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Purpose: Faculty play a unique role in universities performing duties along the three fronts of teaching, research, and service. While it might be teaching that contributes most to the bottom line of a small university, it is often research by which faculty merit is judged. This study explores the relationships between role ambiguity, role conflict, and commitment (affective, normative and continuance) as mediated by job satisfaction among faculty members. Design/Methodology/Approach: A sample of 133 faculty members at a US-accredited university in Lebanon served as the basis for this study. The faculty members completed a survey covering scales on role ambiguity, role conflict, commitment, and job satisfaction in addition to demographic variables. Findings: We find that a decrease in role ambiguity strengthens affective and normative commitment but weakens continuance commitment. Structural equation modeling indicates that job satisfaction partially mediates the relationship between role ambiguity and affective commitment, while not mediating the relationship between role ambiguity and normative and continuance commitments. Similar findings hold for job satisfaction as a mediator in the relationship between role conflict and commitment. Originality: Based on statistical modeling, this work (1) puts forth a revised scale for organizational commitment tailored to academia and (2) provides guidance to higher education institutions in terms of the differential impacts on faculty commitment that stem from reducing role-ambiguity versus role-conflict. Managerial recommendations focus on improving normative and affective commitment through the design of policies to reduce role conflict among faculty.

Purpose This paper uses a cognitive analytics management approach to analyze, understand and solve the problems facing the implementation of information systems and help management do the needed changes to enhance such a critical process; the emergency management system in the health industry is analyzed as a case study. Design/methodology/approach Cognitive analytics management (CAM) framework (Osman and Anouz, 2014) is used. Cognitive process: The right questions are asked to understand the behavior of every process and the flow of its corresponding data; critical data variables were identified, guidelines for identifying data sources were set. Analytics process: Techniques of data analytics were applied to the selected data sets, problems were identified in user–system interaction and in the system design. The analysis process helped the management in the management process to make right decisions for the right change. Findings Using the CAM framework, the analysis to the Lebanese Red Cross case study identified system user-behavior problems and also system design problems. It identified cases where distributed subsystems are vulnerable to time keeping errors and helped the management make knowledgeable decisions to overcome major obstacles by implementing several changes related to hardware design, software implementation, human resource training, operational and human-technology changes. CAM is a novel and feasible software engineering approach for handling system failures. Originality/value The paper uses CAM framework as an approach to overcome system failures and help management do the needed changes to enhance such a critical process. This work contributes to the software engineering literature by introducing CAM as a new agile methodology to be used when dealing with system failures. Furthermore, this study is an action research that validated the CAM theoretical framework in a health emergency context in Lebanon.

Emergency Medical Services (EMS) are essential to the medical healthcare landscape. EMS play a crucial role in maximizing the overall expected survival probability of patients with various health emergencies. During the COVID-19 pandemic, peoples’ lifestyle changed and their decisions to seek medical assistance were mixed with fear. This affected the type and number of missions that EMS responded to worldwide. Furthermore, COVID-19 affected EMS systems in terms of response protocols and personal protective equipment levels. These changes influenced the EMS’ provision of these essential services – fluctuations in demand impacted capacity decisions, the need for PPE altered response times, and lockdowns influenced roadway dynamics. This research focuses on describing and analyzing the performance of a major EMS provider in Lebanon in terms of response time to emergency missions during the COVID-19 pandemic compared to previous years and to the context of EMS in other countries. Results show that the number of calls and number of missions dropped yet, the emergency response time was found to be higher than previous years. The change in response time is most strongly tied to capacity allocation and PPE protocols. This study is an initial step toward exploring EMS system dynamics, with the goal of effectively allocating resources for improved EMS service during periods of heightened uncertainty and variability.

The interconnectedness of actors is an antecedent for collective corruption, which in turn can lead to endemic corruption in a society. As a testbed for studying the effects of social interconnectedness on corrupt behaviours, we examine the domain of maritime customs. We add to our existing agent-based simulation a nuanced model of actor relatedness, consisting of clan, in-group (sect), and place of origin, and encode associated behavioural norms. We examine in simulation the effects of social interconnectedness on domain performance metrics such as container outcomes, delay, revenue, collusion, and coercive demands. Results confirm that, when corruption is widespread, localized punitive- or incentive-based policies are weakened, and that the effect of process re-engineering is frustrated when interconnectedness increases beyond a critical point, for two out of three forms of homophily connections. Our work connects with and provides a complementary methodology to works in the political economy literature., Algorithmics

This paper asks whether agent-based simulation can give insight into social factors surrounding corrupt behaviour in a technical process. The specific case study adopted, for studying the effects of social interconnectedness on corrupt behaviours, is the domain of maritime customs. Taking our previously-developed agent-based simulation, we add to the simulation a nuanced model of actor relatedness, consisting of clan, in-group (sect), and town of origin, and encode selected behavioural norms associated with these factors. Using the simulation, we examine the effects of social interconnectedness on domain performance metrics such as container outcomes, time, revenue, coercive demands, and collusion. Initial results confirm that as actor interconnectedness increases, established policies to combat corruption, such as process re-engineering, become less effective., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Algorithmics

We examine different routing strategies to cope with inaccurate time window in-formation in the context of a dynamic pick-up and delivery problem with time windows. Our experiments show that advance information, even if inaccurate, can provide benefits from a planning perspective. We propose a novel stochastic strategy that consistently performs well compared to several benchmark strategies.

This document reports data from the Lebanese red Cross regarding calls received for ambulance services in 2016. The primary aim of this report is to illustrate facets of the pre-hospital care system in Lebanon via data analysis. As the Lebanese Red Cross operates throughout Lebanon, analysis of their ambulance response data allows for a broad picture regarding how the Lebanese population uses ambulance services and what impact system structures (eg. roadway dynamics and hospital locations) have on pre-hospital care.

This paper introduces a new algorithm for enumerating regular Boolean functions. This algorithm exploits the equivalence between regular Boolean functions and positive threshold functions that can be used to represent instances of the knapsack problem. After proving this equivalence, this paper introduces a novel data structure that may, with further tweaking, enable faster enumeration algorithms for both regular Boolean functions and all-capacities knapsack problem instances.

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?” Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.

This paper describes five sets of 33 Mixed Integer Problem instances each, for a total of 165 instances, derived from a real-world full-truckload pick-up and delivery problem with time windows at the Port of Rotterdam. These instances represent 33 individual days of data encompassing 65 jobs and 40 trucks. We report, in this paper, on the structure of the real-world problem, the mechanism by which the real data was transformed into the test instances, the Mixed Integer Programming formulation used to solve these instances, the results obtained, and sources in the literature describing alternative uses for these instances.

In this paper we derive the worst-case ratio of an online algorithm for the Traveling Salesman Problem (TSP) with two disclosure dates. This problem, a variant of the online TSP with release dates, is characterized by the disclosure of a job's location at one point in time followed by the disclosure of that job's release date at a later point in time. We present an online algorithm for this problem restricted to the positive real number line. We then derive the worst-case ratio of our algorithm and show that it is best-possible in two contexts - the first, one in which the amount of time between the disclosure events and release time are fixed and equal for all jobs; and a second in which the time between disclosure events varies for each job. We conclude that the value of advanced information can be attributed to the location information alone - yielding an optimal solution in favorable instances.

Strategic workforce management decisions (e.g., hiring, training, and staffing) have a direct effect on the cost, schedule, and quality of work. This paper presents the preliminary findings of a survey of construction/site managers representing a set of building construction projects in Lebanon. The survey serves to capture current workforce conditions and management practices in an environment that is dominated by residential construction projects and a transient or migrant workforce. Despite the international location of the survey, the results indicate a worker demographic similar to that found in the southwestern United States and other areas with significant residential construction. The results also serve to highlight the significance of basic management techniques in reducing absenteeism: adopting training programs to increase the skill level of workers, providing incentives to increase the tenure of workers onsite, and arranging the working schedule to favor less overtime.

Several studies recognize the existence of a relationship between learning and experience, on one hand, and productivity and quality of work, on the other hand. This paper examines, through the use of a case study, the relationship between productivity, quality, and learning within the unique labor pool of the Middle East and North Africa (MENA) region. The analysis of the case study confirms the existence of a positive correlation between task repetition (as a proxy for learning) and productivity. The data for the case study was obtained from productivity logs prepared by subcontractors and from material submittal and shop drawings approval logs prepared by the construction manager. The analysis of the case study did not show a positive relationship between learning and quality. This outcome is in opposition with the literature.

This paper examines the value of utilizing dynamic, revenue-maximizing, congestion pricing on a privately operated tolled route, when the only alternative is a highly congested, public, free-access route. Three methods are specified by which revenue can be maximized—through the selection of a fixed percent of users to set the toll price for, through the selection of a fixed level of service on the toll road, and through the use of a nonlinear optimization model. These methods are tested on a case study involving a highly congested corridor. In simulation, the corridor’s flow rates are observed following the posting of a toll, the toll is updated, and the new flow observed until a subsequent round of revenue maximizing is applied to the new conditions. It was concluded that in a highly congested corridor a dynamic, revenue-maximizing toll can finance the construction of new capacity without degrading social welfare.

This paper describes a method for using location data to optimize the routing of pavement data collection vehicles. In much of the developed world, pavement testing is performed on a regular basis; the pavement testing data, in turn, serves as input to Pavement Management Systems. Currently, in the United States of America, state departments of transportation plan this data collection work by providing the list of roads that must be tested and then leave the routing of the vehicles to the equipment operators who typically execute the work in an ad hoc manner. This study presents the processes required to code the list of roads for testing, select appropriate hotels in the region of testing, and apply a Traveling Salesman Problem with Hotel Stops model to derive a route. Applying the processes to a case study shows significant cost savings associated with this method of roadway testing, as opposed to the current ad hoc methods.

Case studies report that corruption is not easily combated by policy changes, and that reform policies can have unexpected side-effects in practice. Using agent-based simulation, this paper studies potential anti-corruption policies in the maritime customs context. We detail an agent-based simulation calibrated on the processes of an archetypal Mediterranean container port (where in-group relationships contribute to a situation of endemic corruption), and describe insights gained about the costs and benefits of various reform policies. Results from the simulation model provide commentary on cases reported in the literature, and offer novel evaluation of policies involving process re-engineering.

In most real-world settings, a transportation plan requires modifications during execution. A thorough evaluation of transportation planning methods thus requires testing and comparison in a dynamic environment. We give conditions on a simulation environment that follow from this requirement, and propose a multi-agent simulator meeting these conditions. In addition, we propose a new measure that captures robustness in such dynamic settings. The multi-agent simulator and the robustness measure are then used to compare three different transportation methods (two multi-agent planners and one online optimization approach) in settings with release time uncertainty and truck breakdown incidents.

Decision support within transport companies should not only use traditional objective functions, but also reason about qualitative effects on all involved actors. We propose a fuzzy logic rule base that can be used in addition to traditional operation research tools to calculate not just optimal solutions, but solutions that are optimal with respect to knowledge about the preferences and long-term effects on customers, employees, and the environment. We propose a fuzzy logic based judge module that is capable of evaluating logistical performance considering all parties involved in the act transporting a container. It is based on measurements of selected key performance indicators that are fuzzified and combined into satisfaction scores of customers, employees and society. Our proposed method not only enables the continuity of the quality of planning by storing and maintaining valuable expert knowledge, but can also explain decisions based on this knowledge.

Port community systems (PCSs) can be defined as holistic, geographically bounded information hubs in global supply chains that primarily serve the interest of a heterogeneous collective of port related companies. These heterogeneous companies often include terminal operators, carriers (ocean, road, and rail), freight forwarders, enforcement agencies (i.e. customs), port authorities, and various lobby groups (including workers’ unions, environmentalists, and other policy makers). Port community systems that bring these diverse parties together in transaction recordkeeping and information sharing can serve to improve the flow of goods. Despite these potential benefits of port community systems it is challenging to find documentation on PCS implementation lessons learned. The process of designing and implementing a PCS can be roughly divided across four stages: project initiation, system analysis and design, implementation and adoption, and maintenance and growth. In this article, we document lessons learned within each life cycle stage as derived from an international scan of PCS deployments.

The term dray dates back to the 14th century when it was used commonly to describe a type of very sturdy sideless cart . In the 1700s the word drayage came into use meaning “to transport by a sideless cart”. Today, drayage commonly refers to the transport of containerized cargo to and from port or rail terminals and inland locations. With the phenomenal growth of containerized freight since the container’s introduction in 1956, the drayage industry has also experienced significant growth. In fact, according to the Bureau for Transportation Statistics, the world saw total maritime container traffic grow to approximately 417 million twenty foot equivalent units (TEUs) in 2006. Unfortunately, the drayage portion of a door-to-door container move tends to be the most costly part of the move. There are a variety of reasons for this disproportionate assignment of costs, including a great deal of uncertainty at the interface of modes. For example, trucks moving containers to and from a port terminal are often uncertain as to how long it will take them to pick up a designated container coming from a ship, from the terminal stack, or from customs. This uncertainty leads to much difficulty and inefficiency in planning a profitable routing for multiple containers in one day. We study this problem from three perspectives using both empirical and theoretical techniques.

The operation of a port, including its customs import processes, is an instance of a complex socio-technical system with multiple stakeholders. In order to provide insight for policy analysis, we posit the appropriateness of agent-based modelling techniques for the simulation of potential anti-corruption policies in the maritime customs context. We outline the design of an agent-based simulation calibrated on the customs processes of an archetypal Mediterranean container port.

Federal laws require regular condition reporting for portions of the national highway system. Most agencies use instrumented vehicles to collect data on and report the required information. The routing of these vehicles, however, is often managed in an ad hoc manner with significant expense. This paper introduces a practical optimization method for designing the routes of pavement data collection equipment. The proposed model requires, as input, only the global positioning system (GPS) coordinates of the segments' start and end points along with a set of viable hotels. The authors tested the model on two real-world instances provided by a pavement testing consultant. The optimized routes show a cost savings of up to 30% relative to the routes generated using current best-practices in the field.

In many real-life routing problems there is more uncertainty with respect to the required timing of the service than with respect to the service locations. We focus on a pickup and delivery problem with time windows in which the pickup and drop-off locations of the service requests are fully known in advance, but the time at which these jobs will require service is only fully revealed during operations. We develop a sample-scenario routing strategy to accommodate a variety of potential time realizations while designing and updating the routes. Our experiments on a breadth of instances show that advance time related information, if used intelligently, can yield benefits. Furthermore, we show that it is beneficial to tailor the consensus function that is used in the sample-scenario approach to the specifics of the problem setting. By doing so, our strategy performs well on instances with both short time windows and limited advance confirmation.

This paper brings forth from the literature a series of learning curve models and evaluates them through the lens of the construction industry. The review suggests that there is still no consensus on which model provides the best fit and predictability for construction data. As such, this paper puts forth a new model that is suitable for the modern construction industry as it accommodates for both mechanization and forgetting. The proposed model is similar to the Wright model (an exponential model of learning), but, through recursion, places more emphasis on recent data. The proposed model shows an error of less than 1% when predicting the cumulative average completion times in three out of four cases examined.

Many full truckload pick-up and delivery problems in the intermodal freight container transport industry can be modeled as Asymmetric Traveling Salesman Problems (ATSPs). Several authors have noted that while ATSPs are NP-hard, some instances are readily solved to optimality in only a short amount of time. Furthermore, the literature contains several references to the Stacker Crane Problem (SCP) as an “easy” problem amidst the ATSPs. We put this hypothesis to test by using statistical methods to build a model relating measurable distance matrix structures to the amount of time required by two existing exact solvers in finding solutions to over 500 ATSP instances. From this analysis we conclude that SCPs are not necessarily easier than other ATSPs, but a special subset of SCPs, termed drayage problems, are more readily solved. We speculate that drayage problems are “easy” because of a comparatively high number of zeros in symmetric locations within the distance matrix. In real-world drayage problems (i.e. the movement of containers a short distance to/from a port or rail terminal), these zeros correspond to the prevalence of jobs originating at or destined to a fixed number of freight terminals.

Freight planning is an increasingly visible topic in the arena of transportation planning. Freight planning metrics and benchmarks can help state agencies select and assess the impact of improvement projects with respect to freight mobility. Given the unique patterns of freight flow, freight metrics are most valuable when derived from freight-specific data. Extraction of timely freight-specific data to support freight planning efforts is not, however, always straightforward. Washington State, the primary focus of this study, has been developing potential methodologies for extracting and applying freight-specific data in benchmarking freight projects. The Freight Mobility Strategic Investment Board (FMSIB) of Washington State deployed intelligent transportation systems (ITS) technologies [Global Positioning Systems (GPS) and automated vehicle identification (AVI)] with the stated goal of collecting freight-specific data. An examination of the data collected for the FMSIB benchmarking test reveals that both sources of data—GPS and AVI—can support the freight planning process. This paper highlights results of the FMSIB study, the comparative accuracy of the two data sources, and the uses of each for planning purposes.

In many real-life routing problems there is more uncertainty with respect to the required timing of the service than with respect to the service locations. We focus on a pickup and delivery problem with time windows in which the pickup and drop-off locations of the service requests are fully known in advance, but the time at which these jobs will require service is only fully revealed during operations. We develop a sample-scenario routing strategy to accommodate a variety of potential time realizations while designing and updating the routes. Our experiments on a breadth of instances show that advance time related information, if used intelligently, can yield benefits. Furthermore, we show that it is beneficial to tailor the consensus function that is used in the sample-scenario approach to the specifics of the problem setting. By doing so, our strategy performs well on instances with both short time windows and limited advance confirmation.