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• Based on the model decomposition approach, two new parallel algorithmic frameworks are proposed. • The parallel algorithm B-ADMM is developed by employing the concept of bush. • Integrating the parallel block coordinate descent (PBCD) method and the B-ADMM algorithm, then a bi-level parallel algorithm PBCD-ADMM is developed. • To validate the two algorithms proposed with much large-scale networks. This paper introduces two novel parallel algorithmic frameworks to address the user equilibrium traffic assignment problem (UE-TAP). Most of the existing solution algorithms for the UE-TAP are executed in a sequential manner. This study endeavors to explore parallel computing methods based on model decomposition. Considering that the TAPs can be decomposed based on their origins, thus, following the spirit of the alternating direction method of multipliers (ADMM), a new parallel algorithm B-ADMM is proposed, which integrates the concept of the bush. Subsequently, the convergence of the proposed algorithm is rigorously proven. To enhance the algorithmic parallelism while maintaining the convergence efficiency of the B-ADMM algorithm, this paper further employs the parallel block coordinate descent (PBCD) method to improve the B-ADMM algorithm. We develop a bi-level parallel algorithm PBCD-ADMM, in which the independent origins/bushes are separated into several blocks, and the origin/bush-based restricted subproblems in each block can be solved in parallel. Furthermore, for the given bush belonging to a block, the bush links can also be grouped into several sub-blocks based on the original link-blocking scheme. Thus, these link-based subproblems in each sub-block can also be solved in parallel. A numerical experiment is conducted to validate the proposed algorithms, which indicates that the two new parallel algorithms perform better in terms of convergence speed compared with the original ADMM algorithm. [ABSTRACT FROM AUTHOR]

Ridepooling is expected to become more and more important in people's mobility. At the same time, autonomous vehicles are expected to be widely used in such applications in the future. While the driver as a person of trust currently provides security, passengers are alone with strangers in an autonomous ridepooling vehicle. In the paper, we address this aspect by including incompatibilities between customers, meaning that incompatible customers cannot share the vehicle at the same time. Although incompatibilities can be defined in any way by the provider, they can be set, for example, if a customer feels unsafe in sharing the vehicle with customers of a certain type. As other compatible customers can replace the driver as a person of trust, we present a second setting where all customers are allowed to share the vehicle at the same time, but customers are never alone in the vehicle with incompatible customers. We present Branch-and-Cut algorithms for both problem settings including methods to improve the search with respect to the incompatibilities. We show in a computational study that the introduced methods improve the search significantly and evaluate the Branch-and-Cut algorithms on realistic instances with up to 120 customers and 10 vehicles. Moreover, our results suggest that it can be a beneficial option for the provider to allow their customers to participate in the decision process by indicating incompatibilities for types of customers, meaning that they do not want to share a vehicle with a customer of an incompatible type. • A Dial-a-Ride Problem with incompatibilities is investigated. • Two types of incompatibilities are considered. • Branch-and-Cut algorithms are developed for both types. • Zero distance chains where several events occur simultaneously are included. [ABSTRACT FROM AUTHOR]

• We introduce an RVRP where the last-mile delivery of some demands can be outsourced. • We propose construction and improvement heuristics handling outsourcing decisions. • The routes include the driving and rest periods according to European regulations. • We solve 60 artificial instances using our algorithms in classic metaheuristics. • The impact of the construction procedure and the outsourcing decisions are evaluated. This paper addresses a Rich Vehicle Routing Problem (RVRP) characterized by the following attributes: long-haul transport, driver hours regulation, incompatibility among goods, multiple depots and pickup locations, heterogeneous vehicles, time windows, and outsourcing decisions for last-mile delivery. Addressing the latter aspect is especially challenging, since it requires substantial changes in the algorithms for generating initial feasible solutions (construction heuristic) and for modifying existing solutions (improvement heuristics). Our work develops new heuristics adapted to the above attributes, and evaluates their performance in combination with common solving metaheuristics. We also assess the impact and the cost-effectiveness of the outsourcing attribute. [ABSTRACT FROM AUTHOR]

Highlights • We provide a mathematical formulation for the multi-depot Inventory Routing Problem. • We design a three-phase matheuristic algorithm for solving the problem. • We design and implement a branch-and-cut algorithm for solving the problem. • We show the performance of the proposed matheuristic on a real case study. Abstract In this paper we solve a practical Multi-Depot Inventory Routing Problem (MDIRP). One minimizes routing costs by determining how to serve the customers from different depots, managing their inventory levels to avoid stock-outs. The MDIRP optimizes the trade-off between inventory and routing decisions in an integrated way. We formulate this problem as a mixed-integer linear programming model and design a three-phase matheuristic to solve the problem. The solutions of our matheuristic are compared with those from a branch-and-cut algorithm on classical IRP instances, new instances and a real case study, showing to be very effective. [ABSTRACT FROM AUTHOR]

Highlights • A new e-tail order fulfilment problem is studied. • A MIP formulation is developed and its computational complexity is analyzed. • A Benders decomposition algorithm is proposed for solving the problem. • Test instances are generated from the logistic network of JD.com. Abstract This paper studies an order fulfilment problem in an e-tailing environment, where the e-tailer fulfills orders by assigning orders to its fulfilment centers and routing the shipments from the fulfilment centers to its delivery stations. We develop a mixed integer program for the problem, analyze its computational complexity, and propose a Benders decomposition algorithm for solving the problem. Computational performance of the proposed algorithm is evaluated on problem instances generated based on the logistics network of JD.com in Shanghai. Computational results show that the Benders decomposition algorithm is able to achieve satisfactory performance within reasonable computation time. [ABSTRACT FROM AUTHOR]

Highlights • A new time-based policy is proposed for consolidating same customers' shipments. • The model determines shipping time for shipments considering their relationship. • The piecewise-integer structure is solved by a new three-phase heuristic algorithm. • The order consolidation approach outperforms the First-In-First-Out approach. Abstract This paper addresses the last-mile split delivery problem emerging in online retailing, in which multiple shipments are delivered to the same customer multiple times. We propose an innovative order consolidation approach which consolidates same customers' shipments in the delivery station and delivers them in fewer trips. An integer programming model is formulated to make a cost tradeoff between splitting and consolidating shipments, which is solved by a new three-phase heuristic algorithm. Numerical experiments are conducted to demonstrate the superiority of the order consolidation approach over the First-In-First-Out approach on various parameters and generate managerial insights for applying it in practice. [ABSTRACT FROM AUTHOR]

This paper considers a joint scheduling problem of macro-level yard section allocation and transfer manpower assignment in automobile RO-RO terminals. The joint scheduling problem is formulated as a mixed integer programming model with the objective of minimizing the total necessary driving distance of vehicles. Relaxation cases derived from a series of ingenious problem analyses and dual segmentation techniques based on mathematical derivations are presented. In addition, compressed strategies are applied to reduce the complexity of our problem. Then, we develop an amalgamated algorithm consisting of a rule-based stepwise algorithm, a reinforced column generation, and a multi-root branch-and-bound. Numerical experiments based on random data and a case study based on real operation data are conducted to evaluate the performance of our algorithms. The results show that the proposed algorithms are capable of generating high-quality solutions efficiently and are of great practical value in daily operations. • A joint scheduling problem of RO-RO terminals is studied. • A mixed integer programming model is built. • Relaxation and dual segmentation are applied to develop algorithms. • A column generation based heuristic algorithm is proposed. • A series of experiments with case study are conducted. [ABSTRACT FROM AUTHOR]

• Proposed model with harbor constraints can reduce the conflicts in harbor apron. • The model optimizes the efficiency of gate utilization while ensuring safety. • A better solution is obtained by an improved branch-and-price method. • Experiments are designed to verify the value of harbor safety constraints. The airport gate assignment problem is the main problem in airport operation management. Most existing studies on the airport gate assignment problem focus more on the improvement of gate utilization efficiency and ignore apron operation safety. Aiming at addressing this problem, an airport gate assignment problem with harbor safety constraints is proposed in this paper. A two-phase mathematical optimization model is constructed to optimize the efficiency of gate utilization considering the safety constraints in harbors. To the best of our knowledge, this is the first study to explicitly focus on harbor apron security in the gate assignment problem. Then, the exact branch-and-price method is improved by incorporating a label-based pricing algorithm and two acceleration strategies. These strategies include an upper bound prediction strategy for handling the large number of subproblems and a lower parameter symmetry elimination strategy to overcome column generation degradation. The results show that the proposed gate assignment model and improved branch-and-price method optimize the efficiency of gate utilization under the condition of avoiding security conflicts in harbor aprons. The improved branch-and-price method has advantages in terms of both accuracy and efficiency compared with those of other solvers. [ABSTRACT FROM AUTHOR]

This paper focuses on operational level stochastic orienteering problem, in which travel time and service time are stochastic and the vehicle can adjust its routing plan. A real-time adjustment strategy, called Simulation-Aided Multiple Plan Approach (SMPA), is proposed to optimize the real-time vehicle routing plan. We embed a “myopia prevention” strategy into SMPA to improve solution quality. The numerical experiment compares the performance of our proposed algorithm with a strategic level algorithm and another commonly used operational level algorithm called re-optimization algorithm. The results show that our algorithm outperforms previous methods in both solution quality and computing time. [ABSTRACT FROM AUTHOR]

This paper introduces a mixed integer programming model for production routing problems with reverse logistics and remanufacturing, which are closed-loop production routing problems addressed for the first time. A solution method of branch-and-cut guided search algorithm is developed. Computational results from instances adapted from benchmarks of production routing problems show that, the algorithm is more effective when pickup requests are relative high. The problem is also easier to solve when production or transportation costs are lower. The optimal decisions are insensitive to the location of remanufacturing depot whether it is geographically centered or centered with gravity. [ABSTRACT FROM AUTHOR]

This paper addresses a problem of ship type decision considering empty container repositioning and foldable containers, which determines the capacity of ships deployed in a given shipping route at a tactical level and empty container repositioning between ports at an operational level. It considers the use of foldable containers and aims to find under what conditions, a shipping liner needs to use the foldable containers. To solve the problem, we formulate a network flow model with a revised network simplex algorithm, based on which an exact solution approach is designed to determine the optimal ship type. [ABSTRACT FROM AUTHOR]

Shintani et al. (2010) developed five integer programming models to analyze the effects of using foldable containers in hinterland transportation. They solved the models using a general purpose integer programming algorithm and mentioned that other powerful solution methods are needed to solve a large size model. In this paper, we develop analytical solutions for two of their models and show that a network flow algorithm solves another two models. [ABSTRACT FROM AUTHOR]

• A novel flow update policy: successive over relaxation (SOR) iteration method. • The principle of employing the SOR method in the traffic assignment algorithms, e.g., the gradient projection (GP) algorithm. • Three important propositions of the GP-SOR algorithm. • Self-adaptive adjustment of the relaxation factor based on the Armijo Rule. • To validate the SOR method with much large-scale networks. This paper presents a novel flow update policy, namely the successive over relaxation (SOR) iteration method, which can be implemented in traffic assignment algorithms. Most existing solution algorithms for the user equilibrium traffic assignment problem (UE-TAP) mainly use two flow update policies: Jacobi and Gauss-Seidel iteration methods. The proposed flow update policy SOR can be a more efficient replacement. Following the path-based gradient projection (GP) algorithm, we developed a new method GP-SOR for the UE-TAP. This study first provides the complete procedure of applying the GP-SOR algorithm to solve the UE-TAP. Subsequently, a few properties of the proposed method are rigorously proven. However, empirical tests of the GP-SOR algorithm demonstrate serious oscillations and poor convergence. To cope with this problem, the Armijo Rule is employed to determine the relaxation factor, which substantially improves the convergence of GP-SOR algorithm. The preliminary numerical examples show that the GP-SOR algorithm has speedier convergence compared with the known alternatives, which is reflected by the evident reduction of the computing time and the number of iterations. [ABSTRACT FROM AUTHOR]

• Container slot allocation and pricing problem with uncertain shipping demand. • Two chance-constrained programming models under two slot allocation strategies. • A tailored SAA-RLT solution algorithm to solve the models. This paper studies the container slot allocation problem for time-sensitive cargoes with consideration of dynamic pricing and port congestion under uncertain demand. Time-sensitive cargoes call for express delivery. However, port congestion is a non-negligible factor to affect the delivery time. Hence, a new freight rate function with respect to the delivery time considering port congestion is proposed for time-sensitive cargoes, and two different slot allocation strategies are proposed: Loyal Strategy and Expansive Strategy. Accordingly, we formulate the problem as a one-stage container slot allocation model and a two-stage container slot allocation model, respectively. Both of the two models are stochastic mixed-integer quadratic programming models with chance constraints due to the involvement of dynamic pricing, port congestion, and uncertain demand. To solve the proposed models, a tailored algorithm that combines the Sample Average Approximation approach and the Reformulation Linearization Technique (SAA-RLT) is developed in this paper. Finally, numerical experiments are carried out to verify the applicability and effectiveness of the proposed models and the solution algorithm. [ABSTRACT FROM AUTHOR]

• TEAP is much less studied compared to TAP, especially in terms of solution algorithms. • This paper proposes two Newton-type hyperpath-based algorithms for a frequency-based TEAP formulation. • The newly developed algorithms are tested on two real-world transit networks. • Hyperpath-based algorithms significantly outperform the benchmark algorithms in large networks. The transit equilibrium assignment problem (TEAP) aims to predict the distributions of passenger flows on lines or line segments in a transit network. Compared to the traffic assignment problem (TAP) for highway networks, the TEAP is much less studied, especially in terms of solution algorithms. This paper proposes two Newton-type hyperpath-based algorithms for a frequency-based TEAP formulation that considers both the congestion effect related to crowding and the queuing effect related to boarding. These newly developed algorithms, as well as two benchmark algorithms from the literature, are tested and compared on a number of transit networks, including two constructed using real-world data. The results show the proposed hyperpath-based algorithms significantly outperform the benchmark algorithms in large networks. [ABSTRACT FROM AUTHOR]

This paper presents a d -minimal cut based algorithm to evaluate the performance index R d +1 of a distribution network, defined as the probability that a specified demand d + 1 can be successfully distributed through stochastic arc capacities from the source to the destination. To improve the efficiency of solving d -minimal cuts, a novel technique is developed to determine the minimal capacities of arcs. Also, two new judging criteria are proposed to detect duplicate d -minimal cuts. Both theoretical and computational results indicate that our algorithm outperforms the existing methods. Furthermore, a real case study is provided to illustrate the application of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a model of stable vessel-cargo matching with price game mechanism in the dry bulk shipping market. The research aims at mimicking the bid of disadvantaged participants in the process of matching preferred objects. The matching equilibrium and price equilibrium are formulated between the shippers and carriers. A price game mechanism based Gale-Shapley algorithm is developed. Three scenarios of market dominated by shippers, market dominated by carriers, and equilibrium market are discussed in computational experiments. It is showed that if disadvantaged participants bid with the price game mechanism, they may gain more surpluses even in the negative position. [ABSTRACT FROM AUTHOR]

• A more comprehensive multimodal urban transportation network is built, which includes three basic travel modes and two P&R modes. • We develop ageneral fixed-point (FP) model to formulate the combined mode split and traffic assignment (CMSTA) problem. • A new solution algorithm that integrated with several effective methods and strategies is proposed to solve the proposed model. • Numerical results illustrate the performance of the proposed model and algorithm on large-scale networks. Modelling the combined mode split and traffic assignment (CMSTA) problem is essential to capture the travelers' behaviors and predict the flow distribution in multimodal transportation networks. In this paper, a general fixed-point model that combines the CNL-based mode split and the VI-based traffic assignment is developed to formulate the CMSTA problem in a more realistic multimodal network. The transit common line problem and asymmetric travel cost are considered. A new solution algorithm that integrated with several effective methods and strategies is proposed to solve the fixed-point model in large-scale networks. Numerical results show the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Shared mobility has attracted increasing attention due to its advantages in relieving traffic congestion and low-carbon environmental protection. This paper studies the dynamic shared-taxi problem of the on-demand shared-taxi system, where we introduce a rescheduling ratio to measure the proportion of requests that can be rescheduled in the total scheduled requests. To match vehicles and passengers in the on-demand platforms with high quality and efficiency, we formulate the problem into a mixed-integer model and develop two optimality-guaranteed algorithms, the branch-and-price algorithm and the Lagrangian relaxation algorithm. The two algorithms share a common subproblem which is solved by dynamic programming in parallel to speed up the solving. Computational results reveal that the branch-and-price algorithm and the Lagrangian relaxation algorithm are significantly superior to the commercial solver (Gurobi) in terms of the solution quality, solvable problem size, and the computational time. Specifically, the branch-and-price algorithm is superior in solution quality, while the Lagrangian relaxation algorithm can obtain high-quality solutions for several large-scale instances faster. After that, we further compare the proposed algorithms with several commonly used heuristic algorithms to verify the necessity of developing optimality-guaranteed algorithms. Finally, sensitivity analyses of the rescheduling ratio factor are carried out to provide several insights for the shared-taxi platform on balancing the relationship between the system-wide profit and user experience. • A rescheduling ratio is introduced into the shared-taxi problem. • Two optimality-guaranteed algorithms are proposed and verified. • The proposed algorithms are compared with heuristics in dynamic and static environments. • Sensitive analyses of the rescheduling ratio are carried out to deduce managerial insights. [ABSTRACT FROM AUTHOR]

Conventional discrete transportation network design problem deals with the optimal decision on new link addition, assuming the capacity of each candidate link addition is predetermined and fixed. In this paper, we address a novel yet general discrete network design problem formulation that aims to determine the optimal new link addition and their optimal capacities simultaneously, which answers the questions on whether a new link should be added or not, and if added, what should be the optimal link capacity. A global optimization method employing linearization, outer approximation and range reduction techniques is developed to solve the formulated model. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to identify when freight consolidation strategies are cost-efficient in the less-than-truckload carriers operations. Shipments are assigned based on proximity and cost criteria to build an initial long-haul shipment solution. This initial solution is later improved by the implementation of Tabu Search algorithm. The proximity criterion takes into account the spatial distribution of shipments loads among centers. The results show that the proposed methodology may reduce the transportation cost by 20% compared to the solution of those heuristics only considering cost criterion. [ABSTRACT FROM AUTHOR]

This paper presents several heuristics for a variation of the vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need for recharge during their duties. In addition to the routing plan, the amount of energy recharged and the technology used must also be determined. Constructive and local search heuristics are proposed, which are exploited within a non deterministic Simulated Annealing framework. Extensive computational results on varying instances are reported, evaluating the performance of the proposed algorithms and analyzing the distinctive elements of the problem (size, geographical configuration, recharge stations, autonomy, technologies, etc.). [ABSTRACT FROM AUTHOR]

Although long-haul intermodal container transportation combines multiple transportation modes including shipping, the most critical aspect of enabling door-to-door service is container port drayage operation. Container port drayage connects customers and container ports and provides an effective solution to the first/last-mile problem faced by the container logistics industry. Therefore, container port drayage has a significant impact not only on the economic advantages of the long-haul intermodal container transportation chain, but also the management, environment and social benefits of the stakeholders involved. In this paper, we comprehensively review the existing literature on container port drayage studies from different methodological angles (e.g., model development and algorithm design) and technological angles (e.g., autonomous truck and blockchain). Moreover, with the shifting of industrial focus from traditional operations and management of container port drayage towards e-commerce platforms, we identify potential issues for future research directions. • Critically review the studies on container port drayage from the perspective of operations and management. • Systematically examine the interactions between main stakeholders of container port drayage services. • Identify future research focuses and directions of container port drayage operations and management. [ABSTRACT FROM AUTHOR]

This paper addresses risk-averse stochastic hub location problems where the risk is measured using the conditional β -mean criterion. Three variants of the classical multiple allocation hub location problem, namely the p -hub median, the p -hub maximal covering, and the weighted p -hub center problems are studied under demand data uncertainty represented by a finite set of scenarios. Novel mixed-integer linear programming formulations are proposed for the problems and exact algorithms based on Benders decomposition are developed for solving large instances of the problems. A large set of computational tests are conducted so that the efficiency of the proposed algorithms is proved and the effect of various input parameters on the optimal solutions is analyzed. • New risk-averse hub location problems are introduced to the literature. • Novel MILP formulations are proposed for the problems. • Exact algorithms based on Benders decomposition are proposed to solve the problems. • Extensive computational study is carried out using the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present a green location routing problem with multi-type charging infrastructure (GLRP-CI), which aims to determine simultaneous decisions on locating depots, reinforcing them with battery swapping infrastructure (BSI) or recharging infrastructure (FCI), and routing electric vehicles (EVs) in the distribution system. The problem is formulated as an arc-based formulation, and the objective is to minimize the total cost, including the daily fixed costs of depots equipped with BSI or FCI, the travel cost of EVs, the holding cost of batteries, and the electricity cost for charging, with the potential profit of sending energy back into the electric grid subtracted from the objective function. Some analytical properties of special cases of the problem are also investigated. We develop a branch-and-price (B&P) algorithm to solve this problem, in which initial feasible columns are given by a hybrid heuristic algorithm, the pricing subproblems are solved by the label-setting algorithm, and the global lower bound is raised by the Lagrangian lower bound. The proposed B&P algorithm is validated by extensive computational experiments, and it performs well compared with commercial branch-and-bound/cut solvers such as CPLEX in terms of computational speed and solution quality. Through sensitivity analysis, we explore the interaction between key factors (such as the configuration of EVs and the price of energy sent back to the grid) and the use of BSI- or FCI-type depot location strategies. • It is essentially a location-routing problem with multiple types of facilities. • The analytical properties of some special cases of the problem are proved. • This is the first work to apply an exact algorithm to this general model. [ABSTRACT FROM AUTHOR]

• A budgeted uncertainty set to capture the correlation between facilities' disruptions and customers' demand changes. • Three two-stage robust optimization models to reflect decision-makers' risk preferences. • Column-and-constraint generation algorithm for two-stage robust models. • Numerical tests demonstrate the importance of considering disruption-correlated uncertain demand. This paper studies a capacitated facility location problem under disruptions, where disruption risk not only diminishes facilities' capacity partially or completely but also affects customers' demand patterns. We use a budgeted uncertainty set to simultaneously capture facilities' disruptions and customers' uncertain demand caused by the failures of nearby facilities. Three two-stage robust optimization formulations with different objectives and performance bounds are built to model the problem. Both column-and-constraint generation algorithm and Benders decomposition method are implemented to solve the models. Numerical tests are conducted to study the impact of disruption correlated demand uncertainty and the performance of models and algorithms. [ABSTRACT FROM AUTHOR]

• Propose a transfer-based customized bus network design with passenger-route assignment and a modular fleet. • Formulate the problem into a mixed-integer non-linear programming model. • Propose an exact solution method by reformulating the problem into a linear model. • Present a customized Particle Swarm Optimization (PSO) algorithm for large-scale problems. • Verify the hybrid PSO algorithm can produce high quality near-optimal solutions efficiently. • Show that the incorporation of passenger-route assignment optimization and transfer operation benefits customized bus network design. Customized bus (CB) is an increasingly popular mode of transportation in many cities around the world. However, studies on CB network design have mostly overlooked three options that may further improve system performance: passenger-route assignment, passenger transfer, and modular vehicles. To bridge this gap, this paper proposes to design a transfer-based CB network with a modular fleet while simultaneously optimizing the passenger-route assignment. To solve the optimal network structure with this new design paradigm, we formulate the network design problem into a nonlinear mixed integer optimization model. A linearization approach and a particle swarm optimization (PSO) algorithm are proposed to solve the exact and near-optimal solution(s) to the model, respectively. Numerical experiments are conducted on the Sioux Falls network and a large-scale network in Chengdu, China. Results show that the customized PSO algorithm efficiently provides high quality near-optimal solutions compared with CPLEX, the genetic algorithm, and the simulated annealing algorithm. Results also show that incorporating passenger-route assignment optimization and the transfer operation produces a more cost-effective CB operational network with less operational costs and higher service quality. The benefit increases as the passenger demand grows. [ABSTRACT FROM AUTHOR]

• Studied the generalized vehicle routing problem with time windows (GVRPTW). • Proposed a column generation based heuristic for the GVRPTW. • Several types of benchmark instances can be solved efficiently and effectively. The generalized vehicle routing problem with time windows (GVRPTW) is defined on a directed graph G = (V , A) where the vertex set V is partitioned into clusters. One cluster contains only the depot, where is located a homogeneous fleet of vehicles, each with a limited capacity. The other clusters represent customers. A demand is associated with each cluster. Inside a cluster, the vertices represent the possible locations of the customer. A time window is associated with each vertex, during which the visit must take place if the vertex is visited. The objective is to find a set of routes such that the total traveling cost is minimized, exactly one vertex per cluster is visited, and all the capacity and time constraints are respected. This paper presents a set covering formulation for the GVRPTW which is used to provide a column generation based heuristic to solve it. The proposed solving method combines several components including a construction heuristic, a route optimization procedure, local search operators and the generation of negative reduced cost routes. Experimental results on benchmark instances show that the proposed algorithm is efficient and high-quality solutions for instances with up to 120 clusters are obtained within short computation times. [ABSTRACT FROM AUTHOR]

• A new real-life inspired problem combining zone-based pricing with vehicle routing. • Customers accept the service if the zone price is below their individual thresholds. • Pricing and routing decisions to maximize total revenue minus routing cost. • Mathematical modeling of the proposed problem. • Branch-and-price algorithm that finds optimum for instances up to 50 customers. In this paper we study the vehicle routing problem with zone-based pricing where the transporter defines a service price for all customers in a geographical zone. Each customer accepts or rejects the transporter's offer according to a personal threshold value. Once the customer accepts the offer, the delivery service becomes compulsory. Two decisions need to be made: the price per zone and the routing to serve all customers that accept that price. The objective is to maximize the profit, which is the difference of total service revenue and the routing costs. We present a mathematical model for the problem and propose a branch-and-price algorithm. A numerical analysis shows promising results on the proposed set of instances. [ABSTRACT FROM AUTHOR]

• A novel two-stage robust optimization model is developed for the line planning with passenger demand uncertainty. • A Lagrange relaxation-based heuristic as well as some strengthening techniques is introduced to solve the robust model. • The model and algorithm are tested for case studies of a Chinese high-speed railway line. A railway line-planning problem is one of the fundamental problems in the strategic planning of railway operations. Operation zone, frequency, stop schedule, and passenger distribution for each train based on passenger demand are determined to minimize both the operation cost of the railway enterprise and the total travel cost to the passengers. However, passenger demand uncertainty makes balancing transport capacity and fluctuating demand challenging. Therefore, this paper proposes a two-stage robust optimization model wherein the nominal line plan is determined in the first stage (i.e., the case in which passenger demand has no fluctuation) and the rescheduled line plan based on the nominal line plan is determined according to the demand realization in the second stage. A Lagrangian relaxation algorithm and some strengthening techniques are designed. The model and algorithm are tested on real-world instances of the Wuhan–Guangzhou high-speed railway line under uncertain passenger demands. Computational results indicate that the proposed solution algorithm can yield a high-quality solution in 3600 s. In addition, the proposed two-stage robust solution can help minimize both operation and travel costs on average when compared with the nominal solution under uncertainty realization. [ABSTRACT FROM AUTHOR]

• Evaluate research articles in the field of airline crew scheduling from four aspects. • Identify the modelling and algorithmic advancements for each crew type and planning stage. • Examine a number of prior representative studies to generate insights on modelling and algorithm design. • Present the problem and model features in airline crew scheduling for different aspects. • Suggest a future research agenda with the consideration of Covid-19 and blockchain technology. The airline crew scheduling problem has become a crucial but challenging task for commercial airlines for decades. Airlines are operating with two types of air crew: cockpit crew and cabin crew. Due to the unique operating characteristics, the scheduling problems for these two crew types are very different. Besides, according to the planning stage, the airline crew scheduling problem can be classified as tactical planning problems (traditional scheduling and robust scheduling, weeks or months before the actual operations) and operational planning problems (recovery, after disruptions have occurred during the operational stage). Realizing the significance of the airline crew scheduling problems and a lack of review on the modelling and algorithmic advancements in terms of each crew type and planning stage, we develop this paper to review the related literature from four aspects: the scheduling for cabin crew, the scheduling for both cabin crew and cockpit crew, the robust scheduling for cockpit crew, and the recovery for cockpit crew. For each stream, we examine a number of prior representative studies to review the advancements in model development and solution algorithm construction to generate insights. Finally, we conclude the review by proposing a future research agenda for the airline crew scheduling problem. [ABSTRACT FROM AUTHOR]

In this paper we study a Multi-Attribute Inventory Routing Problem (MAIRP). A mathematical formulation and exact solution algorithms are introduced for this problem. More precisely, we extend the Multi-Depot Inventory Routing Problem (MDIRP) in order to consider the multi-product case with a heterogeneous fleet of vehicles and explicit constraints for the route duration. The MAIRP is an NP-hard problem more complex than the classical Inventory Routing Problem. Moreover, it captures many features that can be found in real applications of a vendor-managed inventory strategy. We introduce a hybrid exact algorithm to solve it, in which several Mixed Integer Programming (MIP) models are solved to explore the neighborhoods of a Variable Neighborhood Search (VNS) scheme applied to the MAIRP. We design several neighborhoods that are based on the features of the problem. The impact of this hybridization is a faster convergence of the model and an accelerated resolution process with respect to a branch-and-cut algorithm applied to the regular MIP formulation. Extensive computational results on new and existing instances from the literature on two benchmark problems and a real data set confirm the high efficiency of our algorithm. • We provide a mathematical formulation for the multi-attribute IRP. • We design a variable MIP neighborhood descendent for solving the problem. • We design a branch-a-cut algorithm that embeds a local search scheme. • The algorithm is very effective in terms of solution quality and computational time. • The algorithm is tested on four classes of both classical and real data instances. [ABSTRACT FROM AUTHOR]

This paper studies an integrated network planning and operation problem of sustainable closed-loop supply chain (CLSC) under demand uncertainty with particularly interest in the configuration of retail network. The problem is modeled as a two-stage stochastic program which is further extended to evaluate the impact of various carbon polices on planning and operation decisions of the CLSC. A branch and bound based integer L-shaped algorithm has been proposed to solve these models. Extensive numerical experiments are conducted and impacts of carbon policies are analyzed. Sensitivity analysis is carried out and several managerial insights are drawn. • Consider an integrated network planning and operation problem of sustainable closed-loop supply chain with retail network configuration. • Model the problem and its extension under various carbon policies into a two-stage stochastic program. • Propose a branch and bound based integer L-shaped algorithm to solve these models. [ABSTRACT FROM AUTHOR]

• Addressing a multi-commodity hub-and-spoke network design under hub congestion. • Considering congestion in both hubs and connection links. • Developing a queuing-based aggregation model to model the congestion. • Developing a learning-based metaheuristic to solve the model. This paper models a single allocation multi-commodity hub-and-spoke network problem through a bi-objective mathematical model, considering the congestion in both hubs and connection links. A novel aggregation model is developed based on a general GI/G/c queuing system to evaluate the congestion of the flow of the multiple products in the hubs. The proposed model is then solved using a novel learning-based metaheuristic based on NSGA-II, k -Means clustering method, and an Iterated Local Search (ILS) algorithm. The proposed model and solution algorithm are validated through a set of experiments against optimal solutions, and benchmarked against four existing well-known algorithms. [ABSTRACT FROM AUTHOR]

• Evaluate 178 research articles in the field of blockchain implementation in supply chains. • Identify the current trends of research on blockchain in different domains of supply chain operations. • Examine various supply chain functions that can be enhanced through blockchain technology. • Present strong excerpts of blockchain applications in supply chains across various industrial sectors. • Suggest managerial implications, highlight challenges and build a future research agenda. Blockchain is a technology with unique combination of features such as decentralized structure, distributed notes and storage mechanism, consensus algorithm, smart contracting, and asymmetric encryption to ensure network security, transparency and visibility. Blockchain has immense potential to transform supply chain (SC) functions, from SC provenance, business process reengineering to security enhancement. More and more studies exploring the use of blockchain in SCs have appeared in recent years. In this paper, we consider a total of 178 articles and examine all the relevant research done in the field associated with the use of blockchain integration in SC operations. We highlight the corresponding opportunities, possible societal impacts, current state-of-the-art technologies along with major trends and challenges. We examine several industrial sectors such as shipping, manufacturing, automotive, aviation, finance, technology, energy, healthcare, agriculture and food, e-commerce, and education among others that can be successfully revamped with blockchain based technologies through enhanced visibility and business process management. A future research agenda is established which lays the solid foundation for further studies on this important emerging research area. [ABSTRACT FROM AUTHOR]

• A novel healthcare waste location-routing problem is developed with a new perspective in healthcare logistics networks. • A stochastic essence for the emission of contamination is considered. • The management of medical wastes in Coronavirus Disease 2019 is focused. • A real case study is modeled to manage the logistics of infectious and non-infectious healthcare wastes. • A Multi-Objective Water-Flow like Algorithm with novel operators is developed and compared with the others. This paper presents a novel healthcare waste location-routing problem by concentrating on a new perspective in healthcare logistics networks. In this problem, there are healthcare, treatment, and disposal centers. Locations of healthcare centers are known, however, it is required to select appropriate locations for treatment, recycling, and disposal centers. Healthcare wastes are divided into infectious and non-infectious wastes. Since a great volume of healthcare wastes are infectious, the emission of contamination can have obnoxious impacts on the environment. The proposed problem considers a stochastic essence for the emission of contamination which depends on the transferring times. In this respect, transferring times between healthcare and treatment centers have been considered as normal random variables. As transferring time increases, it is more likely for the contamination to spread. Having visited a treatment center, infectious wastes are sterilized and they will no longer be harmful to the environment. This research develops a bi-objective mixed-integer mathematical formulation to tackle this problem. The objectives of this model are minimization of total costs and emission of contamination, simultaneously. Complexity of the proposed problem led the researchers to another contribution. This study also develops a Multi-Objective Water-Flow like Algorithm (MOWFA), which is a meta -heuristic, to solve the problem. This algorithm uses a procedure based on the Analytical Hierarchy Process (AHP) to rank the non-dominated solutions in the archive set. By means of a developed mating operator, the MOWFA utilizes the best ranked solutions of the archive in order to obtain high quality offspring. Two neighborhood operators have been designed for the MOWFA as the local search methods. Extensive computational experiments have been conducted to evaluate the effectiveness of the MOWFA on several test problems compared with other meta -heuristics, namely the Multi-Objective Imperialist Competitive Algorithm (MOICA) and Multi-Objective Simulated Annealing (MOSA). These experiments also include a real healthcare waste logistic network in Iran. The computational experiments demonstrate that our proposed algorithm prevails these algorithms in terms of some well-known performance evaluation measures. [ABSTRACT FROM AUTHOR]

• We concern a joint planning of relief logistics and casualty distribution considering secondary disasters. • We propose a scenario-based robust programming under the uncertainty of evacuee scales and transportation time. • We develop a customized progressive hedging algorithm based on the augmented Lagrangian relaxation to solve the problem. This paper proposes a scenario-based three-stage hybrid robust and stochastic model that optimally designs the response network and distributes casualties effectively under uncertain combinational scenarios of primary and secondary disasters. Following the stochastic severity of combinational disasters, the robust counterparts are derived against the ambiguous uncertainty of evacuee scales and transportation time, respectively. A customized progressive hedging algorithm based on the augmented Lagrangian relaxation is developed to solve the problem. We decompose the problem based on the scenario and iteratively solve the adaptively penalized sub-problems with decision variables independent of stages. The results of an illustrative example show that incorporating secondary disaster scenarios can contribute to improving relief coverage. The proposed algorithm is competitive with some benchmarks. [ABSTRACT FROM AUTHOR]

• The largest eigenvalue of the weighted adjacency matrix measures network connectivity. • Adding a link always increases the largest eigenvalue of the adjacency matrix. • A max-min integer optimization model is proposed to maximize the largest eigenvalue. • Results for the world container shipping network are presented. • First to use the eigenvector centrality to improve shipping network connectivity. Container shipping accounts for most of the world merchandise trade. Better maritime connectivity leads to lower freight rates and greater economic growth. This paper presents a novel max–min integer optimization model to facilitate better shipping network connectivity by analysing the largest eigenvalue and its corresponding eigenvector of the (asymmetric) frequency weighted adjacency matrix. An algorithm is presented that can quickly identify which link not currently in the container shipping network would best improve its connectivity. A demand matrix is not required by this method of analysis and network symmetry is not assumed. The method could strengthen direct connection between port pairs. [ABSTRACT FROM AUTHOR]

• Districting consideration is successfully integrated with the multi-customer JRP. • This study is vital to a company outsourcing logistics to a 3PL service provider. • Our numerical experiments are based on a real-life example in a bank system. • An effective GA-based algorithm is proposed for solving the problem. • Managerial insights provide baseline information before contract negotiation. This paper studies a multi-customer joint replenishment problem with districting consideration (MJRPDC) which is of particular importance to a company that outsources its transportation and delivery operations to a third-party logistics (3PL) service provider. To solve the problem, we first propose an innovative search algorithm for solving the traditional multi-customer joint replenishment problem in a given zone. Then we design a GA-based framework to handle the corresponding districting problem based on the performance of each district evaluated by using the proposed search algorithm. The proposed methodologies are demonstrated by using an example of solving MJRPDC for a bank. [ABSTRACT FROM AUTHOR]