Set Of Trips Research Articles

The effect of inner ring modification of standard 37-element fuel on CHF enhancement

A CANDU-6 reactor has 380 fuel channels of a pressure tube type, which provides an independent flow passage, and each pressure tube contains twelve fuel bundles horizontally. The CHF of a CANDU fuel bundle in a horizontal fuel channel is one of the important parameters determining the thermalhydraulic safety margin as well as the trip set point of the Regional Overpower Protection (ROP) system. Hence, the CHF enhancement of a CANDU fuel bundle has been an issue for a long time and can be affected by the geometric configuration of the fuel elements as well as several appendages such as the end-plates, bearing pads, and spacers attached to the fuel elements. It is known that the first CHFs of a standard 37-element fuel bundle preferably occur at the peripheral subchannels of the center rod, owing to the relative small flow area or high flow resistance under high flow conditions or the normal operating conditions of a CANDU reactor.An increase of the inner ring radius of a standard 37-element fuel bundle without any changes in the fuel bundle or element sizes is introduced to enhance the CHF in consideration of the CHF characteristics of a standard 37-element fuel bundle for a CANDU reactor. Subchannel analysis techniques using the ASSERT-PV code were applied to investigate the local CHF characteristics according to the inner ring radius variation for the original diameter of the pressure tube. It was found that the modification of the inner ring radius is very effective in enhancing the dryout power of the fuel bundle through an enthalpy re-distribution of the subchannels and change in the local locations of the first CHF occurrences.

A hybrid constructive heuristic and simulated annealing for railway crew scheduling

Railway crew scheduling problem is the process of allocating train services to the crew duties based on the published train timetable while satisfying operational and contractual requirements. The problem is restricted by many constraints and it belongs to the class of NP-hard. In this paper, we develop a mathematical model for railway crew scheduling with the aim of minimising the number of crew duties by reducing idle transition times. Duties are generated by arranging scheduled trips over a set of duties and sequentially ordering the set of trips within each of duties. The optimisation model includes the time period of relief opportunities within which a train crew can be relieved at any relief point. Existing models and algorithms usually only consider relieving a crew at the beginning of the interval of relief opportunities which may be impractical. This model involves a large number of decision variables and constraints, and therefore a hybrid constructive heuristic with the simulated annealing search algorithm is applied to yield an optimal or near-optimal schedule. The performance of the proposed algorithms is evaluated by applying computational experiments on randomly generated test instances. The results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time for large-sized problems.

Toward Personalised and Dynamic Cultural Routing: A Three-Level Approach

This paper introduces the concept of “smart routing” as a recommender system for tourists that takes into account the dynamics of their personal user profiles. The concept relies on three levels of support: 1) programming the tour, i.e. selecting a set of relevant points of interests (POIs) to be included into the tour, 2) scheduling the tour, i.e. arranging the selected POIs into a sequence based on the cultural, recreational and situational value of each, and 3) determining the tour's travel route, i.e. generating a set of trips between the POIs that the tourist needs to perform in order to complete the tour. The “smart routing” approach intends to enhance the experience of tourists in a number of ways. The first advantage is the system's ability to reflect on the tourists’ dynamic preferences, for which an understanding of the influence of a tourist's affective state and dynamic needs on the preferred activities is required. Next, it arranges the POIs together in a way that creates a storyline that the tourist will be interested to follow, which adds to the tour's cultural value. Finally, the POIs are connected by a chain of multimodal trips that the tourist will have to make, also in accordance with the tourist's preferences and dynamic needs. As a result, each tour can be personalised in a “smart” way, from the perspective of both the cultural and the overall experience of taking it. We present the building blocks of the “smart routing” concept in detail and describe the data categories involved. We also report on the current status of our activities with respect to the inclusion of a tourist's affective state and dynamic needs into the preference measurement phase, as well as discuss relevant practical concerns in this regard.

Vehicle Scheduling of an Urban Bus Line via an Improved Multiobjective Genetic Algorithm

It is complex and difficult to perform the vehicle scheduling of urban bus lines, which is important to reduce the operational cost and improve the quality of public transportation services. One has to assign vehicles to cover a set of trips contained in a timetable while minimizing multiple objectives that may conflict with each other. Existing approaches combine these objectives in a weighted fashion to form a single objective and then use a single-objective optimization approach to solve it. However, they can only produce one solution, and it is not easy to assign a proper weight for each objective to obtain a superior solution that can balance different objectives. In this paper, a methodology is presented to create a set of Pareto solutions for this problem. First, a set of candidate vehicle blocks is generated. Then, multiple block subsets are selected from this candidate set by an improved multiobjective genetic algorithm combined with a departure-time adjustment procedure to obtain multiple Pareto solutions. To encode a solution, we propose a coding scheme that has a relatively short coding length and low decoding complexity. This approach is applied to a real-world vehicle scheduling problem of a bus line in Nanjing, China. Experiments show that this approach is able to quickly produce satisfactory Pareto solutions that outperform the actually used experience-based solution.

Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm

The vehicle routing problem (VRP) is a well-known combinatorial optimisation problem and holds a central place in logistics management. Many exact, heuristic and metaheuristic approaches have been proposed to solve VRP. An important variant of the VRP arises when a fleet of vehicles is fixed and characterised by different capacities for distribution activities. The problem is known as the heterogeneous fixed fleet VRP (HFFVRP). The HFFVRP is a natural generalisation of the VRP with several vehicle types, each type being defined by a capacity, a fixed cost and a cost per distance unit, and can cover more practical situations in transportation. This problem consists of determining a set of vehicle trips of minimum total length in which a set of customers is to be satisfied in the demand constraints using identical vehicles with limited capacity. If open routes instead of closed ones are considered in the HFFVRP, the problem becomes a heterogeneous fixed fleet Open VRP (HFFOVRP) which has numerous applications in industrial and service problems. In this paper, a bone route algorithm which uses the tabu search as an improved procedure is utilised to solve the HFFOVRP. The proposed algorithm was tested empirically on a 24 of generated VRPs, and compared with elite ant system and ant colony system. In all cases, the proposed algorithm finds the best-known solutions within a reasonable time.

A GRASP×ELS for the vehicle routing problem with basic three-dimensional loading constraints

This paper addresses an extension of the capacitated vehicle routing problem where the client demand consists of three-dimensional weighted items (3L-CVRP). The objective is to design a set of trips for a homogeneous fleet of vehicles based at a depot node which minimizes the total transportation cost. Items in each vehicle trip must satisfy the three-dimensional orthogonal packing constraints. This problem is strongly connected to real-life transportation systems where the packing of items to be delivered by each vehicle can have a significant impact on the routes. We propose a new way to solve the packing sub-problem. It consists of a two-step procedure in which the z-constraints are first relaxed to get a (x,y) positioning of the items. Then, a compatible z-coordinate is computed to get a packing solution. Items can be rotated but additional constraints such as item fragility, support and LIFO are not considered. This method is included in a GRASP×ELS hybrid algorithm dedicated to the computation of VRP routes. The route optimization alternates between two search spaces: the space of VRP routes and the space of giant trips. The projection from one to the other is done by dedicated procedures (namely the Split and the concatenation algorithms). Moreover, a Local Search is defined on each search space. Furthermore, hash tables are used to store the result of the packing checks and thus save a substantial amount of CPU time. The effectiveness of our approach is illustrated by computational experiments on 3L-CVRP instances from the literature. A new set of realistic instances based on the 96 French districts are also proposed. They range from 19 nodes for the small instances to 255 nodes for the large instances and they can be stated as realistic since they are based on true travel distances in kilometers between French cities. The impact of the hash tables is illustrated as well.

A new exact algorithm to solve the multi-trip vehicle routing problem with time windows and limited duration

This article tackles the multi-trip vehicle routing problem with time windows and limited duration. A trip is a timed route such that a succession of trips can be assigned to one vehicle. We provide an exact two-phase algorithm to solve it. The first phase enumerates possible ordered lists of clients which match the maximum trip duration criterion. The second phase uses a Branch and Price scheme to generate and choose a best set of trips so that all customers are visited. We propose a set covering formulation as the column generation master problem, where columns (variables) represent trips. The sub-problem selects appropriate timing for trips and has a pseudo-polynomial complexity. Computational results on Solomon’s benchmarks are presented. The computational times obtained with our new algorithm are much lower than the ones recently obtained in the only two studies published on this problem to date.

IMPROVING REGIONAL OVERPOWER PROTECTION TRIP SET POINT VIA CHANNEL OPTIMIZATION

In recent years, a new algorithm has been introduced to perform the regional overpower protection (ROP) detector layout optimization for CANDU ® reactors. This algorithm is called DETPLASA. This algorithm has been shown to successfully come up with a detector layout which meets the target trip set point (TSP) value. Knowing that these ROP detectors are placed in a number of safety channels, one expects that there is an optimal placement of the candidate detectors into these channels. The objective of the present paper is to show that a slight improvement to the TSP value can be realized by optimizing the channelization of these ROP detectors. Depending on the size of the ROP system, based on numerical experiments performed in this study, the range of additional TSP improvement is from 0.16%FP (full power) to 0.56%FP.

Heuristics for the multi-depot petrol station replenishment problem with time windows

In the multi-depot petrol station replenishment problem with time windows (MPSRPTW), the delivery of petroleum products stored in a number of different petroleum depots to a set of petrol distribution stations has to be optimized. Each depot has its own fleet of heterogeneous and compartmented tank trucks. Stations specify their demand by indicating the minimum and maximum quantities to be delivered for each ordered product and require the delivery within a predetermined time window. Several inter-related decisions must be made simultaneously in order to solve the problem. For this problem, the set of feasible routes to deliver all the demands, the departure depot for each route, the quantities of each product to be delivered, the assignment of these routes to trucks, the time schedule for each trip, and the loading of the ordered products to different tanks of the trucks used need to be determined. In this paper, we propose a mathematical model that selects, among a set of feasible trips, the subset that allows the delivery of all the demands while maximizing the overall daily net revenue. If this model is provided with all possible feasible trips, it determines the optimal solution for the corresponding MPSRPTW. However, since the number of such trips is often huge, we developed a procedure to generate a restricted set of promising feasible trips. Using this restricted set, the model produces a good but not necessarily optimal solution. Thus the proposed solution process can be seen as a heuristic. We report the results of the extensive numerical tests carried out to assess the performance of the proposed heuristic. In addition, we show that, for the special case of only one depot, the proposed heuristic outperforms a previously published solution method.

Utilization of reduced fuelling ripple set in ROP detector layout optimization

The ADORE (Alternative Detector layout Optimization for REgional overpower protection system) algorithm for performing the optimization of regional overpower protection (ROP) for CANDU® reactors has been recently developed. This algorithm utilizes the simulated annealing (SA) stochastic optimization technique to come up with an optimized detector layout for the ROP systems. For each history in the SA iteration where a particular detector layout is evaluated, the goodness of this detector layout is measured in terms of its trip set point value which is obtained by performing a probabilistic trip set point calculation using the ROVER-F code. Since during each optimization execution thousands of candidate detector layouts are evaluated, the overall optimization process is time consuming. Since for each ROVER-F evaluation the number of fuelling ripples controls the execution time, reducing the number of fuelling ripples will reduce the overall execution time. This approach has been investigated and the results are presented in this paper. The challenge is to construct a set of representative fuelling ripples which will significantly speedup the optimization process while guaranteeing that the resulting detector layout has similar quality to the ones produced when the complete set of fuelling ripples is employed.

REACTIVITY INSERTION ACCIDENT ANALYSIS OF KARTINI REACTOR

REACTIVITY INSERTION ACCIDENT ANALYSIS OF KARTINI REACTOR. A transient analysis of reactivity insertionaccident of Kartini reactor during start up from the minimum critical condition has been performed to estimate the effect onthe fuel temperature increase. Two cases of reactivity insertion limits had been assumed in this study i.e. the reactivityinsertions were limited by the actuation of overpower trip system (110 %) for the 1st case and by manual scram when thecontrol rod reached the 100 % UP position, assuming the overpower trip system was failed to function for the 2nd case.Adiabatic condition was assumed in this study, to get the most severe condition. The result shows that based on theassumed power level of trip setting for the 1st case, the total reactivity insertion was 0.82 $, corresponding to the reactorperiod of about 2 s and causing the maximum fuel temperature increase of around 11 oC or the maximum fuel temperature of45 oC. For the 2nd case the total reactivity insertion at the trip point was 1.367 $, resulting in the maximum fuel temperatureincrease of about 103 oC or maximum fuel temperatur of around 137 oC which is still far below the defined design limit of1115 oC for transient condition and 700 oC for steady state as well. This result concludes that by limiting the available coreexcess of reactivity at reasonably low, it could prevent the fuel from possible of undergoing an excessive temperatureincrease, during the postulated reactivity insertion accident.Keywords: Transient analysis, reactivity insertion, accident, reactor kartini, fuel temperature.

Multi-treatment capacitated arc routing of construction machinery in Taiwan's smooth road project

This study investigates the routing problems of road resurfacing in Taiwan's smooth road project, incorporating multiple treatments served by different construction machinery in a way that propagates extra time window constraints to ensure that a subsequent treatment starts after the required preceding treatment has started or been completed. The routing problem is modeled as a multi-treatment capacitated arc routing problem with time windows (MTCARPTW) in order to determine a set of trips at a minimum total cost that covers all required links of road resurfacing work. The MTCARPTW is first transformed into a traveling salesman problem (TSP); then, a heuristics method based on ant colony optimization (ACO) is applied and evaluated based on the set of given circumstances. The computational results indicate that the proposed algorithm is efficient. This research contributes to identifying a new routing problem, modeling this MTCARPTW, and introducing the ACO to solve the problem efficiently.

On the aggregate grid load imposed by battery health-conscious charging of plug-in hybrid electric vehicles

This article examines the problem of estimating the aggregate load imposed on the power grid by the battery health-conscious charging of plug-in hybrid electric vehicles (PHEVs). The article begins by generating a set of representative daily trips using (i) the National Household Travel Survey (NHTS) and (ii) a Markov chain model of both federal and naturalistic drive cycles. A multi-objective optimizer then uses each of these trips, together with PHEV powertrain and battery degradation models, to optimize both PHEV daily energy cost and battery degradation. The optimizer achieves this by varying (i) the amounts of charge obtained from the grid by each PHEV, and (ii) the timing of this charging. The article finally computes aggregate PHEV power demand by accumulating the charge patterns optimized for individual PHEV trips. The results of this aggregation process show a peak PHEV load in the early morning (between 5.00 and 6.00a.m.), with approximately half of all PHEVs charging simultaneously. The ability to charge at work introduces smaller additional peaks in the aggregate load pattern. The article concludes by exploring the sensitivity of these results to the relative weighting of the two optimization objectives (energy cost and battery health), battery size, and electricity price.

Selective Coordination

The Underwriters Laboratories (UL) 845 standard for low-voltage (LV) motor control centers (MCCs) requires a short-circuit withstand rating on the bus for only three cycles. The LV power-circuit breakers are typically rated for up to 30 cycles short delay, which can be greater than the UL 845 short-circuit withstand test requirements. The trip settings on these LV power-circuit breakers may include short time delay that can result in the MCC-enduring fault currents for much more than the three cycles for which it is rated. In this article, it is proposed that MCC bus designed and tested for higher number of cycles of withstand can address the above potential issue by offering better coordination and safer power distribution design options.

A hybrid evolutionary algorithm for the periodic location-routing problem

The well-known vehicle routing problem (VRP) has been studied in depth over the last decades. Nowadays, generalizations of VRP have been developed for tactical or strategic decision levels of companies but not both. The tactical extension or periodic VRP (PVRP) plans a set of trips over a multiperiod horizon, subject to frequency constraints. The strategic extension is motivated by interdependent depot location and routing decisions in most distribution systems. Low-quality solutions are obtained if depots are located first, regardless of the future routes. In the location-routing problem (LRP), location and routing decisions are tackled simultaneously. Here for the first time, except for some conference papers, the goal is to combine the PVRP and LRP into an even more realistic problem covering all decision levels: the periodic LRP or PLRP. A hybrid evolutionary algorithm is proposed to solve large size instances of the PLRP. First, an individual representing an assignment of customers to combinations of visit days is randomly generated. The evolution operates through an Evolutionary Local Search (ELS) on visit day assignments. The algorithm is hybridized with a heuristic based on the Randomized Extended Clarke and Wright Algorithm (RECWA) to create feasible solutions and stops when a given number of iterations is reached. The method is evaluated over three sets of instances, and solutions are compared to the literature on particular cases such as one-day horizon (LRP) or one depot (PVRP). This metaheuristic outperforms the previous methods for the PLRP.

A multi-start evolutionary local search for the two-dimensional loading capacitated vehicle routing problem

This paper addresses an extension of the capacitated vehicle routing problem where customer demand is composed of two-dimensional weighted items (2L-CVRP). The objective consists in designing a set of trips minimizing the total transportation cost with a homogenous fleet of vehicles based on a depot node. Items in each vehicle trip must satisfy the two-dimensional orthogonal packing constraints. A GRASP×ELS algorithm is proposed to compute solutions of a simpler problem in which the loading constraints are transformed into resource constrained project scheduling problem (RCPSP) constraints. We denote this relaxed problem RCPSP-CVRP. The optimization framework deals with RCPSP-CVRP and lastly RCPSP-CVRP solutions are transformed into 2L-CVRP solutions by solving a dedicated packing problem. The effectiveness of our approach is demonstrated through computational experiments including both classical CVRP and 2L-CVRP instances. Numerical experiments show that the GRASP×ELS approach outperforms all previously published methods.

Public-transport vehicle scheduling with multi vehicle type

The public-transport (transit) operation planning process commonly includes four basic activities, usually performed in sequence: (1) network route design, (2) timetable development, (3) vehicle scheduling, and (4) crew scheduling. The purpose of this work is to address the vehicle scheduling problem, while taking into account the association between the characteristics of each trip (urban, peripheral, inter-city, etc.) and the vehicle type required for the particular trip. The problem is based on given sets of trips and vehicle types, where the categories are arranged in decreasing order of vehicle cost. Therefore, each trip can be carried out by its vehicle type, or by other types listed in prior order. This problem can be formulated as a cost-flow network problem with an NP-hard complexity level. Thus, a heuristic algorithm is developed in this work, based on the Deficit Function theory. Two examples are used as an expository device to illustrate the procedures developed, along with a real-life example of a bus company.

Statistical Inference for Transit System Origin-Destination Matrices

We consider inference for the matrix of origin-destination (O-D) trip rates for a transit system, based on counts of the passengers boarding and alighting at each stop. The observed data provide only indirect information about the O-D rates through a highly indeterminate system of linear equations. Enumeration of the solution set of feasible trips is necessary to calculate the model likelihood, but this would be computationally prohibitive for even moderately large systems. We therefore adopt a sampling-based Bayesian approach. Existing work on the wider problem of O-D traffic rate estimation for general transport networks has failed to produce an efficient sampling methodology for sizeable applications; however, we were able to derive a suitable Markov chain Monte Carlo algorithm by generating candidate trip vectors directly from the feasible set using a Markov model of passenger behavior. The resulting sampler moves freely around the posterior support without the need for any explicit specification of the feasible trip set. This methodology is applicable regardless of whether or not the O-D matrix is assumed to have any given structure. We illustrate our methods with a case study on O-D trip rates for a bus service in San Francisco Bay.

A new model and heuristic algorithms for the multiple‐depot vehicle scheduling problem

The multiple‐depot vehicle scheduling problem (MDVSP) addresses the work of assigning vehicles to serve a given set of time trips with the consideration of certain requirements representing the market rules. Extensive studies in the literature address the MDVSP, but because of the complexity of the problem, the findings of those researchers are still not enough to represent real world situations in Taiwan. Formulation for the MDVSP typically contains the following assumptions: (1) the size of fleet or maximum number of available vehicles at each depot is known already, (2) all trip serving costs are usually simplified as a single term in the objective function, which fails to reflect public transit operator concerns, (3) the applied deadheading strategy is static less flexibility, (4) there is no discussion of differences of route change frequency in the problem. This paper presents a new MDVSP model to address the above issues. A greedy heuristic algorithm based on the divide‐and‐conquer technique is also proposed to solve the MDVSP effectively. Computational tests are performed on the Kinmen Bus Administration (KBA) and results demonstrate that the proposed new model and the greedy heuristic algorithm for the MDVSP are effective in solving real world problems.

Origin–Destination Matrix Generation from Boarding–Alighting and Household Survey Data

Approaches to demand estimation for public transport systems often use either primarily boarding–alighting data counts or travel survey data, based on a sampling of households. However, in the absence of a mechanism to capture transfer trips, boarding–alighting data have difficulty providing accurate demand estimation for large, complex transit systems. Travel survey data also present challenges in the estimation of total demand, when the demand is extrapolated from a limited sample of the population. The errors in this extrapolation often limit the estimation of demand to a coarse-grained zone-to-zone travel, as opposed to a fine-grained bus–rail stop to bus–rail stop. This paper presents the hybrid demand estimation (HDE) algorithm that synthesizes both types of data to produce an accurate, fine-grained demand estimation. Initially, HDE uses boarding–alighting data and a fluid flow model to estimate direct trips that match passenger boarding and alighting counts. Then, in an iterative process, three heuristics are used to introduce transfer trips and alter the set of no-transfer trips to create a better match to the household survey data. Both boarding–alighting and household travel survey data were collected for the Greater Mumbai, India, metropolitan area. The HDE algorithm was evaluated for Mumbai's multimodal suburban rail and bus system. In this network, the HDE algorithm produced significantly better matches to both types of data than other heuristic approaches. The demand estimation produced by HDE deviated from boarding–alighting and survey data by less than 1% across a range of metrics.