existing models to be composed together to form sim- ulations of large-scale systems, or large models to be divided into models that execute on separate computers. Among its claimed benefits are model reuse, speedup, data pri- vacy and data consistency. DS is arguably widely used in the defence sector. However, it is rarely used in Operations Research and Management Science (OR/MS) applications in areas such as manufacturing and healthcare, despite its potential advantages. The main barriers to use DS in OR/MS are the technical complexity in implementation and a gap between the world views of DS and OR/MS communities. In this paper, we propose a new method that attempts to link together the methodological practices of OR/MS and DS. Using a rep- resentative case study, we show that our methodological framework simplifies significantly DS implementation.This research was funded by the Multidisciplinary Assessment of Technology Centre for Healthcare (MATCH), an Innova- tive Manufacturing Research Centre (IMRC) funded by the Engineering and Physical Sciences Research Council (EPSRC) (Ref: EP/F063822/1 )

Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria

Floods have become a global concern because of the vast economic and ecological havoc that ensue. Thus, a flood risk mitigation strategy is used to reduce flood-related consequences by a long-lead identification of its occurrence. A wide range of causative factors, including the adoption of hybrid multi-spatiotemporal data framework is considered in implementing the strategy. Besides the structural or homogenous non-structural factors, the adoption of various Information Systems-based tools are also required to accurately analyse the multiple natural causative factors. Essentially, this was needed to address the inaccurate flood vulnerability classifications and short time of flood prediction. Thus, this study proposes a framework named: Hybrid Multi-spatiotemporal data Framework for Long-lead Upstream Flood Analysis (HyM-SLUFA) to provide a new dimension on flood vulnerability studies by uncovering the influence of multiple factors derived from topography, hydrology, vegetal and precipitation features towards regional flood vulnerability classification and long-lead analysis. In developing the proposed framework, the spatial images were geometrically and radiometrically corrected with the aid of Quantum Geographic Information System (QGIS). The temporal data were cleaned by means of winsorization methods using STATA statistical tool. The hybrid segment of the framework classifies flood vulnerability and performs long-lead analysis. The classification and analysis were conducted using the corrected spatial images to acquire better understanding on the interaction between the extracted features and rainfall in inducing flood as well as producing various regional flood vulnerabilities within the study area. Additionally, with the aid of regression technique, precipitation and water level data were used to perform long-lead flood analysis to provide a foresight of any potential flooding event in order to take proactive measures. As to confirm the reliability and validity of the proposed framework, an accuracy assessment was conducted on the outputs of the data. This study found the influence of various Flood Causative Factors (FCFs) used in the developed HyM-SLUFA framework, by revealing the spatial disparity indicating that the slope of a region shows a more accurate level of flood vulnerability compared to other FCFs, which generally causes severe upstream floods when there is low volume of precipitation within regions of low slope degree. Theoretically, the HyM-SLUFA will serve as a guide that can be adopted or adapted for similar studies. Especially, by considering the trend of precipitation and the pattern of flood vulnerability classifications depicted by various FCFs. These classifications will determine the kind(s) of policies that will be implemented in town planning, and the Flood Inducible Precipitation Volumes can provide a foresight of any potential flooding event in order to take practical proactive measures by the local authority

Hybrid Simulation-based Planning Framework for Agri-Fresh Produce Supply Chain

The ever-increasing demand for fresh and healthy products raises the economic importance of managing Agri-Fresh Produce Supply Chain (AFPSC) effectively. However, the literature review has indicated that many challenges undermine efficient planning for AFPSCs. Stringent regulations on production and logistics activities, production seasonality and high yield variations (quantity and quality), and products vulnerability to multiple natural stresses, alongside with their critical shelf life, impact the planning process. This calls for developing smart planning and decision-support tools which provides higher efficiency for such challenges. Modelling and simulation (M&S) approaches for AFPSC planning problems have a proven record in offering safe and economical solutions. Increase in problem complexity has urged the use of hybrid solutions that integrate different approaches to provide better understanding of the system dynamism in an environment characterised by multi-firm and multi-dimensional relationships. The proposed hybrid simulation-based planning framework for AFPSCs has addressed internal decision-making mechanisms, rules and control procedures to support strategic, tactical and operational planning decisions. An exploratory study has been conducted using semi-structured interviews with twelve managers from different agri-fresh produce organisations. The aim of this study is to understand management practices regarding planning and to gain insights on current challenges. Discussions with managers on planning issues such as resources constraints, outsourcing, capacity, product sensitivity, quality, and lead times have formed the foundation of process mapping. As a result, conceptual modelling process is then used to model supply chain planning activities. These conceptual models are inclusive and reflective to system complexity and decision sensitivity. Verification of logic and accuracy of the conceptual models has been done by few directors in AFPSC before developing a hybrid simulation model. Hybridisation of Discrete Event Simulation (DES), System Dynamics (SD), and Agent-Based Modelling (ABM) has offered flexibility and precision in modelling this complex supply chain. DES provides operational models that include different entities of AFPSC, and SD minds investments decisions according to supply and demand implications, while ABM is concerned with modelling variations of human behaviour and experience. The proposed framework has been validated using Table Grapes Supply Chain (TGSC) case study. Decision makers have appreciated the level of details included in the solution at different planning levels (i.e., operational, tactical and strategic). Results show that around 58% of wasted products can be saved if correct hiring policy is adopted in the management of seasonal labourer recruitment. This would also factor in more than 25% improved profits at packing house entity. Moreover, an anticipation of different supply and demand scenarios demonstrated that inefficiency of internal business processes might undermine the whole business from gaining benefits of market growth opportunities

An agent-based approach to modeling sustainable sociotechnical systems

A gradual evolution in the food and energy sectors towards decentralized decision-making is requiring participant organizations to consider new approaches to the design of policies and processes. Increasing consumer preference for food sourced from small-scale regional farmers has led to changing logistics requirements. Similarly, as consumers have become conscious of the impacts of climate change on the environment, they have begun to adopt renewable sources to meet their energy needs. Moreover, the emergence of new technologies has enabled consumers to generate their own energy. Such shifts in decision-making power to consumers necessitates the consideration of their perspectives and preferences when designing policies and business structures. In food and energy systems, which can be considered sociotechnical systems, the role of human behavior influences system dynamics as strongly as the technical artifacts. This dissertation utilizes an agent-based modeling approach to study such sociotechnical systems. Although agent-based modeling (ABM) has demonstrated the potential to understand and predict the dynamic behavior of sociotechnical systems, the biggest barriers in implementing ABM widely are its replicability and validation. This dissertation aims to address these two issues by developing empirical ABMs for applications in regional food supply chains and renewable energy systems. The applications of the ABMs in this dissertation are motivated by United Nations Sustainable Development Goals, specifically those goals that are focused on the objectives of responsible consumption and production, climate action, and affordable and clean energy for all