This study investigates the location-routing problem in the pallet pooling system, which is an extended combinatorial optimization problem in the field of logistics management. A mixed-integer linear programming model is formulated for the location-routing problem, incorporating simultaneous pickup and delivery, and the limitations on carbon emissions and transportation time. To address the demand uncertainty, this study extends the model using a robust optimization approach to enhance solution reliability. Due to the NP-hard nature and high computational complexity of the problem, a heuristic algorithm integrating genetic algorithm and ant colony optimization is developed to obtain efficient solutions. Numerical experiments are conducted on classical benchmark instances to validate the model feasibility, demonstrate that the integrated optimization model outperforms the two-stage model, and highlight the superior performance of the heuristic algorithm in terms of solution quality and computational efficiency. Furthermore, the study provides valuable insights for logistics managers in the pallet pooling system.

The integration of passenger and freight transport by urban–rural buses is an effective approach to address two critical issues: the inefficiency of parcel delivery services and the financial struggles of public transport operators. This paper studies the county–township–village station location-routing problem for the integration of passenger and freight transport by urban–rural buses, aiming to develop an efficient transport network by establishing rational stations and designing optimal operation routes. A three-level county–township–village station network is proposed for the integration of passenger and freight transport, and a mixed-integer linear programming model is developed, including the constraints of location, allocation, capacity, and routing. A comprehensive series of numerical experiments is conducted on a randomly generated dataset to evaluate the feasibility and advantages of the proposed model. Lastly, key managerial insights are discussed.

The pallet pooling system provides standard transportation packaging in a shared manner, which improves the logistics efficiency and resource utilization. It involves three key and interrelated decisions on the location of operation centers, inventory management and vehicle routing in this system, which significantly influence the overall efficiency of the system. To address this integrated location-inventory-routing problem, we develop a comprehensive mixed-integer linear programming model that incorporates the system's characteristics of multi-scale operation center location, multi-period inventory management with scheduling between operation centers, and vehicle route planning incorporating simultaneous delivery and pickup. Owing to the computational complexity, we propose a hybrid heuristic algorithm that combines genetic operators, K-Medoids clustering, and ant colony optimization to obtain high-quality solutions efficiently. Numerical experiments using benchmark datasets demonstrate both the feasibility of the developed model and the efficiency of the proposed algorithm.

This paper studies the location-routing problem in the pallet pooling system. Considering the characteristic of simultaneous delivery and pickup, a mixed integer linear programming model is proposed for the location-routing problem with the limitations on carbon emissions and transportation time in the pallet pooling system. Then, a robust optimization model is proposed to deal with the uncertainty of demand. Considering the computational complexity, a heuristic algorithm combining the genetic algorithm and the ant colony optimization is designed to solve the problem efficiently. Numerical experiments are carried out on classical benchmark instances, and the results validate the feasibility and effectiveness of the proposed model.

This paper addresses a hierarchical cost-minimizing hub covering problem with uncertain demand in the urban agglomeration comprehensive transport system. A deterministic model is first proposed considering cost-minimizing objective, service-level constraints, and hierarchical hub network. To characterize the demand uncertainty and identify the value of acquiring parameter information, two robust optimization methods are adopted on the basis of distribution information. In the case of lacking distribution information, a robust optimization model with the price of robustness is presented; given partial distribution information, a distributionally robust optimization model based on the worst-case value-at-risk criterion is developed. Owing to the computational complexity, a heuristic algorithm combing the breadth search and the depth search is designed to obtain high-quality solutions. To demonstrate the efficiency of designed algorithm and the advantages of proposed models, numerical experiments are conducted on the dataset generated randomly and the real case study of central Yunnan urban agglomeration of China.

The intermodal express network of high-speed railways and highways can fully utilize the flexibility of highways and the advantages of high-speed railways, such as low cost, high efficiency, and low carbon emission. This paper studies the hub location and flow assignment problem in the intermodal express network of high-speed railways and highways, which can not only increase the transportation efficiency but also provide door-to-door service. Considering the characteristics of multiple modes, flow balance, carbon emission, capacity constraints, and time constraints in the intermodal express network, a mixed-integer linear programming model is proposed with the objective of minimizing the total cost by determining the hub locations, allocations, mode selections, and flow assignments. Owing to the NP-hard computational complexity, an improved genetic algorithm with local search is designed by combining the genetic operators and two optimization strategies to solve the problem effectively. Lastly, numerical experiments are conducted to validate the feasibility of the model and the effectiveness of the algorithm.

As a new mode of freight transportation, the high-speed railway freight express has received widespread attention. This paper studies the hub location and flow assignment problem in the high-speed railway and highway co-transportation network for freight express. A mixed-integer linear programming model is proposed with the objective function of minimizing the total cost and the constraints of location-allocation, mode selection, flow balance, capacity limitation and time limitation. Owing to the NP-hard computational complexity, a heuristic algorithm is designed combining the genetic operators and three optimization strategies to solve the problem effectively. Lastly, numerical experiments are conducted to validate the feasibility of the proposed model and the effectiveness of the proposed algorithm.

The pallet pooling center is responsible for the leasing and recycling of shared pallets, which is the key node of the pallet pooling system and plays an important role in improving the efficiency of logistics and promoting the recycling of resources. The location and allocation optimization of pallet pooling centers has important theoretical and practical significance. In this paper, a linear mathematical programming model for the location-allocation optimization problem of pallet pooling centers with multiple allocation is proposed, aiming to minimize the total cost including the transportation cost and the construction cost. A number of numerical experiments are conducted, where the location-allocation results are analyzed and sensitivity analysis on the number of pallet pooling centers are performed.

PurposeThis paper studies the location-inventory problem (LIP) in pallet pooling systems to improve resource utilization and save logistics costs, which is a new extension of the classical LIP and also an application of the LIP in pallet pooling systems.Design/methodology/approachA mixed-integer linear programming is established, considering the location problem of pallet pooling centers (PPCs) with multi-level capacity, multi-period inventory management and bi-directional logistics. Owing to the computational complexity of the problem, a hybrid genetic algorithm (GA) is then proposed, where three local searching strategies are designed to improve the problem-solving efficiency. Lastly, numerical experiments are carried out to validate the feasibility of the established model and the efficiency of the proposed algorithm.FindingsThe results of numerical experiments show that (1) the proposed model can obtain the integrated optimal solution of the location problem and inventory management, which is better than the two-stage model and the model with single-level capacity; (2) the total cost and network structure are sensitive to the number of PPCs, the unit inventory cost, the proportion of repairable pallets and the fixed transportation cost and (3) the proposed hybrid GA shows good performance in terms of solution quality and computational time.Originality/valueThe established model extends the classical LIP by considering more practical factors, and the proposed algorithm provides support for solving large-scale problems. In addition, this study can also offer valuable decision support for managers in pallet pooling systems.

针对医疗资源匮乏和经济不发达的国家或地区,如何选择定点收治医院、分配患者,有效控制新型冠状病毒肺炎(COVID-19)扩散是亟待解决的问题.首先,考虑疫情患者数量和症状等级动态变化特征,以最大化患者的收治率和最小化医院总费用为目标,构建有限医疗资源约束下定点收治医院动态选址-分配双目标优化模型;其次,分析所构建模型的结构特征,设计基于Epsilon约束方法的求解框架,得到Pareto最优解集;最后,基于北京市卫生健康委发布的疫情数据进行数值实验,以验证所提出模型的可行性与方法的有效性.实验结果表明,双目标优化模型可以有效地权衡定点医院的总费用与患者的收治率,对于COVID-19疫情下医疗资源的合理配置具有重要的指导意义.

This paper investigates sequential ordering and pricing decisions in a retailer-led supply chain consisting of a supplier and a retailer. It is distinct from previous studies on asymmetric demand, cost and productivity information in that asymmetric delivery reliability information representing the proportion of received quantity to order quantity, particularly for fresh products because of high perishability, is specifically considered. Such asymmetric delivery reliability information can be described as an uncertain variable upon the retailer’s belief (subjective assessment) due to insufficient data. In the presence of asymmetric information, we construct two supply-contracting models based on uncertainty theory with two different strategies: Strategy I occurs when the ordering decision is made before the pricing decision, and Strategy II occurs when the pricing decision is made before the ordering decision. We first derive the optimal order quantity, side payment and retail price using the variational method and evaluate how the decision strategies affect the structure of the optimal contracts. We then investigate the impact of information asymmetry on the retailer’s optimal expected profit. Finally, we conduct a series of simulation experiments to identify the value of information and highlight how system parameters affect the performance of the retailer-led supply chain. The computational results show that acquiring the supplier’s delivery reliability information can benefit the retailer, and Strategy I dominates Strategy II regardless of the system parameters.

Motivated by a real-world healthcare supply case of a medical implant company, this paperstudies a supply network configuration problem that integrates warehouse selections forvendor managed inventory (VMI), inventory policy, and delivery routing optimization together.The problem is a variant of the classic location-inventory-routing problem (LIRP) with bothdeterministic demand and uncertain demand, where multi-product, multi-period, multi-typedelivery, delivery time limit and VMI are considered. Two types of delivery are used: one is thescheduled bulk delivery to the VMI warehouses and the other is direct shipping for hospitals.To address the problem, first, a deterministic MILP model is presented for the integrated LIRP.Then, to deal with the uncertainty in demand, we propose a robust optimization model andtransform it into a tractable linear equivalent formulation. Further, considering the effect ofCOVID-19 pandemic on the demand and delivery time, a new robust model is proposed toaccount for this special situation. Numerical experiments are conducted to verify the advantageof the proposed robust optimization models. The sensitivity analysis provides some interestingmanagerial insights, and a real-world case of medical implant supply configuration with 78hospitals is solved.

This paper introduces the stochastic multi-modal hub location problem with direct link strategy and multiple capacity levels for cargo delivery systems under demand uncertainty. For capturing the uncertain nature of demand, we present a stochastic optimization model to formulate this problem formally via the expected value and the chance-constrained programming techniques. Under some mild assumptions, we propose a computationally tractable approach to turn the original model into a crisp equivalent integer second-order cone programming model, which can be efficiently solved by CPLEX for only small instances. Hence, we design a memetic algorithm incorporating genetic search and local intensification for realistic size instances. Furthermore, we provide extended analyses of the original model by considering the mode-specific hub and incorporating the fixed transportation cost. To demonstrate the superiority of the proposed model and the effectiveness of the solution approach, we conduct a series of computational experiments based on Turkish network data set.

Research on multimodal accessibility under uncertain travel time has become a significant issue. Existing studies on accessibility lack a direct integration of multi-source data accessibility evaluation methods. This paper develops a multimodal random accessibility model (MR model). Multi-source data is integrated with a built-in joint calculation method of walking time, waiting time, and transit time while considering the effects of both the travel time budget in the time dimension and distance friction parameter in the spatial dimension. Taking Beijing as an example, accessibility generally shows a downward trend from the center of the city to the suburbs, especially along the subway lines, and there is a positive correlation between traffic flow and accessibility. The low-accessible high-flow area is mainly distributed in areas away from the city center and at the end of subway lines. These results could help transport planners formulate more reasonable public transport planning policies.

This study is motivated by a real-world problem in a food company, where production planning is restricted by the available warehouse space for the finished goods. A novel integrated strategy that combines production planning with a randomized storage assignment policy is presented. The strategy takes advantage of greater visibility and traceability of items provided by IoT-enabled tracking systems in order to increase space utilization. An integer linear programming model is developed to formulate the strategy to minimize the total cost of production and warehouse operations. Our model is the first dynamic model for a randomized storage assignment policy. The model's feasibility, complexity, and its lower bound are presented. A heuristic algorithm is developed to obtain the near-optimal solution for the large-scale real-world problem. Based on numerical experiments, comparisons between our solutions and the solutions to model with a dedicated storage policy are also presented. The results show that the integrated strategy with a randomized storage policy can significantly reduce the total cost (up to 16.84% with an average of 9.95%) and increase space utilization (up to 26.1% with an average of 14.8%), compared to the strategy with a dedicated policy. Such results provide evidence that may justify the cost of applying the new technologies, such as IoT-enabled tracking systems, in warehouse management.

This paper introduces a distributionally robust cluster-based hierarchical hub location problem for the integration of urban and rural public transport system at the strategic level. Lacking complete information on the true probability distributions of construction cost and travel time, a distributionally robust optimization (DRO) model is proposed to formulate this problem formally. The proposed DRO model is demonstrated to be semi-infinite and computationally hard but admits a safe approximation and an equivalent under two kinds of ambiguity sets: bounded perturbations with zero mean and Gaussian perturbations, respectively. Since the resulting formulations can be solved to optimality by the CPLEX software for only small instances, a variable neighbor-hood search algorithm and a population-and-searching based heuristic algorithm are designed to handle the realistic-sized instances. To validate the superiority of the DRO model and the efficiency of the proposed methods, two sets of numerical experiments are implemented on a case study of Guangrao in Shandong Province of PR China and a randomly generated example, respectively. Suggestions for possible extensions of the original problem are also discussed.

This paper studies the fuzzy hierarchical multimodal hub location problem for cargo delivery systems. It differs from traditional hub location problem in two ways. First, this paper constructs a hierarchical multimodal hub-and-spoke distribution network for the cargo delivery systems, which involves two transportation modes (road and air), two types of hubs (ground and airport) and three corresponding layers. Second, this paper develops a credibility-based fuzzy programming model capturing the uncertainty in travel time and handling time of the cargo delivery systems. This new model aims to minimize the latest arrival time (travel time plus handling time) for delivering cargoes from each pair of origin and destination nodes under diverse credibility of chance constraints. Under mild assumptions, the original model can be turned into an equivalent deterministic integer linear programming model. However, even for small instances of the problem, the equivalent model becomes too hard to be tackled by a general solver, e.g., CPLEX. This fact motivates the development of a two-stage heuristic procedure, wherein the first stage for the hub location subproblem is solved by a variable neighborhood search algorithm. These location solutions are then embedded into the second-stage process for the link assignment subproblem based on a shortest path method. To verify the proposed model and method, extensive numerical experiments are conducted on the well-known Turkish network data set.

This study aims at developing a stochastic hierarchical multimodal hub location modeling framework for cargo delivery systems to capture uncertainty in hub construction cost and travel time at the strategic level. From a ring-star-star type network design perspective, a stochastic model is established to formulate this problem formally via the expected value and chance-constrained programming techniques. In particular, three types of chance constraints are proposed to ensure that the on-time delivery with pre-specified confidence levels in their respective layer networks. For normal distributions, the original stochastic model can be reformulated as a crisp equivalent mixed-integer linear programming (MILP) model by invoking the central limit theorem. Since the number of constraints and variables increases drastically with the size of cargo delivery distribution network, a memetic algorithm (MA) is designed. This algorithm incorporates genetic search and local intensification to obtain optimal/near-optimal solutions for realistic instance size within a reasonable time limit. For general distributions, it is difficult to convert the stochastic model into its deterministic counterpart. Hence, a hybrid methodology is further designed by combining the MA and Monte Carlo (MC) simulation to solve the proposed stochastic model. To demonstrate the properties of the proposed model and the performance of the designed algorithm, a series of numerical experiments are set up based on the Civil Aeronautics Board (CAB) and Turkish network data sets. Computational results indicate as the confidence level increases, the airport hubs are located further apart in the cargo delivery distribution network for gaining a greater time advantage. In addition, comparative results demonstrate that the MA algorithm proposed herein performs better than the genetic algorithm (GA) in terms of computing speed and quality of the solution.

In this paper, we introduce an extended version of hub location problem, called bi-objective hierarchical multimodal hub location problem to simultaneously minimize the overall system-wide costs and the maximum delivery time. This problem is distinct from the classic hub location problem in designing a hierarchical multimodal hub-and-spoke network involving multiple transportation modes, multi-class hubs and corresponding layers. Combining cost and time dimensions, we first propose a bi-objective mixed-integer linear programming to model this problem formally with diverse flow balance constraints. We then show that the proposed model can be efficiently solved by a reformulation approach based on the epsilon-constraint method for only small instances. Hence, we develop two heuristics, a variable neighborhood search algorithm and an improved non-dominated sorting genetic algorithm-II to obtain high-quality Pareto solutions for realistic-sized instances. We further illustrate the application of the proposed model to provide decision support for cargo delivery systems. Finally, we conduct extensive numerical experiments based on Turkish network to demonstrate the superiority of the proposed solution methods compared to the standard non-dominated sorting genetic algorithm-II. The statistical results confirm the efficacy of the developed heuristic algorithms by adopting the Wilcoxon test. (C) 2020 Elsevier Inc. All rights reserved.

Quay crane scheduling and yard truck scheduling are two highly interrelated important sub-problems in container terminal operations. To increase the efficiency, it is necessary to solve these two sub-problems in an integrated manner. The dual-strategies are adopted to increase the utilization rate of resources and reduce the empty running rate of yard truck. This paper proposed a mixed integer programming model using dual-strategies to solve quay crane scheduling and yard truck scheduling jointly. In this model, outbound and inbound containers, container precedence, quay crane interference, and quay crane safety margin are considered. Due to the intractability, genetic algorithm is designed to obtain near-optimal solutions.