Maximising the patient flows throughout the emergency care patient pathway is one of the most important objectives in the healthcare system. The emergency department (ED) is the critical point of this pathway in most hospitals, as the potential delays reduce the number of patients seen in the recommended time. One of the key delays in the ED is the waiting time of a patient prior to treatment, which can be reduced by optimising the patient treatment schedules with priorities. In this paper, a novel blocking patient flow (BPF) algorithm is developed and tested using the real data from a hospital in Brisbane, Australia. Initially, a simulation model of real-life ED operations is developed by characterising patient interarrival and treatment times according to different disease categories. Subsequently, a BPF heuristic algorithm is designed and benchmarked via computational experiments using two dominance rules: first come first served (FCFS) and shortest processing time (SPT). The computational results show that the proposed approach leads to a reduction of the total waiting time by more than 8 % in comparison to the current hospital practice, which implies that more patients will be served in a specified time window.

In this paper a mixed-integer linear programming (MILP) model is developed to be used as a decision support tool for the chemical company Perstorp Oxo AB. The intention with the mathematical model is to maximize the profit and the model can be used in the process of planning the supply chain for the company. Perstorp Oxo is classified as a global company in the process industry and is has production sites in Gent, Castellanza, Stenungsund and Perstorp. The site in Stenungsund is in focus in this paper. The company produces chemicals that later are used for example in textiles, plastic and glass production. Perstorp Oxo also uses inventories in other countries for enabling the selling abroad. It has two larger inventories in Antwerp and in Tees and two smaller in Philadelphia and in Aveiro. The larger facilities store five different products and the smaller take care of one type each. To be able to find feasible and profitable production plans for the company we have developed and implemented rolling horizon techniques for a time horizon of one year and used real sales data. The outcomes from the model show the transportation of products between different production sites, the different production rates, the levels of inventory, setups and purchases from external suppliers. The numerical results are promising and we conclude that a decision support tool based on an optimization model could improve the situation for the planners at Perstorp Oxo AB.

Autocorrelation and measurement errors have a negative effect on the performance of any monitoring scheme; therefore, more efficient monitoring schemes are required to monitor such special processes. Hence, in this paper, the use of improved synthetic and runs-rules X̅ schemes with an embedded variable sample size and sampling interval (VSSI) approach to efficiently monitor the mean of a process under the combined effect of autocorrelation and measurement errors is proposed. These new monitoring schemes incorporate a linearly covariate error model with a constant standard deviation and a first-order autoregressive model to the variability of this special process in order to account for measurement errors and autocorrelation, respectively. Moreover, in order to evaluate the zero- and steady-state run-length properties of the proposed monitoring schemes, a dedicated Markov chain matrix that takes into account the following is constructed: (i) VSSI approach, (ii) improved charting regions design of the synthetic and runs-rules X̅ schemes, and (iii) the combined effect of autocorrelation and measurement errors. Also, the probability elements of the Markov chain matrix incorporate two special sampling methods that aid in the reduction of the negative effect of autocorrelation and measurement errors. A real life example is given to illustrate the implementation of the proposed monitoring schemes.

The set covering problem is to find the minimum cardinality set of locations to site the facilities which cover all of the demand points in the network. In this classical problem, it is assumed that the potential facility locations and the demand points are limited to the set of vertices. Although this problem has some applications, there are some covering problems in which the facilities can be located along the edges and the demand exists on the edges, too. For instance, in the public service environment the demand (population) is distributed along the streets. In addition, in many applications (like bus stops), the facilities are not limited to be located at the vertices (intersections), rather they are allowed to be located along the edges (streets). For the first time, this paper develops a novel integer programming formulation for the set covering problem wherein the demand and facility locations lie continuously along the edges. In order to find good solutions in a reasonable time, a matheuristic algorithm is developed which iteratively adds dummy vertices along the edges and solves a simpler problem which does not allow non-nodal facility locations. Finally, a Benders decomposition reformulation of the problem is developed and the lower bounds generated by the Benders algorithm are used to evaluate the quality of the heuristic solutions. Numerical results show that the Benders lower bounds are tight and the matheuristic algorithm generates good quality solutions in short time.

After three decades of its introduction, the dynamic vehicle routing problem (DVRP) remains a fertile field for new studies. The technological evolution, which continues to progress day by day, has allowed better communication between different actors of this model and a more encouraging execution time. This encouraged researchers to investigate new variants of the DVRP and use more complicated algorithms for the resolution. Among these variants is the multi-tour DVRP (MTDVRP) with overtime (MTDVRPOT), which is the subject of this article. This paper proposes an approach with a memetic algorithm (MA). The results obtained in this study are better than those of the ant colony system (ACS) applied to the same problem and published in an earlier paper.

This study primordially aimed at determining the effects of different factors affecting employee’s motivation at Groz-Beckert Vietnam Co. Ltd. Upon perusal and review of related literature, the researchers found out that there is a dearth of studies that have been conducted relative to this subject appertaining to MNCs in Asian countries. Thus, this paper intends to provide empirical evidences towards the link between and among selected variables being covered in this study. The researchers used the quantitative research analysis method which have surveyed and floated questionnaires to 300 employees from various units, departments, divisions at Groz-Beckert Vietnam Company Ltd and retrieved 265 validated responses. Research result further revealed that, out of the 7 factors: income and benefit, superior, work result, colleague, work condition, training and promotion, work nature positively and significantly impacts the staffs and workers of the said organization. Based on research results and findings, the researchers will provide some viable and palatable recommendations for the human resource development policies and such proposals can enhance employee’s motivation in the said entity. For ease of data collection, convenience sampling was adopted. Data were analyzed using SPSS version 21 that included descriptive and inferential statistics. The outcomes of the study showed that almost all the variables have positive correlation with the workers motivation in performing their jobs at the workplace.

Intrinsic leadership is a new concept of leadership and is built based on philosophically and psychologically of the intrinsic meaning. The basic philosophy of its development is a holistic leadership philosophy that integrates cognitive, affective, conative, and spiritual as the core of leadership. The purpose of developing intrinsic leadership is to create a vision and value congruence, empower cooperation between all stakeholders and especially build an organization like a family so that it creates attachment, trust, a motivation that has an impact on organizational performance. Intrinsic leadership is conceptually different from the previous concept of leadership that only rests on one or several elements that the leader has as a human. Development of measurement scales and validity testing provides a foundation that facilitates quantitative research in intrinsic leadership. There are three studies carried out, the first is scale development and testing the content validity of intrinsic leadership measurements. Testing the content validity is accomplished by analyzing and calculating the content validity index (CVI) and Kappa coefficient data are obtained from a panel expert consisting of eight experts. The second study is related to testing construct validity of intrinsic leadership using EFA from 186 data collected from owner-managers of SMEs. The results show that there are twenty-six indicators extracted into eight factors that are intrinsic leadership measurement scale. The third study aims to test empirically or confirm measurement model of intrinsic-leadership using CFA.

The main objective of this paper is to investigate the effect of affective and cognitive trust in bank advertising toward its potential customers. An advertising experiment was conducted by creating two bank advertisements focusing on affective and cognitive trust in bank advertising. SEM analysis is conducted with a sample of 254 customers who banked with the Central Bank of Ghana using STATA 14.1. Results showed that customers of a bank are more drawn to affective advertising than cognitive advertising. Results also showed that cognitive trust is a stronger predictor of anticipation towards future interactions with a bank than affective trust. Uncertainty and commitment had insignificant relationships with affective trust. Cognitive trust had a positive relationship with commitment, while affective trust had no significant relationship with commitment. Managerial implications and future research of the findings are discussed.

This study reveals the worth of Emotional Intelligence (EI) to act as a moderator in undertaking the worst effects of Counterproductive Work Behavior (CWB) influenced by negative emotions and work interference. Contract based employees of the non-profit organizations in development sector of Pakistan were targeted. The responses were obtained in time lags of two weeks to overcome the issues of cross-sectional data and self-serving bias. The 258 fully responded questionnaires by the targeted employees were analyzed in SPSS. The results emphasized that negative emotions and work interference predicts CWB and EI act as a moderator in this relationship. Employees having low EI engaged more in CWB confirming the predictive relationship. It is recommended that the management needs to underline the significance of EI at all levels of the organization for desirable behaviors at workplace. Implications were deliberated to overcome CWB through high and low levels of EI.

The purpose of this study is to identify and analyze the interaction between customer satisfaction and trust in a structural model. Sample consists of 210 Suzuki motor bikes’ users, aged 17-64, domiciled in Medan. Structural equation model approach is used to process raw data. Respondents' opinions were taken proportionally throughout the Medan city sub-districts. Questionnaires were submitted accidentally. The results show that customer satisfaction and trust partially had positive and significant effect on brand equity and repurchase intention. Brand equity did not show any indirect causal relationship mediator of the three exogenous variables to repurchase intention. Programs related to customer satisfaction and trust better increase brand equity and repurchase intention directly. Interaction variable existence in research model increases Chi-Square probability, decreases standard deviation, and increases t-value. Interaction variable creates synergy on the influence of customer satisfaction and trust to increase brand trust. Any programs to increase brand equity through customer satisfaction and trust should be implemented, simultaneously. The two exogenous variables will bring out the synergy that comes from this interaction.