Social, environmental and economic impacts of alternative energy and fuel supply chains

Energy supply nowadays, being a vital element of a country’s development, has to independently meet diverse, sustainability criteria, be it economic, environmental and social. The main goal of the present research work is to present a methodological framework for the evaluation of alternative energy and fuel Supply Chains (SCs), consisting of a broad topology (representation) suggested, encompassing all the well-known energy and fuel SCs, under a unified scheme, a set of performance measures and indices as well as mathematical model development, formulated as Multi-objective Linear Programming with the extension of incorporating binary decisions as well (Multi-objective Mixed Integer-Linear programming). Basic characteristics of the current modelling approach include the adaptability of the model to be applied at different levels of energy SCs decisions, under different time frames and for multiple stakeholders. Model evaluation is carried for a set of Greek islands, located in the Aegean Archipelagos, examining both the existing energy supply options as well future, more sustainable Energy Supply Chains (ESCs) configurations. Results of the specific research work reveal the social and environmental costs which are underestimated under the traditional energy supply options' evaluation, as well as the benefits that may be produced from renewable energy based applications in terms of social security and employment

Food industry supply chain planning with product quality indicators

Quantitative supply chain modelling has contributed substantially to a number of fields, such as the automotive industry, logistics and computer hardware. The inherent methods and optimisation techniques could also be explored in relation to the food industry in order to offer potential benefits. One of the major issues of the food industry is to overcome supply seasonality and on-shelf demand. On the shelf demand is the consumer’s in store demand which could also be seasonal. Objective of this work is to add flexibility to seasonal products (i.e. soup) in order to meet the on-shelf demand. In order to achieve this, a preparation process is introduced and integrated into the manufacturing system. This process increases the shelf-life of raw materials before starting the production process. This process, however, affects the quality of fresh raw materials and requires energy. Therefore, a supply chain model is developed, which is based on the link between the quality of the raw material and the processing conditions, which have an effect on the process’ energy consumption and on the overall product quality. It is challenging to quantify the quality by looking at the processing conditions (degrees of freedom) and by linking it with energy in order to control and optimise the quality and energy consumption for each product. The degrees of freedom are defined differently for each process and state. Therefore, the developed model could be applied to all states and processes in order to generate an optimum solution. Moreover, based on the developed model, we have determined key factors in the whole chain, which are most likely to affect the product quality and consequently overall demand. There are two main quality indicator classes to be optimised, which are both considered in the model: static and time dependent indicators. Also, this work considers three different preparation processes – the air-dry, freeze-dry and freezing process – in order to increase the shelf-life of fresh raw materials and to add flexibility to them. A model based on the interrelationship between the quality and the processing conditions has been developed. This new methodology simplifies and enables the model to find the optimum processing conditions in order to obtain optimum quality across all quality indicators, whilst ensuring minimum energy consumption. This model is later integrated into the supply chain system, where it generates optimum solutions, which are then fed into the supply chain model. The supply chain model optimises the quality in terms of customer satisfaction, energy consumption and wastage of the system linked to environmental issues, and cost, so that the final products are more economical. In this system, both the manufacturing and inventory systems are optimised. This model is later implemented with a real world industrial case study (provided by the industrial collaborator). Two case studies are considered (soya milk and soup) and interestingly enough only one of them (soup) corresponds with this model. The advantage of this model is that it compares the two systems and then establishes which system generates an optimum end product.Open Acces

Process optimization under uncertainty

The ability of a production plant to be flexible by adjusting the operating conditions to changing demands, prices of the products and the raw materials is crucial to maintain a profitable operation. In this respect, the application of mathematical optimization techniques is unanimously recognized to be successful to improve the decision-making process. Typical examples are production planning, scheduling, real-time optimization and advanced process control. The more information are available to the optimization approach, the more "optimal" are the resulting decisions: the "optimal" production strategy cannot reduce the inventory costs if no supply-chain model is integrated into the production planning optimization. This thesis lies in the context of Enterprise-wide optimization with the goal of integrating decision layers and functions while accounting for uncertain information. A stochastic programming approach is adopted to integrate production scheduling with energy management and production planning with predictive maintenance. The approaches are analysed from a formulation perspective and from a computational point of view, which is necessary to deal with one of the challenges of the presented methods consisting in the size of the resulting optimization problems. To reduce the electricity cost that is generated by the uncertain peaks of the dayahead price, a two-stage risk-averse optimization is proposed to simultaneously define the optimal bidding curves for the day-ahead market and the optimal production schedule. The large-scale MILP problem is solved with a scenario-based decomposition technique, the progressive hedging algorithm. Heuristic procedures are applied to speed up the solution phase and to avoid the oscillatory behaviour due to the integer variables. Since large electricity consumers rely on Time-Of-Use power contracts to handle the volatility of the day-ahead price, the two-stage formulation is expanded into a multi-stage optimization to optimally purchase electricity from different sources and to generate electric power with a power plant. The unpractical size of the resulting problem is handled by approximating the multi-stage tree with a series of two-stage scenario-trees within a rolling horizon procedure. A mixed time grid handles the multi-scale nature of the problem by making short-term decisions with a detailed model and catching their effect on the long-term future with an aggregated model. While the electricity prices introduce exogenous uncertain information into the optimization problem, the predictive maintenance optimization carries endogenous uncertain sources into the production planning problem. Endogenous uncertainties, contrary to the exogenous ones, are uncertain information that can be modified (in the probability or in the timing of the realization) by the decision maker. The prognosis technique of the Cox model is embedded into a multi-stage stochastic program to consider an uncertain Remaining Useful Life of the equipment when the optimal operating conditions of the plant are defined. Two modelling approaches (based on superstructure-scenario trees and on conditional non-anticipativity constraints) are proposed to formulate the optimization problem with endogenous uncertainties. Two Benders-like decomposition techniques and several branching priority schemes are applied to handle the high complexity of the resulting optimization problems

Global supply chains of high value low volume products

Imperial Users onl

Methods and algorithms for integrated multi-scale optimisation of production planning and scheduling

Imperial Users onl

A Model-based Approach for Designing Cyber-Physical Production Systems

The most recent development trend related to manufacturing is called "Industry 4.0". It proposes to transition from "blind" mechatronics systems to Cyber-Physical Production Systems (CPPSs). Such systems are capable of communicating with each other, acquiring and transmitting real-time production data. Their management and control require a structured software architecture, which is tipically referred to as the "Automation Pyramid". The design of both the software architecture and the components (i.e., the CPPSs) is a complex task, where the complexity is induced by the heterogeneity of the required functionalities. In such a context, the target of this thesis is to propose a model-based framework for the analysis and the design of production lines, compliant with the Industry 4.0 paradigm. In particular, this framework exploits the Systems Modeling Language (SysML) as a unified representation for the different viewpoints of a manufacturing system. At the components level, the structural and behavioral diagrams provided by SysML are used to produce a set of logical propositions about the system and components under design. Such an approach is specifically tailored towards constructing Assume-Guarantee contracts. By exploiting reactive synthesis techniques, contracts are used to prototype portions of components' behaviors and to verify whether implementations are consistent with the requirements. At the software level, the framework proposes a particular architecture based on the concept of "service". Such an architecture facilitates the reconfiguration of components and integrates an advanced scheduling technique, taking advantage of the production recipe SysML model. The proposed framework has been built coupled with the construction of the ICE Laboratory, a research facility consisting of a full-fledged production line. Such an approach has been adopted to construct models of the laboratory, to virtual prototype parts of the system and to manage the physical system through the proposed software architecture

Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span

As an energy-intensive industry, the steel industry grapples with increasing energy costs and decarbonisation pressures. Therefore, multi-objective optimisation is widely applied in the production scheduling of the steelmaking plant. However, the optimal solution prioritising energy savings and emission reductions may lead to impractical or less economically efficient solutions, since the processing time requirement (PTR) of steel production orders in real-world production is neglected. This study fills the research gap by discussing the impact of PTR on the make-span of the steelmaking process and incorporating it into the optimisation model. Considering the variability of PTR, the solving of the multi-objective scheduling problem is transformed into the selection from Pareto solutions with different make-spans. To better leverage the temporal flexibility of the steelmaking process, a what-if-analysis-based strategy coupled with the Normal Boundary Intersection method is proposed to generate a series of evenly distributed Pareto solutions. The energy storage system is integrated to improve the time granularity of the steelmaking plant's flexibility. Our case studies demonstrate that the electricity and emission costs are reduced by 68.5%, indirect emissions are reduced by 83.5%, and the on-site renewable energy self-consumption rate increases by 12.1%. The effectiveness of the proposed method implies that it is of great relevance to the development of a cleaner steel industry in the future