Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research

Scheduling is a decision-making process that is concerned with the allocation of limited resources to competing tasks (operations of jobs) over a time period with the goal of optimising one or more objectives. In theory, the objective is usually to optimise some classical system performance measures such as makespan, tardiness/earliness and flowtime under deterministic and static assumptions. In practice, however, scheduling systems operate in dynamic and stochastic environments. Hence, there is a need to incorporate both uncertainty and dynamic elements into the scheduling process. In this paper, the major issues involved in scheduling decisions are discussed and the basic approaches to tackle these problems in manufacturing environments are analysed. Proactive scheduling is then focused on and several robustness and stability measures are presented. Previous research on scheduling robustness and stability is also reviewed and further research directions are suggested

The Demand Absorption Coefficient of a Production Line

AbstractIn this article, the demand absorption coefficient is proposed as a measure to quantify the degree of flexibility of a process against the variations of its environment in a context of robust planning. The demand absorption coefficient is defined as the slope on the function relating throughput and demand rates. This coefficient measures how demand disturbances are translated into output production rates depending on the capacity and inventory buffers of the production system. Models of serial production lines with different numbers of machines, capacities and sizes of buffers are solved by means of a decomposition method using phase-type distributions to study the behavior of this coefficient

Flow shop rescheduling under different types of disruption

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 2013, available online:http://www.tandfonline.com/10.1080/00207543.2012.666856Almost all manufacturing facilities need to use production planning and scheduling systems to increase productivity and to reduce production costs. Real-life production operations are subject to a large number of unexpected disruptions that may invalidate the original schedules. In these cases, rescheduling is essential to minimise the impact on the performance of the system. In this work we consider flow shop layouts that have seldom been studied in the rescheduling literature. We generate and employ three types of disruption that interrupt the original schedules simultaneously. We develop rescheduling algorithms to finally accomplish the twofold objective of establishing a standard framework on the one hand, and proposing rescheduling methods that seek a good trade-off between schedule quality and stability on the other.The authors would like to thank the anonymous referees for their careful and detailed comments that helped to improve the paper considerably. This work is partially financed by the Small and Medium Industry of the Generalitat Valenciana (IMPIVA) and by the European Union through the European Regional Development Fund (FEDER) inside the R + D program "Ayudas dirigidas a Institutos tecnologicos de la Red IMPIVA" during the year 2011, with project number IMDEEA/2011/142.Katragjini Prifti, K.; Vallada Regalado, E.; Ruiz García, R. (2013). Flow shop rescheduling under different types of disruption. International Journal of Production Research. 51(3):780-797. https://doi.org/10.1080/00207543.2012.666856S780797513Abumaizar, R. J., & Svestka, J. A. 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Essays on the Market Design of the EU Emissions Trading System

This thesis studies the economics and the market design of the European Union Emissions Trading System (EU ETS). It entails four essays. Chapter 2 analyses the empirical literature on carbon leakage in the EU ETS and thereby assesses the effectiveness of EU ETS regulation in reducing global CO2 emissions. The results show that ETS regulation and market developments, particularly low carbon prices, caused low carbon leakage in the past. Due to a change in carbon leakage regulation and higher expected prices, this might change in the future. The remaining three chapters use a partial equilibrium model of the EU ETS that accurately depicts current EU ETS regulation. The model set up in chapter 2 assumes perfectly rational actors and is used to evaluate the cost-effectiveness of the latest EU ETS amendments. These comprise of the introduction of a Market Stability Reserve, a Cancellation Mechanism, and the tightening of the allowance cap through the increase of the Linear Reduction Factor. The results indicate that the Market Stability Reserve shifts allowances to the future but preserves them over time, decreasing the cost-effectiveness of the market. While the Cancellation Mechanism reduces the overall allowance supply by roughly 2 billion allowances, the increase of the Linear Reduction Factor has the largest impact on market results and is hence considered the key driver of the reforms. Chapter 3 amends the model built in the previous chapter as it deviates from the assumption of perfectly rational market participants. Firms are assumed to be myopic and risk averse. Given these two forms of bounded rationality, the historic market outcomes in Phase III of the EU ETS can be replicated. The steep price increase and the large private bank seen in the market can, thus, be explained by the reform fundamentals. Chapter 4 evaluates the EU ETS in light of the Corona pandemic, which serves as an unforeseen economic shock to the European economy. The model results show that due to the endogenous supply adjustment of the Market Stability Reserve and the Cancellation Mechanism, COVID-19 decreases emissions in the EU ETS in the long run. This holds, even if the crisis is followed by an economic expansion in the same or even large magnitude. Further, the Market Stability increases the market’s resilience towards shocks as it increases the relative price stability of the EU ETS

On the Theory of Emissions Trading - Applications to the EU ETS

This thesis comprises four research articles covering different aspects of emissions trading systems and, in particular, the EU ETS. Chapter 2 decomposes the different amendments of the EU ETS reform and evaluates the cost effectiveness of the single amendments. It finds that the increased Linear Reduction Factor has the most significant impact on the reformed EU ETS. Chapter 3 analyzes the effects of a carbon price floor in the reformed EU ETS and compares two different designs of a CPF. The buyback design raises the market price immediately to the discounted level, whereas the top-up tax causes ambiguous effects depending on the precise design. Chapter 4 includes behavioral aspects such as myopia and risk aversion into the model of the reformed EU ETS. The observed market outcomes between 2013 and 2019 can be replicated with a combination of myopic firms and hedging requirements. Finally, Chapter 5 challenges a common assumption on the shape of marginal abatement cost curves and discusses the implications for the EU ETS. Marginal abatement cost curves are convex and flatten over time, which alters the banking rationale of firms in the EU ETS

A robust flexible flow shop problem under processing and release times uncertainty

The aim of this paper is to present a simheuristic approach that obtains robust solutions for a multi-objective hybrid flow shop problem under uncertain processing and release times. This approach minimizes the expected tardiness and standard deviation of tardiness, as a robustness measure for the stated problem. The simheuristic algorithm hybridizes the NSGA-II with a Monte Carlo Simulation process. Initially, the deterministic scenario was tested on 32 different created small size instances and 32 medium and large benchmarked instances. As a result, the proposed algorithm improved quality of solutions by 1.21% against the MILP model and it also performed better than ERD, NEHedd, and ENS2, while consuming a reasonable computational time. Afterwards, one experimental design was carried out using 10 random instances from the same benchmark as a blocking factor, where four factors of interest were considered. The factors and their respective values are number of generations (50, 100), crossover probability (0.8, 0.9), mutation probability (0.1, 0.2), and population size (60, 100). Results show that the factors instance, mutation probability and number of generations, as well as other interactions between them, have a significant effect in the total tardiness for the deterministic scenario, proving the importance of an appropriate selection of parameters when using genetic algorithms to obtain quality solutions. Then, the performance of the proposed NSGA-II was compared against ERD, NEHedd, and ENS2 methods. Results show that our algorithm improves the quality of the solutions for both objective functions, proving the robustness of our solutions for the HFS problem. Finally, two additional generalized experiments were carried out to analyze the effect of number of jobs (10, 20), number of stages (2, 3), shop condition (0.2, 0.6), probability distribution (uniform, lognormal), and CV (0.05, 0.25, 0.4) on both objective functions. The shop condition, probability distribution and CV were proven to be highly influential on the variability of the results, with the only exception being the coefficient of variation having no statistically significant effect on the total tardiness.The aim of this paper is to present a simheuristic approach that obtains robust solutions for a multi-objective hybrid flow shop problem under uncertain processing and release times. This approach minimizes the expected tardiness and standard deviation of tardiness, as a robustness measure for the stated problem. The simheuristic algorithm hybridizes the NSGA-II with a Monte Carlo Simulation process. Initially, the deterministic scenario was tested on 32 different created small size instances and 32 medium and large benchmarked instances. As a result, the proposed algorithm improved quality of solutions by 1.21% against the MILP model and it also performed better than ERD, NEHedd, and ENS2, while consuming a reasonable computational time. Afterwards, one experimental design was carried out using 10 random instances from the same benchmark as a blocking factor, where four factors of interest were considered. The factors and their respective values are number of generations (50, 100), crossover probability (0.8, 0.9), mutation probability (0.1, 0.2), and population size (60, 100). Results show that the factors instance, mutation probability and number of generations, as well as other interactions between them, have a significant effect in the total tardiness for the deterministic scenario, proving the importance of an appropriate selection of parameters when using genetic algorithms to obtain quality solutions. Then, the performance of the proposed NSGA-II was compared against ERD, NEHedd, and ENS2 methods. Results show that our algorithm improves the quality of the solutions for both objective functions, proving the robustness of our solutions for the HFS problem. Finally, two additional generalized experiments were carried out to analyze the effect of number of jobs (10, 20), number of stages (2, 3), shop condition (0.2, 0.6), probability distribution (uniform, lognormal), and CV (0.05, 0.25, 0.4) on both objective functions. The shop condition, probability distribution and CV were proven to be highly influential on the variability of the results, with the only exception being the coefficient of variation having no statistically significant effect on the total tardiness.Ingeniero (a) IndustrialPregrad

Financing Economic Development

We understand that both the level as well as the composition of investment play a crucial role in the economic development process. However, it needs to be understood that investment contributes to the growth process by increasing the productive capacity, improving the technology, and enhancing the competitiveness of an economy. And when it is supplemented with investment in the social sectors, it also results in human development. The demand for investment depends on strong macroeconomic fundamentals comprising stability of exchange rates, fiscal prudence, feasible structure of financial market, including the regulatory and supervisory framework and the size and quality of the securities and bond markets, and continuity of a consistent investment policy.1 Two types of capital formation may be distinguished, viz., physical capital and human capital. Since there are significant differences between private and social profitabilities in the social sectors, an optimal level of investment in human resources would depend on the perception of and the decisions taken by the policy-makers to bridge the gap between the two types of profitabilities. Nevertheless, implementation of an investment decision, whether related to physical or social investment, is contingent on the availability of sufficient domestic and external investible resources.

Design of a Reference Architecture for Production Scheduling Applications based on a Problem Representation including Practical Constraints

Changing customer demands increase the complexity and importance of production scheduling, requiring better scheduling algorithms, e.g., machine learning algorithms. At the same time, current research often neglects practical constraints, e.g., changeovers or transportation. To address this issue, we derive a representation of the scheduling problem and develop a reference architecture for future scheduling applications to increase the impact of future research. To achieve this goal, we apply a design science research approach and, first, rigorously identify the problem and derive requirements for a scheduling application based on a structured literature review. Then, we develop the problem representation and reference architecture as design science artifacts. Finally, we demonstrate the artifacts in an application scenario and publish the resulting prototypical scheduling application, enabling machine learning-based scheduling algorithms, for usage in future development projects. Our results guide future research into including practical constraints and provide practitioners with a framework for developing scheduling applications

Optimization of schedule robustness and stability under random machine breakdowns and processing time variability

In practice, scheduling systems are subject to considerable uncertainty in highly dynamic operating environments. The ability to cope with uncertainty in the scheduling process is becoming an increasingly important issue. This paper takes a proactive scheduling approach to study scheduling problems with two sources of uncertainty: processing time variability and machine breakdowns. Two robustness (expected total flow time and expected total tardiness) and three stability (the sum of the squared and absolute differences of the job completion times and the sum of the variances of the realized completion times) measures are defined. Special cases for which the measures can be easily optimized are identified. A dominance rule and two lower bounds for one of the robustness measures are developed and subseqently used in a branch-and-bound algorithm to solve the problem exactly. A beam search heuristic is also proposed to solve large problems for all five measures. The computational results show that the beam search heuristic is capable of generating robust schedules with little average deviation from the optimal objective function value (obtained via the branch-and-bound algorithm) and it performs significantly better than a number of heuristics available in the literature for all five measures. © 2010 "IIE"