Anticipatory Batch Insertion To Mitigate Perceived Processing Risk

The literature reviewed on lot-sizing models with random yields is limited to certain random occurrences such as day to day administrative errors, minor machine repairs and random supply due to faulty delivery of parts. In reality however, the manufacturing industry faces other risks that are non random in nature. One example would be yield discrepancies caused by non random triggers such as a change in the production process, product or material. Yield uncertainties of these types are temporary in nature and usually pertain until the system stabilizes. One way of reducing the implications of such events is to have additional batches processed earlier in the production that can absorb the risk associated with the event. In this thesis, this particular approach is referred to as the anticipatory batch insertion to mitigate perceived risk. This thesis presents an exploratory study to analyze the performance of batch insertion under various scenarios. The scenarios are determined by sensitivity of products, schedule characteristics and magnitude of risks associated with causal triggers such as a process change. The results indicate that the highest return from batch insertion can be expected when there are slightly loose production schedules, high volumes of sensitive products are produced, there are high costs associated with the risks, and the risks can be predicted with some degree of certainty

Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

Energy-aware coordination of machine scheduling and support device recharging in production systems

Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers

In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation

Integrated facility location and capacity planning under uncertainty

We address a multi-period facility location problem with two customer segments having distinct service requirements. While customers in one segment receive preferred service, customers in the other segment accept delayed deliveries as long as lateness does not exceed a pre-specified threshold. The objective is to define a schedule for facility deployment and capacity scalability that satisfies all customer demands at minimum cost. Facilities can have their capacities adjusted over the planning horizon through incrementally increasing or reducing the number of modular units they hold. These two features, capacity expansion and capacity contraction, can help substantially improve the flexibility in responding to demand changes. Future customer demands are assumed to be unknown. We propose two different frameworks for planning capacity decisions and present a two-stage stochastic model for each one of them. While in the first model decisions related to capacity scalability are modeled as first-stage decisions, in the second model, capacity adjustments are deferred to the second stage. We develop the extensive forms of the associated stochastic programs for the case of demand uncertainty being captured by a finite set of scenarios. Additional inequalities are proposed to enhance the original formulations. An extensive computational study with randomly generated instances shows that the proposed enhancements are very useful. Specifically, 97.5% of the instances can be solved to optimality in much shorter computing times. Important insights are also provided into the impact of the two different frameworks for planning capacity adjustments on the facility network configuration and its total cost.publishersversionpublishe

On multi-stage production/inventory systems under stochastic demand

This paper was presented at the 1992 Conference of the International Society of Inventory Research in Budapest, as a tribute to professor Andrew C. Clark for his inspiring work on multi-echelon inventory models both in theory and practice. It reviews and extends the work of the authors on periodic review serial and convergent multi-echelon systems under stochastic stationary demand. In particular, we highlight the structure of echelon cost functions which play a central role in the derivation of the decomposition results and the optimality of base stock policies. The resulting optimal base stock policy is then compared with an MRP system in terms of cost effectiveness, given a predefined target customer service level. Another extension concerns an at first glance rather different problem; it is shown that the problem of setting safety leadtimes in a multi-stage production-to-order system with stochastic lead times leads to similar decomposition structures as those derived for multi-stage inventory systems. Finally, a discussion on possible extensions to capacitated models, models with uncertainty in both demand and production lead time as well as models with an aborescent structure concludes the paper

Group Scheduling in a Cellular Manufacturing Shop to Minimise Total Tardiness and nT: a Comparative Genetic Algorithm and Mathematical Modelling Approach

In this paper, family and job scheduling in a cellular manufacturing shop is addressed where jobs have individual due dates. The objectives are to minimise total tardiness and the number of tardy jobs. Family splitting among cells is allowed but job splitting is not. Two optimisation methods are employed in order to solve this problem, namely mathematical modelling (MM) and genetic algorithm (GA). The results showed that GA found the optimal solution for most of the problems with high frequency. Furthermore, the proposed GA is efficient compared to the MM especially for larger problems in terms of execution times. Other critical aspects of the problem such as family preemption only, impact of family splitting on common due date scenarios and dual objective scenarios are also solved. In short, the proposed comparative approach provides critical insights for the group scheduling problem in a cellular manufacturing shop with distinctive cases