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A modified adaptive large neighborhood search algorithm for solving the multi-port continuous berth allocation problem with vessel speed optimization

Despite its fast-growing popularity in maritime transportation, container shipping is still fraught with risks and uncertainties with its complex operating environments. This paper studies the multi-port continuous berth allocation problem with speed optimization (MCBAP). In the MCBAP, vessels visit multiple ports sequentially, and the problem aims at minimizing the sum of vessel sailing cost, waiting cost, delay cost and port handling cost, while satisfying various constraints related to vessel sailing and berthing. A mixed integer linear programming (MILP) model for MCBAP is formulated, and a modified adaptive large neighborhood search (MALNS) algorithm is proposed for solving large-scale MCBAPs. In the MALNS, an efficient initial solution generation strategy is developed, and a series of neighborhood solution generation operators are proposed. Finally, the proposed MILP model and MALNS algorithm are tested on a range of MCBAP instances. The numerical results demonstrate that the MILP model can be solved to optimality with CPLEX, and the MALNS can efficiently solve instances at various scales. In addition, sensitivity analyses on fuel prices and vessel design speeds (the planned maximum speeds) are performed, and management insights have been provided.

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Joint optimization of recyclable inventory routing problem under uncertainties in an incentive-based recycling system

Due to the value of resource recovery and the development of a circular economy, waste recycling has gathered global attention. Recently, many emerging cities designed new systems like an incentive-based recycling system (IBRS). In such systems, recyclables are collected through community recycling nodes by offering incentives, then transported to street recycling stations and sorted before being finally recycled. The increased recycling nodes and the incentives enhance the convenience and residents’ enthusiasm for waste recycling, but also intensify the uncertainty of recycling quantities and the complexity of the recycling operation management. Poor recycling operation management may result in increased recycling costs or greater loss of recyclables, which discourages residents from participating in recycling. Based on an existing IBRS, this study investigates the joint optimization problem of the recyclable inventory management at each community recycling node and the vehicle routing from the recycling nodes to the recycling station. A two-stage dual-objective multi-period stochastic programming model is established to minimize the loss of recyclables and logistics costs, which is further reformulated using the weighting method and transportation cost approximation parameters. To solve the reformulated model, a three-phase iterative algorithm is designed by combining the progressive hedging algorithm and route splitting algorithm based on the Lin-Kernighan heuristic. A case study is conducted using data from Shanghai’s IBRS. The proposed joint decision model is superior to separate decisions and the three-phase iterative algorithm can reduce the average total cost by up to 42.12% compared to the genetic algorithm and the Iteration-Move-Search method in the literature. Additionally, a sensitivity analysis is conducted to provide managerial insights.

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Graduation-inspired manufacturing system and synchronization mechanism for hybrid assembly cell lines

This paper presents an innovative flexible assembly system – hybrid assembly cell lines (HACL) to adapt to small batch, high product variety and dynamic demands. HACL provides specially designed trolleys for assembly operations, materials and tools to form the workstations flexibly and quickly at low reconfiguration cost. To conduct accurate and effective control for this complex assembly system, this paper presents a modified Graduation-inspired Manufacturing System (GMS), a recently proposed new card-based control system. GMS for HACL designs three kinds of tickets and the ticketing mechanism to absorb the uncertainties of HACL. Job tickets are for workload control and synchronization of part deliveries; setup tickets are for cell line formation and synchronization of resources of tools and machines; operation tickets are for production execution and synchronization of workers and operations in cell lines. GMS for HACL develops Internet of Things-enabled (IoT-enabled) architecture for managing the smart tickets to achieve automatic and intelligent interoperability between GMS and the physical workstations of HACL. This work designs synchronization mechanism based on specific model and genetic algorithm to synchronize manufacturing resources under GMS for HACL

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Product upgrade and advanced quality disclosure in a supply chain

Retailers frequently preannounce the quality of upcoming products, highlighting the importance of information disclosure in supply chains. This study examines the interaction between advanced quality disclosure and product upgrades within a supply chain, where a supplier can release either upgraded products exclusively or alongside regular products in a subsequent period. Using a game-theoretical model, we investigate how the supplier’s upgrade decisions and the retailer’s disclosure strategy affect consumer behaviour, particularly strategic waiting and product cannibalisation. Our results show that when only upgraded products are offered, the retailer discloses quality in advance if the innovation level is high. When both regular and upgraded products are available, the retailer chooses advanced disclosure when the innovation level is either high or low. Even when the product improvement is minimal, advanced disclosure reduces cannibalisation and attracts more consumers to the upgraded products, thereby boosting the retailer’s profits. Additionally, the supplier may choose not to upgrade products when the innovation cost is moderate. These insights suggest that advanced disclosure can mitigate both cannibalisation and consumer waiting, particularly in high-tech industries or incremental innovation scenarios, such as electronics and smart home sectors, where the preannouncement of product information is crucial.

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Train assignment and handling capacity arrangement in multi-yard railway container terminals: An enhanced adaptive large neighborhood search heuristic approach

Expanding terminal scale and constructing multiple railway handling yards have become popular strategies for leading railway container terminals globally to cope with the ever-increasing handling volume. Although such an approach greatly enhances the terminal’s productivity, it also intensifies handling operations and introduces additional inter-yard interactions, complicating terminal management. To address these challenges, this paper investigates an integrated optimization approach for multi-yard railway container terminals, which feature both rail-road and rail-rail container transshipment operations. The train assignment plan for each yard and the handling capacity arrangement for each train are jointly optimized while considering inter-yard container transits, workload allocation for yards, and safety requirements in train shunting. This problem is formulated as a nonlinear programming model, with the objective to minimize operational delay and service time for each incoming train, and to minimize workload differences and container transit volume among yards. To efficiently solve this problem, this research develops an enhanced adaptive large neighborhood search (EALNS) heuristic, which includes several customized operators and feasibility repair methods, and is further enhanced with a local search method and backtracking mechanism compared to the standard ALNS framework. Computational experiments with different data scales and problem settings demonstrate the superiority of the EALNS in terms of solution quality and stability compared with three other solution methods. Additionally, practical insights for terminal operations are drawn through detailed analysis of different infrastructure configurations, transshipment train characteristics, and unit cost settings.

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On-line recognition of mixture control chart patterns using hybrid CNN and LSTM for auto-correlated processes

Statistical process control (SPC), designed to detect and identify process disturbances, is an effective quality control method for ensuring process stability. Owing to the growing automation of the industry, the manufacturing process can be autocorrelated. Engineer process control (EPC) is typically used to address autocorrelation. However, process disturbances can be offset by feedback compensation, making control chart patterns (CCPs) difficult to identify. Most studies on control chart pattern recognition (CCPR) techniques are based on a single abnormal control chart pattern (CCP). However, a mixture of CCPs can occur during real-world manufacturing processes. With the development of intelligent manufacturing systems, early detection of abnormal concurrent CCPs has become an important issue. In this study, a hybrid model combining a convolutional neural network (CNN) and long short-term memory (LSTM) in an online detection system was used to recognize the concurrent CCPR problem in SPC-EPC processes. The results showed that the average accuracy of the deep learning CNN-LSTM method was 99.83%, which was significantly better than that of the machine learning method. In addition, the running time of the CNN-LSTM model was shortened. In a comparison of online monitoring between the deep learning method and the machine learning method, the CNN-LSTM model for an online monitoring system identified abnormal concurrent patterns faster. Therefore, the proposed CNN-LSTM online monitoring scheme can be applied confidently and successfully to identify mixture CCPs in an SPC-EPC process.

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Design of a multi-component system-based fixed-wing unmanned aerial vehicle maintenance policy and its case study

At present, flight vehicle structure maintenance is undergoing a paradigm shift from preventive maintenance depending on calendar time and flight cycle to condition-based maintenance (CBM) using structural condition monitoring data. This shift is particularly relevant as unmanned aerial vehicle (UAV) systems increasingly undertake aerial missions. However, unlike CBM for single components, most UAV systems consist of multiple interconnected components. Optimizing pre-flight maintenance and ensuring traceability of inspections from a system-level perspective remains challenging. This study introduces a multi-layered decision-making policy for UAV reliability and stability multi-component evaluation, enhancing service condition monitoring and maintenance evaluation. The system adopts a knowledge-driven approach to develop a comprehensive maintenance framework for multi-component UAVs. It enables detailed damage assessment, effective management, predictive capabilities, and optimization strategies. By integrating and reasoning across knowledge-based, geometric, and decision-making models, the system supports dynamic maintenance and continuous iterative enhancements. To further reduce system complexity, this paper created a risk grating evaluation system. Within the framework of individual components, decision-making rules are established to optimally determine the components needing preventive maintenance when activated by decisions from risk assessments. Additionally, a new soft failure threshold model to determine the optimal maintenance decision variables is defined. This model incorporates general knowledge of the system and subsystems, further optimizing predictive reliability and economic dependency. Finally, the effectiveness and feasibility of this policy are validated through a fixed-wing UAV maintenance case study. The results demonstrate that the proposed framework holds significant promise for maintenance management in aircraft systems.

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Multi-objective model for electric vehicle charging station location selection problem for a sustainable transportation infrastructure

The transportation industry mostly depends on conventional vehicles, leading to significant adverse effects on the environment. The widespread usage of electric vehicles can be seen as a relief for this problem. However, the success of electric vehicles largely depends on the availability and proper deployment of charging station infrastructure. It is crucial for cities to strategically select suitable locations for charging stations with adequate capacity levels to promote sustainable and environmentally-friendly transportation options. Hence, in this study, a multi-objective model is proposed for the electric vehicle charging station location selection and capacity allocation problem. The model aims to maximize customer satisfaction, minimize total risk, and minimize costs as key objective functions. To manage the demand effectively, the region of interest is divided into grids. The proposed multi-objective model is applied to the European side of Istanbul and solved by using AUGMECON2 technique. Finally, computational analyses are presented based on scenarios including different demand values. These analyses provide valuable insights into the effectiveness of the proposed model and its implications for achieving sustainable transportation in Istanbul.

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