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In this paper a manufacturing process is introduced to treat the bulk surfaces of surfaces via graphite powder blasting to directly produce an adherent layer of graphene. Subsequently, the study aims to correlate optical, thermal and electrochemical modifications with treatment parameters such as blasting pressure, nozzle distance and number of passes. After the treatment, the target surface has enhanced spectral, thermal and electrochemical properties because of the graphene’s turbostratic nature to adhesion to its surface. The thermal stability performance showed a consistent 5% increase relative to a bare aluminum substrate. A two-fold increase in corrosion resistance is seen in the sample compared to bare aluminum while diffuse absorbance values enhancement is around three-fold. This proposed manufacturing method provides straightforward and effective treatment at various degrees of automation. Since the deposited graphene substrate can cover a large area, it can be applied as a final layer on thermal collectors, PV panels and for other applications. It is possible to adopt this process to other well-established treatment plants without requiring high investments in the overhead expenses.

This paper addresses a multi-objective single-product network design and operations problem that consists of selecting the suppliers, allocating the orders, routing a single vehicle to collect the ...

As part of the global efforts to make aviation activities more environmentally friendly, the worldwide goal is to achieve a 50% reduction in the 2005 emissions by 2050. In this context, aviation emissions represent a critical challenge to aviation activities, especially with the increasing travel demand up to the beginning of the COVID-19 crisis, starting in 2020. One of the potential drivers that would help the aviation industry reduce its emissions is the use of sustainable aviation fuel (SAF). In this study, we analyzed the impact of SAF from an air traffic flow management (ATFM) perspective, considering delay and re-routing costs. We developed an optimization model that considers, in addition to the traditional ATFM costs, fuel costs and carbon dioxide emissions. We investigated the impact of accounting for these two new aspects, that is, fuel costs and emissions, on ATFM performance, and we compared SAF with conventional fuel. The analysis of a real case study revealed that, in addition to delay and re-routing costs, fuel cost should be included in the ATFM model so that the resulting solution becomes economically and environmentally realistic for airlines. The increase in the fuel cost and network delays when using SAF requires setting an appropriate carbon price under an emission policy, such as the carbon offsetting and reduction scheme for international flights policy, to make SAF more attractive. Furthermore, flexible re-routing programs for flights operated using SAF make it advantageous from an ATFM perspective. Funding Agencies|University of Sharjah, Sharjah, United Arab Emirates [1702040585]; VINNOVA, Swedish innovation agency [2018-02998]; Swedish innovation agency

We discuss a widely used air traffic flow management formulation. We show that this formulation can lead to a solution where air delays are assigned to flights during their take-off which is prohibited in practice. Although air delay is more expensive than ground delay, the model may assign air delay to a few flights during their take-off to save more on not having as much ground delay. We present a modified formulation and verify its functionality in avoiding incorrect solutions.

An evaluation of the competitiveness of the Northern Sea Route (NSR) for container shipping services, considering ice thickness changes during the year, is presented in the present work. The variation in ice thickness has three implications. Firstly, it entails a probability of blockage in ice and reduces the number of days in which a round-trip liner service can be completed. Secondly, ice thickness impacts schedule integrity. Thirdly, it impacts costs (icebreaker fees and fuel consumption), transit time, and the amount of CO2 emitted per TEU. Accounting for these elements in a model and then in a business case, this study concludes that NSR liner services are only competitive, compared with the Suez Canal Route or the Trans-Siberian Railway Connection, for a limited period of 1.5 months per year.

International audience; This paper models a single liner service design and operations problem. The model selects the portsto be included, their sequence, the sailing speed of vessels, the number of vessels and the amountsof cargo to transport by the service. The objective is to maximise profit. First, a relaxation with amixed-integer nonlinear programming (MINLP) formulation is proposed. We show how to obtainthe optimal speed value. Once this value is obtained, the mathematical programming formulation becomes a mixed-integer linear program (MILP). Then, a two-step exact algorithm is presented to solve the problem. Using real data, the optimal solution was found in less than 1 min for small-size problems and in few hours for relatively large-size problems. More tests were carried out on randomly generated data sets with up to 25 ports. The results of these tests are rather promising, and they enabled us to identify the performance limits of the algorithm.

Despite various planning efforts, airspace capacity can sometimes be exceeded, typically because of disruptive events. Air traffic flow management (ATFM) is the process of managing flights in this situation. In this paper, we present an ATFM model that accounts for different rerouting options (path rerouting and diversion) and preexisting en route flights. The model proposes having a central authority to control all decisions, which is then compared with current practice. We also consider interflight and interairline fairness measures in the network. We use an exact approach to solve small- to medium-sized instances, and we propose a modified fix-and-relax heuristic to solve large-sized instances. Allowing a central authority to control all decisions increases network efficiency compared with the case where the ATFM authority and airlines control decisions independently. Our experiments show that including different rerouting options in ATFM can help reduce delays by up to 8% and cancellations by up to 23%. Moreover, ground delay cost has much more impact on network decisions than air delay cost, and network decisions are insensitive to changes in diversion cost. Furthermore, the analysis of the tradeoff between total network cost and overtaking cost shows that adding costs for overtaking can significantly improve fairness at only a small increase in total system cost. A balanced total cost per flight among airlines can be achieved at a small increase in the network cost (0.2%-3.0%) when imposing airline fairness. In conclusion, the comprehensiveness of the model makes it useful for analyzing a wide range of alternatives for efficient ATFM. Funding Agencies|University of Sharjah, Sharjah, United Arab Emirates [1702040585]; VINNOVA, the Swedish innovation agencyVinnova [2018-02998]

Design is a process through which customer needs are transformed into product or service specifications, and then used to develop a model or prototype. The prototype is tested, and modifications are brought to it before the production process starts. Moreover, the design process may be divided into different stages, starting from the definition of the customer needs, going through the conceptual design phase and ending up with the detailed design. In this article, we address the conceptual design phase, where the customer needs are assumed to be known. The proposed approach considers, based on customer needs, primary and secondary design criteria. Each design criterion has a set of predetermined possible values (options) from which the designer may select. Making the best selection of all the design features while satisfying the customer needs in terms of cost, quality (customer preference) and environmental performance is a combinatorial problem and therefore a decision-making framework would be helpful for the designers. In this article, the design criteria are evaluated using fuzzy technique for order preference by similarities to ideal solution based on cost, quality and environmental sustainability. A multiobjective and a single-objective binary programming models are then developed and solved, and their optimal solutions are obtained. The multiobjective solutions provide the decision makers with the possible trade-offs, whereas the single-objective model solution can be used as a final decision-making tool. The proposed approach is implemented in a user-friendly software developed by the authors. A case study is conducted using a baby car seat for which three main and six secondary design criteria are considered. The obtained results show the effectiveness of the approach used.

We propose a single product two-stage supply contract between a retailer and a main supplier. The retailer buys a number of supply options for a non-refundable price from the main supplier at the beginning of the first decision stage (first period), which can be seen as capacity reservation. At the beginning of the second decision stage (second period), these options can be exercised for a given unit cost and transformed, by the retailer, fully or partially, into orders that are delivered by the supplier immediately. Moreover, the retailer faces a stochastic demand, which is concentrated in the second period and is modeled jointly with a correlated exogenous information using a joint probability distribution. In addition, a second supply opportunity from a risky supplier whose availability is random and modeled using a Bernoulli distribution may be available. At the beginning of the second decision stage, the stochastic exogenous information is revealed and the information about the availability or unavailability of the risky supplier becomes known. Therefore, the demand forecast is updated conditionally to the value of the exogenous information. Thus, if the risky supplier is available, another quantity may be ordered from this supplier by the retailer for a given unit cost and delivered immediately. The end-customer demand occurs during the second period and every satisfied demand is charged a given unit price by the retailer. At the end of the selling season, any remaining units are salvaged by the retailer at a salvage value. We model this problem using a dynamic programming approach and we exhibit some characteristics of the structure of the optimal inventory control policy for the retailer. More specifically, we provide the structure of the second decision stage optimal policy and some analytical insights concerning the first stage optimal policy. Furthermore, through a numerical study, we analyze the effect of some of the model parameters on the optimal policy such as the information quality, the probability of the availability of the risky supply option, and the difference between the costs of the two supply options.

Purpose This paper aims to examine containership routing and speed optimization for maritime liner services. It focuses on a realistic case in which the transport demand, and consequently the collected revenue from the visited ports depend on the sailing speed. Design/methodology/approach The authors present an integer non-linear programming model for the containership routing and fleet sizing problem, in which the sailing speed of every leg, the ports to be included in the service and their sequence are optimized based on the net line's profit. The authors present a heuristic approach that is based on speed discretization and a genetic algorithm to solve the problem for large size instances. They present an application on a line provided by COSCO in 2017 between Asia and Europe. Findings The numerical results show that the proposed heuristic approach provides good quality solutions after a reasonable computation time. In addition, the demand sensitivity has a great impact on the selected route and therefore the profit function. Moreover, the more the demand is sensitive to the sailing speed, the higher the sailing speed value. Research limitations/implications The vessel carrying capacity is not considered in an explicit way. Originality/value This paper focuses on an important aspect in liner shipping, i.e. demand sensitivity to sailing speed. It brings a novel approach that is important in a context in which sailing speed strategies and market volatility are to be considered together in network design. This perspective has not been addressed previously.

International audience; Arctic shipping is controversial due to the trade-off between its positive economic impact and its negative environmental effects. As both effects are affected by the uncertainty related to sailing conditions encountered along the route, we develop a model that estimates how ice conditions change the probability of blockage along the route and play on the economic and environmental attractiveness of the Northern Sea Route. The model is applied to a business case that compares the performance of two ice-class oil tankers (1A and 1AS) that use Marine Gas or Low Sulfur Fuel, and considers historical data on daily ice thickness records (2006–2016) for the probability of blockage. Our main finding is that the probability of blockage affects the choice of the optimal vessel to deploy along the Arctic Route as well as the effectiveness of various environmental policy options.

To curb emissions, containerized shipping lines face the traditional trade-off between cost and emissions (CO2 and SOx) reduction. This paper considers this element in the context of liner service design and proposes a mixed integer linear programming (MILP) model based on a multi-commodity pickup and delivery arc-flow formulation. The objective is to maximize the profit by selecting the ports to be visited, the sequence of port visit, the cargo flows between ports, as well as the number/operating speeds of vessels on each arc of the selected route. The problem also considers that Emission Control Areas (ECAs) exist in the liner network and accounts for the vessel carrying capacity. In addition to using the MILP solver of CPLEX, we develop in the paper a specific genetic algorithm (GA) based heuristic and show that it gives the possibility to reach an optimal solution when solving large size instances.

This article is motivated by growing concerns related to shipping $$\hbox {CO}_{2}$$ and $$\hbox {SO}_{\mathrm{x}}$$ emissions in the hope that ship operators further consider the environmental impacts of their activities when attempting to maximize profit. The article proposes a liner shipping multi-objective optimization (MOO) model based on profit maximization, $$\hbox {CO}_{2}$$ emissions minimization, and $$\hbox {SO}_{\mathrm{x}}$$ emissions minimization for which all objective functions are a function of vessel sailing speed. Two demand configurations are considered: elastic and inelastic. The MOO model is solved using three different methods and is applied to two liner services deployed on the trans-Pacific and Europe–Far East markets. A single-objective optimization approach is also proposed in which the monetary value of the emissions is considered in an objective function. The main conclusion of the article is that the sensitivity of demand to transit time is based on the gap between economic and environmental optimal solutions and that policies considering imposing a tax on $$\hbox {CO}_{2}$$ or $$\hbox {SO}_{\mathrm{x}}$$ to reduce the negative externalities from international shipping should account for this element.

International audience; This paper aims to solve a multi-period green supplier selection and order allocation problem with all-unit quantity discounts, in which the availability of suppliers differs from one period to another. The proposed approach involves three stages. In the first stage, decision makers use fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to assign two preference weights to every potential supplier based on the supplier's performance in two sets of criteria considered separately: traditional and green. In the second stage, top management uses the analytic hierarchy process to assign an importance weight to each of the two sets of criteria based on the organization's strategy. The outputs of the first and second stages serve as inputs for a single-product bi-objective integer linear programming model with deterministic demand that takes into account all-unit quantity discounts and a varying number of suppliers in each period of the planning horizon. We implement the proposed mathematical model in MATLAB R2014a software using the weighted comprehensive criterion method and the branch-and-cut algorithm. Statistical analysis helps determine the most suitable ranking approach for suppliers when their availability changes in each period. This paper presents a numerical comparison between two settings: the first considers all-unit quantity discounts, and the second does not. Moreover, a time study shows that the proposed bi-objective integer linear programming model has an exponential computation time.

This data article provides detailed optimization input and output datasets and optimization code for the published research work titled “Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability” (Hamdan and Cheaitou, 2017, In press) [1]. Researchers may use these datasets as a baseline for future comparison and extensive analysis of the green supplier selection and order allocation problem with all-unit quantity discount and varying number of suppliers. More particularly, the datasets presented in this article allow researchers to generate the exact optimization outputs obtained by the authors of Hamdan and Cheaitou (2017, In press) [1] using the provided optimization code and then to use them for comparison with the outputs of other techniques or methodologies such as heuristic approaches. Moreover, this article includes the randomly generated optimization input data and the related outputs that are used as input data for the statistical analysis presented in Hamdan and Cheaitou (2017 In press) [1] in which two different approaches for ranking potential suppliers are compared. This article also provides the time analysis data used in (Hamdan and Cheaitou (2017, In press) [1] to study the effect of the problem size on the computation time as well as an additional time analysis dataset. The input data for the time study are generated randomly, in which the problem size is changed, and then are used by the optimization problem to obtain the corresponding optimal outputs as well as the corresponding computation time. Keywords: Green supplier selection data, Computation time data, All-unit quantity discounts, Supplier availability, Bi-objective optimization, Multi-criteria decision-making

Green and traditional criteria for supplier selection were split into two sets.Two bi-objective and one multi-objective optimization models were proposed.The models use a combination of three tools: AHP, Fuzzy TOPSIS and optimization.The results show that the proposed approach is more flexible than the existing ones. This research provides a decision-making tool to solve a multi-period green supplier selection and order allocation problem. The tool contains three integrated components. First, fuzzy TOPSIS (technique for order of preference by similarity to ideal solution) is used to assign two preference weights to each potential supplier according to two sets of criteria taken separately: traditional and green. Second, top management uses an analytic hierarchy process (AHP) to assign a global importance weight to each of the two sets of criteria based on the strategy of the company and independently of the potential suppliers. Third, for each supplier, the preference weight obtained from fuzzy TOPSIS regarding the traditional criteria is then multiplied by the global importance weight of the set of traditional criteria. The same is done for the green criteria. The two combined preference weights obtained for each supplier are then used in addition to total cost to select the best suppliers and to allocate orders using multi-period bi-objective and multi-objective optimization. The mathematical models are solved using the weighted comprehensive criterion method and the branch-and-cut algorithm. The approach of this research has a major advantage: it provides top management with flexibility in giving more or less importance weight to green or traditional criteria regardless of the number of criteria in each category through the use of AHP, which reduces the effect of the number of criteria on the preference weight of the suppliers. Contrary to the case in which each supplier is evaluated on the basis of all criteria at the same time, the proposed approach would not necessarily result in a supplier with poor green performance being ranked among the best for a situation in which the number of green criteria is smaller than the number of traditional criteria. In this case, the final ranking would mainly depend on the global weight of the green criteria set given by the top management using AHP as well as on the ranking of the supplier in terms of green criteria obtained from fuzzy TOPSIS. Extensive numerical experiments are conducted in which the bi-objective and multi-objective models are compared and the effect of the separation between green and traditional criteria is investigated. The results show that the two optimization approaches provide very close solutions, which leads to a preference for the bi-objective approach because of its lower computation time. Moreover, the results confirm the flexibility of the proposed approach and show that combining all criteria in one set is a special case. Finally, a time study is performed, which shows that the bi-objective integer linear programming model has a polynomial computation time.

International audience

Intense drone traffic, exceeding human capabilities of manual control, is expected to occur during the last stage of Unified Traffic Management (UTM) and Unmanned Airspace System (UAS) service deployment in cities. In this paper, we discuss how humans and automation could collaborate to manage this airspace. We review theory on options for UTM airspace structure (volumes, points, networks, layers), machine learning, optimization, and human-automation collaboration. Based on simulation and visualization of two cities, we discuss four abilities: to discern traffic patterns, to recognize situations, to predict situational developments, and to function in varying conditions of rule-following habits of airspace users. We then discuss the challenge of collaborating though the use of advanced visual dashboards, for human-in-the loop AI but also for society-in-the-loop. Finally, we discuss how the challenge of human-automation collaboration can be expected to shift, as the capabilities of the machine increases.

Despite the critical effect of CO 2 emissions of the aviation sector on the environment, none of the previous works on air traffic flow management (ATFM) has addressed this aspect from the perspective of the ATFM network operations. In this paper, we study this effect by proposing an ATFM bi-objective mathematical model that minimizes the total delay cost and the total CO 2 emissions. The proposed model considers ground delay, air delay, flight rerouting, speed controls, and CO2 emissions and is solved using the weighted comprehensive criterion method. The illustrative example showed that reducing CO 2 emissions by an average of 0.07% results in an average of 1.2% increase in the total delay cost. The consideration of CO 2 emissions leads to a solution that favors ground holding rather than air holding or rerouting to longer paths.

Navigation via the Northern Sea Route (NSR) requires specific vessels in comparison with the Suez Canal Route (SCR). We develop a profit decision model that defines the best option mixing the shipping lane (NSR or SCR) and the ice-class of the vessel (1A, 1AS, and Open Water (OW)) for oil producers operating in the Russian Arctic zone. The effects of the Brent barrel price, bunker cost and ice-thickness are analyzed. The best option depends in fact on the strategy implemented by the shipper. For instance, the solutions of a profit focused strategy or of a cost-oriented strategy are different.

Selecting the best construction contractor plays a critical role in the success of any construction project. Moreover, in the public sector, the traditional lowest bid method is still broadly used and contracts are often awarded based on the lowest price. However, this method has been criticized by many researchers because even if it might guarantee the lowest cost for a project, it does not guarantee the maximum value in terms of time and quality. More particularly, the risk exposure during the tendering process is usually very high and the success of the construction project is strongly related to managing this risk in an appropriate way. Therefore, the selection of the most appropriate contractor should be based on a set of criteria such as technical capability, financial stability, risk, safety, etc., in addition to cost in order to avoid problems that may happen after the contract is awarded. This study aims therefore to develop a decision making framework (DMF) to assist the public organizations in selecting the most appropriate construction contractor(s). The proposed DMF uses a combination of multi-criteria decision making (MCDM) tools and fuzzy logic theory and consists of three stages. In the first stage, data envelopment analysis (DEA) is used to reduce the number of contractors, based on multiple criteria, such that only efficient contractors are considered. In the second stage, the risk factors that are related to each efficient contractor are identified and assessed using a fuzzy logic based approach. Finally, in the third stage, a bi-objective mixed integer linear programming (MILP) model is developed to select the best contractor(s) based on cost and risk and to determine the optimal quantity of work to be assigned to each selected contractor. Indeed, the proposed approach considers the multiple award contract case in which more than one contractor can be selected to share the amount of work to be performed. We assume that prior to using the proposed DMF, a pre-qualification study is conducted using multiple criteria chosen by the public organization, and only pre-qualified contractors are included in the first stage of the proposed DMF. Moreover, a case study inspired from the contractor selection process in a public organization in the United Arab Emirates (UAE) is used to show the effectiveness of the proposed approach.

International audience; This paper presents a fairness model for the network air traffic flow management with rerouting under deterministic capacity. In addition to ground delay and air delay, the model provides control on the aircraft speed through the control of the time spent in each sector, and the ability to reroute the aircraft to avoid or reduce delays. The model covers two aspects of fairness, which are delay distribution and flights reversal. The model outperforms existing works in terms of the number of delayed flights per time period as well as the total and maximum delay.

The automotive engine contains different systems that operate to fulfill the continuous function of the combustion. To insure un-interrupted function and optimal performance, various types of condition monitoring technologies are being used with different new advanced data processing techniques. Based on the results of a comprehensive literature review it was found that there is scarce research dealing with CM technologies and soft computing methods used with automotive engines. Thus, the purpose of this paper is to conduct an up-to-date literature review of the CM technologies used with internal combustion engines and identify the fault detection and diagnostic methods used in this context. Consequently, it was found that literature concentrate on four types of condition monitoring technologies and four operational parameters when it comes to multi-sensor diagnosis of engine systems. The paper also highlights the data processing techniques that is not much utilized in this topic.

The literature of supply chain management and concurrent engineering indicates that many benefits can be achieved if suppliers are involved in product design and development. This paper proposes a single product, multiple sourcing model for evaluating and ranking potential suppliers using a multi-criteria decision making tool, the analytic hierarchy process (AHP). In addition to the criteria related to the operational aspects of the production process, such as quality and price, AHP considers other criteria related to the early stage of product design, such as end customer requirements satisfaction, technical product specifications, and supplier flexibility. The integration between operational criteria and predesign criteria, gives the decision makers the opportunity to select the suppliers that have the potential to satisfy their future demand of raw materials, components, subassemblies or services effectively and efficiently, as well as the capability to satisfy the end customers’ requirements by positively affecting the design of the finished products. The output from AHP is then used in a two-stage optimization model where a utility function that includes suppliers’ relative weights is first maximized to select the best suppliers, then a cost function is minimized to determine the amounts of raw materials, components, subassemblies or services to be ordered from every selected supplier. An application to the lubricants industry is carried out where predesign criteria such as base oil dynamic viscosity and kinematic viscosity are taken into account in the supplier selection phase to show the effectiveness of the model.