In the case of disruptions in the blood supply chain, rapid reorganization of distribution processes is required to ensure an effective response to emergency situations. This paper considers the problem of redistributing available blood stocks from the institute and hospitals to hospitals affected by a disruption. A mathematical formulation of the problem is developed, with a multi-objective function aiming to: (i) minimize the blood delivery time to the locations of disruption, (ii) minimize violations of predefined safety stock levels at the institute and hospitals, and (iii) minimize the amount of blood taken from hospitals not affected by the disruption. The formulation also introduces constraints that ensure balanced violations of safety stock levels across unaffected facilities. Computational experiments are conducted on test scenarios generated from real case studies from the healthcare system of the Republic of Serbia. Small-sized instances can be solved exactly, providing benchmarks for evaluating a General Variable Neighborhood Search metaheuristic designed for larger problem instances. The results indicate that the proposed metaheuristic produces high-quality solutions within negligible CPU time.

We consider the optimization problem related to the sugar beet transportation when supplying sugar mills in the sugar production. The sugar beet transportation comprises of loading the beet collected at storage piles and then delivering it to sugar mills. An essential prerequisite to guarantee a viability of the considered sugar mill, is to transport the required quantities of sugar beet while maximizing technological quality and minimizing transportation costs. Some growers may be privileged to conduct collection activities in days of a planning period when sugar beet is fresh and contains larger amount of sucrose, while others do not. This unfair collect scheduling plan should be avoided to provide equal treatment of growers. We propose an Integer Linear Programming (ILP) model with the aim of simultaneously maximizing the amount of collected sucrose during the planning period while minimizing the number of vehicles of a homogenous vehicle fleet, including constraints that provide equal opportunities for growers in sugar beet collection. The problem is denoted by the Sugar Beet Transportation Problem under Growers’ Equity Regulations (SBT-GER). By applying the weighted sum method, the two objective functions are combined to transform the bi-objective problem into a single-objective one. Equity regulations are expressed through the requirement that the minimum percentage of the quantity of sugar beet is guaranteed to be collected from each grower on the day of harvest. For real-sized instances, we propose two metaheuristic algorithms, based on Variable Neighborhood Search (VNS) and Greedy Randomized Adaptive Search Procedure (GRASP), respectively. The developed mathematical model and the proposed metaheuristic approaches are evaluated on a set of randomly generated test instances. The obtained results show that VNS outperforms exact solver and GRASP for the majority of examples.

With the growing environmental concerns and the rising number of electric vehicles, researchers and companies are paying more and more attention to green logistics. This paper studies the Electric Vehicle Routing Problem with time-dependent speeds and soft time windows. The purpose is to minimize the total distance travelled, while penalizing early or late arrivals at the customers’ locations. For this purpose, we formulated the Mixed Integer Linear Program (MILP) and developed a General Variable Neighborhood Search (GVNS) metaheuristic, an efficient way to tackle this problem. To prove the efficiency of our approach, we tested the GVNS against the Adaptive Large Neighborhood Search (ALNS) algorithm and our MILP model, using a set of available benchmark instances. After an extensive experimental evaluation, we concluded that GVNS can find better quality solutions than other methods considered in this research or the same quality solution in less time.

The current study tries to identify the impact of COVID-19 on human resource management practices, business processes, and organizational sustainability. Furthermore, it identified the impact of sustainable HRM practices on hypermarkets' sustainability. The outbreaks of COVID-19 have considerably impacted organizations and businesses all over the world. Most companies were not ready to face such force majeure. Moreover, like everywhere else in the world COVID-19 has significantly affected HRM functions and organizational sustainability in the organizations in Gulf Cooperation Council (GCC) countries. The research is based on a survey of 363 HR practitioners working in hypermarkets in the GCC countries to examine the impact of the pandemic COVID-19 on organizational sustainability. The findings reveal the negative impact of COVID-19 on human resource management practices, business processes, and organizational sustainability. Furthermore, they identify the positive impact of sustainable HRM practices and effective business processes on hypermarkets' sustainability. Finally, the results show that effective business processes and sustainable HRM practices annihilate the negative effect of COVID-19 on organizational sustainability in hypermarkets operating in the GCC countries. This study is unique since it is conducted during the pandemic period and analyses the negative impact of Covid-19 on organizations in the GCC countries. Moreover, it suggests solutions to minimize the negative effect of COVID-19 on organizational sustainability.

The pandemic of the coronavirus known as ‘COVID-19’ has spread rapidly across the globe, resulting in a worldwide economic recession. Although global efforts are in place to combat the virus, it continues to spread on a massive scale. This is not just a medical crisis; rather, it is a business crisis as well. Therefore, the aim of this paper is to develop a research scale that could be used to analyze the impact of COVID-19 on business. We adopted a scale variables approach that is generated from topics covered in the papers of leading academic business journals to form the basis of our analysis. We exposed the scale to qualitative and quantitative testing and concluded that it is reliable for research on the negative effects of COVID-19 on enterprises. The scale was used to investigate the impact of the COVID-19 on businesses in two countries, namely Serbia and Kuwait, to represent two different continents. The results of this research indicate that the influence of this coronavirus is equally devastating in both countries, Kuwait with its otherwise good economic conditions and Serbia with relatively poor ones. The findings of the research are beneficial for both academics in producing quality output papers, as well as their support to managers in various business industries in their fight against coronavirus to keep their businesses sustainable.

The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

In this paper, an attempt has been made to develop a mathematical model in order to study the relationship between laser cutting parameters such as laser power, cutting speed, assist gas pressure and focus position, and kerf taper angle obtained in CO2 laser cutting of AISI 304 stainless steel. To this aim, a single hidden layer artificial neural network (ANN) trained with gradient descent with momentum algorithm was used. To obtain an experimental database for the ANN training, laser cutting experiment was planned as per Taguchi’s L27 orthogonal array with three levels for each of the cutting parameters. Statistically assessed as adequate, ANN model was then used to investigate the effect of the laser cutting parameters on the kerf taper angle by generating 2D and 3D plots. It was observed that the kerf taper angle was highly sensitive to the selected laser cutting parameters, as well as their interactions. In addition to modeling, by applying the Monte Carlo method on the developed kerf taper angle ANN model, the near optimal laser cutting parameter settings, which minimize kerf taper angle, were determined.

Enhancing the overall machining performance implies optimization of machining processes, i.e. determination of optimal machining parameters combination. Optimization of machining processes is an active field of research where different optimization methods are being used to determine an optimal combination of different machining parameters. In this paper, multi-stage Monte Carlo (MC) method was employed to determine optimal combinations of machining parameters for six machining processes, i.e. drilling, turning, turn-milling, abrasive waterjet machining, electrochemical discharge machining and electrochemical micromachining. Optimization solutions obtained by using multi-stage MC method were compared with the optimization solutions of past researchers obtained by using meta-heuristic optimization methods, e.g. genetic algorithm, simulated annealing algorithm, artificial bee colony algorithm and teaching learning based optimization algorithm. The obtained results prove the applicability and suitability of the multi-stage MC method for solving machining optimization problems with up to four independent variables. Specific features, merits and drawbacks of the MC method were also discussed.

Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS) algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed.

Production engineers are frequently faced with the multi-criteria selection problems in the manufacturing environment. Over the years, many multi-criteria decision making (MCDM) methods have been proposed to help decision makers in solving different complex selection problems. This paper introduces the use of an almost unexplored MCDM method, i.e. range of value (ROV) method for solving cutting fluid selection problems. The main motivation of using the ROV method is that it offers a very simple computational procedure compared to other MCDM methods. Applicability and effectiveness of the ROV method have been demonstrated while solving four case studies dealing with selection of the most suitable cutting fluid for the given machining application. In each case study the obtained complete rankings were compared with those derived by the past researchers using different MCDM methods. The results obtained using the ROV method have excellent correlation with those derived by the past researchers which validate the usefulness and effectiveness of this simple MCDM method for solving cutting fluid selection problems.