Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm

The idea of non-hierarchical production networks consisting of autonomous enterprises has been present in scientific community for more than 20 years. Although some global corporations are using their own production networks across continents, they are not similar to the original idea of non-hierarchical production networks in many aspects. It seems that this idea waited for production systems to acquire proper information and communications technology (ICT) or new industrial platforms, like Industry 4.0. The result is a new type of production network called Cyber-Physical Production Network (CPPN). The CPPN is, from ICT point of view, ready to act as non-hierarchical production networks consisting of autonomous production systems with many automated processes. One of the most important processes of the CPPN is a selection of optimal partners (enterprises) to be part of a new virtual enterprise, created inside production network. An optimisation problem emerges in this process, and it is called Partner Selection Problem (PSP). It is non-polynomial-hard combinatorial problem. Since metaheuristic algorithms are well-proven in solving that kind of problem, a specially designed metaheuristic algorithm derived from ant colony optimisation and named the HUMANT (HUManoid ANT) algorithm is used in this paper. It is multi-objective optimisation algorithm that successfully solves different instances of PSP with two, three, four or more objectives.     

Optimisation of maintenance strategy for rural road network using genetic algorithm

The objective of this paper is to develop an optimised maintenance strategy for the rural road network of Kerala state. This is accomplished with the development of a bi-objective deterministic optimisation model which simultaneously satisfies the objectives of both minimisation of total maintenance cost and maximisation of performance of the road network. The model is capable of planning the maintenance activities over a multi-year planning period. The performance of the road network is accounted using the composite index, namely Pavement Condition Index. The constraint-based genetic algorithm was used as the optimisation tool since it very well takes care of the combinatorial nature of the network-level pavement maintenance programming. The applicability of the model is illustrated using a case study for the rural road network of Kerala state in India. The effort made through this research work to develop a suitable Pavement Maintenance and Management System for rural road network can lead to the proper maintenance and upkeep of the rural roads, thereby triggering a positive impact on the Indian economy.     

Interactive minimax optimisation for integrated performance analysis and resource planning

Analysing performances for future improvement and resource planning is a key management function. Data Envelopment Analysis (DEA) provides an analytical mean for performance modelling without assuming parametric functions. Multiple Objective Optimisation (MOO) is well-suited for resource planning. This paper reports an investigation in exploring relationships between DEA and MOO models for equivalent efficiency analysis in a MOO process. It is shown that under certain conditions minimax reference point models are identical to input-oriented dual DEA models for performance assessment. The former can thus be used for Hybrid Efficiency and Trade-off Analyses (HETA). In this paper, these conditions are first established and the equivalent models are explored both analytically and graphically to better understand HETA. Further investigation in the equivalence models leads to the modification of efficiency measures and the development of a minimax reference point approach for supporting integrated performance analysis and resource planning, with the Decision Maker’s (DM) preferences taken into account in an interactive fashion. Both numerical and case studies are conducted to demonstrate the proposed approach and its potential applications.     

Scheduling of open-pit phosphate mine extraction

Open-pit deposits are often characterised by a stack of layers of different geological nature. Some layers are worthless while the ore of the others is of a varying economic value depending on grade. To reach a layer, it is necessary to have first removed the upper layers above the extraction zone. This action results in uncovering the layer in this particular place and in facilitating access to the layers below. This extraction process involves a series of 2–7 basic operations; each one is performed by a machine, some of which are able to perform up to three different operations. Ensuring the consistency of mining extraction scheduling over a few months, in order to meet known or forecast demand, is a challenging task. A mining extraction model based on mathematical programming is proposed but it is hardly usable, due to its size. Therefore, a model based on a Discrete Event Simulation, is created to test how ore supplies are affected by the tactical and operational decisions relating to the choice of parcels to be processed and to the allocation of machines to the different basic operations.     

Line of sight controller tuning using Bayesian optimisation: application to a double stage stabilisation platform

The inertial stabilisation of the line of sight of an imager fixed on a mobile carrier is considered in order to acquire good quality images despite the disturbances generated by the carrier. A double stage mechanical stabilisation architecture is proposed, where a second stabilisation stage, based on a piezoelectric actuator, is added to the usual structure. The piezoelectric actuator transfer function and hysteresis are characterised through experiments. In order to design the controllers of both stages, a high-level image quality criterion (the modulation transfer function (MTF)) is considered, together with design constraints on the main variables of interest. The criterion and the constraints are evaluated by realistic simulations based on some input and noise profiles measured on a real-life system. The MTF evaluation being time-consuming, a Bayesian optimisation method specially dedicated to expensive-to-evaluate functions is used to obtain the parameters of the controllers. The obtain experimental results are displayed and their performances discussed.     

Process optimisation and physicochemical characterisation of enzymatic hydrolysates of proteins from co‐products of Atlantic Salmon (Salmo salar) and Yellowtail Kingfish (Seriola lalandi)

The aim of this study was to develop an enzymatic hydrolysis process of protein co‐products for two major commercial fish species in Australia: Atlantic salmon (AS) and Yellowtail kingfish (YTK). The outcomes are to produce high protein recovery of fish protein hydrolysates within controlled molecular weight ranges that display enhanced physicochemical properties of oil binding and emulsification. Three enzymes (Flavourzyme, Neutrase and Alcalase) were applied to processing co‐products. Protein recovery and physicochemical properties were evaluated with increasing hydrolysis time from 30 min to 180 min and ratio of enzyme to substrate (E/S) from 0.5% to 3.0%. In order to achieve a product with optimum emulsifying capacity (50 ± 0.6 m² g^?1), an E/S ratio of 0.6–1.3% Flavourzyme was applied for 30–111 min with a protein recovery of 55%; in order to achieve a product with optimum oil‐binding capacity (8.3 ± 0.3 g oil g hydrolysates^?1), an E/S ratio of 2.3–3.0% Flavourzyme was applied for 25–64 min with a protein recovery of 70%. YTK protein hydrolysates were further membrane‐fractionated into five fractions (>100 kDa, 50–100 kDa, 30–50 kDa, 10–30 kDa and <10 kDa), and of these, the 10–30 kDa exhibited the best properties of oil binding (19 ± 0.3 g oil g hydrolysates^?1) and emulsification (57 ± 0.7 m² g^?1). These results demonstrate the importance of enzymatic hydrolysis of seafood co‐products into high‐value ingredients for food products and processing.     

Optimisation of the fuelling of hydrogen vehicles using cascade systems and ejectors

The present study investigates the replacement of expansion valves, used in the cascade system of hydrogen fuelling stations, by a series of ejectors. The major advantage of using ejectors is to recover part of the kinetic energy lost during the expansion of a high-pressure primary flow, in order to entrain a lower pressure secondary flow; thus resulting in a more efficient fuelling.Firstly, a quasi-steady 1-D simulation model of the ejector was calibrated using computational fluid dynamics in terms of the main geometry and pressure conditions.Secondly, the quasi-steady 1-D model of the ejector was used in a dynamic model of the hydrogen fuelling station, in order to investigate the influence of its geometry on the transient fuelling performances. Different fuelling scenarios were explored with varying number of buffer tanks in the cascade system of the fuelling station, and different initial pressures in the vehicle's tank. The results show that the replacement of the expansion valve by an ejector may reduce the energy consumption for hydrogen compression by up to 6.5% using two buffer tanks in the cascade system. On the other hand, increasing the number of buffer tanks reduces the energy savings as the driving pressure ratio decreases.     

A comparison of mixed integer programming formulations of the capacitated lot-sizing problem

We propose a novel mixed integer programming formulation for the capacitated lot-sizing problem with set-up times and set-up carryover. We compare our formulation to two earlier formulations, the Classical and Modified formulations, and a more recent formulation due to Suerie and Stadtler. Extensive computational experiments show that our formulation consistently outperforms the Classical and Modified formulations in terms of CPU time and solution quality. It is competitive with the Suerie–Stadtler (S&S) formulation, but outperforms all other formulations on the most challenging instances, those with low-capacity slack and a dense jobs matrix. We show that some of the differences in the performance of these various formulations arise from their different use of binary variables to represent production or set-up states. We also show that the LP relaxation of our Novel formulation provides a tighter lower bound than that of the Modified formulation. Our experiments demonstrate that, while the S&S formulation provides a much tighter LP bound, the Novel formulation is better able to exploit the intelligence of the CPLEX solution engine.     

Multistage PRIM: patient rule induction method for optimisation of a multistage manufacturing process

Industries such as automotive, LCD, PDP, semiconductor and steel produce products through multistage manufacturing processes. In a multistage manufacturing process, performances of stages are not independent. Therefore, the relationship between stages should be considered when optimising the multistage manufacturing process. This study proposes a new procedure of optimising a multistage manufacturing process, called multistage PRIM (patient rule induction method). Multistage PRIM extends the scope of process optimisation from a single stage to the multistage process, and it can use the information encapsulated in the relationship between stages when maximising each stage's performance. A case study in a multistage steel manufacturing process is conducted to illustrate the proposed procedure.     

Reducing CO2 emission from exhaust gases using molten carbonate fuel cells: a new approach

The aim of this study is to couple molten carbonate fuel cell (MCFC) stack with integrated gasification combined cycle fed by refinery residues, to remove CO₂ from gas turbine exhaust gases that have CO₂ emission rate of 14,200 ton/year. By applying multi-objective optimisation (MOO) using genetic algorithm, the optimal values of operating load and the corresponding values of objective functions are obtained. The MOO of the MCFC system regarding two scenarios is performed. The first scenario is minimisation of cost of electricity (COE) and CO₂ emission rate. Objective functions of the second scenario are the same as in the first scenario while CO₂ tax is taken into account. Results show that the second scenario has 29.5% lower average optimal COE and 2.5% lower average emission rate in comparison with the first scenario. A sensitivity analysis is also performed to study the effect of fuel price and CO₂ tax variations on optimal solutions.