Relocation Operations Research Articles

Performance of one-way carsharing systems under combined strategy of pricing and relocations

ABSTRACT A bilevel nonlinear mathematical programing model is formulated to determine the optimal pricing and operator-based relocations in a one-way station-based carsharing system in competition with private cars. In the upper level, the carsharing operator determines the vehicle fleet, prices, and relocation operations with the objective of maximizing profits, considering the potential reaction of travelers. In the lower level, travelers choose travel modes from a cost-minimization perspective. Travel utilities are calculated through a logit model. The Karush–Kuhn–Tucker conditions are used to transform the bilevel model into a single-level model and then a genetic algorithm is proposed to solve it. Computational tests in four different scenarios show the combined strategy is the best one. The four scenarios are base, relocations, dynamic pricing, and a combination of relocations and pricing separately. The combined strategy can make the best trade-offs between the operator’s profit and the travelers’ cost.

Scheduling optimization for two crossover automated stacking cranes considering relocation

In this paper, we focus on the two crossover automated stacking cranes (ASCs) scheduling problem that arises at a storage block in an automated container terminal. To address relocation during retrieval operations, we use two methods with job precedence constraints: (1) adjusting the operation sequence of jobs to avoid relocation operations and (2) optimizing the dispatching for relocation operations to improve the efficiency of ASCs. Therefore, the method proposed in this paper optimizes the dispatching for storage, retrieval, and relocation and the routes of the ASCs while considering interferences between the ASCs and job precedence constraints. A branch-and-cut algorithm based on the characteristics of the problem is designed to decompose the problem into two problem classes connected via logic-based Benders constraints. Numerical experiments indicate that the proposed algorithm is efficient for solving realistically sized problems. Furthermore, the influence of the number of relocations is investigated based on experimental results.

Public Engagement in The Policy of Arrangement and Empowerment for Street Vendors in Garut Kota Subdistrict

This paper aims to examine the cause of public engagement ineffectiveness in the policy of arrangement and empowerment for street vendors in Garut Kota subdistrict. The research method used is descriptive qualitative. Based on the results of the study, it can be concluded that public engagement in this policy has not been effective, because it has not been related to: 1) Context and Settings; 2) Sponsors, Conveners, and Their Motivations for Direct Public Engagement; 3) Process Design; and 4) Outcomes. Regarding Context and Settings, there are no specific rules for public engagement, while in Regent’s Decree Number 511.23 / Kep. 790-Admrek / 2014 concerning the Team of Arrangement and Empowerment for Street Vendors in Garut Regency, the position of street vendors is only as a team member, without being accompanied by a clear definition of the roles, main tasks and functions. Public engagement for street vendors has become a formality to fulfill the principles of democratic governance, but in its implementation, most of the local government decisions and actions that are considered crucial in this policy do not represent street vendors desire. Regarding Sponsors, Conveners, and Their Motivations for Direct Public Engagement, public engagement in the policy of arrangement and empowerment for street vendors in Garut Kota subdistrict does not have sponsors yet in its sustainability, as for those who are organizers of public engagement in this policy based on the Regent’s Decree, especially in the fields that give positions to street vendors to be engaged as implementing elements, the fields in question are: 1) Fields of Socialization, Data Collection, and Structuring; and 2) Structuring / Relocation Operations. Some information, especially related to Process Design and Outcomes, is difficult to obtain. In substance, the author does not find information that is truly relevant to the aspects contained therein, because of the unavailability of Context and Settings that specifically regulate public engagement.

Machine learning-driven algorithms for the container relocation problem

The container relocation problem is one of important issues in seaport terminals which could bring a significant saving on the operating cost even with a slight improvement due to the huge number of containers processed across the world each year. Given a specific layout and container retrieval priorities, the container relocation problem aims to find the optimal movement sequence to minimize the total number of container relocation operations. In this paper, we propose novel machine learning-driven algorithms, which integrate optimization methods and machine learning techniques, to solve the problem. More specifically, we propose a new upper bound method called MLUB that incorporates branch pruners. These pruners are derived from some machine learning techniques through using the optimal solution values of many small-scale instances. The tightened upper bounds generated by MLUB are used subsequently both in the exact branch-and-bound algorithm called IB&B and the hybrid beam search heuristic called MLBS. Moreover, we also provide a tighter lower bound for the problem by additionally considering the interaction between consecutive target containers. Based on the benchmark data published recently in the literature, extensive experiments are conducted to test the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms outperform the state-of-the-art algorithms reported in the literature, and some managerial insights regarding the load intensity of the bay and some algorithm parameters such as the look-ahead depth and the beam width are drawn from the results.

Planning station capacity and fleet size of one-way electric carsharing systems with continuous state of charge functions

This paper presents a method for determining the deployment of one-way electric carsharing services within a designated region that maximizes the total profit of the operator. A mixed integer non-linear program model is built, with a strategic planning level that decides the fleet size and the station capacity and an operational level that decides on the required relocation operations. The state of charge (SOC) of the vehicles parked in one station is assumed to follow a continuous distribution. A rolling horizon method is used to optimize the operational decisions over the course of a day, considering demand fluctuations and the limited battery capacity of the vehicles. A golden section line search method and a shadow price algorithm are developed to optimize the fleet size and station capacity, with the results feeding back to the carsharing operations. To demonstrate the applicability of the formulated models and solution algorithms, a large-scale case study is conducted for Suzhou Industrial Park, China as the region of operation. A two-step verification method that combines an optimization model via tracking of individual vehicle SOC and a discrete event simulation, demonstrates the accuracy of the SOC distribution model. Managerial insights from the application are also presented.

EV-sharing station location and vehicle relocation with parking rewards and punishments mechanism

EV-sharing has played a more and more important role in the traffic modes of the resident. The vehicle relocation cost takes up a great proportion of the total operation cost. To reduce the vehicle relocation operations between the EV-sharing stations, the parking rewards and punishments mechanism along with the deployment of social parking lots is designed. Under this mechanism, a model for EV-sharing station location and vehicle relocation is developed. Then, the model is applied to a real-world problem with available data from WuHan, China. Numerical experiments reveal that the mechanism makes the vehicle relocation cost and station construction cost decrease by 77.99% and 21.05% respectively, as well as the demand fill rate and the profit increase by 142.72% and 15.94% respectively.

Automated taxis’ dial-a-ride problem with ride-sharing considering congestion-based dynamic travel times

In this paper, we study the dial-a-ride problem of ride-sharing automated taxis (ATs) in an urban road network, considering the traffic congestion caused by the ATs. This shared automated mobility system is expected to provide a seamless door-to-door service for urban travellers, much like what the existing transportation network companies (TNC) do, but with decreased labour cost and more flexible relocation operations due to the vehicles’ automation. We propose an integer non-linear programming (INLP) model that optimizes the routing of the ATs to maximize the system profit, depending on dynamic travel times, which are a non-linear function of the ATs’ flows. It is important to involve traffic congestion in such a routing problem since for a growing number of ATs circulating in the city their number will lead to delays. The model is embedded within a rolling horizon framework, which divides a typical day into several horizons to deal with the real-time travel demand. In each horizon, the routing model is solved with the demand at that interval and assuring the continuity of the trips between horizons. Nevertheless, each horizon model is hard to solve given its number of constraints and decision variables. Therefore, we propose a solution approach based on a customized Lagrangian relaxation algorithm, which allows identifying a near-optimal solution for this difficult problem. Numerical experiments for the city of Delft, The Netherlands, are used to demonstrate the solution quality of the proposed algorithm as well as obtaining insights about the AT system performance. Results show that the solution algorithm can solve the proposed model for hard instances. Ride-sharing makes the AT system more capable to provide better service regarding delay time and the number of requests that can be attended by the system. The delay penalty on the profit objective function is an effective control parameter on guaranteeing the service quality while maintaining system profitability.

Synthesis of NiOx@NPC composite for high-performance supercapacitor via waste PET plastic-derived Ni-MOF

Metal–organic frameworks (MOFs) have come up as potential advance materials for energy applications owing to high surface area and tuneable porosity. The poor electrical conductivity and stability of MOFs restrict their vital use in supercapacitor applications. Herein, a smart approach was employed to synthesize NiOx nanoparticles decorated with nitrogenous porous carbon (NiOx@NPC nanocomposite) via PET-derived MOFs. The nitrogenous porous carbon not merely enhanced the electrical conductivity and stability but as well improved the charge relocation operation for the better performance of supercapacitor devices. The as-prepared NiOx@NPC composite demonstrated high specific surface area of 1523 m2/g for the first time. In a three-electrode setup the as synthesized NiOx@NPC composite electrode demonstrated an excellent specific capacitance of 581.30 F/g at a scan rate of 5 mV/s. Moreover, the fabricated symmetric supercapacitor (2E system) device demonstrated a specific capacitance of 291 F/g at a scan rate of 5 mV/s using 6 M KOH electrolyte. The excellent cyclic stability of the NiOx@NPC composite was obtained after running 5000 segments of CV at a sweep rate of 50 mV/s. Thus, the obtained results show that NiOx@NPC composites certify and support the novelty of the materials for next-generation energy applications.

Local search algorithm for solving periodic location routing problem

This study examines the problem of facility location and vehicle routes determination in supply chain system periodically. The problem is determining the location and route to meet the supply of commodity to the power plant that taken from the case of an electricity company in Indonesia. In this study, the problem is called Periodic Location Routing Problem (PLRP). Periodic Location Routing Problem is a non-polynomial hard problem (NP-hard), if locations considered is increase, the computation time used is increasing exponentially. Therefore, to solve a large-scale of this problem, a local search algorithm is proposed. Local search is a very simple metaheuristic algorithm. To explore the solution space for the problem, this algorithm will use the relocation operator. The results of computational experiments from 10 instances indicate that the results of the solution have a small standard deviation with short computing time in less than 2 hours.

Simulated annealing algorithm for solving pickup and delivery problem with LIFO, time duration and limited vehicle number

Nowadays, the process of delivering goods or documents from one place to another can be done faster by utilizing a courier service. This courier must be able to create a travel route to pick goods and deliver them with a short time. Route creation will be more difficult if there is a time window for each customer and the goods being transported are heavy or dangerous items. In this case, the courier company must implement a last-in-first-out rules. In addition, with the limitation of vehicles number, companies have to minimize the number of underserved customers. This problem called pickup and delivery problem with last-in-first-out, time duration, and limited vehicle number. In this study, to solve the problem, simulated annealing algorithm was used for some dataset. To explore the solution space for the problem, this algorithm will use pair relocation operator. In several experiments, the results show a good solution and short computation time.

Optimizing the Relocation Operations of Free-Floating Electric Vehicle Sharing Systems

Free-floating electric vehicle sharing (FFEVS) systems require nightly relocation and recharging operations to better meet the next day's spatial demand with sufficient battery-levels. Such operations involve not only a crew of drivers to move the shared electric vehicles (EVs), but also a fleet of shuttles to transport those drivers. We consider a decision-making problem for routing shuttles and drivers to recharge and relocate EVs in FFEVS systems. The free-floating nature of FFEVS systems makes their operations costly and cumbersome. Comprehensive studies for relocating EVs and routing shuttles are limited in the literature, and an optimal mix of shuttles and drivers is unknown. We fill this gap by providing a modeling framework for joint decision making and efficient computational tools. We formulate mixed integer programs to model the relocation and recharging operations. Two approaches are devised: sequential and synchronized approaches. In the sequential approach, the movement of EVs is first decided, then the routing of shuttles and drivers is determined. In the synchronized approach, all decisions are made simultaneously. To solve large-scale problems, we devise an efficient computational method, called an exchange-based neighborhood-search method. Our computational method can solve real large-scale instances of car2go in Amsterdam within 10 minutes on a generic computer. Our synchronized approach saves the total shuttle route up to 15% compared to the sequential approach. Wait times of drivers and EVs are also reduced. Our extensive numerical experiments show that when the service area is large, increasing the number of shuttles is more cost efficient than increasing the number of drivers. We also find that when the service area is small, the charging infrastructure is scarce, or the recharging requirements are low, increasing the number of drivers can be more beneficial.

Investigating the effect of temporal and spatial flexibility on the performance of one-way electric carsharing systems

One-way electric carsharing systems provide an environmentally friendly option for facilitating urban mobility needs. However, the management of one-way electric carsharing systems presents operational challenges stemming from the need to relocate cars in order to strike an optimum balance between demand and supply. As a result, the cost associated with vehicle relocation operations represents a significant proportion of the total operating cost. In the context of electric carsharing systems, the problem of vehicle relocation is further exacerbated by the car battery charging requirements. The introduction of temporal and spatial flexibility regarding the pick-up and drop-off of vehicles provides the means of improving the efficiency of one-way electric carsharing systems. However, the literature currently lacks models that can be used to investigate the effect of temporal and spatial flexibility on the performance of one-way electric carsharing systems. In this paper, we are introducing an integrated modeling and solution framework for investigating the effect of temporal and/or spatial flexibility, and different options for processing trip requests to the profitability and utilization of one-way electric carsharing systems. The application of the proposed framework to a realistic size system suggests that spatial flexibility has a stronger effect on the system performance than temporal flexibility. Furthermore, both spatial and temporal flexibility can increase the profitability of the system by serving more customers with fewer vehicle relocation needs.

Fleet sizing for one-way electric carsharing services considering dynamic vehicle relocation and nonlinear charging profile

This study aims to determine the electric vehicle fleet size for one-way carsharing services by maximizing the profit of carsharing operators while taking into account the vehicle relocation operations and nonlinear electric vehicle charging profile. We formulate a set partitioning model for the considered problem. A tailored branch-and-price (B&P) approach is proposed to find the exact optimal solution of the model. In particular, an effective multi-label correcting method is developed to solve the pricing problem (i.e., generate columns) within the B&P approach. A novel non-dominated charging strategy is put up to avoid the exponential growth of labels caused by the allowance of partial charging with a nonlinear charging profile. In addition to the B&P approach, two heuristic methods are put forward for solving the large-scale problems or reinforcing the B&P approach. Numerical experiments with randomly generated instances and a case study based on a one-way carsharing operator in Singapore are conducted to further assess the efficiency and applicability of the proposed solution methods. The effects of several key parameters, i.e., the fixed cost of EV, relocation cost, electricity cost, service charge, EV driving range, the charging efficiency, and the number of rentals on the performance of a one-way electric carsharing system are also examined.

Mathematical formulation and heuristic algorithm for the block relocation and loading problem

AbstractThis study considers the block relocation and loading problem in container terminals. The optimal loading sequence and relocation location are simultaneously decided on the basis of the desired ship‐bay and initial yard space configuration. An integer linear programming model is developed to minimize the number of relocations in the yard space on the basis of no shifts in the ship bay. The accuracy of the model is tested on small‐scale scenarios by using CPLEX. Considering the problem size in the real world, we present a rule‐based heuristic method that is combined with a mathematical model for the removal, loading, and relocation operations. The influence of rules on algorithm performance is also analyzed, and the heuristic algorithm is compared with different types of algorithms in the literature. The extensive numerical experiments show the efficiency of the proposed heuristic algorithm.

The outbound container slot allocation based on the stowage plan in rail–water intermodal container terminals

In the past, most researchers focused on the storage space allocation problem or container block allocation problem in maritime container terminals, while few studied the container slot allocation problem in rail–water intermodal container terminals. Container slot allocation problem is proposed to reduce relocation operations of containers in railway container yards and improve the efficiency of rail–water intermodal container terminals. In this paper, a novel outbound container slot allocation model is introduced to reduce the rehandling operations, considering stowage plan, containers left from earlier planning periods and container departure time. A novel heuristic algorithm based on the rolling planning horizon approach is developed to solve the proposed problem effectively. Computational experiments are carried out to validate that the proposed model and algorithm are feasible and effective to enhance the storage effect. Meanwhile, some other experiments are conducted to verify that our approach is better than the regular allocation approach, which is a common method in marine and railway container terminals, and container weight is the most important influence factor when storing containers.

Is dynamic traffic sensor network profitable for network-level real-time information prediction?

Optimally deploying sensors into a complex highway network can significantly enhance the socio-economic benefits through accurately providing system users and operators with the latest traffic state information. This paper presents a linear optimization framework for designing a sensor network for travel time surveillance. The presented framework consists of a traditional static network model and a novel dynamic network model as well as an integer cut-based speed-up solution approach. The aims of the study are twofold. First, the proposed optimization model maximizes the network-level travel time surveillance benefits by taking into account the values of real-time measurements induced from both temporal and spatial traffic state patterns. Second, the mechanism of sensor relocation operations is designed and incorporated into the proposed model. This mechanism enables one to evaluate whether appropriately relocating sensors can optimize the utilization of the surveillance resources and further enhance the surveillance benefits over a given time horizon. We applied the proposed optimization framework to a mixed arterial-freeway commuting network located in Washington. D.C.-Baltimore metropolitan area. The study empirically revealed and illustrated two interesting findings: (1) relocation operations do bring additional surveillance benefits on travel time prediction, and the marginal benefit is highly correlated with the sensor fleet size; (2) spatial measurement is not a trivial part in travel time prediction, and one should fully recognize the effectiveness of temporal and spatial measurements when designing a traffic sensor network for real-time travel time surveillance.

Optimization of dynamic relocation operations for one-way electric carsharing systems

This study developed a new approach for the relocation operations of one-way electric carsharing systems without advanced reservation information. The developed model consists of two parts: relocation needs computation and execution plan generation. In the first part, thresholds and a Station Ruin Prevention Method for relocation computation are proposed. In the second part, an Integer Linear Programming model for relocation plan optimization is formulated. Both Vehicle relocation and the Staff rebalancing are optimized in a unified framework. Furthermore, in order to improve the efficiency of the programming, a zoning scheme was proposed to divide the large-scale network into separate zones. Finally, a simulation model, which was calibrated and validated using the field data, was built to conduct tests on the performance of the proposed method. The results showed that the number of allocation tasks and distance for allocation have been greatly reduced because of the proposed methods, and the computing time can be reduced significantly with a small loss of accomplished tasks under the zoning scheme.

Ambulance Deployment With Relocation Through Robust Optimization

This paper investigates the deployment issue of an emergency medical service (EMS) system to maintain the preferred service coverages under different considerations. Specifically, two coverage levels are introduced to reflect the requirements under the regular situation and the situation with ambulance unavailable. We propose the two-stage robust optimization (RO) models to design a reliable ambulance system subject to unavailability of the ambulances, with and without the ambulance relocation. For the RO problem with mixed-integer recourse for relocation, we customize the column and constraint generation method with an approximation strategy to handle the computational challenge. Our numerical study: 1) demonstrates that our RO formulations have a strong modeling capacity on designing the EMS system; 2) shows that our approximation algorithm performs very well; and 3) provides a quantitative evaluation of, including, relocation operations on the system performance. Note to Practitioners —This paper presents the novel optimization models to help ambulance deployment. Due to ambulance unavailability and relocation operations, traditional optimization formulations might not be sufficient for modeling or might be hard for computation. In this paper, we provide an uncertainty set-based approach to capture ambulance unavailability and to build the robust optimization models (with relocation recourse decisions). Also, efficient algorithms are designed to support practical instances. Numerical results are very supportive to our new models and computational methods.

Multiobjective car relocation problem in one-way carsharing system

In this paper, we present a multiobjective approach for solving the one-way car relocation problem. We fix three objectives that include the number of remaining rejected demands, the number of jockeys used for the relocation operations, and the total time used by these jockeys. For this sake, we propose to apply two algorithms namely NSGA-II and an adapted memetic algorithm (MA) that we call MARPOCS which stands for memetic algorithm for the one-way carsharing system. The NSGA-II is used as a reference to compare the performance of MARPOCS. The comparison of the approximation sets obtained by both algorithms shows that the hybrid algorithm outperforms the classical NSGA-II and so solutions generated by the MARPOCS are much better than the solutions generated by NSGA-II. This observation is proved by the comparison of different quality indicators’ values that are used to compare the performance of each algorithm. Results show that the MARPOCS is promising to generate very good solutions for the multiobjective car relocation problem in one-way carsharing system. It shows a good performance in exploring the search space and in finding solution with very good fitness values.

Dynamic response investigation of a small size floating dock unit in irregular oblique waves

The floating dock units, according to the operation schedules, from time to time have to be relocated to different shipyards, involving river or costal routes. In this study, the dynamic behaviour of a small size floating dock during relocation is analyzed, with two constructive types of the floating structure above the dock pontoon deck and a maximum length of 60 m. By the floating dock and tug ship system resistance prediction, results a maximum towing speed around 18 km/h (9.72 kn). So, the numerical analysis is carried on for a set of five speed values, between 0 and 18 km/h. The dynamic response in irregular waves of the floating dock, full heading angle set, is computed by own program code. The irregular waves intensity is prescribed in terms of statistical wave significant height, with the maximum values corresponding to the Lower Danube River way and the Black Sea Coastal on Romanian area. The statistical dynamic responses, in terms of motion and acceleration significant amplitudes, are checked out against the short-term seakeeping criteria, in order not flood the dock pontoon deck. The study results are pointing out the limit navigation conditions for safety during the small size floating dock relocation operation.