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

A Hybrid Process Mining Framework for Automated Simulation Modelling for Healthcare

Advances in data and process mining algorithms combined with the availability of sophisticated information systems have created an encouraging environment for innovations in simulation modelling. Researchers have investigated the integration between such algorithms and business process modelling to facilitate the automation of building simulation models. These endeavors have resulted in a prototype termed Auto Simulation Model Builder (ASMB) for DES models. However, this prototype has limitations that undermine applying it on complex systems. This paper presents an extension of the ASMB framework previously developed by authors adopted for healthcare systems. The proposed framework offers a comprehensive solution for resources handling to support complex decision-making processes around hospital staff planning. The framework also introduces a machine learning real-time data-driven prediction approach for system performance using advanced activity blocks for the auto-generated model, based on live-streams of patient data. This prediction can be useful for both single and multiple healthcare units management

Hybrid Modelling for Vineyard Harvesting Operations

Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in the vineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, and increasing in grape losses rates. The objective of this research is to investigate how the variation in workers experience could impact vineyard harvesting productivity and operational cost. The complexity of the problem means that it is difficult to analyze the system parameters and their relationships using individual analytical model. Hence, a hybrid model integrating discrete event simulation (DES) and agent based modeling (ABM) is developed and applied on a vineyard to achieve research objective. DES models harvesting operation and simulates process performance, while ABM addresses the seasonal workers heterogeneous characteristics, particularly experience variations and disparity of working days in the vineyard. The model is used to evaluate two seasonal recruiting policies against vineyard productivity, grape losses quantities, and total operational cost

Seasonal recruiting policies for table grape packing operations: A hybrid simulation modelling study

The packing process is a critical post-harvesting activity in table grape industry. Workers in packing stations are hired under seasonal contracts because of product seasonality and operations labor intensity. Seasonal workers, however, are usually characterized by inconsistent performance, high turnover and experience variation which leads to low productivity and high waste. Few mathematical models were used for evaluating fresh products packing operations, but in a deterministic nature which hinders the complexity and dynamics of the business processes. Hence, a hybrid Discrete Event Simulation (DES) and Agent-Based Modelling (ABM) are employed to evaluate a set of seasonal recruiting policies in a large grape packing station. The paper aims to investigate the impact of workers experience on packing operations efficiency. The model outcomes demonstrate the improvement in operations efficiency and total running cost (about 20% savings) that can be achieved when applying optimal recruiting policies that reduce labors variations

Hybrid modeling for vineyard harvesting operations

Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in the vineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, and increasing in grape losses rates. The objective of this research is to investigate how the variation in workers experience could impact vineyard harvesting productivity and operational cost. The complexity of the problem means that it is difficult to analyze the system parameters and their relationships using individual analytical model. Hence, a hybrid model integrating discrete event simulation (DES) and agent based modeling (ABM) is developed and applied on a vineyard to achieve research objective. DES models harvesting operation and simulates process performance, while ABM addresses the seasonal workers heterogeneous characteristics, particularly experience variations and disparity of working days in the vineyard. The model is used to evaluate two seasonal recruiting policies against vineyard productivity, grape losses quantities, and total operational cost

An Empirical Estimation of Statistical Inferences for System Dynamics Model Parameters

For system dynamics simulation (SD) models, an estimation of statistical distributions for uncertain parameters is crucial. These distributions could be used for testing models sensitivity, quality of policies, and/or estimating confidence intervals for these parameters. Assumptions related to normality, independence and constant variation are often misapplied in dynamic simulation. Bootstrapping holds a considerable theoretical advantage when used with non-Gaussian data for estimating empirical distributions for unknown parameters. Although it is a widely acceptable approach, it has had only limited use in system dynamics applications. This paper introduces an application of Direct Residual Bootstrapping (DRBS) for statistical inference in system dynamic model. DRBS has been applied successfully to ‘The Irish Elderly Patient Delayed Discharge’ dynamic model to estimate empirical distribution for some unknown parameters with a minimal computation effort. The computational results show that bootstrapping offers an efficient performance in cases of no availability of prior information of model parameters

UNDERSTANDING THE DYNAMIC BEHAVIOUR OF THREE ECHELON RETAIL SUPPLY CHAIN DISRUPTIONS

It is often taken for granted that the right products will be available to buy in retail outlets 7 days a week, 52 weeks a year. Challenges in achieving this continued on-shelf availability range from recession hit demand patterns to cost reduction driven strategies. Irish government initiatives to brand the country as a sustainable, reliable provider of food retail supply chains has resulted in increased importance on decision maker accuracy. The vulnerability of retail supply chain’s (RSC) to disruption is another catalyst in the complexity of the decision making process and a more robust understanding of disruption behavior is needed. The aim of this paper is to illustrate the advantages of integrating balanced scorecard system thinking to system dynamic modeling of an extended retail supply chain. With this approach, decision makers can gain a better understanding of disruptions within their own organization and the partnerswithin their extended RSC