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The purpose of this paper is to investigate the best frequency description of a chain dependent Markov process for the daily simulation of precipitation. The influence of the order of the Markov chain model to simulate daily precipitation occurrence is evaluated. A mixed‐order model is constructed and compared to a simple first‐order model to evaluate the importance of the model order for the pricing of a rainfall index put option.

For the first time a mixed‐order Markov chain model is presented where the monthly varying order was chosen based on a Bayesian information criteria analysis of rainfall data for one weather station in the US. The outcome of this model is compared to simpler Markov models and to burn analysis results.

The comparison indicate that there is only a slightly better representation of the rain statistics in the theoretically best mixed‐order Markov chain model compared to a more simple first‐order model. Clear differences between the daily simulation and the burn method are found when pricing a put option on a rainfall index. All daily simulation models underestimate the volatility of the monthly rainfall amount especially in the summer months.

To assess the robustness and any geographical dependence of the bias in the volatility a systematic analysis could be applied to more weather stations across the US in further studies.

The bias in the volatility has significant influence on the price of the put option considered here and limits the use of such a model for risk analyses, e.g. for an extreme event cover.

For the first time a multi‐order Markov chain model is applied to price a precipitation derivative. While the focus of previous studies was the appropriate choice for the intensity process, the importance of the frequency process is investigated in this paper.

The purpose of this paper is to analyze the extent to which weather index‐based insurances can contribute to reducing shortfall risks of revenues of a representative average farm that produces corn or wheat in the North China Plain (NCP). The geographical basis risk is quantified to analyze the spatial dependency of weather patterns between established weather stations in the area and locations where the local weather patterns are unknown.

Data are based on the Statistical Yearbook of China and the Chinese Meteorological Administration. Methods of insurance valuation are burn analysis and index value simulation. Risk reduction is measured non‐parametrically and parametrically by the change of the standard deviation and the value at risk of revenues. The geographical basis risk is quantified by setting up a decorrelation function.

Results suggest significant differences in the potential risk reduction between corn and wheat when using insurance based on a precipitation index. The spatial analysis suggests a potential to expand the insurance around a reference weather station up to community level.

Findings are limited by a weak database in China and, in particular, by the unavailability of individual farm data. Moreover, the low density of weather stations currently limits the examination of the approach in a broader context.

The risk reduction potential of the proposed insurance is encouraging. From a policy point of view, the approach used here can support the adjustment of insurers towards different crops.

This paper is believed to be the first that investigates a weather index‐based insurance designed for an average farm in the NCP and the quantification of geographical basis risk.

The use of weather derivatives is impaired with a basis risk which diminishes the hedging effectiveness and hinders the distribution of these risk management instruments in the agricultural sector. A frequently suggested approach to reduce the basis risk is the use of mixed indices composed of several weather variables. The purpose of this paper is to compare the hedging effectiveness of a simple temperature‐based and a simple precipitation‐based weather derivative with that of a derivative based on a mixed index of two weather variables.

The basis of this comparison are empirical yield time series of the winter wheat production of 32 farms located in central Germany, as well as daily temperature and precipitation data collected by selected weather stations over several years. Insurance is structured as an option on an accumulated weather index and priced by index‐value simulation. In addition, the bootstrapping method is used to improve statistical reliability. The hedging effectiveness is measured non‐parametrically regarding the relative reduction of the standard deviation of winter wheat revenues caused by using weather derivatives.

The results reveal that mixed index‐based weather derivatives have a significantly higher potential to reduce the risk of winter wheat revenues than simple index‐based weather derivatives. However, using mixed index‐based weather derivatives does not lead to a significantly higher hedging effectiveness than the simultaneous use of several simple index‐based weather derivatives. Moreover, simple index‐based weather derivatives may more easily raise the interest of other industries which could serve as potential trading partners for the agricultural sector.

The authors analyzed the hedging effectiveness of weather derivatives based on simple and mixed indices with regard to the production of winter wheat in Central Germany. To confirm that the present results are generalizable, further research is required for other types of production apart from winter wheat cultivation and with respect to other regions besides Germany.

The focus and results of the present study are very relevant for farmers as well as for potential providers of weather derivatives. The results reconfirm that weather derivative providers should better offer different weather derivatives based on a simple index than complex derivatives that are based on a mixed index.

To the best of the authors' knowledge, this paper is the first that provides a comparative impact analysis of simple and mixed index‐based weather derivatives conducted for real individual farms with regard to their hedging effectiveness.

The main operations of the powder-coating process are staggered along a closed-loop conveyor. Given the volatile market demands, using a fixed level of staffing may result in significant productivity losses. The present study aims to capture stochastic behavior and optimize operator assignment problems in a practical powder-coating process. By using the proposed methodology, when demand changes, the optimal operator assignment configuration can be provided, ensuring high labor productivity.

The powder-coating process is an important industrial application and is often a labor-intensive system. The present study adopts a practical case to optimize its staffing level. Because of its operational complexity, the problem is solved by a proposed simulation-optimization approach. The results are promising, and the proposed methodology is shown to be an effective approach.

The proposed methodology was tested for various demand levels. The optimized operator assignment configuration always improves on the performance of other staffing levels. Given the same daily throughput, the optimized operator assignment configuration can improve performance by as much as 19%. In scenarios where there is increased demand, the resulting reduction in overtime work improves performance by between 20.33% and 56.72%. In scenarios where there is reduced demand, the optimized staffing level produces improvements between 3.13% and 50%. Compared with the fixed staffing policy of the case company, the flexible staffing policy of the proposed methodology can maintain high labor productivity across demand variations. The results are consistent with the Shojinka philosophy of the Toyota Production System.

This study proposes a solution to the operator assignment decision in a labor-intensive manufacturing system – a powder-coating processing system. Powder coating provides a solid powder coating without any solvent. Because of its excellent application performance and environmental protection, it is widely used in the field of metal coating, especially appliances for offices and homes. Most of the existing literature has solved the problem by making unrealistic assumptions. The present study proposes a simulation-optimization method to solve a practical problem in powder-coating processing. The effectiveness of the proposed methodology is illustrated by a practical application. According to the experimental results, five operators can be saved for the same daily throughput. An average of 35 and 19 min of overtimes can be saved when demand increases by 10% and 20% with one less operator; between 2 and 16 operators can be saved when demand falls by 10%–60%.

Despite the difference in information sets, we are able to compare the asymptotic distribution of volatility estimators involving data sampled at different frequencies. To do so, we propose extensions of the continuous record asymptotic analysis for rolling sample variance estimators developed by Foster and Nelson (1996, Econometrica, 64, 139–174). We focus on traditional historical volatility filters involving monthly, daily and intradaily observations. Theoretical results are complemented with Monte Carlo simulations in order to assess the validity of the asymptotics for sample sizes and filters encountered in empirical studies.

The spatial conflicts and congestion of construction resources are challenges that lead to the reduction in efficiency. The purpose of this paper is to enable users to detect and resolve workspace conflicts by implementing four resolution strategies in a five-dimensional (5D) CAD model. In addition to resolving conflicts, the model should be able to optimize time and cost of the projects. In other words, three variables of spatial conflicts, time and cost of project are considered simultaneously in the proposed model to find the optimum solution.

In the first step, a 5D simulation model is developed that includes time, cost and geometrical information of a project. Then, time-cost trade-off analysis was carried out to distinguish optimum schedule. The schedule was imported to the 5D CAD model to detect spatial conflicts. Finally, a novel algorithm was implemented to solve identified conflicts while imposing minimum project’s time and cost. Several iterations are performed to resolve all clashes using conflict resolution algorithm and visual simulation model.

The proposed methodology in this research was applied to a real case. Results showed that in comparison to the normal and initial schedule with 19 conflicts, the finalized schedule has no conflict, while time and cost of the project are both reduced.

Implementing the proposed methodology in construction projects requires proper technical basis in this field. In this regard, the executive user should have a proper understanding of the principles, concepts and tools of building information modeling and have project management knowledge. Also, the implementation conditions of the basic model requires the determination of the construction methods, estimated volumes of working items, scheduling and technical specification. The designed methodology also has two limitations regarding to its implementation. The first is the fact that strategies should be applied manually to the schedule. The other one pertains to the number of strategies used in the research. Four strategies have been used in the conflict resolution algorithm directly and the two others (spatial divisibility and activities breakdown strategies) have been used as default strategies in the visual simulation model. Since the unused strategies including the changing of construction method and the activity resources are subjective and depend upon the planner and project manager’s personal opinion, the authors have avoided using them in this research.

The method proposed in this research contributes the coordination of the working teams at the planning and execution phases of the project. In fact, the best location and work direction for each working team is presented as a schedule, so that the space conflict may not come about and the cost can be minimized. This visual simulation not only deepens the planners’ views about the executive barriers and the spatial conditions of the worksite, it also makes the construction engineers familiar on a daily basis with their executive scope. Therefore, it considerably improves the interactions and communication of the planning and construction teams. Another advantage and application of this methodology is the use of initial and available projects’ documents including the schedule and two-dimensional drawings. The integration of these basic documents in this methodology helps identify the spatial conflicts efficiently. To achieve this, the use of the existing and widely-used construction tools has facilitated the implementation of the methodology. Using this system, planners have applied the strategies in an order of priority and can observe the results of each strategy visually and numerically in terms of time, cost and conflicts. This methodology by providing the effective resolution strategies guides the practitioner to remove conflicts while optimum time and cost are imposed to project.

Contrary to the previous models that ignore cost, the proposed model is a 5D visual simulation model, which considers the variable of cost as a main factor for conflict identification and resolution. Moreover, a forward-pass approach is introduced to implement resolution strategies that are novel compared to other investigations.

In a volatile marketplace, warehouse management is fundamentally contingent of changes in its supply network environment. Flexibility is therefore a key logistics issue in distribution centre management. This study probes into the nature of warehouse flexibility in a supply network through simulation.

By using the FlexSim simulation tool the dynamic behaviour of the warehouse system are conceptualised, documented, simulated, analysed and evaluated.

Simulation revealed that external changes affect daily processes and the reorganisation of warehouse processes. Given the extensity of resource use, simulation also revealed that process reorganisation should not be a daily undertaking. This is because warehouse processes react in unpredictable and different manners to even the smallest disturbance from the environment. This reaction is not necessarily negative impending more long-term change of warehouse processes.

The warehouse is a complex system that self-adapts with limited need to calculate new optimised warehouse processes to counter changes in its environment. Rather than following deterministic optimisation procedures, the development of flexible resources is a key issue in warehouse management. The applied simulation model is generic and therefore applicable in other distribution centres pointing to how to monitor warehouse processes to in a pre-emptively develop warehouse flexibility through change of process context.

This study examines rainfall variability and its implications for wheat production risk in northeast Germany. The hedging effectiveness of rainfall options and the role of geographical basis risk are analyzed using a daily precipitation model. Simpler pricing methods such as the burn analysis and the index value simulation serve as benchmarks for comparison. It is found that the choice of statistical approach may lead to considerable differences in the estimation results. Daily precipitation models should be used with some caution in the context of derivative pricing because they tend to underestimate rainfall variability. This is unexpected, because daily simulation models are usually preferred in the context of temperature‐based weather indexes.

A methodology of structuring a garment production simulation model using a spreadsheet is described to minimize the average daily production cost through the investigation of various man‐machine combinations. The capability and usability of an easily available modern spreadsheet Excel 7.0 to simulate a simple garment production system is accessed with an attempt to demonstrate the simulation model building in a user friendly environment rather than learning and using costly simulation programming languages or simulation software packages. Simulation has evaluated the resource utilization and measured the system performance and developed strategies for taking operational decisions in a logical and better way to minimize the garment production cost. It may also assist and benefit the garment production managers to plan, design and operate their systems in an efficient manner in a competitive environment.

The food processing industry is growing with retail and catering supply chains. With the rising complexity of food products and the need to address food customization expectations, food processing systems are progressively shifting from production line to job-shops that are characterized by high flexibility and high complexity. A food job-shop system processes multiple items (i.e. raw ingredients, toppings, dressings) according to their working cycles in a typical resource and capacity constrained environment. Given the complexity of such systems, there are divergent goals of process cost optimization and of food quality and safety preservation. These goals deserve integration at both an operational and a strategic decisional perspective. The twofold purpose of this paper is to design a simulation model for food job-shop processing and to build understanding of the extant relationships between food flows and processing equipment through a real case study from the catering industry.

The authors designed a simulation tool enabling the analysis of food job-shop processing systems. A methodology based on discrete event simulation is developed to study the dynamics and behaviour of the processing systems according to an event-driven approach. The proposed conceptual model builds upon a comprehensive set of variables and key performance indicators (KPIs) that describe and measure the dynamics of the food job-shop according to a multi-disciplinary perspective.

This simulation identifies the job-shop bottlenecks and investigates the utilization of the working centres and product queuing through the system. This approach helps to characterize how costs are allocated in a flow-driven approach and identifies the trade-off between investments in equipment and operative costs.

The primary purpose of the proposed model relies on the definition of standard resources and operating patterns that can meet the behaviour of a wide variety of food processing equipment and tasks, thereby addressing the complexity of a food job-shop. The proposed methodology enables the integration of strategic and operative decisions between several company departments. The KPIs enable identification of the benchmark system, tracking the system performance via multi-scenario what-if simulations, and suggesting improvements through short-term (e.g. tasks scheduling, dispatching rules), mid-term (e.g. recipes review), or long-term (e.g. re-layout, working centres number) levers.