Asynchronous optimisation with the use of a cascade search algorithm

This paper introduces the development of an asynchronous approach coupled with a cascade optimisation algorithm. The approach incorporates concepts of asynchronous Markov processes and introduces a search process that is benefiting from distributed computing infrastructures. The algorithm uses concepts of partitions and pools to store intermediate solutions and corresponding objectives. Population inflections are performed periodically to ensure that Markov processes, still independent and asynchronous, make arbitrary use of intermediate solutions. Tested against complex optimisation problems and in comparison with commonly used Tabu Search, the asynchronous cascade algorithm demonstrates a significant potential in distributed operations with favourable comparisons drawn against synchronous and quasi-asynchronous versions of conventional algorithms.     

Tidal turbine array optimisation using the adjoint approach

Oceanic tides have the potential to yield a vast amount of renewable energy. Tidal stream generators are one of the key technologies for extracting and harnessing this potential. In order to extract an economically useful amount of power, hundreds of tidal turbines must typically be deployed in an array. This naturally leads to the question of how these turbines should be configured to extract the maximum possible power: the positioning and the individual tuning of the turbines could significantly influence the extracted power, and hence is of major economic interest. However, manual optimisation is difficult due to legal site constraints, nonlinear interactions of the turbine wakes, and the cubic dependence of the power on the flow speed. The novel contribution of this paper is the formulation of this problem as an optimisation problem constrained by a physical model, which is then solved using an efficient gradient-based optimisation algorithm. In each optimisation iteration, a two-dimensional finite element shallow water model predicts the flow and the performance of the current array configuration. The gradient of the power extracted with respect to the turbine positions and their tuning parameters is then computed in a fraction of the time taken for a flow solution by solving the associated adjoint equations. These equations propagate causality backwards through the computation, from the power extracted back to the turbine positions and the tuning parameters. This yields the gradient at a cost almost independent of the number of turbines, which is crucial for any practical application. The utility of the approach is demonstrated by optimising turbine arrays in four idealised scenarios and a more realistic case with up to 256 turbines in the Inner Sound of the Pentland Firth, Scotland.     

Managing information and supplies inventory operations in a manufacturing environment. Part 2: An order-timing and sizing algorithm

We develop a new, flexible independent demand forecasting-optimisation algorithm, and apply it to nine difficult-to-manage maintenance and repair products at the AREVA nuclear fuel rod manufacturing facility. The algorithm results in a 27% reduction in inventory holding and ordering costs relative to AREVA's baseline ERP method. This is in addition to improving the line item fill rates from 96 to 98%. This new algorithm is more flexible than the baseline method in that (1) our forecast error distribution is not assumed to be normal—we automatically find the best-fitting distribution from a large family of distributions, (2) we jointly optimise the order quantity and reorder point by using an optimisation routine that is embedded in a simulation methodology. Our algorithm can therefore handle a non-stationary demand process during the planning horizon, and (3) we dynamically select the best time series forecaster for demand based on the most recent history. This flexibility drove the performance improvements. Our algorithm can be easily adapted to any independent demand situation across any industry's supply chain.     

Cycle time-oriented mid-term production planning for semiconductor wafer fabrication

Wafers are produced in an environment with uncertain demand and failure-prone machines. Production planners have to react to changes of both machine availability and target output, and revise plans appropriately. The scientific community mostly proposes WIP-oriented mid-term production planning to solve this problem. In such approaches, production is planned by defining targets for throughput rates and buffer levels of selected operations. In industrial practice, however, cycle time-oriented planning is often preferred over WIP-oriented planning. We therefore propose a new linear programming formulation, which facilitates cycle time-oriented mid-term production planning in wafer fabrication. This approach plans production by defining release quantities and target cycle times up to selected operations. It allows a seamless integration with the subordinate scheduling level. Here, least slack first scheduling translates target cycle times into lot priorities. We evaluate our new methodology in a comprehensive simulation study. The results suggest that cycle time-oriented mid-term production planning can both increase service level and reduce cycle time compared to WIP-oriented planning. Further, it requires less modelling effort and generates plans, which are easier to comprehend by human planners.     

Adjustable Robust Optimisation approach to optimise discounts for multi-period supply chain coordination under demand uncertainty

In this research, a problem of supply chain coordination with discounts under demand uncertainty is studied. To solve the problem, an Affinely Adjustable Robust Optimisation model is developed. At the time when decisions about order periods, ordering quantities and discounts to offer are made, only a forecasted value of demand is available to a decision-maker. The proposed model produces a discount schedule, which is robust against the demand uncertainty. The model is also able to utilise the information about the realised demand from the previous periods in order to make decisions for future stages in an adjustable way. We consider both box and budget uncertainty sets. Computational results show the necessity of accounting for uncertainty, as the total costs of the nominal solution increase significantly even when only a small percentage of uncertainty is in place. It is testified that the affinely adjustable model produces solutions, which perform significantly better than the nominal solutions, not only on average, but also in the worst case. The trade-off between reduction of the conservatism of the model and the uncertainty protection is investigated as well.     

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.     

A PSO-based optimum consumer incentive policy for WEEE incorporating reliability of components

The world is going digital, human infatuation with gadgets is increasing and a very short product lifecycle of these products has resulted in a horrendous threat to the environment. Waste electrical and electronic equipment (WEEE) reverse logistic management has been a key area for researchers to innovate and produce insights that could sustain businesses without compromising the environment. In this paper, we aim to understand the consumer return behaviour of end of life goods at different incentive levels and make an attempt to incorporate the latest research practices. A novel attempt is made in this paper, where we have for the first time applied product and component reliability in determining the incentives given to the consumer so that the efforts of WEEE recycling and disposal could be conducted in an economically sustainable manner to promote ecological sustainability, thereby envisaging upon the fact that there is a hidden synergy between the trade and environment, and only innovative managerial approaches that are thoroughly pragmatic, could exploit them. The optimisation model has been solved using particle swarm optimisation (PSO) and the insights produce highlight upon the feasibility of such a model.     

Optimisation and modelling green bean's ultrasound blanching

Using of ultrasound in food processing is a novel and interesting technique, which is often complementary to classical methods. This study reports on the ultrasound blanching (USB) of green bean. Response surface methodology (RSM) was used to study the effect of process variables on the USB. Three independent variables including temperature (50–90 °C), time (45–225 s) and duty cycle (0.2–0.8 s) were examined. The optimal USB conditions were obtained with a temperature of 90 °C, USB time of 58.27 s and duty cycle of 0.79 s. At these conditions, the residual peroxidase activity (RPA) determined as 9.64% and the vitamin C loss as 8.92%. The experimental values under optimal condition were in good consistent with the predicted values. According to the results, the USB process is more efficient process and as well as less damage to the product compared to the conventional blanching method.     

Optimisation of the extraction conditions of natural colourant carthamin from safflower (Carthamus tinctorius L.) by response surface methodology

Alkali‐solution and acid‐isolation method (ASAI) and aqueous two‐phase system separation method (ATSS) have been reported to extract natural colourant carthamin from Carthamus tinctorius. In this study, these two methods were compared based on the optimisation results of extraction conditions by response surface methodology. In ASAI, the maximum extraction yield reached 0.779% at pH value of potassium carbonate solution of 11.16, ratio of potassium carbonate solution to raw material of fifteen and extraction time of 18 min, while 2.652% was achieved with concentration of acetone of 58%, ratio of acetone solution to raw material of twenty‐three and extraction time of 41 min in ATSS. From the point of view of extraction yield, ATSS had more superiority than ASAI. However, more pure carthamin was provided in ASAI according to HPLC assay. The obtained results in our experiments could be utilised for further researches of carthamin.     

Joint optimisation of tracking capability and price in a supply chain with endogenous pricing

Tracking systems have been widely used to resolve the issues of product recall and food safety. Thus far, few researches have been done on designing the tracking capability from the perspective of supply chain. In this paper, using the traceable unit size at the manufacturer level to measure the tracking capability, we propose a non-convex non-linear programming to jointly optimise the tracking capability and price considering the tracking cost and recall cost in a supply chain with endogenous pricing. Results show that, in both centralised and decentralised supply chains, there is a unique tracking capability and retailing/wholesale price with closed-form solutions to optimise the supply chain profit. When the cost ratio (unit tracking cost/unit recall cost) is sufficiently large and small, the optimal tracking strategy is barcode tracking and unit tracking, respectively, and otherwise, the optimal tracking strategy is batch tracking with an economic traceable unit size which depends on the cost ratio, quality inspection threshold, supply defection rate and the supplier’s tracking capability. Furthermore, in the context of large and small cost ratio, we find that improving tracking capability will enlarge and mitigate the effect of double marginalisation, respectively. In particular, we find that the strict tracking regulation policy is more robust than the subsidy policy to improve the supply chain tracking capability.