Multi-objective integrated acyclic crew rostering and vehicle assignment problem in public bus transportation

In this study, we try to solve a real planning problem faced in public bus transportation. It is a multi-objective integrated crew rostering and vehicle assignment problem. We model this problem as a multi-objective set partitioning problem. Most of the time, crew rostering problem with a single-objective function is considered, and the output may not satisfy some transport companies. To minimize the cost and maximize the fairness of the workload among the drivers, we define many criteria. Although crew rostering problem and its integrated versions appear in the literature, it is the first time these two problems are integrated. We propose a new multi-objective tabu search algorithm to obtain near Pareto-optimal solutions. The algorithm works with a set of solutions using parallel search. We test our algorithm for the case with ten objectives and define a method to choose solutions from the approximated efficient frontier to present to the user. We discuss the performance of our meta-heuristic approach.

     

A static empty equipment allocation model for long-haul networks with constrained crew routes

A realistic and efficient tactical model is developed that is able to optimize equipment and crew movements in long-haul trucking networks so that drivers are able to return home within a reasonable amount of time. A unique feature of the model is that driver, tractor, and trailer routes are simultaneously optimized. An underlying assumption is that routes remain in place for extended periods; though the trailers, tractors, and drivers may flow across these routes at variable rates. We formulate a static linear programming model for this tactical problem, and propose a column generation algorithm. The algorithm is tested with real data from a less-than-truckload carrier and randomly generated test data. Networks with up to 40 nodes (including the 30-node real-life example) are successfully optimized. For our sample problems, we found that static routes produced solutions within an average 0.22% of the optimum under dynamic conditions, provided that variable flows are permitted across the static routes.     

A graph partitioning strategy for solving large-scale crew scheduling problems

Railway crew scheduling deals with generating driver duties for a given train timetable such that all work regulations are met and the resulting schedule has minimal cost. Typical problem instances in the freight railway industry require the generation of duties for thousands of drivers operating tens of thousands of trains per week. Due to short runtime requirements, common solution approaches decompose the optimization problem into smaller subproblems that are solved separately. Several studies have shown that the way of decomposing the problem significantly affects the solution quality. An overall best decomposition strategy for a freight railway crew scheduling problem, though, is not known. In this paper, we present general considerations on when to assign two scheduled train movements to separate subproblems (and when to rather assign them to the same subproblem) and deduct a graph partitioning based decomposition algorithm with several variations. Using a set of real-world problem instances from a major European railway freight carrier, we evaluate our strategy and benchmark the performance of the decomposition algorithm both against a common non-decomposition algorithm and a lower bound on the optimal solution schedule. The test runs show that our decomposition algorithm is capable of producing high-quality solution schedules while significantly cutting runtimes compared to the non-decomposition solution algorithm. We are following a ”greenfield” approach, where no information on previous schedules is needed. Hence, our approach is applicable to any railway crew scheduling setting, including network enlargement, integration of new customers, etc.     

Strategic network design for multi-zone truckload shipments

In recent years, the truckload trucking industry has faced a serious problem in retaining drivers. The primary reason for this problem is the very-long driver tour lengths that keep drivers on the road for extended time periods. In this paper, we present a mathematical model for a multi-zone dispatching method that addresses this issue. In addition to reducing driver tour lengths to desirable levels, the model captures important aspects of other practical problems via unique constraints that address the perspectives of both the truckload company and the customers. Using this model, we first provide insights into various aspects of the problem as well as the nature of the interaction among the problem components that pertain to these perspectives. We later develop a construction heuristic and a tabu search framework, which considers driver tour length constraints (a major determinant of driver turnover rate) to solve the model. We provide computational results illustrating the excellent performance of the proposed solution procedure.     

An integrated approach to the vehicle routing and container loading problems

Real-world distribution problems raise some practical considerations that usually are not considered in a realistic way in more theoretical studies. One of these considerations is related to the vehicle capacity, not only in terms of cubic meters or weight capacity but also in terms of the cargo physical arrangements. In a distribution scene, two combinatorial optimization problems, the vehicle routing problem with time windows and the container loading problem, are inherently related to each other. This work presents a framework to integrate these two problems using two different resolution methods. The first one treats the problem in a sequential approach, while the second uses a hierarchical approach. To test the quality and efficiency of the proposed approaches, some test problems were created based on the well-known Solomon, Bischoff and Ratcliff test problems. The results of the integrated approaches are presented and compared with results of the vehicle routing problem with time windows and the container loading problem applied separately.     

Factors influential in making an injury severity difference to older drivers involved in fixed object-passenger car crashes

To identify factors influencing severity of injury to older drivers in fixed object-passenger car crashes, two sets of sequential binary logistic regression models were developed. The dependent variable in one set of models was driver injury severity, whereas for the other it was the crash severity (most severe injury in the crash). For each set of models, crash or injury severity was varied from the least severity level (no injury) to the highest severity level (fatality) and vice versa. The source of data was police crash reports from the state of Florida. The model with the best fitting and highest predictive capability was used to identify the influence of roadway, environmental, vehicle, and driver related factors on severity. Travel speed, restraint device usage, point of impact, use of alcohol and drugs, personal condition, gender, whether the driver is at fault, urban/rural nature and grade/curve existence at the crash location were identified as the important factors for making an injury severity difference to older drivers involved in single vehicle crashes.     

The influence of driver distraction on the severity of injuries sustained by teenage drivers and their passengers

Studies show that teenage drivers are at a higher risk for crashes. Opportunities to engage in technology and non-technology based distractions appear to be a particular concern among this age group. An ordered logit model was developed to predict the likelihood of a severe injury for these drivers and their passenger using a national crash database (the 2003, U.S. DOT-General Estimate System [GES]). As one would expect, speeding substantially increases the likelihood of severe injuries for teenage drivers and their passengers. The results of the analysis also reveal that teenage drivers have an increased likelihood of more severe injuries if distracted by a cell phone or by passengers than if the source of distraction was related to in-vehicle devices or if the driver was inattentive. Additionally, passengers of teenage drivers are more likely to sustain severe injuries when their driver is distracted by devices or passengers than with a non-distracted or inattentive driver. This supports the previous literature on teenage drivers and extends our understanding of injuries for this age group related to distraction-related crashes.     

Integrated planning of loaded and empty container movements

Efficiently planning drayage operations is an important task for transportation companies since these operations constitute a large part of the cost of an intermodal transport. In this paper, a full truckload vehicle routing problem for transporting loaded and empty containers in drayage operations is studied. For empty container transports, either the origin or the destination is not predefined. The problem is formulated as an asymmetric multiple vehicle Travelling Salesman Problem with Time Windows (am-TSPTW). Two solution approaches are proposed: a sequential and an integrated approach. For both approaches, a single- and a two-phase deterministic annealing algorithm are presented. Results show that the proposed algorithms are able to find good quality solutions in a small amount of computation time. The integrated approach clearly outperforms the sequential one and the results confirm the advantage of using a two-phase algorithm for vehicle routing problems with hierarchical objectives. Finally, it is shown that the proposed integrated solution method improves previous results on a similar problem.     

An integrated formulation for hierarchical cast design problems in the steel making industry

Production design is a key decision problem of steel making industry but due to its complexity, solution properties are not well understood. The design problem can be viewed as a multi-stage problem of bin packing, matching and sequencing sub-problems. Traditionally a sequential approach which treats each sub-problem separately has been applied to the simple case where mould width change is not allowed. However, for more general cases where casting with width change and different width is allowed, the sequential approach fails to generate good solutions. In this paper, we propose a mathematical programming formulation which can solve the design problem in an integrative way. By introducing multi-layer network representation of the cast design problem, it is now possible to generate an integrated formulation for the proposed problem. Based on the formulation, we derive a heuristic algorithm. The algorithm is adopted and tested at a large international integrated steel mill. The computational tests with real data-sets show that the proposed algorithm is quite effective and practical.     

An on-road network analysis-based approach to studying driver situation awareness at rail level crossings

Crashes between cars and trains at rail level crossings are problematic worldwide. Despite this, key facets of driver behaviour at rail level crossings, such as situation awareness and decision making, remain ambiguous. This is largely down to the inability of existing methodologies to describe or evaluate the cognitive aspects of driver behaviour when negotiating rail level crossings. This paper showcases an on-road approach for examining driver situation awareness at rail level crossings. The study presented involved participants, classified either as novice or experienced drivers, providing concurrent verbal protocols as they drove a pre-determined urban route incorporating four rail level crossings. Driver situation awareness was modelled using a network analysis-based approach and the structure and content of the networks was assessed. The analysis revealed key differences between novice and experienced drivers situation awareness at rail level crossings. In closing, the benefits of the on-road approach are discussed and a series of wider driver behaviour applications are proposed.     

Logistics optimisation of slab pre-marshalling problem in steel industry

We study the slab pre-marshalling problem to re-position slabs in a way that the slabs are stored in the least number of stacks and each stack contains only the slabs of the same group, which can be utilised interchangeably. In this way, when a slab of any group is required, the topmost slab can always be picked up without shuffling. During pre-marshalling, however, at most two slabs can be moved by one operation. In this paper, we present a network model with three valid inequalities to solve this problem. With a small amount of labelled data from the model approach, a self-training technique is applied to train a function for predicting the best next move. Then, a new hybrid algorithm is developed to solve the practical problems by combining the self-training technique, heuristics, and the branch-and-bound algorithm with five dominance rules. The experimental results demonstrate the effectiveness of this network model and valid inequalities, and the performances of different components of this algorithm. The new algorithm produces high-quality solutions within seconds.     

Book review of Global Strategic Management,by P. Lasserre,Palgrave Macmillan,Basingstoke, 2003, xx + 454 pp., £24.99, softcover (ISBN 0 333 79375 7)

This paper considers the simultaneous scheduling of material handling transporters (such as automatic guided vehicles or AGVs) and manufacturing equipment (such as machines and workcentres) in the production of complex asembled product. Given the shipping schedule for the end-items, the objective of the integrated problem is to minimize the cumulative lead time of the overall production schedule (i.e. total makespan) for on-time shipment, and to reduce material handling and inventory holding costs on the shop-floor. The problem of makespan minimization is formulated as a transportation integrated scheduling problem, which is NP-hard. For industrial size problems, an effective heuritsic is developed to simultaneouly schedule manufacturing and material handling operations by exploting the critical path of an integrated operation network. The performance of the proposed heuristic is evaluated via extensive numerical studies and compared with the traditional sequential scheduling approach. The superiority of the integrated heuristic is well documented.     

Quasi-induced exposure method: Evaluation of not-at-fault assumption

Crash rates are used to establish the relative safety of various variables of concern such as driver classes, vehicle types and roadway components. Appropriate exposure data for estimating crash rates is critical but crash databases do not contain information on driver or vehicle exposure. The quasi-induced exposure method, which uses not-at-fault driver/vehicle data as an exposure metric, is a technique used in order to overcome this problem. The basic assumption made here is that not-at-fault drivers represent the total population in question. This paper examines the validity of this assumption using the Kentucky crash database to define two samples of not-at-fault drivers. One sample included only not-at-fault drivers selected from the first two vehicles in a multi-vehicle crash (two or more vehicles involved) while the other included the not-at-fault drivers from multi-vehicle crashes with more than two vehicles involved and excluding the first two drivers. The assumption is that the randomness of the involvement of drivers in the second sample is more reasonable than the drivers in the first two vehicles involved in crashes. The results indicate that these two samples are similar; there is no statistical evidence demonstrating that both samples represent two different populations in the maneuvers and other variables/factors examined here; and they are representative simple random samples of the driver population with respect to the distribution of the driver age when there is no reasonable doubt about investigating officers’ judgments. Thus, estimating relative crash propensities for any given driver type by using the quasi-induced exposure approach will yield reasonable estimates of exposure.     

Estimating the accident potential of an Ontario driver

To run a "demerit point" program, one uses routinely available information about drivers to identify those who are most likely to have an accident in the near future. On the basis of a four-year record for a large sample of Ontario drivers, we have examined several tools for the identification of such drivers and investigated how they perform. Each driver is thought to have an expected number of accidents, m. In a group of drivers with common traits (such as age, gender, record of convictions and accidents) the ms have a mean E(m) and a variance VAR(m). Estimates of E(m) and VAR(m) for all combinations of traits can be obtained within the framework of a multivariate statistical model. The same estimates can then be used to judge how well a model identifies drivers who have a large m. In such a multivariate model it is important to use data about previous accidents and convictions. However, the accuracy with which the m of a driver can be estimated is not improved much by distinguishing between offence type or between accidents as being "at fault" or "not at fault". Without much loss in estimation accuracy, one may attach a weight 1 to a conviction and 2 to an accident. Model performance is described in tangible terms: how many accidents are recorded by the drivers identified by a model, what proportion of identified drivers are "false positives," how many drivers with high m remain unidentified. We conclude that by using a multivariate statistical model one can do substantially better than by using a demerit point scheme in which points are assigned to offenses on the basis of their perceived seriousness. However, even when the best model is used to identify a large group of drivers, many will be false positives.     

The driver,the road,the rules … and the rest? A systems-based approach to young driver road safety

The persistent overrepresentation of young drivers in road crashes is universally recognised. A multitude of factors influencing their behaviour and safety have been identified through methods including crash analyses, simulated and naturalistic driving studies, and self-report measures. Across the globe numerous, diverse, countermeasures have been implemented; the design of the vast majority of these has been informed by a driver-centric approach. An alternative approach gaining popularity in transport safety is the systems approach which considers not only the characteristics of the individual, but also the decisions and actions of other actors within the road transport system, along with the interactions amongst them. This paper argues that for substantial improvements to be made in young driver road safety, what has been learnt from driver-centric research needs to be integrated into a systems approach, thus providing a holistic appraisal of the young driver road safety problem. Only then will more effective opportunities and avenues for intervention be realised.     

The impact of driver age on lost life years for other road users in France: A population based study of crash-involved road users

BACKGROUND: One of the concerns in road safety is the threat older drivers may pose to other road users. Using the rate of lost life years, the present study provides a public health approach to quantify this potential threat. METHODS: A total of 1570686 motorised vehicle drivers or motorcycle riders and 652246 non-drivers, i.e. vehicle passengers, pedestrians and cyclists involved in injury crashes in France between 1996 and 2004, were included in a population based cross-sectional study. Fatality rates and rate of lost life years for each crash-involved driver age class were calculated for the drivers themselves and for other road users. RESULTS: The study has shown a significant reduction in the rate of lost life years for crash-involved other road users (whether passengers, pedestrians, cyclists or opposing drivers) as driver age increases. Other road users lost half as many years of life when involved in crashes with drivers aged over 85 than with drivers under 65 (1.26 and 2.32 per 100 expected remaining life years, respectively). CONCLUSION: Our findings suggest that among road users involved in injury crashes, older drivers are less dangerous for the other road users. By attributing other road users' lost life years to each driver age, this study represents a new contribution to the debate about ageing and road safety.     

Revisiting the concept of the ‘problem young driver’ within the context of the ‘young driver problem’: Who are they?

For decades there have been two young driver concepts: the ‘young driver problem’ where the driver cohort represents a key problem for road safety; and the ‘problem young driver’ where a sub-sample of drivers represents the greatest road safety problem. Given difficulties associated with identifying and then modifying the behaviour of the latter group, broad countermeasures such as graduated driver licensing (GDL) have generally been relied upon to address the young driver problem. GDL evaluations reveal general road safety benefits for young drivers, yet they continue to be overrepresented in fatality and injury statistics. Therefore it is timely for researchers to revisit the ‘problem young driver’ concept to assess its potential countermeasure implications. This is particularly relevant within the context of broader countermeasures that have been designed to address the ‘young driver problem’ Personal characteristics, behaviours and attitudes of 378 Queensland novice drivers aged 17–25 years were explored during their pre-, Learner and Provisional 1 (intermediate) licence as part of a larger longitudinal project. Self-reported risky driving was measured by the Behaviour of Young Novice Drivers Scale (BYNDS), and five subscale scores were used to cluster the drivers into three groups (high risk n = 49, medium risk n = 163, low risk n = 166). High risk ‘problem young drivers’ were characterised by greater self-reported pre-Licence driving, unsupervised Learner driving, and speeding, driving errors, risky driving exposure, crash involvement, and offence detection during the Provisional period. Medium risk drivers were also characterised by more risky road use than the low risk group. Interestingly problem young drivers appear to have some insight into their high-risk driving, since they report significantly greater intentions to bend road rules in future driving. The results suggest that tailored intervention efforts may need to target problem young drivers within the context of broad countermeasures such as GDL which address the young driver problem in general. Experiences such as crash-involvement could be used to identify these drivers as a preintervention screening measure.     

Optimization redundancy allocation problem with nonexponential repairable components using simulation approach and artificial neural network

Redundancy allocation is one of the adopted approaches that is used by system designers to improve the performance of systems. In this article, a new model and a novel‐solving method are provided to address the nonexponential redundancy allocation problem in series‐parallel systems with repairable components based on optimization via simulation approach and artificial neural network technique. Despite the previous researches, in this model the failure and repair times of the each component were considered to have nonnegative exponential distributions. This assumption makes the model closer to the reality where most of used components have greater chance to face a breakdown in comparison to new ones. The main aim of this research is the optimization of mean time to the first failure of the system via allocating the best redundant components for each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique and artificial neural network were applied to model the problem, and different experimental designs were produced using design of experiment methods. To solve the problem, some metaheuristic algorithms were integrated with the simulation method. Several experiments were performed to test the proposed approach, and as the results show, the proposed approach is much more real than previous models, and also the near optimum solutions are promising.     

Fatality risk for belted drivers versus car mass

This study was performed to determine how the likelihood of a belted driver being killed in a single car crash depends on the mass of the car. This was done by applying the pedestrian fatality exposure approach to the subset of fatalities in the Fatal Accident Reporting System (FARS) for which the driver was coded as using a shoulder belt and/or a lap belt. Combining the 1975 through 1982 data provided a sufficiently large population of belted drivers to perform the analysis. In the exposure approach used, the number of car drivers killed in single car crashes is divided by the number of nonoccupant fatalities (pedestrians or motorcyclists) associated with the same group of cars. The ratio is interpreted to reflect the physical effect of car mass, essentially independent of driver behavior effects. In the present application, car mass effects for belted drivers were determined by considering the number of belted drivers killed divided by the number of nonoccupants killed in crashes involving cars whose drivers were coded in the FARS files as being belted. Because the belt use of surviving drivers is, to some extent, self-reported, it is considered that the data given in the report should be not used to estimate the effectiveness of seat belts in preventing fatalities. The results are presented as graphical and analytical comparisons of fatality likelihood versus car mass for belted and unbelted drivers. It is concluded that the effect of car mass on relative driver fatality likelihood is essentially the same for belted and unbelted drivers (for example, the present analysis gives that a belted driver in a 900 kg car is 2.3 times as likely to be killed in a single car crash as is the belted driver in an 1800 kg car. The corresponding ratio determined here for unbelted drivers is 2.4). As a consequence of this conclusion, the relative effectiveness of seat belts in preventing driver fatalities is similar for cars of different masses.     

Optimisation of facility layout design problem with safety and environmental factors by stochastic DEA and simulation approach

This article presents an integrated computer simulation–stochastic data envelopment analysis (SDEA) approach to deal with the job shop facility layout design problem (JSFLD) with stochastic outputs and safety and environmental factors. Stochastic outputs are defined as non-crisp operational and deterministic inputs. At first, feasible layout alternatives are generated under expert decision. Then, computer simulation network is used for performance modelling of each layout design. The outputs of simulation are average time-in-system, average queue length and average machine utilisation. Finally, SDEA is used with Lingo software for finding the optimum layout alternative amongst all feasible generated alternatives with respect to stochastic, safety and environmental indicators. The integrated approach of this study was more precise and efficient than previous studies with the stated outputs. The results have been verified and validated by principal component analysis. The unique features of this study are the ability of dealing with multiple inputs (including safety) and stochastic (including environmental) outputs. It also uses mathematical programming for optimum layout alternatives. Moreover, it is a practical tool and may be applied in real cases by considering safety and environmental aspects of the manufacturing process within JSFLD problems.