Repositioning Problem Research Articles

Overview of shared-bike repositioning optimization with artificial intelligence

Abstract Rapid developments in artificial intelligence (AI) present unprecedented opportunities to enhance the operational and management performance of shared bikes. heuristic algorithms, supervised algorithms, unsupervised algorithms, and reinforcement learning (RL) in AI technologies enable the consideration of more possibilities in the bike repositioning problem (BRP), including addressing challenges such as large-scale bike sharing, real-time dynamic repositioning, and dynamic policy interaction with the environment. This paper provides an overview of research on bike-sharing repositioning utilizing AI techniques. The applications of Heuristic Search methods and Machine Learning, including RL for docked and dock-less shared bikes, are summarized based on dynamic and static environments, respectively. We provide a comprehensive analysis of the advanced development in AI-based BRP and review the application of AI technologies in obtaining scientifically repositioning strategies that effectively balance supply and demand conflicts. Moreover, this study delves into the constraints and potential advancements of AI methods for shared bike reallocation, offering valuable recommendations for future research.

Data-Driven Empty Container Repositioning for Large Scale Railway Network With Fuzzy Demands

The Chinese rail network has a fast-growing number of containers in service to promote intermodal transport service and to attract more freight demand to the railway for a sustainable transport system. The repositioning of empty containers induces a significant problem for the large-scale rail network, due to the spatial imbalance between freight supply and demand. Traditionally, the empty container repositioning (ECR) problem is solved with the estimated parameters of a given demand distribution, which may difficult known to decision maker. This article develops a data-driven framework for the ECR problem based on the large datasets available. This framework employs machine-learning algorithms to forecast the supply/demand of empty containers, and to draw parameters identifying the factors that can be hardly integrated into optimization models. Based on those parameters, two optimization models for different ECR modes are then proposed to explore the optimal ECR plan. An integrated weight coefficient is introduced into the objective functions, which minimizes the total kilometers that empty containers transported. The models are solved by CPLEX after constraints conversion based on triangular fuzzy supply/demand. The numerical results based on a real-world case show that the proposed solution method can yield the optimal empty container repositioning plan. The total container-kilometer may increase after data-driven parameters are introduced, as some of the empty container repositioning may no longer follow the shortest path, while the final plan becomes more practicable and executable.

Distribution-Free Approaches for an Integrated Cargo Routing and Empty Container Repositioning Problem with Repacking Operations in Liner Shipping Networks

Cargo (laden container) routing and empty container repositioning are crucial components in liner shipping, which are closely relevant. However, most existing works focus on cargo routing and empty container repositioning separately, which cannot optimize the global shipping cost. In addition, (1) appropriate repacking operations for laden containers can curtail the total handling costs, and (2) the perfect information for uncertain laden and empty container demands may be difficult to obtain due to insufficient historical data. This work investigates an integrated cargo routing and empty container repositioning problem with repacking operations in which only partial information about uncertain laden and empty container demands is known. The objective is to minimize the total costs including repacking operation cost and laden and empty container transportation costs. For the problem, a new chance-constrained programming model based on moment-based ambiguous sets is formulated. Then, four distribution-free solution approaches are adopted to solve the investigated problem. Numerical experiments are conducted to compare the proposed methods.

A Multi-Stage Approach for Empty Container Repositioning Under Coordination Among Liner Carriers

This paper studies the empty container repositioning (ECR) problem considering the exchange of slots and empty containers among liner shipping companies. It is common for an individual shipping company to seek an optimal solution for ECR and cargo routing to maximize its own benefits. To achieve cooperation among shipping companies, a multi-stage solution strategy is proposed. With the inverse optimization technique, the guide leasing prices of slots and empty containers among shipping companies are derived considering the schedule of vessels and cargo routing. Based on the guide leasing price, a cooperative model is formulated to minimize the total cost, which includes the transportation cost for laden containers, the inventory holding cost, the container leasing cost, and the repositioning cost. All the involved shipping companies are expected to follow the best solution of ECR and cargo routing to achieve a cooperative and stable optimum. A real-world shipping network operated by three liner shipping companies is used as a case study with promising numerical results.

An enhanced artificial bee colony algorithm for the green bike repositioning problem with broken bikes

The Bike Repositioning Problem (BRP) has raised many researchers’ attention in recent years to improve the service quality of Bike Sharing Systems (BSSs). It is mainly about designing the routes and loading instructions for the vehicles to transfer bikes among stations in order to achieve a desirable state. This study tackles a static green BRP that aims to minimize the CO2 emissions of the repositioning vehicle besides achieving the target inventory level at stations as much as possible within the time budget. Two types of bikes are considered, including usable and broken bikes. The Enhanced Artificial Bee Colony (EABC) algorithm is adopted to generate the vehicle route. Two methods, namely heuristic and exact methods, are proposed and incorporated into the EABC algorithm to compute the loading/unloading quantities at each stop. Computational experiments were conducted on the real-world instances having 10–300 stations. The results indicate that the proposed solution methodology that relies on the heuristic loading method can provide optimal solutions for small instances. For large-scale instances, it can produce better feasible solutions than two benchmark methodologies in the literature.

A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem

A common challenge faced by liner operators in practice is to effectively allocate empty containers now in a way that minimizes the expectation of costs and reduces inefficiencies in the future with uncertainty. To incorporate uncertainties in the operational model, we formulate a two-stage stochastic programming model for the stochastic empty container repositioning (ECR) problem. This paper proposes a separable piecewise linear learning algorithm (SPELL) to approximate the expected cost function. The core of SPELL involves learning steps that provide information for updating the expected cost function adaptively through a sequence of piecewise linear separable approximations. Moreover, SPELL can utilize the network structure of the ECR problem and does not require any information about the distribution of the uncertain parameters. For the two-stage stochastic programs, we prove the convergence of SPELL. Computational results show that SPELL performs well in terms of operating costs. When the scale of the problem is very large and the dimensionality of the problem is increased, SPELL continues to provide consistent performance very efficiently and exhibits excellent convergence performance.

A hybrid algorithm for the static bike-sharing re-positioning problem based on an effective clustering strategy

This paper studies the bike-sharing re-positioning problem (BSRP) frequently encountered in modern bike-sharing systems that are widely deployed around the world. To cope with customer demand fluctuations and improve service level, BSRP aims to identify the optimal vehicle routes to visit bike-sharing stations in order to balance their inventories, picking up excess bikes from surplus stations and adding needed bikes to insufficient stations, with the objective of minimizing total traveling cost and inventory cost. The mathematical model of the studied problem is first given, detailing the considerations of multiple depots available for re-positioning vehicles and the extra objective of inventory cost minimization. An effective clustering strategy is then proposed to put bike-sharing stations into self-sufficient groups, which is shown to be able to greatly decompose the problem complexity for large-scale instances. A destroy-and-repair algorithm is developed to improve the clusters, and an adaptive variable neighborhood search algorithm is designed to conduct intra-cluster and inter-cluster vehicle routing optimization. Performance of the hybrid algorithm is validated on three sets of benchmark instances, and compared with CPLEX as well as state-of-the-art algorithms from the literature, which demonstrates that the proposed algorithm is highly competitive in solving BSRPs.

Addressing the Bike Repositioning Problem in Bike Sharing System: A Two-Stage Stochastic Programming Model

In this paper, a bike repositioning problem with stochastic demand is studied. The problem is formulated as a two-stage stochastic programming model to optimize the routing and loading/unloading decisions of the repositioning truck at each station and depot under stochastic demands. The goal of the model is to minimize the expected total sum of the transportation costs, the expected penalty costs at all stations, and the holding cost of the depot. A simulated annealing algorithm is developed to solve the model. Numerical experiments are conducted on a set of instances from 20 to 90 stations to demonstrate the effectiveness of the solution algorithm and the accuracy of the proposed two-stage stochastic model.

Pricing and coordination of marine service chain with empty equipment repositioning

The empty equipment repositioning (EER) problem is deemed an inefficiency of the marine industry. However, owing to trade imbalance as the fundamental cause of EER, such problem is inevitable and requires consideration in pricing decisions. Accordingly, this study explores a marine service chain with one carrier and two dominant forwarders providing transportation service between two ports. We built a mathematical model to examine how carrier and forwarders determine pricing decisions with and without EER cost sharing. We find that the smaller one between empty equipment balancing and EER costs determines pricing decisions. We also investigate whether the EER cost-sharing mechanism can coordinate the service chain and subsequently found that the EER cost-sharing mechanism aggravates double marginalization. Then, we design a two-part tariff coordination mechanism to coordinate the service chain. Lastly, we study the effect of EER cost sharing and coordination mechanism on the environment. Interestingly, we find that EER cost-sharing and coordination should be avoided from the perspective of environmental protection.

A Bilevel Programming Model and Algorithm for the Static Bike Repositioning Problem

In this paper, by taking the outsourcing transportation mode into account, a bilevel programming model is proposed to formulate the static bike repositioning (SBR) problem, which can be used to determine the number of bikes loaded and unloaded at each station and the optimal truck routes in bike sharing systems (BSS). The upper-level BSS providers determine the optimal loading and unloading quantities at stations to minimize the total penalties. The lower-level truck owner pursues the minimum transportation route cost. An iterated local search and tabu search are developed to solve the model. Computational tests on a set of instances from 20 to 200 bikes demonstrate the effectiveness of the model and algorithms proposed, together with some insightful findings.

An optimization model for container inventory management

This paper formulates the empty container repositioning (ECR) problem, which is one of the most important issues in the container shipping industry, by running a model to generate the service route of feeder shipping in a dynamic environment. The objective is to reduce the disequilibrium of laden and empty containers. This paper first provides a literature review with the emphasis on the shipping scheduling and modelling of ECR. Second, a network model has been designed to find the optimum size of ship servicing a particular region. A simply ECR policy is tested to demonstrate an essential impact on the profit of a container transport system. With the aid of this heuristic shipping model, ECR is integrated with the tramp ship routing problem under dynamic condition.

Solving the Liner Shipping Fleet Repositioning Problem with Cargo Flows

We solve a central problem in the liner shipping industry called the liner shipping fleet repositioning problem (LSFRP). The LSFRP poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between routes in a liner shipping network. Liner carriers wish to reposition vessels as cheaply as possible without disrupting cargo flows. The LSFRP is characterized by chains of interacting activities with a multicommodity flow over paths defined by the activities chosen. Despite its industrial importance, the LSFRP has received little attention in the literature. We introduce a novel mathematical model and a simulated annealing algorithm for the LSFRP with cargo flows that makes use of a carefully constructed graph; we evaluate these approaches using real-world data from our industrial collaborator. Additionally, we compare the performance of our approach against an actual repositioning scenario, one of many undertaken by our industrial collaborator in 2011. Our simulated annealing algorithm is able to increase the profit from $18.1 to $31.8 million using only a few minutes of CPU time. This shows that our algorithm could be used in a decision support system to solve the LSFRP.

Sample average approximation under non-i.i.d. sampling for stochastic empty container repositioning problem

The stochastic empty container repositioning (ECR) problem can be solved by the sample average approximation (SAA) method, in which a sample of random scenarios are generated and then deterministic optimization techniques can be applied to solve the problem based on this sample. Considering the scale of SAA problem with multiple scenarios and the large number of random parameters in the ECR problem, we propose a design which combines Latin hypercube design and supersaturated design, which is able to get a satisfying approximation by using only a few scenarios. The impact of several non-independent and identical distribution (i.i.d). sampling methods for the stochastic ECR problem has been computationally examined. It is shown that all these non-i.i.d. sampling methods in consideration could help to enhance the performance of the SAA method for the stochastic ECR problem. Moreover, by using the proposed sampling method, it is less probable to provide bad solutions and the bias of the expected objective value of the sampled problem could also be reduced.

Automated Planning for Liner Shipping Fleet Repositioning

The Liner Shipping Fleet Repositioning Problem (LSFRP) poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between services in a liner shipping network. The LSFRP is characterized by chains of interacting activities, many of which have costs that are a function of their duration; for example, sailing slowly between two ports is cheaper than sailing quickly. Despite its great industrial importance, the LSFRP has received little attention in the literature. We show how the LSFRP can be solved sub-optimally using the planner POPF and optimally with a mixed-integer program (MIP) and a novel method called Temporal Optimization Planning (TOP). We evaluate the performance of each of these techniques on a dataset of real-world instances from our industrial collaborator, and show that automated planning scales to the size of problems faced by industry.