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This paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.

A stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.

Compared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.

The results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.

Extending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.

A novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.

Human resources are one of the most important and effective elements for companies. In other words, employees are a competitive advantage. This issue is more vital in the supply chains and production systems, because of high need for manpower in the different specification. Therefore, manpower planning is an important, essential and complex task. The purpose of this paper is to present a manpower planning model for production departments. The authors consider workforce with individual and hierarchical skills with skill substitution in the planning. Assuming workforce demand as a factor of uncertainty, a two-stage stochastic model is proposed.

To solve the proposed mixed-integer model in the real-world cases and large-scale problems, a Benders’ decomposition algorithm is introduced. Some test instances are solved, with scenarios generated by Monte Carlo method. For some test instances, to find the number of suitable scenarios, the authors use the sample average approximation method and to generate scenarios, the authors use Latin hypercube sampling method.

The results show a reasonable performance in terms of both quality and solution time. Finally, the paper concludes with some analysis of the results and suggestions for further research.

Researchers have attracted to other uncertainty factors such as costs and products demand in the literature, and have little attention to workforce demand as an uncertainty factor. Furthermore, most of the time, researchers assume that there is no difference between the education level and skill, while they are not necessarily equivalent. Hence, this paper enters these elements into decision making.

The purpose of this paper is to present the honey‐bee mating optimization (HBMO) algorithm tested with, first, a well‐known, non‐linear, non‐separable, irregular, multi‐modal “Fletcher‐Powell” function; and second, with a single hydropower reservoir operation optimization problem, to demonstrate the efficiency of the algorithm in handling complex mathematical problems as well as non‐convex water resource management problems. HBMO and genetic algorithm (GA) are applied to the second problem and the results are compared with those of a gradient‐based method non‐linear programming (NLP).

The HBMO algorithm is a hybrid optimization algorithm comprised of three features: simulated annealing, GA, and local search. This algorithm uses the individual features of these approaches and combines them together, resulting in an enhanced performance of HBMO in finding near optimal solutions.

Results of the “Fletcher‐Powell” function show more accuracy and higher convergence speed when applying HBMO algorithm rather than GA. When solving the single hydropower reservoir operation optimization problem, by disregarding evaporation from the model structure, both NLP solver and HBMO resulted in approximately the same near‐optimal solutions. However, when evaporation was added to the model, the NLP solver failed to find a feasible solution, whereas the proposed HBMO algorithm resulted in a feasible, near‐optimal solution.

This paper shows that the HBMO algorithm is not complicated to use and does not require much mathematical sophistication to understand its mechanisms. A tool such as the HBMO algorithm can be considered as an optimization tool able to provide alternative solutions from which designers/decision makers may choose.

The combined heat and power dispatch (CHPD) aims to optimize the outputs of online units in a power plant consisting thermal generators, co-generators and heat-only units. Identifying the operating point of a co-generator within its feasible operating region (FOR) is difficult. This paper aims to solve the CHPD problem in static and dynamic environments.

The CHPD plant operation is formulated as an optimization problem under static and dynamic load conditions with the objectives of minimizations of cost and emissions subject to various system and operational constraints. A novel bio-inspired search technique, grey wolf optimization (GWO) algorithm is used as an optimization tool.

The GWO-based algorithm has been developed to determine the preeminent power and heat dispatch of operating units within the FOR region. The proposed methodology provides fuel cost savings and lesser pollutant emissions than those in earlier reports. Particularly, the GWO always keeps the co-generator’s operating point within the FOR, whereas most of the existing methods fail.

The GWO is applied for the first time to solve the CHPD problems. New dispatch schedules are reported for 7-unit system with the objectives of total fuel cost and emission minimizations, 24-unit system for economic operation and 11-unit system in dynamic environment. The simulation experiments reveal that GWO converges quickly, consistent and the statistical performance clears its applicability to CHPD problems.

An integrated model for distribution network design is proposed which explicitly represents the required level of customer service.

The literature on the facilities location problem is quite extensive with a wide variety of solution methods for addressing these problems where the objective is cost minimization. Develops a branch and bound algorithm for solving the uncapacitated, multi‐period facility location problem where the objective is to maximize profits. The solution method uses a number of simplification and branching decision rules to solve the problem efficiently. Extensive computational results on the algorithm’s performance are provided. The results indicate that the algorithm provides optimal solutions in substantially less time than LINDO.

In this paper, multi-period location–inventory–routing problem (LIRP) considering different vehicles with various capacities has been investigated for the supply chain of red meat. The purpose of this paper is to reduce variable and fixed costs of transportation and production, holding costs of red meat, costs of meeting livestock needs and refrigerator rents.

The considered supply chain network includes five echelons. Demand considered for each customer is approximated as deterministic using historical data. The modeling is performed on a real case. The presented model is a linear mixed-integer programming model. The considered model is solved using general algebraic modeling system (GAMS) software for data set of the real case.

A real-world case is solved using the proposed method. The obtained results have shown a reduction of 4.20 per cent in final price of red meat. Also, it was observed that if the time periods changed from month to week, the final cost of meat per kilogram would increase by 43.26 per cent.

This paper presents a five-echelon LIRP for the meat supply chain in which vehicles are considered heterogeneous. To evaluate the capability of the presented model, a real case is solved in Iran and its results are compared with the real conditions of a firm, and the rate of improvement is presented. Finally, the impact of the changed time period on the results of the solution is examined.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

The purpose of this paper is to deal with a new dispatching and optimization of reactive power sources in power systems.

The methodology is first based on an optimal movement of existing reactive sources as a first phase, then an optimal investment in a second phase and finally a combination of the two previous phases as the third one. The methodology showed also the advantage of a two‐levels procedure, considering an initial minimal compensation before minimizing the active losses. The solution of the global non‐linear problem is performed using the projected and augmented Lagrange method associated with the reduced gradient and the DFP methods.

In waiting for new investment programs which are planned for limited periods, the study presents an alternative of optimizing the reactive power compensation by a movement of the power sources or some of them, satisfying all system constraints and minimizing also the active power losses.

The planners and operators are able to decide what cases are to be considered for reactive power dispatch; the proposed program gives a proposal solution to almost all changes that can occur to the power system (incident, contingency, load variation, development).