13 research outputs found
An Improved Robust Optimization Approach for Scheduling Under Uncertainty
In practice, the uncertainty in processing time data frequently affects the feasibility of
optimal solution of the nominal production scheduling problem. Using the unit-specific
event-based continuous time model for scheduling, we develop a novel multi-stage robust
approach with corrective action to ensure robust feasibility of the worst case solution
while reducing the conservatism arising from traditional robust optimization approaches.
We quantify the probability of constraint satisfaction by using a priori and a posteriori
probabilistic bounds for known and unknown uncertainty distributions, consequently, improving
the objective value for a given risk scenario. Computational experiments on several
examples were carried out to measure the effectiveness of the proposed method. For
a given constraint satisfaction probability, the proposed method improves the objective
value compared to the traditional robust optimization approaches
An Improved Robust Optimization Approach for Scheduling Under Uncertainty
In practice, the uncertainty in processing time data frequently affects the feasibility of
optimal solution of the nominal production scheduling problem. Using the unit-specific
event-based continuous time model for scheduling, we develop a novel multi-stage robust
approach with corrective action to ensure robust feasibility of the worst case solution
while reducing the conservatism arising from traditional robust optimization approaches.
We quantify the probability of constraint satisfaction by using a priori and a posteriori
probabilistic bounds for known and unknown uncertainty distributions, consequently, improving
the objective value for a given risk scenario. Computational experiments on several
examples were carried out to measure the effectiveness of the proposed method. For
a given constraint satisfaction probability, the proposed method improves the objective
value compared to the traditional robust optimization approaches
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
Simultaneous Design, Scheduling and Operation Through Process Integration
Processing facilities are normally designed with sufficient flexibility to handle nominal
variations. When the process features planned changes in feedstock and products,
scheduling is often used to optimize process operation. The objective of this dissertation
is to develop a new approach to design and scheduling with economic, environmental,
heat integration and inherently safer design objectives. Specifically, this work introduces
a systematic framework and the associated mathematical formulation for simultaneous
process design and scheduling while simultaneously addressing economic,
environmental, heat integration and inherently safer design objectives. Therefore, more
than one type of proper tradeoffs are established between these objectives. The
environmental issues pertaining to the parameterized process retrofitting, scheduling,
and operation strategies are simultaneously considered along with the environmental impact of these changes. Similarly, the design synthesis of heat-exchange networks
(HENs) is addressed in the context of optimizing energy consumption under scheduling
scenarios. Finally, the goal of inherently safer design is simultaneously considered with
the expected schedules of the process. Several optimization formulations are developed
for the projected schedules while allowing design modifications and retrofitting changes.
The modifications and changes include new environmental management units, synthesis
of flexible and optimal HENs, and design of an inherently safer process. Process models
with the appropriate level of relevant details are included in the formulations. A
discretization approach has been adopted to allow for a multiperiod optimization
formulation over a given time horizon. The resulting framework identifies opportunities
for synergism between the economic, environmental, heat integration and inherently
safer design objectives. It also determines points of diminishing return beyond which
tradeoffs between the above mentioned objectives are established. The devised
procedure is illustrated with case studies
Supply chain management for the process industry
This thesis investigates some important problems in the supply chain management
(SCM) for the process industry to fill the gap in the literature work, covering
production planning and scheduling, production, distribution planning under
uncertainty, multiobjective supply chain optimisation and water resources
management in the water supply chain planning. To solve these problems, models
and solution approaches are developed using mathematical programming, especially
mixed-integer linear programming (MILP), techniques.
First, the medium-term planning of continuous multiproduct plants with sequence-dependent
changeovers is addressed. An MILP model is developed using Travelling
Salesman Problem (TSP) classic formulation. A rolling horizon approach is also
proposed for large instances. Compared with several literature models, the proposed
models and approaches show significant computational advantage.
Then, the short-term scheduling of batch multiproduct plants is considered. TSP-based
formulation is adapted to model the sequence-dependent changeovers between
product groups. An edible-oil deodoriser case study is investigated.
Later, the proposed TSP-based formulation is incorporated into the supply chain
planning with sequence-dependent changeovers and demand elasticity of price.
Model predictive control (MPC) is applied to the production, distribution and
inventory planning of supply chains under demand uncertainty.
A multiobjective optimisation problem for the production, distribution and capacity
planning of a global supply chain of agrochemicals is also addressed, considering
cost, responsiveness and customer service level as objectives simultaneously. Both ε-
constraint method and lexicographic minimax method are used to find the Pareto-optimal
solutions Finally, the integrated water resources management in the water supply chain
management is addressed, considering desalinated water, wastewater and reclaimed
water, simultaneously. The optimal production, distribution and storage systems are
determined by the proposed MILP model. Real cases of two Greek islands are
studied
Scheduling of crude oil and product blending and distribution operations in a refinery
Ph.DDOCTOR OF PHILOSOPH