The airport gate assignment problem (AGAP) is one of the most important problems operations managers face daily. Many researches have been done to solve this problem and tackle its complexity. The objective of the task is assigning each flight (aircraft) to an available gate while maximizing both conveniences to passengers and the operational efficiency of airport. This objective requires a solution that provides the ability to change and update the gate assignment data on a real time basis. In this paper, we survey the state of the art of these problems and the various methods to obtain the solution. Our survey covers both theoretical and real AGAP with the description of mathematical formulations and resolution methods such as exact algorithms, heuristic algorithms, and metaheuristic algorithms. We also provide a research trend that can inspire researchers about new problems in this area.

Sustainable development is now the focus of researchers and organizations worldwide. Several concepts, such as reverse logistics (RLs) and closed-loop supply chains, have been introduced to encourage sustainability in supply chains. RLs refers to the set of activities needed by consumers to collect the product used for reuse, repair, remanufacturing, recycling, or disposal of the used product. There are various processes involved in RL, and one of them is collection systems. Collection refers to a company obtaining custody of specific items. We review the literature on RLs collection systems. A bibliometric analysis was conducted to provide better insight into the field and establish any trends. Firstly, we present the classification methods used in the field, based on available review papers. Secondly, we evaluate literature from several fields that are related to either the problem setting or the technical features. Different perspectives are presented and classified. This method facilitates the identification of manuscripts related to the reader’s specific interests. Throughout the literature review, trends in measuring the performance of collection systems are identified, and directions for future research are identified and presented.

The advancements in human lifestyle result in growth in daily demands of products, and accordingly, an increased rate of manufacturing. However, the resources on the planet Earth are limited, thus depleting day-by-day. More goods also contribute to more end-of-life (EOL) dumping or even before EOL in some cases. Therefore, an interest in remanufacturing has appeared, and it offers a solution that can solve or perhaps mitigate the risks of consuming more resources and increasing waste. Remanufacturing is a procedure of bringing used products to “like-new” functional status with a matching warranty. However, due to its relative novelty in terms of research field and industry, remanufacturing is poorly understood. People often mix it with other terms such as recycling, reconditioning, or repair. Therefore, in this research, the focus is on the remanufacturing systems’ definition, relevance, main phases, case studies, and solution methods proposed by various researchers. The word ‘remanufacturing’ is clearly described in this paper by differentiating it from alternative green manufacturing initiatives. Both qualitative and quantitative analysis of literature are performed. The quantitative analysis is conducted using a bibliometric method. For quantitative analysis, a systematic approach is utilized for research papers’ selection. The qualitative analysis has been carried out by discussing different aspects of remanufacturing and how the researchers are working on its different domains and phases. The review showed that researchers focused on some phases more as compared with others. Moreover, it is also revealed from the literature that the common solutions methods applied in this domain are optimization techniques. Future research directions are also identified and presented.

In this paper, we develop solutions to the problem of identifying the k most vital cut-sets of a network under limited attacking resources in a network interdiction context. We treat three different versions of the problem, each having a different objective: namely, the highest probability of a successful attack, the least cost attack, and the least expected cost attack combining both success probability and attacking cost. We introduce a budget constraint to reflect the resource limitation of the attacker. We consider both the case of disjoint and non-disjoint cut-sets to target. We develop a simulation-optimization approach accounting for various techniques including the shortest path problem and the min-cut problem. The solution method is found to be very efficient even for large-scale networks. We implement the suggested algorithms for illustration purposes.

The paper investigates the most critical k cut-sets to target in the context of attacks on networks. The targeted network fails if the attack successfully disables a full cut-set. The attack process is dynamic and targets one link at a time. We assume perfect information, so that the attacker picks the next link to target after identifying the outcome of the attack on the previous one. The attack may continue to reach up to k cut-sets. The problem is to identify which cut-sets to target (referred to as the k-critical cut-sets of the network) that maximize the probability of a successful attack. We distinguish both cases of disjoint and non-disjoint cut-sets. We develop an algorithm for each case with illustrations. We explore the large scale of networks and offer guidelines on the corresponding defensive strategies.

This paper considers a network under intelligent threats. The defender of the network attempts to identify which disjoint K-critical path sets to be fortified under limited resources in order to better resist to attacks. Strengthening paths consists on increasing the path survivability by increasing individual survival probabilities of some or all arcs within the path accounting for limited resources. These resources are expressed through a defensive budget. The model assumes different reinforcement scenarios using different levels of resources. The paper proposes two solution methods. The first method maximizes the smallest survival probability over the K breakthrough paths to select under a budget constraint. The second one minimizes the cost of the overall survivability improvement while maintaining the smallest survival probability over the K selected breakthrough paths to a predefined threshold value. The formulated integer programs are solved optimally in a short time. This is the case even for large networks. The sensitivity analysis indicates in particular that, beyond some levels of defensive resources consumption, the survivability improvement becomes very slow suggesting an efficient re-allocation of the budget.