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    Efficiency analysis and CO2 emission reduction strategies in the US forest sector: a data envelopment analysis approach
    (Springer, 2024) Amirteimoori, Alireza; Zadmirzaei, Majid; Susaeta, Andres; Amirteimoori, Arash
    Industrial economic activities produce pollutants and environmentally sustainable production systems in forestry aim to minimize these undesirable outputs while maintaining high production and economic growth. In this contribution, we assume that in addition to plot-specific inputs and outputs, there are some contextual variables that may be exogenously fixed or may be under the control of the decision-makers. In this sense, we first propose a novel and practical approach to calculate environmental efficiency by reducing undesirable products. Then, we utilize an inverse data envelopment analysis (IDEA) model to effectively manage and reduce CO2 emissions. In doing so, the applied models have been utilized to evaluate the efficiencies of 89 forest plots in the USA. Given our estimations in a real application to the forest plots, the study revealed that the average environmental efficiency score is nearly 0.75 (out of 1). However, there is potential for improvement by adjusting the impacts of contextual factors, which could raise the score to approximately 0.8. Furthermore, the analysis indicates a positive correlation between ownership and environmental efficiency, suggesting that increased ownership leads to higher environmental efficiency. Conversely, temperature exhibits a negative correlation with environmental efficiency. Finally, the results obtained from the IDEA indicate that in order to reduce undesirable outputs by a specific level of 5-10%, it is necessary to decrease other inputs and outputs. This is because, under the assumption of weak disposability, reducing the level of undesirable outputs requires a reduction in certain factors that influence production capacity. In other words, achieving the desired reduction in undesirable outputs inevitably involves diminishing certain aspects of the production process. As the major conclusion, the emergence of IDEA as a powerful tool for sensitivity analysis, along with its flexible nature, offers exciting opportunities for research and practical applications in various fields, including forestry activities. It has the potential to enhance overall environmental efficiency and enable better control over GHG emissions levels.
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    Neutrosophic LOPCOW-ARAS model for prioritizing industry 4.0-based material handling technologies in smart and sustainable warehouse management systems
    (Elsevier, 2023) Simic, Vladimir; Dabic-Miletic, Svetlana; Tirkolaee, Erfan Babaee; Stevic, Zeljko; Ala, Ali; Amirteimoori, Arash
    Industry 4.0 technologies embedded in the warehouse management system (WMS) are needed to improve the automation of material handling activities such as receiving, storing, picking, sorting, packaging, and delivering. This research aims to introduce a neutrosophic multi-criteria group decision -making tool that is intelligible in supporting the transition and upgrading of WMS with Industry 4.0-based solutions. This advanced two-stage model is based on the integration of the logarithmic percentage change-driven objective weighting (LOPCOW) method and the additive ratio assessment (ARAS) method under the type-2 neutrosophic number (T2NN) environment. In the first stage, T2NN-LOPCOW generates an objective importance vector of decision-making criteria. In the second stage, T2NN-ARAS based on the generalized weighted Heronian mean operator provides an advantageous order of Industry 4.0-based material handling technologies. T2NN-LOPCOW-ARAS brings the following novelties: ((i) to straightforwardly represent and explore interconnection levels between weights of criteria, ((ii) to provide wide-scoping insight into the stability of initial priority order, as well as a broad spectrum of flexible solutions, ((iii) to control the normalization procedure and minimize distortions due to the double-normalization backbone. The real-life case study of a logistics company from the Serbian grocery retail sector illustrates the practical applicability of T2NN-LOPCOW-ARAS. A practical evaluation framework is defined to comprehensively assess automated guided vehicles (AGVs), collaborative robotics, and drones. The sensitivity analyses show the high robustness of the proposed framework. The comparative investigation shows that T2NN-LOPCOW-ARAS is superior to the extant methods. The research findings show that AGVs are the most favorable Industry 4.0-based material handling solution.& COPY; 2023 Elsevier B.V. All rights reserved.
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    A novel cross-docking EOQ-based model to optimize a multi-item multi-supplier multi-retailer inventory management system
    (Springer, 2024) Khakbaz, Amir; Alfares, Hesham K.; Amirteimoori, Arash; Tirkolaee, Erfan Babaee
    Nowadays, the retail industry accounts for a large share of the world's economy. Cross-docking is one of the most effective and smart inventory management systems used by retail companies to respond to demands efficiently. In this study, the aim is to develop a novel cross-docking EOQ-based model for a retail company. By considering a two-stage inventory procurement process, a new multi-item, multi-supplier, multi-retailer EOQ model is developed to minimize the total inventory costs. In the first stage, the required items are received from suppliers and are held in a central warehouse. In the second stage, these items are delivered to several retail stores. The total inventory costs include four main parts, i.e., holding costs at the central warehouse, holding costs at the retail stores, fixed ordering costs from the suppliers, and fixed ordering costs from the central warehouse. The optimal inventory policy is obtained by analyzing extrema, and a numerical example is used to confirm the efficiency of the proposed model. Based on the obtained results, it is evident that the proposed model produces the optimal policy for the cross-docking system. Furthermore, the model enables managers to analyze the effects of key factors on the costs of the system. Based on the obtained results, the annual demand of each retailer, the ordering cost by the central warehouse, the ordering cost at each retail store, and the holding cost at each retail store have a direct impact on the optimal cost. Furthermore, it is not possible to describe the effects of the holding cost at the central warehouse on the optimal cost of the system generally.
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    A parallel heuristic for hybrid job shop scheduling problem considering conflict-free AGV routing
    (Elsevier, 2023) Amirteimoori, Arash; Tirkolaee, Erfan Babaee; Simic, Vladimir; Weber, Gerhard-Wilhelm
    In this study, a novel and computationally efficacious Parallel Two-Step Decomposition-Based Heuristic (PTSDBH) and a Mixed Integer Linear Programming (MILP) are developed to tackle the concurrent scheduling of jobs and Automated Guided Vehicles (AGVs) or transporters in a hybrid job shop system. Finite multiple AGVs, AGV eligibility, job's alternative process routes, job re-entry, and conflict-free AGV routing are considered. As far as the authors know, the importance of conflict-free routing for AGVs has not been featured in any of the past studies. Conflict-free AGV routing is an indispensable technicality, specifically where AGVs are the main mean of transportation as AGVs may collide on routes and the whole system ends up in breakdown. To avoid this issue, a conflict-free routing strategy is considered. Utilizing the parallel computing approach, PTSDBH is capable of tackling large-sized problems in remarkably shorter runtimes. To support this, PTSDBH is compared against three literarily well-known metaheuristics; i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) along with TSDBH (i.e., the single-core variant of PTSDBH) on three different-sized sets of benchmark instances. The results reveal that PTSDBH and TSDBH produce the same objective values and outperform the metaheuristics in terms of the quality of objective value. However, the runtimes of TSDBH are considerably higher than those of PTSDBH as it only uses one core to process. Finally, employing Nemenyi's post-hoc procedure for Friedman's test and the convergence plot, it is supported that the objective values generated by PTSDBH and TSDBH are significantly more desirable than those generated by the metaheuristics.
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    A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation
    (Elsevier Ltd, 2022) Amirteimoori, Arash; Mahdavi, Iraj; Solimanpur, Maghsud; Ali, Sadia Samar; Tirkolaee, Erfan Babaee
    In this paper, a Mixed-Integer Linear Programming (MILP) model to simultaneously schedule jobs and transporters in a flexible flow shop system is suggested. Wherein multiple jobs, finite transporters, and stages with parallel unrelated machines are considered. In addition to the mentioned technicalities, the jobs are able to omit one or more stages, and may not be executable by all the machines, and similarly, transportable by all the transporters. To the best of our knowledge, no study in the literature has featured efficacy of the parallel computing in simultaneous scheduling of jobs and transporters in the flexible flow shop system which remarkably shortens run time if the solution approaches are designed accordingly. To this end, we employ Gurobi solver, Parallel Genetic Algorithm (PGA), Parallel Particle Swarm Optimization (PPSO) and hybrid Parallel PSO-GA Algorithm (PPSOGA) to deal with the problem instances. Furthermore, a parallel version of Ant Colony Optimization (ACO) algorithm adapted from the state-of-the-art literature is developed to verify the performance of our suggested solution methods. Using 60 problem instances generated via uniform distribution, the suggested solution approaches are compared against one another. After assessing the results of the computational experiments, it is deduced that PPSOGA algorithm outperforms PGA, PPSO, Parallel Ant Colony Optimization (PACO) and Gurobi solver in terms of the quality of the solutions. The efficiency and run time of the suggested approaches are then assessed through two prominent statistical tests (i.e., Wald and Analysis of Variance (ANOVA)). Eventually, it comes to spotlight that PPSOGA algorithm is computationally rewarding and dependable.