“The Harem Within”: The Complexity of Female Identity in Elif Shafak’s Black Milk

In a post-modern world where selfhood is defined by diversity and multiplicity, Elif Shafak’s Black Milk outlines how women’s experiences depict a tragic fate. In this memoir Elif Shafak writes about motherhood and authorship and the many stereotypes women face in a patriarchal society. For many women writers, motherhood became a burden, because they had to choose between being a “good” mother and a “good” author. This article aims to explore the complexity of women’s identity in Black Milk through a feminist perspective and also to analyse Elif Shafak’s feminine discourse and its empowerment process. Elif Shafak questions the norms of the patriarchal society, because it enforces a “given” identity for both women and men. Black Milk also outlines the anxiety women face when it comes to writing, motherhood and many other experiences, describing an enormous pressure put on women to reflect an ideal

Portfolio formation with preselection using deep learning from long-term financial data

Portfolio theory is an important foundation for portfolio management which is a well-studied subject yet not fully conquered territory. This paper proposes a mixed method consisting of long short-term memory networks and mean-variance model for optimal portfolio formation in conjunction with the asset preselection, in which long-term dependences of financial time-series data can be captured. The experiment uses a large volume of sample data from the UK Stock Exchange 100 Index between March 1994 and March 2019. In the first stage, long short-term memory networks are used to forecast the return of assets and select assets with higher potential returns. After comparing the outcomes of the long short-term memory networks against support vector machine, random forest, deep neural networks, and autoregressive integrated moving average model, we discover that long short-term memory networks are appropriate for financial time-series forecasting, to beat the other benchmark models by a very clear margin. In the second stage, based on selected assets with higher returns, the mean-variance model is applied for portfolio optimisation. The validation of this methodology is carried out by comparing the proposed model with the other five baseline strategies, to which the proposed model clearly outperforms others in terms of the cumulative return per year, Sharpe ratio per triennium as well as average return to the risk per month of each triennium. i.e. potential returns and risks

An ordering policy to reduce transportation disruption impact on multi-echelon supply chain systems

In today's global competition, due to shorter product life cycles, cost and time pressure, companies adopt lean production concepts, global outsourcing and collaboration strategies. However, it is observed that these strategies increase supply chain vulnerability. The bad experiences from the disruptive events such as 9/11 attacks and Taiwan earthquake have attracted both researchers' and practitioners' attention on supply chain disruptions. In this study, an ordering policy considering possible impacts of supply chain disruptions is proposed. In this context, widely known beer distribution model which is suitable for observing the complex dynamics of multi-echelon systems is handled by considering transportation disruptions. In order to reduce the impact of the transportation disruptions on the system, a new ordering policy is proposed. Finally, the best values for the parameters of the proposed policy are determined by implementing a differential evolution algorithm under different disruption scenarios

(not) Understanding Improvisational Dance through the Lens of the Kantian Sublime

Collective dance improvisation, as an experimental form of dance-making, presents a challenge and dissatisfaction to the audience member who attempts to interpret it by asking what it is about, or what it is supposed to represent. These pieces are characterized by their collaborative, spontaneous nature, and by their blurring of the lines between quotidien and virtuosic, expected and unexpected, natural and unnatural. In this paper, I argue that questions about representation stand in the way of having an aesthetic appreciation of improvised dance pieces. On the other hand, I find Kant’s aesthetic theory in The Critique of Judgment promising to be used in this case, for its dissociation of representation from aesthetic judgments. I ultimately argue that Kant’s notion of “the sublime,” as opposed to “the beautiful,” is applicable to improvisational dance, as it accounts for the negative pleasure we feel in experiencing something we cannot fully comprehend. This ends up challenging Kant’s own framework, as he never acknowledges such connections between the two and only uses “the sublime” in the context of nature

Solving the flexible job shop scheduling and lot streaming problem with setup and transport resource constraints

This article addresses the Flexible Job Shop Scheduling and Lot Streaming Problem (FJSSP-LS) under setup and transport resource constraints. While the related literature emphasises the lot streaming policy for time-based objectives, setup and transport resource constraints were not considered simultaneously with this policy, limiting the resulting schedule's applicability in practice. For this reason, we propose a novel Constraint Programming (CP) model enriched by an efficient variable and value ordering strategy specifically designed for the FJSSP-LS with resource constraints. We also present a CP-based iterative improvement method, CP-based Large Neighbourhood Search (CP-based LNS), that focuses on exploring large neighbourhoods through the CP model. Both models are initially tested for the FJSSP and have been shown to provide the best solutions to well-known benchmark instances. Next, they are used for the FJSSP-LS, and the proposed CP-based LNS improves the objective function value by 4.68 percent on average compared to the CP model for the generated test problems.</p

Solving the flexible job shop scheduling and lot streaming problem with setup and transport resource constraints

This article addresses the Flexible Job Shop Scheduling and Lot Streaming Problem (FJSSP-LS) under setup and transport resource constraints. While the related literature emphasises the lot streaming policy for time-based objectives, setup and transport resource constraints were not considered simultaneously with this policy, limiting the resulting schedule's applicability in practice. For this reason, we propose a novel Constraint Programming (CP) model enriched by an efficient variable and value ordering strategy specifically designed for the FJSSP-LS with resource constraints. We also present a CP-based iterative improvement method, CP-based Large Neighbourhood Search (CP-based LNS), that focuses on exploring large neighbourhoods through the CP model. Both models are initially tested for the FJSSP and have been shown to provide the best solutions to well-known benchmark instances. Next, they are used for the FJSSP-LS, and the proposed CP-based LNS improves the objective function value by 4.68 percent on average compared to the CP model for the generated test problems

Solving the flexible job shop scheduling and lot streaming problem with setup and transport resource constraints -2

This article addresses the Flexible Job Shop Scheduling and Lot Streaming Problem (FJSSP-LS) under setup and transport resource constraints. While the related literature emphasises the lot streaming policy for time-based objectives, setup and transport resource constraints were not considered simultaneously with this policy, limiting the resulting schedule's applicability in practice. For this reason, we propose a novel Constraint Programming (CP) model enriched by an efficient variable and value ordering strategy specifically designed for the FJSSP-LS with resource constraints. We also present a CP-based iterative improvement method, CP-based Large Neighbourhood Search (CP-based LNS), that focuses on exploring large neighbourhoods through the CP model. Both models are initially tested for the FJSSP and have been shown to provide the best solutions to well-known benchmark instances. Next, they are used for the FJSSP-LS, and the proposed CP-based LNS improves the objective function value by 4.68 percent on average compared to the CP model for the generated test problems