Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

Quick Response Practices at the Warehouse of Ankor

In the warehouse of Ankor, a wholesaler of tools and garden equipment, various problems concerning the storage and retrieval of products arise. For example, heavy products have to be retrieved prior to light products to prevent damage. Furthermore, the layout of the warehouse differs from the layout generally assumed in literature. The goal of this research was to determine storage locations for the products and a routing method to obtain sequences in which products are to be retrieved from their locations. It is shown that despite deviations from the "normal" case, similar savings in route length can be obtained by adapting existing solution techniques. Total labor savings are far less than expected on basis of assumptions made in literature. With a minimum of adaptations to the current situation the average route length can be decreased by 30 %. There is no need for complex techniques.storage;warehousing;optimization;case study;routing

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Product Return Handling

In this article we focus on product return handling and warehousingissues. In some businesses return rates can be well over 20% andreturns can be especially costly when not handled properly. In spiteof this, many managers have handled returns extemporarily. The factthat quantitative methods barely exist to support return handlingdecisions adds to this. In this article we bridge those issues by 1)going over the key decisions related with return handling; 2)identifying quantitative models to support those decisions.Furthermore, we provide insights on directions for future research.reverse logistics;decision-making;quantitative models;retailing and warehousing

Predictive Irrigation Scheduling Modeling in Nurseries

Water requirement allocation plays an important role in modern farming management. Evapotranspiration-based irrigation controllers can ideally provide irrigation according to the water requirements of the plant. This chapter describes predictive irrigation scheduling in nurseries with multiple crop species and high-frequency water requirements under limited resources. Based on historical data, time-series analysis is used to forecast evapotranspiration, an essential element in water balance equation. An algorithm based on a hierarchical research including dispatching priority rules and taking into account crop characteristics, available water, and constraints of the hydraulic network is proposed to predict irrigation schedules, with the objective of minimizing crop’s water stress periods and optimizing resource materials. Simulation results with different climatic conditions show on the one hand the ability of the time-series model to forecast potential evapotranspiration, and on the other hand that, given a typical nursery, the proposed predictive approach of irrigation scheduling compared to the non-predictive approach makes it possible to prevent crop’s water stress

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

Sequence-Based Simulation-Optimization Framework With Application to Port Operations at Multimodal Container Terminals

It is evident in previous works that operations research and mathematical algorithms can provide optimal or near-optimal solutions, whereas simulation models can aid in predicting and studying the behavior of systems over time and monitor performance under stochastic and uncertain circumstances. Given the intensive computational effort that simulation optimization methods impose, especially for large and complex systems like container terminals, a favorable approach is to reduce the search space to decrease the amount of computation. A maritime port can consist of multiple terminals with specific functionalities and specialized equipment. A container terminal is one of several facilities in a port that involves numerous resources and entities. It is also where containers are stored and transported, making the container terminal a complex system. Problems such as berth allocation, quay and yard crane scheduling and assignment, storage yard layout configuration, container re-handling, customs and security, and risk analysis become particularly challenging. Discrete-event simulation (DES) models are typically developed for complex and stochastic systems such as container terminals to study their behavior under different scenarios and circumstances. Simulation-optimization methods have emerged as an approach to find optimal values for input variables that maximize certain output metric(s) of the simulation. Various traditional and nontraditional approaches of simulation-optimization continue to be used to aid in decision making. In this dissertation, a novel framework for simulation-optimization is developed, implemented, and validated to study the influence of using a sequence (ordering) of decision variables (resource levels) for simulation-based optimization in resource allocation problems. This approach aims to reduce the computational effort of optimizing large simulations by breaking the simulation-optimization problem into stages. Since container terminals are complex stochastic systems consisting of different areas with detailed and critical functions that may affect the output, a platform that accurately simulates such a system can be of significant analytical benefit. To implement and validate the developed framework, a large-scale complex container terminal discrete-event simulation model was developed and validated based on a real system and then used as a testing platform for various hypothesized algorithms studied in this work

Warehouse design and control: framework and literature review

In this paper we present a reference framework and a classification of warehouse design and control problems. Based on this framework, we review the existing literature on warehousing systems and indicate important gaps. In particular, we emphasize the need for design oriented studies, as opposed to the strong analysis oriented research on isolated subproblems that seems to be dominant in the current literature

The Multi Crane Scheduling Problem: A Comparison Between Genetic Algorithm and Neural Network Approaches based on Simulation Modeling

The internal logistics for warehouses of many industrial applications, based on the movement of heavy goods, is commonly solved by the installment of a multi-crane system. The job scheduling of a multi-crane system is an interesting problem of optimization, solved in many ways in the past. This paper describes a comparison between the optimization by the use of Genetic Algorithms (GA) and introduce a framework for the solution of the problem using machine learning driven by Neural Networks (NN). Even though this last approach is not implemented in this paper, performances very close to GA ones are expected with NN. A case-study for steel coil production is proposed as a test frame for two different simulation software tools, one based on a heuristic solution and one on machine learning; performances and data achieved from reviews and simulations are compared