Relocation Operations Research Articles

Optimizing container relocation operations by using deep reinforcement learning

Optimizing container relocation operations by using deep reinforcement learning

A three-stage heuristic for optimizing container relocations in maritime container terminals

The Container Relocation Problem (CRP) is one of the most important optimization problems in maritime container terminals. The objective is to minimize the number of relocation operations for retrieving containers in a sequence. If the container to be retrieved next is not at the top of a stack, unproductive relocations have to be carried out. Due to the large number of containers handled by busy terminals, a slight reduction in relocation rates can result in significant savings in operating costs. Most of the existing heuristics make relocation decisions for the blocking containers one by one, based on simple indicators. In this article, we propose a Three-Stage Heuristic (3SH) that extends the decision horizon to multiple containers to achieve a higher-quality solution. Computational experiments are conducted on 3 sets of benchmark instances, and the results show that the proposed heuristic outperforms the state-of-the-art heuristics documented in the research literature.

Optimization of vehicle charging and dynamic relocation in free-floating electric carsharing systems with advanced reservations

The electric free-floating carsharing (FFCS) system is an emerging type of shared mobility and is widely adopted in urban areas due to its eco-friendliness, effectiveness, and flexibility. However, as most trips in FFCS are uni-directional, the distributions of vehicles in FFCS are usually spatially uneven. The operators of FFCS must relocate cars during the operation period to maintain the availability of cars to the users and improve the profitability of the system for dealing with imbalanced vehicle distribution caused by uneven demand. To optimize the relocation operations for FFCS operators, this study formulates the problem as a mixed-integer linear programming (MILP) model, which simultaneously addresses dynamic relocation, battery charging of electric vehicles, and scheduling of relocation staff in an FFCS with advanced reservation. A novel, efficient algorithm using a new structure of relocation tuples (RTs) is proposed to solve the problem that facilitates the search for feasible solutions using an RT exchange-based local search algorithm. Acceleration techniques are introduced to strengthen the solution search in large-scale scenarios. Numerical examples are used to evaluate the performance of the solution algorithm and validate the benefit of the proposed methodology. This shows that the methodology could be used by operators for decision-making at different levels.

Algiers master plan, land use and forced relocation: Monitoring change with a spatial decision support system

Over the past two decades, Algerian authorities have made significant investments in the housing sector, leading to substantial changes in Algiers' urban landscape. The implementation of forced housing relocation programs, such as the Program for the Resorption of Precarious Housing (RHP), has been pivotal in transforming Algiers into the first African city without slums, recognized by the United Nations in 2016. However, the selection of receiving sites has presented challenges for the authorities, as the actual implementation deviated from the initial plans outlined in the PDAU master plan. This study focuses on examining the site selection process for relocation in Algiers and delineating the disparities between planned and implemented land mobilization. Using an integrated approach, the analysis targets an in-depth study of the PDAU documents. It draws on a comprehensive data collection process, complemented by interviews with all stakeholders which have been engaged in either in the relocation operations in Algiers or in the elaboration of the PDAU. Furthermore, the study is supported with by a large cartographic overlay of the PDAU investigated sites, which allows a better understanding of the subject. Similar studies conducted worldwide have shown that failures in master planning often result from difficulties in execution and compliance, which are common issues in developing countries. In Algiers, the slow process of land mobilization has compelled authorities to adopt unplanned alternative solutions, making land management a blind spot in the planning practice and hindering the implementation of the PDAU as well as limiting the monitoring of urban expansion and development. Additionally, the study shows that despite attempts to incorporate a Spatial Decision Support System (SDSS) within the PDAU, its execution was to a large extent clearly ineffective. The paper contends that failures in urban land management and data collection constitute weak links impeding spatial development. This has shed light on deficiencies in the cadastral system in Algiers, with ambiguities in the legal framework of land management. The study also shows that the strict adherence to master plans can be challenging when dealing with relocation operations due to various factors. Addressing these issues is crucial for the elaboration of more effective relocation programs. By learning from the gaps and weaknesses in the selection process and implementing improved strategies, Algerian authorities can optimize the outcomes of relocation projects and promote more sustainable urban development.

Profit and utility optimization through joint dynamic pricing and vehicle relocation in carsharing operations

Pricing is one of the main determinants of a successful carsharing business plan. Companies develop different pricing strategies to increase attractiveness, profit, and service usage. Using dynamic pricing strategies can lead to service improvement in terms of profit and better customer satisfaction. This paper presents a novel research contribution to the field of transportation policy by introducing a new framework for designing dynamic pricing strategies in carsharing operations. We develop two hybrid-pricing strategies to increase profit and user utility in car sharing and analyze the service key performance indicators. These two different hybrid-pricing strategies are based upon two different approaches: one relying on demand related information (i.e., fixed price and time-based dynamic price) and one relying on supply related characteristics (i.e., maximum profit price and availability-based dynamic price). By considering both user utility and company indicators, this model features a bi-level structure that allows for rapid implementation. The framework relies on real-world data, typically available to carsharing companies, including membership data, geographic distribution of users, fleet composition, and the location of vehicles and stations. Additionally, we propose a relocation procedure that relocates vehicles on a day-to-day adjustment process. We study the impact of these strategies in an agent-based environment capable to accurately replicate a real carsharing service that operated in the city of Munich, Germany. Once these policies are in place, results show how it is possible to increase profit and customers’ utility. Moreover, we show how an increment in profit corresponds to a reduction of the utility and vice versa. Overall, the effectiveness of the proposed hybrid-pricing strategies in improving key performance indicators such as profit and score in carsharing services is demonstrated through the positive impact of demand-based pricing combined with relocation operations, while supply-based pricing strategies were found to be ineffective in enhancing profit and booking time.

An adaptive large neighborhood search method for rebalancing free-floating electric vehicle sharing systems

Free-floating electric vehicle sharing systems allow users to pick up an available electric vehicle (EV) and return it to any permissible parking location within a service area. Such service flexibility can drive a severe spatial imbalance between vehicle availability and trip demands. Hence, it is an important part of their operations to relocate the EV fleet to meet the next day’s demand with sufficient battery levels. This relocation operation involves a complicated routing problem for a fleet of shuttles to transport the staff drivers who recharge, if necessary, and relocate the EVs to proper demand locations. Characterized by unique hierarchical and interdependent decisions, the EV relocation and shuttle routing problem poses significant computational challenges for large-scale problems. We devise an efficient algorithm that adapts the Adaptive Large Neighborhood Search (ALNS) metaheuristic framework to overcome such unique challenges. The algorithm is tested on two sets of data: randomly generated data and real-world EV-sharing usage data in Amsterdam. The results validate the efficiency and effectiveness of our ALNS algorithm. In addition, the analysis of total operational cost and waiting time percentage provides practical recommendations to decision-makers on choosing the mode of staff transportation (e.g., shuttles vs. personal mobility such as scooters). Lastly, our numerical results also highlight the usefulness of our ALNS method, which is quite flexible to be applied to a dynamic environment where some EV demands are removed or added in the course of EV relocation operations.

A real-coded chicken swarm optimisation algorithm for solving travelling salesman problem

Chicken swarm optimisation (CSO) algorithm, which is inspired by the hierarchal structure and the behaviours of the chicken flock, was first presented for continuous optimisation problems. The paper proposes a real-coded scheme of CSO algorithm (RCCSO) to solve travelling salesman problem (TSP). In the RCCSO algorithm, each position vector represents a visiting sequence of cities. In a position vector, each dimension represents a city and is coded with a real number. The integer part of the real number represents the index number of the city, and the decimal part denotes the visiting order of the city. Using this coding scheme, the discrete neighbourhood of TSP is converted into a continuous neighbourhood. Two repair operators, relocation operator and replacement operator, are designed to guarantee that position vector is always a valid solution of TSP. Finally, the RCCSO algorithm is compared with many different types of intelligent optimisation algorithms. Experimental results prove that the RCCSO algorithm can find the shortest path more quickly and effectively on most TSP datasets.

The benefit of complete trip information in free-floating carsharing systems

Free-floating and instant-access carsharing systems are two features of the most flexible carsharing systems. In the former, users are allowed to freely park the car in any legal parking spot within the boundaries defined by the service operator. In the latter, users are not requested to make any reservation in advance before picking up the car. This paper aims at evaluating the importance of complete trip information in free-floating instant-access carsharing systems. To this aim, we consider a system, referred to as the look-ahead system, where users can reserve a car in advance and are also allowed to directly pick up a car without any reservation. In both cases, the user is requested to specify complete trip details, including the estimated usage duration and the location where the car will be returned. Taking advantage of a complete knowledge of the trip details, the service operator can assign a reservation request to a car that is in use at the time of reservation, provided it will become available at the time and location the user will need it. In its nature, the operational setting we consider is dynamic, as trip information is revealed to the service operator at the time the user requests a car. We also investigate the possibility of suggesting to the user that makes a reservation a pickup location different from the desired one, provided it is not too distant from the latter. We compare the performance of the look-ahead system with the case no information is anticipated, which resembles the service provided by the main carsharing operators currently active in Milan (Italy). Additionally, we use, as a benchmark, the static case where all the information about the users requests (pickup and return locations, as well as delivery time) is known before the start of the planning horizon. We consider a system with no car relocations performed by ad-hoc operators. This enables us to measure the pure benefit of anticipated information, i.e., the benefit coming only from knowing in advance where and when the vehicles will be returned, purged from potential vehicles availability related to relocation operations. The matching between user requests and cars is obtained, for the look-ahead system, iteratively at fixed time intervals through the solution of a Binary Linear Program (BLP) and, for the benchmark case, through the solution of a single BLP. No optimization model is needed for the case where no information is anticipated. A simulation study, based on real-world data from the city of Milan, shows that the look-ahead system can satisfy a number of requests much greater than the case without anticipated information and close to the benchmark case. Moreover, we perform a sensitivity analysis on different parameters, including the maximum distance a user is available to walk and the minimum amount of anticipation requested to users for booking requests, showing their impact on the performance of the systems.

Parameters of optimization of transfer operations of the harvesting process

The development of the forestry complex, its technical equipment, the increase in the volume of harvested wood in the Republic of Belarus, as well as increased competition in the timber market have become prerequisites for the need to study issues in the field of optimizing transport processes and ensuring the rhythm of logging production. Moving operations in the cutting area, including the collection and loading of timber, their sorting and skidding, the formation of stacks in an intermediate warehouse, are the most labor-intensive in logging production. In turn, the effectiveness of these operations largely depends on the optimal placement of technological elements in the cutting area and their geometric parameters. Among the ways to improve the efficiency of the development of the forest fund, one can single out the solution of the problem of optimizing the relocation operations of logging production, for which it is necessary to determine the appropriate parameters and restrictions.

Load distribution strategy of decentralized databases

Load management in databases typically involves segmentation strategies that determine the distribution and placement of information within data storage systems. This article explores a critical aspect of data segmentation and the challenges associated with selecting the right strategy. Some of the discussed strategies, such as range-based strategy and hashing strategy, come with their limitations and risks that can complicate scalability and data migration operations. The article also highlights issues related to choosing a specific data segmentation pattern in databases, including data movement complexity, uneven load distribution, transactional problems, and data integrity preservation. Choosing the right segmentation strategy is crucial to ensure the efficiency and productivity of a data storage system, and it should be a thoughtful choice tailored to the specific requirements of the system. The article discusses important data segmentation strategies in storage systems, which play a key role in the distribution and management of large datasets. The authors explore three main strategies: the search strategy, the range strategy, and the hashing strategy. Each of these strategies has its unique advantages and characteristics. The search strategy provides maximum control over data placement and the ability to use virtual segments for data balancing. This strategy is particularly useful for multi-user applications but may introduce additional overhead during segment retrieval. The range strategy groups related data into one segment and works well with queries for ranges of values. However, it can lead to uneven load distribution. The hashing strategy helps evenly distribute the load and avoid highly active segments but may introduce additional computational overhead when calculating the hash. The article also discusses data scaling and relocation operations for each of these strategies. For example, the search strategy allows for data scaling and relocation at the user level without system downtime. This article helps readers better understand the differences between data segmentation strategies and choose the most suitable one for their specific use case.

Dynamic Discrimination Pricing and Freelance Drivers to Rebalance Mixed-Fleet Carsharing Systems

This paper presents pricing strategies for the relocation problem in mixed carsharing systems with heterogeneous users. On the demand side, a dynamic discriminatory pricing approach is introduced, which can obviate the need for operation-based repositioning, partially or fully. Moreover, it’s assumed that there are earnings-sensitive freelance drivers who may decide to participate in relocation operations, and their decisions depend on the wages are offered by the operation manager. As the operational decisions (trip pricing, relocators hiring, and charging scheduling) are tightly connected, and their interplay can significantly affect the performance of the carsharing system, an integrated model is proposed to optimize trip price and staff wage problems along with vehicle allocation and charge scheduling. To cope with computation difficulties for solving the proposed model, this paper presents a solution framework based on a rolling horizon approach. Through extensive numerical experiments, the performance and effectiveness of the proposed model are investigated under different assumptions and parameter settings. The results confirm that the proposed pricing strategies outperform common strategies in the literature and can significantly improve the performances of carsharing systems. The sensitivity analysis of several key parameters provides managerial insights into the operations management of carsharing systems.

Machine Learning Guides the Solution of Blocks Relocation Problem in Container Terminals

The blocks relocation problem (BRP) is a well known and important combinatorial optimization problem, in which the initial storage state and retrieval priority of containers are known, and the containers should be picked up in the retrieval order with the goal of minimizing container relocations. This paper studied how machine-learning techniques guide the solution of this Non-deterministic Polynomial problem(NP-hard problem) NP-hard problem. Through our self-developed data generator, we generated initial state stacking matrices and extracted 22 influencing factors for container relocation operations. The supervised learning method and attribution technique were used to verify the relationship between significant container relocated influence features and the number of container relocations using the unrestricted BRP and restricted BRP models. We characterize the potential patterns in the data based on 22 container relocated influence features using four supervised learning models: random forest (RF), extra trees (ET), support vector machine (SVM), and logistic regression (LR). The experimental results demonstrate that RF has a classification accuracy rate of up to 94% on the restricted BRP model. The attribution technique identifies the most sensitive features to the number of container relocations. We organically integrate machine learning into the BRP problem and propose an interactive iterative framework that may provide a novel method for studying the BRP problem.

Travel time model for multi-deep automated storage and retrieval systems with different storage strategies

Travel time models for automated storage and retrieval systems (AS/RS) are used to define average travel times during storage/retrieval operations in an AS/RS. With an increasing depth of AS/RS racks, storage goods are stored in front of each other. This can lead to relocation operations of blocking goods causing higher travel times. This paper derives analytically and presents four travel time models for multi-deep AS/RS following four storage allocation strategies. Two models handle random strategies, one minimises the variance of storage channel fillings and the fourth maximises this variance. Evaluation and comparison of different models is followed by a discrete event simulation to verify these models. It is shown that the minimal variance strategy achieves the lowest relocation numbers and also the lowest total travel times, the random strategies perform between the minimal and maximal variance strategy.

The Competitive Pickup and Delivery Orienteering Problem for Balancing Car-Sharing Systems

Competition between one-way car-sharing operators is currently increasing. Fleet relocation as a means to compensate demand imbalances constitutes a major cost factor in a business with low profit margins. Existing decision support models have so far ignored the aspect of a competitor when the fleet is rebalanced for better availability. We present mixed-integer linear programming formulations for a pickup and delivery orienteering problem under different business models with multiple (competing) operators. Structural solution properties, including existence of equilibria and bounds on losses as a result of competition, of the competitive pickup and delivery problem under the restrictions of unit-demand stations, homogeneous payoffs, and indifferent customers based on results for congestion games are derived. Two algorithms to find a Nash equilibrium for real-life instances are proposed. One can find equilibria in the most general case; the other can only be applied if the game can be represented as a congestion game, that is, under the restrictions of homogeneous payoffs, unit-demand stations, and indifferent customers. In a numerical study, we compare different business models for car-sharing operations, including a merger between operators and outsourcing relocation operations to a common service provider (coopetition). Gross profit improvements achieved by explicitly incorporating competitor decisions are substantial, and the presence of competition decreases gross profits for all operators (compared with a merger). Using a Munich, Germany, case study, we quantify the gross profit gains resulting from considering competition as approximately 35% (over assuming absence of competition) and 12% (over assuming that the competitor is omnipresence) and the losses because of the presence of competition to be approximately 10%.

Socio-spatial impact of slum dwellers relocation in Algiers: a flow analysis using QGIS

Since 2014, we have witnessed an acceleration of the pace of massive relocation operations in Algerian cities, particularly in the capital, with the ambition of making the city of Algiers the first African capital without slums. This article presents an evaluation of the relocation operations carried out during the period from 2014 to 2016. The objective is to identify the socio-spatial impacts of massive relocation on the province of Algiers and the neighboring cities. For this purpose, we adopted a method based on the flow analysis of slum dwellers relocated with the data visualization technique, widely used in GIS. The results show that a large number of inhabitants were relocated in a relatively short time and that there was a random distribution of slum dwellers from the capital to communes within or outside its administrative boundaries, with negative impacts on socio-spatial transformations of Algiers.

Notes on the Successful Restraining, Transport, and Release of Two Sub-Adult Whale Sharks (Rhincodon Typus) Stranded Inland on the Coast of Abu Dhabi

On November 6th and November 12th, 2020, two sub-adult Whale Sharks (Rhincodon typus) Smith 1828, were successfully restrained and relocated in situ after being stranded for 4 and 2 weeks respectively, in manmade canals inland of Abu Dhabi, UAE. The first relocation operation took ~5 hours to complete a distance of 23.5 km and the second ~3.5 hours for 20km. Both individuals were in good condition after the transportation, with the second R. typus tagged with a SPOT 253 tracker (Wildlife computers).

The block relocation problem with appointment scheduling

In many container terminals, containers are piled vertically and horizontally in the terminal yard, limited mainly by the dimensions of the yard crane. Import and export containers are typically stacked separately. An external truck can access the terminal to pick up an import container only after making an appointment reserving a pickup time. To reduce truck waiting time inside the terminal, container pickup appointments are normally scheduled on a time window basis. However, when a truck arrives at the terminal yard at the appointed time, it is common for the target container not to be at the top of its stack, resulting in unproductive relocations to remove all the containers stacked above the target container and thus increasing the truck's waiting time. To minimize the number of relocations, the Block Relocation Problem (BRP) is usually solved independently, without consideration of appointment scheduling. In this paper, we introduce a new optimization problem—the Block Relocation Problem with Appointment Scheduling (BRPAS)—to jointly address the two issues. To solve the problem, two binary IP models are proposed, and examples from the literature are solved to confirm the performance of the two models. The proposed formulations are further extended to cover several operational aspects related to the flexibility of container pickup operations. Results show that the proposed approach can improve container relocation operations at terminal yards by coordinating with appointment scheduling.

THE POLICY INSTRUMENTS OF STREET VENDOR RELOCATION IN SURABAYA BULAK FISH CENTER

The purpose of this study was to determine the policy instruments used by the Surabaya City Government to move street vendors to Bulak Fish Center. The background is because not all street vendors are ready to occupy the Bulak Fish Center. This study uses Howleet and Ramesh's theory to categorize policy instruments into three groups: Voluntary, Combined, and Compulsory Instruments. The research method used is descriptive qualitative with in-depth interviews. The research results reveal that the Surabaya City Government uses only five types of policy instruments. First, families and communities provide information to the community about the relocation of street vendors to Bulak Fish Center. Second, policies provide space to interact with street vendors, community leaders, and religious groups. Third, the policy of providing information and advice to all Surabaya local governments to keep Bulak Fish Center alive for a month. Fourth, the policy prohibiting selling in the old location. Fifth, the policy is to provide cleaning staff and transport vehicles directly to roadside vendors. However, 50 percent of the street vendors are still willing to occupy the Bulak Fish Center. Thus, the policy tools used by the Surabaya City Government have not been optimal, so that it has become a factor in the failure of roadside trade relocation operations.

Double-balanced relocation optimization of one-way car-sharing system with real-time requests

One-way car-sharing systems, an increasingly prominent transportation means, are facing the vehicle imbalance issue with their emergence. To overcome the problem, operators have adopted a common strategy to relocate vehicles among stations by dispatchers. However, along with this approach come imbalanced dispatchers, demanding double-balanced optimization for relocation operations in vehicle relocation and dispatcher scheduling. In this paper, we propose an integrated model to determine the optimal requests served, relocation tasks, and dispatchers’ routes in order to minimize the generalized daily operational cost. The model adopts two different time granularities to obtain all the possible relocation tasks and the refined scheduling of dispatchers. Due to the dynamic nature and the scale of this double-balanced relocation problem, a hybrid solution algorithm is designed combining a rolling horizon algorithm with a customized decomposition algorithm. The planning horizon consists of several stages, each of which contains a sub-problem for the double-balanced relocation, and a customized decomposition is embedded to optimize it efficiently. Some computational experiments and a case study in Lanzhou, China are conducted to identify critical parameters and illustrate the performance of the proposed method.

Can a bikesharing system reduce fuel consumption? Case study in Tucson, Arizona

Despite the popularity of bikesharing systems, it is still unclear whether bikesharing systems can reduce fuel consumption, and how much of fuel-savings they could achieve. Therefore, this paper evaluated the fuel-savings of a bikesharing system in Tucson with varying substitution rates of bikesharing trips for vehicle-based trips. First, relocation operations were identified by fusing bikesharing trip and station level data. Then, the fuel consumed by the relocation truck and the fuel saved by bikesharing trips were estimated. Sensitivity analyses are conducted to investigate the fuel consumption impacts of adding more stations, optimizing the relocation routes, and using more operation trucks. After adding four more stations and optimizing the relocation routes, the required car substitution rate to offset the fuel consumed by the relocation truck decreases from 30% to 17%. Furthermore, the required substitution rate in a denser bikesharing system is less sensitive to an increase in the number of trucks. Therefore, bikesharing systems have great potential to reduce fuel consumption by incorporating suitable strategies.