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The purpose of this paper is twofold. First, to propose an operational model for aircraft maintenance routing problem (AMRP) rather than tactical models that are commonly used in the literature. Second, to develop a fast and responsive solution method in order to cope with the frequent changes experienced in the airline industry.

Two important operational considerations were considered, simultaneously. First one is the maximum flying hours, and second one is the man-power availability. On the other hand, ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA) approaches were proposed to solve the model, and the upper bound was calculated to be the criteria to assess the performance of each meta-heuristic. After attempting to solve the model by these meta-heuristics, the authors noticed further improvement chances in terms of solution quality and computational time. Therefore, a new solution algorithm was proposed, and its performance was validated based on 12 real data from the EgyptAir carrier. Also, the model and experiments were extended to test the effect of the operational considerations on the profit.

The computational results showed that the proposed solution algorithm outperforms other meta-heuristics in finding a better solution in much less time, whereas the operational considerations improve the profitability of the existing model.

The authors focused on some operational considerations rather than tactical considerations that are commonly used in the literature. One advantage of this is that it improves the profitability of the existing models. On the other hand, identifying future research opportunities should help academic researchers to develop new models and improve the performance of the existing models.

The experiment results showed that the proposed model and solution methods are scalable and can thus be adopted by the airline industry at large.

In the literature, AMRP models were cast with approximated assumption regarding the maintenance issue, while neglecting the man-power availability consideration. However, in this paper, the authors attempted to relax that maintenance assumption, and consider the man-power availability constraints. Since the result showed that these considerations improve the profitability by 5.63 percent in the largest case. The proposed operational considerations are hence significant. Also, the authors utilized ACO, SA, and GA to solve the model for the first time, and developed a new solution algorithm. The value and significance of the new algorithm appeared as follow. First, the solution quality was improved since the average improvement ratio over ACO, SA, and GA goes up to 8.30, 4.45, and 4.00 percent, respectively. Second, the computational time was significantly improved since it does not go beyond 3 seconds in all the 12 real cases, which is considered much lesser compared to ACO, SA, and GA.

The purpose of this paper is to develop a model which schedules activities and allocates resources in a resource constrained project management problem. This paper also considers learning rate and uncertainties in the activity durations.

An activity schedule with requirements of different resource units is used to calculate the objectives: makespan and resource efficiency. A comparisons between non-dominated sorting genetic algorithm – II (NSGA-II) and non-dominated sorting genetic algorithm – III (NSGA-III) is done to calculate near optimal solutions. Buffers are introduced in the activity schedule to take uncertainty into account and learning rate is used to incorporate the learning effect.

The results show that NSGA-III gives better near optimal solutions than NSGA-II for multi-objective problem with different complexities of activity schedule.

The paper does not considers activity sequencing with multiple activity relations (for instance partial overlapping among different activities) and dynamic events occurring in between or during activities.

The paper helps project managers in manufacturing industry to schedule the activities and allocate resources for a near-real world environment.

This paper takes into account both the learning rate and the uncertainties in the activity duration for a resource constrained project management problem. The uncertainty in both the individual durations of activities and the whole project duration time is taken into consideration. Genetic algorithms were used to solve the problem at hand.

Optimal ordering decision for a retailer in a dual-sourcing procurement is an important research area. The main purpose of this paper is to explore a loss-averse retailer’s ordering decision in a dual-sourcing problem.

For a loss-averse retailer, the study obtains the optimal ordering decision to maximize expected utility. Based on sensitivity analysis, the properties of the optimal ordering decision are well discussed.

Under the optimal ordering quantity that maximizes expected loss aversion utility, the relevant expected profit of a retailer turns to be smaller under a bigger loss aversion coefficient. For this point, a retailer needs to balance between expected loss aversion utility maximization and expected profit maximization in deciding the optimal ordering policy in a dual-sourcing problem.

This paper reveals the influence of loss aversion on a retailer’s ordering decision in a dual-sourcing problem. Managerial insights are suggested to devise the optimal ordering policy for retailers in practice.

With the rapid development of e-commerce, multi-sourcing with supply contracts and spot buying has become more and more popular in reality. The main purpose of the paper is to explore a loss-averse buyer's optimal procurement policy in a multi-sourcing under e-commerce surroundings.

The study introduces the loss aversion utility function to characterize the loss aversion effect and derives a loss-averse buyer's optimal procurement policy in a multi-sourcing with a wholesale price contract and spot market.

A loss-averse buyer could order no items in a wholesale price contract and only needs to replenish commodities from spot market under certain conditions. In addition, the study shows that spot capacity has important influences on a loss-averse buyer's optimal ordering decision in the wholesale price contract.

This is the first paper to study the loss aversion effect on a buyer's procurement decision in a multi-sourcing. The results present important managerial insights for a loss-averse buyer to devise optimal ordering policies in a multi-sourcing under e-commerce surroundings.

This paper aims to address the supplier selection (SS) problem under dynamic business environments to optimize the procurement cost of spare-parts in the context of a mining equipment company (MEC). Practically, involved parameters’ value does not remain constant as planning periods due to fluctuation in the demand and their market dynamics. Therefore, dynamicity in the parameter is considered as an important factor when a company forms a responsive chain through most eligible suppliers with respect to planning periods. This area of study may be considered for their complexities to the approaches toward order-allocations with bi-products of unused and repair spare-parts.

An integrated methodology of analytic hierarchy process (AHP) and mixed-integer non-linear programming (MILP) is implemented in the two stages during each planning periods. In the first stage, AHP is used to obtain the relative weights with respect to each spare-parts of each criterion and based on that, the ranking is evaluated in accordance with case considered. And in the second stage, MILP is formulated to find the allocations of each spare-part with two distinct approaches through Model-1 and Model-2 separately. Moreover, Model-1 and Model-2 are outlined based on the ranking and efficient parameters-value under cost, limited capacities, quality level and delay lead time respectively.

The ranking and their optimal order-allocation of potential suppliers are obtained during consecutive planning periods for both unused and repair spare-parts. Subsequently, sensitivity analysis is conducted to deduce the key nuggets with the comparison of Model-1 and Model-2 in the changing of capacity, demand and cost per spare-parts. From this analysis, it is found that suppliers who have optimal parameter settings would be better for order-allocations than ranking during the changing planning period.

This paper points out the situation-specific approach for SS problem for a mining industry which often faces disruptive supplying environments. The managerial implication between ranking and parameters are highlighted through Model-1 and Model-2 by sensitivity analysis.

It provides useful directions for managers who are involved in the procurement of spare-parts in the mining environment. For this, suppliers are selected for order-allocation by using Model-1 and Model-2 in the dynamic business environment. The solvability of the model is presented using LINGO 17. Furthermore, the case company selected in this study can be extended to other sectors.

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.

This paper revisits the recent work of Chan and Qi which proposed an innovative performance measurement method for supply chain management. While the measurement method has many advantages, it can be unwieldy in practice. This paper aims to address these limitations and to propose a more user‐friendly alternative performance measurement model.

The performance measurement model described in this paper is a combination of two existing methods: Chan and Qi's model and the supply chain operations reference (SCOR) model. To demonstrate the applicability of the combined approach, actual SCOR level 1 performance data and the measurement information from a case supply chain (SC) are collected and processed by Chan and Qi's measurement algorithm.

These two methods complement each other when measuring SC performance.

This paper develops a practical and efficient measurement model that can resolve SC performance problems by incorporating the strengths of two different measurement models to create a synergistic new model.

The purpose of this paper is to test the effectiveness of fuzzy time series (FTS) forecasting system in a supply chain experiencing disruptions and also to examine the changes in performance as the authors move across different tiers.

A discrete event simulation based on the popular beer game model is used for these tests. A popular ordering management system is used to emulate the behavior of the system when the game is played with human players.

FTS is tested against some other well-known forecasting systems and it proves to be the best of the lot. It is also shown that it is better to go for higher order FTS for higher tiers, to match auto regressive integrated moving average.

This study fills an important research gap by proving that FTS forecasting system is the best for a supply chain during disruption scenarios. This is important because the forecasting performance deteriorates significantly and the effect is more pronounced in the upstream tiers because of bullwhip effect.

Having a system which works best in all scenarios and also across the tiers in a chain simplifies things for the practitioners. The costs related to acquiring and training comes down significantly.

This study contributes by suggesting a forecasting system which works best for all the tiers and also for every scenario tested and simultaneously significantly improves on the previous studies available in this area.

The purpose of this paper is to study the impacts of time segment modeling approach for berth allocation and quay crane (QC) assignment on container terminal operations efficiency.

The authors model the small time segment modeling approach, based on minutes, which can be a minute, 15 min, etc. Moreover, the authors divided the problem into three sub-problems and proposed a novel three-level genetic algorithm (3LGA) with QC shifting heuristics to deal with the problem. The objective function here is to minimize the total service time by using different time segments for comparison and analysis.

First, the study shows that by reducing the time segment, the complexity of the problem increases dramatically. Traditional meta-heuristic, such as genetic algorithm, simulated annealing, etc., becomes not very promising. Second, the proposed 3LGA with QC shifting heuristics outperforms the traditional ones. In addition, by using a smaller time segment, the idling time of berth and QC can be reduced significantly. This greatly benefits the container terminal operations efficiency, and customer service level.

Nowadays, transshipment becomes the main business to many container terminals, especially in Southeast Asia (e.g. Hong Kong and Singapore). In these terminals, vessel arrivals are usually very frequent with small handling volume and very short staying time, e.g. 1.5 h. Therefore, a traditional hourly based modeling approach may cause significant berth and QC idling, and consequently cannot meet their practical needs. In this connection, a small time segment modeling approach is requested by industrial practitioners.

In the existing literature, berth allocation and QC assignment are usually in an hourly based approach. However, such modeling induces much idling time and consequently causes low utilization and poor service quality level. Therefore, a novel small time segment modeling approach is proposed with a novel optimization algorithm.

The purpose of this paper is to develop a novel recommendation method using online reviews with emotional preferences for facilitating online purchase decisions. This leads to better use of information-rich online reviews for providing users with personalized recommendations.

A novel method is developed for producing personalized recommendations in online purchase decision-making. Such a method fuses the belief structure and the Shapley function together to effectively deal with the emotional preferences in online reviews and adequately tackle the interaction existent between product criteria with the use of a modified combination rule for making better online recommendations for making online purchase decisions.

An example is presented for demonstrating the applicability of the method for facilitating online purchase. The results show that the recommendation using the proposed method can effectively improve customer satisfaction with better purchase decisions.

The proposed method can better utilize online reviews for satisfying personalized needs of consumers. The use of such a method can optimize interface design, refine customer needs, reduce recommendation errors and provide personalized recommendations.

The proposed method adequately considers the characteristics of online reviews and the personalized needs of customers for providing customers with appropriate recommendations. It can help businesses better manage online reviews for improving customer satisfaction and create greater value for both businesses and customers.