Hyesung Seok

Sustainability decision support system based on collaborative control theory

Abstract Industries nowadays have more insight into corporate environmental, social and economic sustainability than ever before. Sustainability issues in various industries are all about choices – finding ways to be more strategic and reducing waste and energy, while benefiting the bottom line. Because of the complexity of sustainability decisions and strategies, these insights call for applying advanced control techniques. In this article we review the concepts of sustainability and its practical issues, specifically focusing on the issues of supply networks. Sustainability issues are usually complex because beyond their inherent challenges, there are conflicts among stakeholders within organizations and between organizations; hence, more useful methods are required for effective solutions. We consider Decision Support Systems (DSSs) to help optimize solutions related with sustainability issues, and review their concepts and usefulness based on previous work. We also suggest strategies applying the Collaborative Control Theory (CCT) principles to augment DSS by a new Sustainability – Decision Support Protocol (S-DSP) in order to overcome certain weaknesses. To model and illustrate the benefit of S-DSP as a control protocol, two practical supply delivery/production problems are analyzed. The results highlight how better collaborative solutions can be achieved to maximize the sustainability of supply networks. It is envisioned that sustainability decision support by such cyber-supported collaboration protocols will contribute to overcome the emerging challenges of sustainability planning and control.

Intelligent contingent multi-sourcing model for resilient supply networks

We develop an intelligent contingent multi-sourcing model when disruptions occur.We evaluate the performance of our model in terms of lost sale and total cost.We analyze the performance of our model under various costs and disruption rates.Our model performs at least equal to or better than single sourcing.Our model also outperforms constant multi-sourcing under specific conditions. As the complexity and uncertainty of supply networks (SNs) increase, strategic management for resilient SNs becomes significant. An intelligent contingency plan, rather than a direct and constant back-up plan, is more suitable for flexible and effective management. In this research, we introduce a unique contingent multi-sourcing decision protocol for effective response when disruptions occur. It is called the Intelligent Contingent Sourcing (ICS) protocol. Unlike previous research, which has mostly considered the constant multi-sourcing11In this paper, constant multi-sourcing means that each manufacturer is supplied from only pre-determined suppliers and the amount of materials supplied from each of them is fixed. Only disrupted supplier cannot supply until recovery is finished. On the other hand, in case of contingent multi-sourcing, the suppliers and the amount of materials supplied can be changed according to the current condition. among internal suppliers, our model is developed for contingent multi-sourcing by collaboration with external suppliers. (When both the supplier and buyer are managed/controlled by the same company, that supplier is considered an internal supplier; in case each supplier is independent in terms of buyers and is self-interested, such supplier is considered an external supplier). Such contingent multi-sourcing requires the theoretical analysis of each participants economic aspect, because each external supplier is independent and thus self-interested. The ICS protocol forms a contingent collaborative coalition based on a distributed decision making process. To evaluate the performance of the ICS protocol, three sourcing models are compared: (1) Single sourcing, (2) Constant multi-sourcing, and (3) Contingent multi-sourcing by the ICS protocol. Statistical analysis reveals that, with statistical significance, the ICS protocol performs at least equal to or better than single sourcing, in terms of less lost sales and less total costs; ICS protocol also outperforms constant multi-sourcing under specific conditions (stated in the article).

AN INTELLIGENT RTP-BASED HOUSEHOLD ELECTRICITY SCHEDULING BY A GENETIC ALGORITHM IN SMART GRID

Electricity scheduling for households based on real-time pricing (RTP) allows flexible and efficient consumption planning. However, this creates errors in predicted costs. Therefore this study used a genetic algorithm (GA) to reduce the error in predicted costs and suggested a model that offered better consumption planning. This model comprises a provider that supplies electricity and a subscriber that consumes electricity. Each subscriber has an energy management controller (EMC) that selects the optimal electricity scheduling. The provider and subscriber exchange real-time predicted costs and consumption plans to achieve an appropriate balance. During this process, the aforementioned prediction error — i.e., the difference between the predicted cost for each time slot and the final actual cost — occurs. This was addressed in this study using a GA. As a result, the presented model produced consumption plans with costs that were 22.60 per cent lower than the non-scheduled case, and 3.34 per cent lower than the model from a previous study. Furthermore, the fairness for each subscriber was improved by 15.96 per cent compared with the non-scheduled case, and by 0.62 per cent compared with the previous study model.

Evaluation of forecasting methods in aggregate production planning: A Cumulative Absolute Forecast Error (CAFE)

Abstract The purpose of forecasting error measures is to estimate forecasting methods and choose the best one. Most typical forecasting error measures are designed based on the gap between forecasts and actual demands and, consequently, a forecasting method yielding forecasts in accordance with real demands is considered as good. However, in some applications such as aggregate production planning, these measures are not suitable because they are not capable for considering any effects caused by forecasting error such as increasing cost or decreasing profit. To tackle this issue, we propose a new measure, CAFE (Cumulative Absolute Forecast Error), to evaluate forecasting methods in terms of total cost. Basically, the CAFE is designed to consider not only forecasting errors but also costs occured by errors in aggregate production planning which is set up based on forecasts. The CAFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We have demonstrated the effectiveness of the proposed measure by conducting intensive experiments with demand data sets from M3-competition.

A simulated annealing algorithm with neighbourhood list for capacitated dynamic lot-sizing problem with returns and hybrid products

ABSTRACT This research addresses the capacitated dynamic lot-sizing problem with returns and hybrid products (). The problem is to identify how many of each product type to produce during each period for a hybrid system with manufacturing capacity constraints. The objective of is to maximise total profit of the production system that consists of new, remanufactured and hybrid products. is a multi-period CLSP, which is modelled as a mixed-integer nonlinear programming problem. The traditional CLSP is NP-hard, and the nonlinearity of makes the problem even harder to solve. Therefore, a Simulated Annealing (SA) algorithm with a neighbourhood list (SA_NL) is proposed. By using a list of several neighbourhoods, the SA algorithm is improved. SA_NL is compared to SA, three variants of Genetic Algorithm (GA) and a Variable Neighbourhood Search (VNS) algorithm. The variants of GA are GA with one-point crossover (), GA with two-point crossover () and GA with one-point period-based crossover (). Over all instances, the results show that the proposed SA_NL outperforms SA, VNS, , and by 0.54%, 0.34%, 1.92%, 1.78% and 2.92%, respectively.

Performance assessment in homogeneous/heterogeneous collaborative enterprise networks with inventory adjustment

Globalization has not only reduced product prices but also increased product and service availability. As a result, demand and inventory reallocation between collaborative enterprise network (CEN) members has become essential to improve performance and competitiveness. Previous research focused on decentralized collaboration and partner selection, with an emphasis on inventory management and demand allocation. However, balanced reallocation strategies with benefits in every period for all network members have not been studied sufficiently. Hence, we have proposed the Proportional Inventory Deficit Satisfaction Algorithm (PIDSA), which supports more efficient inventory- and demand-sharing among all members through balanced collaboration based on single enterprise and network characteristics. The performance of PIDSA has been validated and analyzed by comparing three models: No Collaboration (NC); Partial Collaboration (PC); and Complete Collaboration (CC). Both PC and CC follow PIDSA, but only CC uses inventory adjustments. As CC has a higher collaboration level than NC and PC, we expected CC always to outperform others. However, experimental results show that this is not the fact. It can be concluded that the level of collaboration should be changeable and be dependent on network conditions. It implies that proper collaboration modes should be applied to maximize the performance of different CEN configurations.

The constrained-collaboration algorithm for intelligent resource distribution in supply networks

Abstract Manufacturing and supply strategies have evolved from the notion of mass production focusing on economies of scale, to flexible production systems seeking economies of scope, and recently, to the concept of enterprise and supply networks aiming for economies of collaboration. The emergence of supply networks poses new challenges derived from a growing complexity in coordinating the flow of resources, materials, and information within and among an increasing number of network participants. In such situations, an intelligent resource distribution under various constraints is one of the most critical problems. In this paper, we have developed a novel Constrained-Collaboration Algorithm (CCA) for physically cooperative resource distribution planning. Based on Collaborative Control Theory (CCT) with theoretical formulations and network flow approaches, the CCA addresses an efficient and effective resource distribution by a suitable form of physical cooperation. We have applied it to an actual industry case, in combination with Direct/Indirect Delivery Protocol (DIDP), introduced in previous research. As a result, the integrated model achieves 55% increase in resource utilization and 20% reduction in distribution cost, while accommodating external changes. Besides, the new formalism of the physical dimension of collaboration requirement planning introduced in this research with new CCA approach can be generalized for other improvements in supply-and-demand management decision support.