The Combinatorial Optimization Problem (COPs) is one of the branches of applied mathematics and computer sciences, which is accompanied by many problems such as Facility Layout Problem (FLP), Vehicle Routing Problem (VRP), etc. Even though the use of several mathematical formulations is employed for FLP, Quadratic Assignment Problem (QAP) is one of the most commonly used. One of the major problems of Combinatorial NP-hard Optimization Problem is QAP mathematical model. Consequently, many approaches have been introduced to solve this problem, and these approaches are classified as Approximate and Exact methods. With QAP, each facility is allocated to just one location, thereby reducing cost in terms of aggregate distances weighted by flow values. The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. The performance of the proposed approach has been tested on several sets of instances from the data set of QAP and the results obtained have shown the effective performance of the proposed algorithm in improving several solutions of QAP in reasonable time. Afterwards, the proposed approach is compared with other recent methods in the literature review. Based on the computation results, the proposed hybrid approach outperforms the other methods.

The 0-1 Multidimensional Knapsack Problem (MKP) is an NP-Hard problem that has many important applications in business and industry. However, business and industrial applications typically involve large problem instances that can be time consuming to solve for a guaranteed optimal solution. There are many approximate solution approaches, heuristics and metaheuristics, for the MKP published in the literature, but these typically require the fine-tuning of several parameters. Fine-tuning parameters is not only time-consuming (especially for operations research (OR) practitioners), but also implies that solution quality can be compromised if the problem instances being solved change in nature. In this paper, we demonstrate an efficient and effective implementation of a robust population-based metaheuristic that does not require parameter fine-tuning and can easily be used by OR practitioners to solve industrial size problems. Specifically, to solve the MKP, we provide an efficient adaptation of the two-phase Teaching-Learning Based Optimization (TLBO) approach that was originally designed to solve continuous nonlinear engineering design optimization problems. Empirical results using the 270 MKP test problems available in Beasley’s OR-Library demonstrate that our implementation of TLBO for the MKP is competitive with published solution approaches without the need for time-consuming parameter fine-tuning.

This article presents a new approach to address the problem of joint planning of physical and financial flows. The main contribution of this work is that it integrates supply chain contracts and also focuses on supply chain tactical planning in an uncertain and disrupted environment, taking into account budgetary and contractual constraints. In order to minimize the effect of disturbances due to existing uncertainties, a planning model is developed and implemented on a rolling horizon basis. The goal is to seek the best compromise between the available decision-making levers linked with physical and financial flows by adopting a dynamic process that allows for data update at each planning stage. The results of the implemented approach are analysed to highlight the benefits incurred by the inter-firm collaboration in terms of operational performance and working capital (WC) of the supply chain. Our approach represents a basis for negotiation with the suppliers in order to yield a possibly shared profit.

Three simple metaphor-less optimization algorithms are developed in this paper for solving the unconstrained and constrained optimization problems. These algorithms are based on the best and worst solutions obtained during the optimization process and the random interactions between the candidate solutions. These algorithms require only the common control parameters like population size and number of iterations and do not require any algorithm-specific control parameters. The performance of the proposed algorithms is investigated by implementing these on 23 benchmark functions comprising 7 unimodal, 6 multimodal and 10 fixed-dimension multimodal functions. Additional computational experiments are conducted on 25 unconstrained and 2 constrained optimization problems. The proposed simple algorithms have shown good performance and are quite competitive. The research community may take advantage of these algorithms by adapting the same for solving different unconstrained and constrained optimization problems.

In the classical vehicle routing problem with backhauls (VRPB) the customers are divided into two sets; the linehaul and backhaul customers, so that the distribution and collection services of goods are separated into different routes. This is justified by the need to avoid the reorganization of the loads inside the vehicles, to reduce the return of the vehicles with empty load and to give greater priority to the customers of the linehaul. Many logistics companies have special responsibility to make their operations greener, and electric vehicles (EVs) can be an efficient solution. Thus, when the fleet consists of electric vehicles (EVs), the driving range is limited due to their battery capacities and, therefore, it is necessary to visit recharging stations along their route. In this paper the electric vehicle routing problem with backhauls (EVRPB) is introduced and formulated as a mixed integer linear programming model. This formulation is based on the generalization of the open vehicle routing problem considering a set of new constraints focussed on maintaining the arborescence condition of the linehaul and backhaul paths. Different charging points for the EVs are considered in order to recharge the battery at the end of the linehaul route or during the course of the backhaul route. Finally, a heuristic initialization methodology is proposed, in which an auxiliary graph is used for the efficient coding of feasible solutions to the problem. The operation and effectiveness of the proposed formulation is tested on two VRPB instance datasets of literature which have been adapted to the EVRPB.

The purpose of this paper is to present the relief operations (RO), responding to a sudden, natural, national disaster (SNND) as a multi-mode resource-constrain multi-project scheduling problem (MRCMPSP). A conceptual framework at a strategic level is constructed and the Colombian RO for an earthquake response is shown as an illustrative case. We concluded that RO can be addressed as a MRCMPSP and that for Colombian case, it is a convenient way to board it. Addressing RO as a MRCMPSP allows managers to implement different project scheduling tools successful in other contexts.

Leadership is an important factor in providing direction to employees and it can improve employee performance, significantly. When a leader can grow the employees’ confidence in carrying out their respective duties, he/she contributes to the performance of the organization. In addition to leadership, job satisfaction is also an important factor which influences on employee performance. To be able to maintain existing resources, companies are required to increase employee satisfaction, increase employee organizational commitment and provide job security for employees. This study aims to analyze the influence of leadership on job satisfaction, analyze the influence of leadership on employee performance, study the effect of job satisfaction on employee performance and analyze job satisfaction as a mediate leadership on employee performance based on path analysis. The result of the analysis shows that leadership influenced job satisfaction and employee performance while job satisfaction affects performance. Finally, the results indicate that job satisfaction mediated the effect of leadership on employee performance.

The present paper aims to examine and analyze: 1) the effect of farmers’ work motivation on their performance; 2) the effect of information technology on farmers’ performance; 3) the effect of farmers’ work motivation on their performance as moderated by local knowledge; and 4) the effect of information technology utilization on farmers’ performance as moderated by local knowledge. Our study population comprised of all farmers registered as members of farmer association in Tirtomartani Village, Sleman Regency, Yogyakarta. We adopt cluster random sampling as the sampling method and use a sample of 102 respondents. Data were collected using a four-point Likert scale and analyzed using smartPLS 3.0. The results indicate that: 1) work motivation had a positive effect on farmers’ performance; 2) utilization of information technology had a positive effect on farmers’ performance; 3) local knowledge did not moderate the relationship between work motivation and farmers’ performance; and 4) local knowledge moderated the relationship between utilization of information technology and farmers’ performance.

The present paper attempts to analyse the effect of organizational climate on organizational citizenship behaviour (OCB), as well as the effect of OCB on turnover intention. Data on organizational climate, OCB and turnover intention were obtained from a sample of 78 internal auditors of Jordanian private universities. Hypotheses were proposed and were tested using PLS-SEM, and the outcomes demonstrate the positive linkage between organizational climate and OCB. Meanwhile, OCB and turnover intention were found to have inverse linkage. Strong organizational climate should be created in Jordanian private universities since it improves and predicts OCB. OCB will in turn cause turnover intention to decrease among internal auditors. With the use of PLS-SEM method, this study adds to the extant literature.

Human Resource Management (HRM) has the capacity of influencing the organizational behavior of the employees, thereby ensuring the achievement of the competitive advantage. This study proposes to assess the relationship between human resources (HR) practices and competitive advantage. It is also intended to test organizational commitment as a mediator, between HR practices and competitive advantage among the employees of the private universities in Jordan. The data were gathered from 232 individuals employed at ten private universities located in Jordan. PLS, SEM was performed on the data, using the SmartPLS 3 software. HRM practices result in a statistically significant variation in competitive advantage. Relationship of HRM practices and competitive advantage was partially mediated by organizational commitment. HRM practices resulted in a statistically significant variation in and organizational commitment. Our findings contribute to the existing body of literature of how organizational commitment can mediate the relationships among the HR practices adopted by organizations and competitive advantage. Jordanian private universities should improvise extra attention over HR practices that contribute positively toward the performance of employees.