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The transportation industry is characterized as a capital-intensive industry that plays a crucial role in economic and social development, and the rapid expansion of this industry has led to serious environmental problems, which makes the eco-efficiency analysis of the transportation industry an important issue. Previous research paid little attention to the regulatory scenarios and suffered from the incomparability problem, hence this paper aims to reasonably estimate the eco-efficiency and identify its evolutionary characteristics. We measure the eco-efficiency and the corresponding global Malmquist–Luenberger productivity index using a modified model of the data envelopment analysis framework, in which different regulatory constraints are incorporated. Based on the empirical study on the transportation industry of thirty provinces in China, we find that the eco-efficiency of Chinese transportation industry experienced a slight increase during 2015–2016, a sharp decline during 2016–2017, and a continuous rise since year 2017. The Middle Yangtze River area was the best performer among the eight regions in terms of eco-efficiency, while the Southwest area was placed last. The global Malmquist–Luenberger productivity index showed an earlier increase and later decrease trend, which was quite consistent with the reality of the variation of inputs and outputs and the emergence of COVID-19. Moreover, the best practice gap change was found to be the main driven force of productivity. The empirical results verify the practicability of our measurement models and the conclusions can be adopted in guiding the formulation of corresponding policies and regulations.
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The outbreak of SARS-CoV-2 and the corresponding surge in patients with severe symptoms of COVID-19 put a strain on health systems, requiring specialized material and human resources, often exceeding the locally available ones. Motivated by a real emergency response system employed in Northern Italy, we propose a mathematical programming approach for rebalancing the health resources among a network of hospitals in a large geographical area. It is meant for tactical planning in facing foreseen peaks of patients requiring specialized treatment. Our model has a clean combinatorial structure. At the same time, it considers the handling of patients by a dedicated home healthcare service, and the efficient exploitation of resource sharing. We introduce mathematical programming heuristic based on decomposition methods and column generation to drive very large-scale neighborhood search. We evaluate its embedding in a multi-objective optimization framework. We experiment on real world data of the COVID-19 in Northern Italy during 2020, whose aggregation and post processing is made openly available to the community. Our approach proves to be effective in tackling realistic instances, thus making it a reliable basis for actual decision support tools.
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This paper aims at proposing a novel multiattribute group decision-making (MAGDM) method in complex decision-making environments. To this end, we first introduce a tool, called q-rung interval-valued probabilistic dual hesitant fuzzy sets (q-RIVPDHFSs), for decision makers to express their evaluation information over a set of finite alternatives in MAGDM procedures. The q-RIVPDHFS consists of some possible membership and nonmembership degrees, along with their interval-valued probabilistic information. Due to this structure, q-RIVPDHFSs are more powerful and flexible than the traditional q-rung probabilistic q-rung dual hesitant fuzzy sets, in which probabilistic information of membership and nonmembership degree is denoted by crisp numbers. Second, some other related concepts of q-RIVPDHFSs, such as operational laws, comparison method, distance measure, and aggregation operators, are introduced. Third, based on these novel concepts, two MAGDM methods (Algorithms 1 and 2) are put forward. Last but not least, a practical decision-making example is provided to show the effectiveness of our proposed MAGDM method. We also compare our Algorithms 1 and 2 with some existing decision-making methods to explain why our methods are more powerful and useful.
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PurposeThis paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.Design/methodology/approachThe weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.FindingsCovid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.Originality/valueThis paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors' knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.
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As Chamadas Públicas para compra de Cestas Verdes do Programa de Aquisição da Produção da Agricultura foram criadas pelo governo do Distrito Federal para atender pessoas em situação de vulnerabilidade alimentar e ao mesmo tempo apoiar os agricultores familiares que, em decorrência do fechamento de feiras livres e restaurantes para consumo no local, foram indiretamente afetados pela pandemia de COVID-19. As Cestas Verdes do programa são compostas por uma seleção de frutas, legumes e verduras, divididas em grupos, sendo que cada cesta deve ser composta por uma quantidade mínima em quilogramas de cada grupo. Entre o período de 2020 e 2021 foram realizadas quatro chamadas públicas. Espera-se ser relevante um modelo de apoio à decisão para o agricultor, utilizando a programação matemática, por intermédio de restrições de quantidade demandada e quantidade de produtos disponíveis, direcionando o planejamento tático do agricultor familiar com relação a melhor combinação de alimentos, visando a minimização dos custos de composição das cestas e atendimento das regras do edital. Desta forma, esse trabalho propõe um modelo matemático para apoio à decisão do agricultor que desejar aderir ao programa Cestas Verdes, com aplicação no Solver do Excel. Os resultados das simulações mostraram que os valores pagos pelo governo por Cestas Verdes têm cada vez menos se tornado benéficos para os produtores.Alternate :The Public Calls for the purchase of Green Food Baskets from the Agricultural Production Acquisition Program were created by the Federal District government to serve people in a situation of food vulnerability and at the same time support family farmers who, as a result of the closure of free markets and restaurants for on-site consumption, were indirectly affected by the pandemic of COVID-19. The program's Green Baskets are made up of a selection of fruits, vegetables and greens, divided into groups, with each basket consisting of a minimum amount in kilograms of each group. Between 2020 and 2021 four public calls were made. It is expected to be relevant a decision support model for the farmer, using mathematical programming, through constraints of quantity demanded and quantity of available products, directing the tactical planning of the family farmer with respect to the best combination of food, aiming to minimize the costs of composition of the baskets and compliance with the rules of the public notice. Thus, this work proposes a mathematical model to support the decision of the farmer who wishes to join the Green Food Basket program, with application in Excel's Solver. The results of the simulations showed that the amounts paid by the government for Cestas Verdes have become less and less beneficial to the producers.
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This paper develops an integrated methodology aimed at diagnosing supply chain resilience in terms of (1) internal dynamic capabilities of an enterprise, and (2) resilience of its suppliers. In addition, unlike other research, it integrates the suppliers' resilience evaluation into the order size allocation plan. Multi-attribute decision making (MADM) algorithms were employed to quantify the relative importance to evaluate the internal and external resilience of an enterprise. Furthermore, the MADM output was combined with a multi-objective programming model formulated to solve the order size problem considering economic and resilience objectives. The applicability of the developed methodology is demonstrated via a dairy manufacturing enterprise that suffered from disruptions attributed to COVID-19. The results translate the enterprise's non-viable manufacturing due to its poor external and internal resilience profiles. It is emphasized that if an enterprise fails to develop internal capabilities such as readiness and sensing, the enterprise could also fail in managing external resilience. A resilient supply chain requires a blend of internal and external resilience. This work represents the first quantitative attempt to provide a unified methodology for identifying and measuring internal and external resilience.
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Industrial automation has become increasingly more prominent in many industries, such as manufacturing, automotive, pharmaceuticals, and food processing industries, as the technology evolves and Industry 4.0 revolution advances. Th demand of automation and personnel with automation skills has ever been increasing since Covid-19 Pandemic. Industrial robots and machine vision inspection are essential systems for manufacturing automation. Industrial robots are capable of performing various tasks like part handling, machine tending, assembly, palletizing, arc welding, or laser cutting with high speeds, repeatability and accuracy. Machine Vision Inspection (MVI) systems are used for part quality inspection, manufacturing and assembly supervision and robot guidance. A MVI system integrated with an industrial robot provides a hand-eye coordination to the robot for flexible material handling and operations. Vision-guided robotics serves as the next-generation research instrument that opens new opportunities to advance the boundaries in science and engineering research. This paper focuses on teaching industrial robot programming to engineering students using an offline virtual robotic simulation software, Fanuc ROBOGUIDE and iRVision software. Using a virtual robot and offline programming with ROBOGUIDE reduces a risk by enabling visualization of the robot operations before an actual installation and operations. The ROBOGUIDE software will provide students with an experience of programming an industrial robot and will enhance the effectiveness of the teaching and learning process. The developed programs can be imported and implemented onto a real robot with a minimum configuration setup. The step by step approach of developing and programming a 2D vision guided material handling cell using ROBOGUIDE has been discussed in the paper such that other educators and students can learn and implement the project with ease. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.
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Economists and policy makers are interested in producers' responses to policies in order to achieve some national or sectoral objectives, e.g., growth, employment, food security. The way producers respond to policy depends on their production function. If producers do not have homogenous production function, policy responses will be heterogeneous. We use the underlying functional relationship to derive homogenous groupings. The paper employs finite regression mixture models to specify and estimate farm groups with regard to pre-specified functional relationship. The proposed approach is illustrated with regard to the aggregate production function of Kosovo agriculture, characterised by high prevalence of small farmers. The results point out to two farm clusters. The first one extracts more output from labour and intermediate consumption. The second one makes a better use of land. Perhaps, surprisingly, both clusters appear quite similar in terms of their stock of production inputs. Cluster 1 however appears to be more specialised. We can conclude that in Kosovo agriculture appearances and size are not primary determinants of productivity.
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The year 2020 saw the world turned upside down by the coronavirus pandemic. Countless human activities were suspended or cancelled as the virus spread across the globe. In this paper, we show how the regular season matches of Ecuador's professional football league were rescheduled due to the disruption caused by the pandemic. As with many others, this league had to reschedule its remaining games to fit within in a much shorter period of time than originally planned. To address this problem, we developed two mathematical models that designed new match calendars. The first one, a round assignment model, rescheduled the various rounds in the season still to be played while the second one, a day assignment model, took the solutions of the first model as input to assign the matches within each round to specific days. The implementation of our models secured a well-balanced number of days off before each match across all of the teams. Also, it enabled the league to conclude a full season without cancelling any matches or changing the schedule format, unlike what occurred in many other leagues, and won the approval of all stakeholders including league officials, players, team coaches, the TV broadcaster and fans. © 2022 The Author(s).
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Public health emergencies will pose an enormous challenge to healthcare service systems. As COVID-19 rage across the globe, we realize that COVID-19 exposes the problem of inadequate research on the dispatch of emergency medical personnel in response to a major epidemic outbreak. In the face of major public health emergencies, failure in timely satisfaction of healthcare demands by local healthcare professionals necessitates human resource support from other regions. To address this issue, further research is needed to gain better insights into interregional emergency human resource allocation. This paper aims to offer attention to patients' medical needs and suppose that there are support hubs outside the outbreak region offering an external supply of medical personnel. The hospitals in these support hubs are categorized based on variables such as capacity, medical capability, and the number of dispatched personnel per day. An interregional emergency allocation model was established to consider the proper doctor-patient ratio and nurse-patient ratio in emergency response using methods such as mathematical programming. And relevant management suggestions were then offered via analysis. Research in this paper provides allocation models and proposals that healthcare professionals can refer to when making resource allocation decisions in emergency response. © 2022 ACM.
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With the rapid increase of digitization and desire for contactless shopping during the COVID-19 pandemic, online grocery sales keep growing fast. Correspondingly, optimized policies for order picking are nowadays central in omnichannel supply chains, not only within dedicated warehouses but also in grocery stores while processing online orders. In this work, we apply the Buy-Online-Pick-up-in-Store concept and optimize the in-store picking and packing procedure. The approach we propose, which is based on two mathematical programming models, guides pickers on how to organize articles into bags while collecting items. In this way bags are filled up evenly and they are ready to be handled to the customers at the end of each picking task, with no further rearrangement needed.
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Solving the absolute value equation (AVE) is a nondifferentiable NP-hard and continuous optimization problem with a wide range of applications. Because its solutions have different forms, it is challenging to design the most efficient algorithm that can solve different AVEs without using overcomplicated technical improvement and problem-dependent objectives. Hence, this paper proposed an improved glowworm swarm optimization (GSO) algorithm with an adaptive step size strategy based on the sigmoid function (SIGGSO) that solves the AVEs. Seven test AVEs, including multisolution and high-dimensional AVEs, are selected for testing and compared with seven metaheuristic algorithms. The experimental results show that the proposed SIGGSO algorithm has higher solution accuracy and stability when seeking multiple solution of AVEs compared to the basic GSO. Moreover, it obtains competitive advantages on multisolution and high-dimensional AVEs compared with other metaheuristic algorithms and provides an effective method for engineering and scientific calculations.
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Short-term car rental services, i.e., carsharing, is a solution that has been developing better and better in urban transport systems in recent years. Along with intensive expansion, service providers have to face an increasing number of challenges to compete with each other. One of them is meeting the expectations of customers about the fleet of vehicles offered in the system. While this aspect is noticed in the literature review mainly in terms of fleet optimization and management, there is a research gap regarding the appropriate selection of vehicle models. In response, the article was dedicated to identifying the vehicles that were best suited to carsharing systems from the point of view of frequent customers. The selection of appropriate vehicles was treated as a multi-criteria decision issue, therefore the study used one of the multi-criteria decision support methods—ELECTRE III. The work focuses on researching the opinions of users (experts) who often use carsharing services in Poland. The study included a list of the most popular vehicles in Europe in 2021, including classic, electric, and hybrid cars, and a list of 11 evaluation criteria. The research results indicate for frequent users the advantage of conventional drive vehicles over electric and hydrogen vehicles. Moreover, they indicate that the best vehicles are relatively large cars (European car segments C and D) with the greatest possible length, boot capacity, engine power, number of safety systems, and quality. On the other hand, the least important issues are the number of seats in the vehicle and the number of doors. Interestingly, the vehicles selected by frequent users questioned the concept of small city cars, which occupied a small public space on which carsharing was supposed to focus. The results obtained support the operators of carsharing services in making fleet decisions.
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In order to cope with the sudden disasters such as floods, COVID-19,etc.,a discrete time Markov chain and multi-objective programming model（DTMC-MOP） with the maximum supply satisfaction rate, the shortest supply time and the lowest supply cost was proposed to dynamically identify, analyze and respond to the emergency supply chain risk.The improved self-adaptive Non-dominated Sorting Genetic Algorithm-Ⅱ（NSGA-Ⅱ） was used to solve the optimization model, and the feasibility and effectiveness of the model were verified by testing and evaluation with standard test functions.Through the example analysis, the Pareto optimal front with higher precision and more uniform distribution was obtained.The results showed that the decision-maker could choose the appropriate emergency scheme based on the core objective of emergency management or different preferences.It provide a scientific method for the decision-making optimization of emergency supply chain, which has positive significance for ensuring the life safety of victims and maintaining the social harmony and stability.
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Postal delivery is the world's longest-running service business process. Every city on the planet has this service, which is supported by a network of various depots serving as collection and delivery points. In distributing postal item delivery, Pos Indonesia through the Postal Processing Centre (PPC) is a distribution streamline that connects cities and regions with delivery centres closer to the final customer. Pos Indonesia has previously established a distribution pattern based on a zone-based system comprising a primary distribution centre and various delivery centres, delivery schedules, internal fleets, and predetermined routes.However, with reduced production capacity, as demonstrated by reducing traditional mail and less than truckload operations and anticipating customer needs for timely delivery, delivery patterns with fixed schedules and the internal fleet are unable to address this issue. Close-Open Mixed Vehicle Routing Problem (COMVRP) is proposed to optimize delivery by involving external fleets, and this is to reduce transportation costs where external fleets do not have fixed costs and do not need to return to PPC after delivery. The Genetic Algorithm is used to find heuristic solutions for each delivery history after Nearest Neighbor (NN)implementationto validate vehicle routes. Combined COMVRP-NN-GA produces a set of solutions, which will be used in the Monte Carlo Simulation (MCS). The results demonstrate that COMVRP outperforms existing scenarios, from optimizing six-vehicle routes to estimating five routes using a single external vehicle route, significantly reducing total mileage. The simulation findings based on the historical delivery dataset may be used to develop future delivery patterns based on the number of vehicles and total route distance and reduce transportation costs.
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Selecting the best place for constructing a renewable power plant is a vital issue that can be considered a site-selection problem. Various factors are involved in selecting the best location for a renewable power plant. Therefore, it categorizes as a multi-criteria decision-making (MCDM) problem. In this study, the site selection of a wind power plant is investigated in a central province of Iran, Semnan. The main criteria for classifying various parts of the province were selected and pairwise compared using experts’ opinions in this field. Furthermore, multiple restrictions were applied according to local and constitutional rules and regulations. The Analytic Hierarchy Process (AHP) was used to weigh the criteria, and according to obtained weights, wind speed, and slope were the essential criteria. Moreover, a geographic information system (GIS) is used to apply the weighted criteria and restrictions. The province’s area is classified into nine classes according to the results. Based on the restrictions, 36.2% of the total area was unsuitable, mainly located in the north part of the province. Furthermore, 2.68% (2618 km2) and 4.98% (4857 km2) of the total area are the ninth and eightieth classes, respectively, which are the best locations for constructing a wind farm. The results show that, although the wind speed and slope are the most essential criteria, the distance from power facilities and communication routes has an extreme impact on the initial costs and final results. The results of this study are reliable and can help to develop the wind farm industry in the central part of Iran.
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Purpose>With the proliferation of e-commerce companies, express delivery companies must increasingly maintain the efficient expansion of their networks in accordance with growing demands and lower margins in a highly uncertain environment. This paper provides a framework for leveraging demand data to determine sustainable network expansion to fulfill the increasing needs of startups in the express delivery industry.Design/methodology/approach>While the literature points out several hub assignment methods, the authors propose an alternative spherical-clustering algorithm for densely urbanized population environments to strengthen the accuracy and robustness of current models. The authors complement this approach with straightforward mathematical optimization and simulation models to generate and test designs that effectively align environmentally sustainable solutions.Findings>To examine the effects of different degrees of demand variability, the authors analyzed this approach's performance by solving a real-world case study from an express delivery company's primary market. The authors structured a four-stage implementation framework to facilitate practitioners applying the proposed model.Originality/value>Previous investigations explored driving distances on a spherical surface for facility location. The work considers densely urbanized population and traffic data to simultaneously capture demand patterns and other road dynamics. The inclusion of different population densities and sustainability data in current models is lacking;this paper bridges this gap by posing a novel framework that increases the accuracy of spherical-clustering methods.
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Decision-making in complex systems is undoubtedly quite difficult, mostly under exceptional circumstances. Indeed, in the context of international market selection, the COVID-19 pandemic has made pharmaceutical export decisions more complex. Several scientific approaches are used by researchers as well as practitioners to guide in this area. In particular, Operations Research techniques, including linear programming, discrete event simulation and queuing theory, are called by organizational leaders to make highquality decisions. This study presents a Benchmarking methodology to support the decision-making process for international market selection based on the Data Envelopment Analysis method. A computational numerical study was conducted to highlight the performance of the proposed approach. © 2022 IEEE.
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With the increasing demand for fast, accurate, and reliable biological sensor systems, miniaturized systems have been aimed at droplet-based sensor systems and have been promising. A micro-electrode dot array (MEDA) biochip, which is one kind of the miniaturized systems for biochemical protocols such as dispensing, dilutions, mixing, and so on, has become widespread due to enabling dynamical control of the droplets in microfluidic manipulations. In MEDA biochips, the electrowetting-on-dielectric (EWOD) technique stands out since it can actuate droplets with nano/picoliter volumes. Microelectrode cells on MEDA actuate multiple droplets simultaneously to route locations for the purpose of the biochemical operations. Taking advantage of the feature, droplets are often routed in parallel to achieve high-throughput outcomes. Regarding parallel manipulation of multiple droplets, however, the droplets are known to be initially placed at a distant position to avoid undesirable mixing. The droplets thus result in traveling a long way for a manipulation, and the required biochip size for routing is also enlarged. This paper proposes a routing method for droplets to reduce the biochip size on a MEDA biochip with the allowance of splitting during routing operations. We mathematically derive the routing problem, and the experiments demonstrate that our proposal can significantly reduce the biochip size by 70.8% on average, compared to the state-of-the-art method.