73 research outputs found
The capacitated lot-sizing and energy efficient single machine scheduling problem with sequence dependent setup times and costs in a closed-loop supply chain network
No full textIn this paper, the capacitated lot-sizing and scheduling problem with sequence dependent setup times and costs in a closed loop supply chain is addressed. The system utilizes the closed-loop supply chain strategy so that the multi-class single-level products are produced through both manufacturing of raw materials and remanufacturing of returned recovered products. In this system, a single machine with a limited capacity in each time period is used to perform both the manufacturing and remanufacturing operations. The sequence-dependent setup times and costs (both between two lots of products of different classes and between two lots belonging to the same class of products produced through different methods) are considered. A large-bucket mixed integer programming formulation is proposed for the problem. This model minimizes not only the manufacturing and remanufacturing costs, the setup costs and the inventory holding and backlogging costs over the planning horizon, but also the energy costs paid for the utilization of machine and the compression of processing times. Since the problem is NP-hard, a matheuristic and a grey wolf optimization algorithm are proposed to solve it. To evaluate the efficiency of the proposed algorithm, some experimental instances are generated and solved. The obtained results show the effectiveness of the proposed algorithms.</p
A Diploid Evolutionary Algorithm for Sustainable Truck Scheduling at a Cross-Docking Facility
No full textSupply chains have become more time-sensitive in recent years. Delays in supply chain operations may cause significant negative externalities, including lost sales and customers. In order to facilitate the product distribution process within supply chains, reduce the associated delays, and improve sustainability of the supply chain operations, many distribution companies started implementing the cross-docking technique. One of the challenging problems in management of the cross-docking facilities is efficient scheduling of the arriving trucks. This study proposes a novel Diploid Evolutionary Algorithm for the truck scheduling problem at a cross-docking facility, which—unlike the Evolutionary Algorithms presented in the cross-docking literature to date—stores the genetic information from the parent chromosomes after performing a crossover operation. The objective of the formulated mathematical model is to minimize the total truck service cost. The conducted numerical experiments demonstrate that the optimality gap of the developed algorithm does not exceed 0.18% over the considered small size problem instances. The analysis of the realistic size problem instances indicates that deployment of the developed solution algorithm reduces the total truck handling time, the total truck waiting time, and the total truck delayed departure time on average by 6.14%, 32.61%, and 34.01%, respectively, as compared to a typical Evolutionary Algorithm. Furthermore, application of the diploidy concept decreases the total truck service cost by 18.17%
A New Simulation Model for a Comprehensive Evaluation of Yard Truck Deployment Strategies at Marine Container Terminals
No full textTaking into account increasing volumes of the international seaborne trade and increasing port congestion, marine container terminal operators have to improve efficiency of their operations in order to provide timely service of vessels and avoid product delivery delays to customers. This paper focuses on improvement of container transfer operations between the seaside and the marshaling yard and proposes five yard truck deployment strategies. Performance of the considered marine container terminal is evaluated under each one of the yard truck deployment strategies via simulation. Different performance indicators are estimated to determine how the suggested yard truck deployment strategies will affect all equipment types, involved in container handling and transfer. Results from the extensive simulation experiments showcase that all five yard truck deployment strategies provide similar values of vessel service and quay crane performance indicators, while the shortest distance based yard truck deployment strategy yields superior gantry crane and yard truck performance indicators. The worst values of performance indicators are recorded for the random yard truck deployment strategy. Furthermore, the developed simulation model can serve as an efficient planning tool for marine container terminal operators and enhance productivity of the available equipment
Bunker Consumption Optimization in Liner Shipping: A Metaheuristic Approach
No full textTaking into account increasing volumes of the international seaborne trade, liner shipping companies have to ensure efficiency of their operations in order to remain competitive. The bunker consumption cost constitutes a substantial portion of the total vessel operating cost and directly affects revenues of liner shipping companies. “Slow steaming” became a common strategy among ocean carriers to decrease vessel sailing speeds and reduce bunker consumption costs. However, decreasing vessel sailing speeds may require deployment of more vessels on a given shipping route to provide the agreed service frequency at each port of call. Several bunker consumption optimization methods were developed in the past to capture those conflicting decisions. This paper describes existing bunker consumption optimization methods, outlines their drawbacks, and proposes a new metaheuristic approach. Numerical experiments demonstrate efficiency of the suggested metaheuristic in terms of solution quality and computational time.
DOI: 10.17762/ijritcc2321-8169.15065
A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals
No full text
Purpose
The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost.
Design/methodology/approach
A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search.
Findings
Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space.
Research limitations/implications
This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured.
Practical implications
The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases.
Originality/value
The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.
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Harnessing Digital Twins in Transportation: Adaptation and Mitigation Strategies for Sustainability and Resilience
No full textThe transportation sector is essential for global economic and social progress but also significantly contributes to greenhouse gas emissions and climate change. While the application of digital twin technology has been noted in various sectors, such as aerospace, it has only recently begun to expand into the transportation field. This chapter explores the transformative potential of digital twin technology in developing sustainable and resilient transportation systems. Digital twins are dynamic virtual replicas of physical systems that incorporate real-time data and advanced analytics to enhance transportation networks' performance, adaptability, and efficiency. This study is grounded in a systematic literature review that analyzes the latest research, specifically, peer-reviewed publications from 2018 onwards, focused on the use of digital twin technology in future mobility and optimizing transport network management and supply chains. Real-world applications, such as the Thameslink Railway Program and the Port of Rotterdam, demonstrate how digital twins can enhance energy consumption and streamline logistics operations. The study presented in this chapter contributes to the current understanding of digital twin technology by critically examining its dual benefits and highlighting its role in adaptation and mitigation strategies to improve transportation resilience against climate change and extreme weather conditions. Furthermore, it addresses the implementation challenges and provides recommendations for future research
A comprehensive multi-objective optimization model for the vessel scheduling problem in liner shipping
No full textHighway - Rail Grade Crossing Identification and Prioritizing Model Development
No full textThe United States Department of Transportation (US DOT) provides funding to state DOTs to implement highway-rail grade crossing improvement programs. These programs are suspected to develop particular safety improvement actions in order to decrease the number of accidents at highway-rail grade crossings. The current work is directed to consider various hazard index/accident prediction methodologies, carefully investigate hazard index/accident prediction methods, applied by Tennessee Department of Transportation (TDOT), develop a model to allocate available monetary resources for upgrades of highway-rail grade crossings in the State of Tennessee and maximize the total benefits in terms of accident and severity reduction. Two different approaches are proposed and a number of numerical experiments are presented to evaluate each approach
Evaluation of Non-Parametric Selection Mechanisms in Evolutionary Computation: A Case Study for the Machine Scheduling Problem
No full textGreen vessel scheduling in liner shipping: Modeling carbon dioxide emission costs in sea and at ports of call
No full textConsidering a substantial increase in volumes of the international seaborne trade and drastic climate changes due to carbon dioxide emissions, liner shipping companies have to improve planning of their vessel schedules and improve energy efficiency. This paper presents a novel mixed integer non-linear mathematical model for the green vessel scheduling problem, which directly accounts for the carbon dioxide emission costs in sea and at ports of call. The original non-linear model is linearized and then solved using CPLEX. A set of numerical experiments are conducted for a real-life liner shipping route to reveal managerial insights that can be of importance to liner shipping companies. Results indicate that the proposed mathematical model can serve as an efficient planning tool for liner shipping companies and may assist with evaluation of various carbon dioxide taxation schemes. Increasing carbon dioxide tax may substantially change the design of vessel schedules, incur additional route service costs, and improve the environmental sustainability. However, the effects from increasing carbon dioxide tax on the marine container terminal operations are found to be very limited
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