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An integrated scheduling method for AGV routing in automated container terminals Yongsheng Yanga,? , Meisu Zhonga , Yasser Dessoukyb , Octavian Postolachec
時間: 2021-08-11 10:05:26

1. Introduction The continuous development of global trade as well as logistics technologies has been pushing up the demand for container terminals, including loading and unloading operations, and the storage area. Automated Container Terminals (ACTs) have appeared to meet this ever-increasing demand and contribute to the higher efficiency and productivity of port operations as well as reduction in the cost of human resources and emissions at a port. More than 20 ACTs have taken off around the world but how to continue to improve their efficiency is still one of the most frequently discussed topics on port operations and management. The research in the literature on this topic can be categorized into three groups. One is concerned with the methodology of how to describe operations of an ACT. Liu, Jula, and Ioannou (2002) presented a setup and analysis of a microcosmic simulation model for the operation mode of AC. They showed that the model significantly improved the performance of the traditional terminal and reduced costs. Hu, Lee, Huang, Lee, and Chew (2013) considered an automated terminal system that was decomposed into three subsystems through the Markov chain model to analyse and forecast the capacity of containers in ACT. The sensitivity analysis illustrated the high efficiency of ACT in the latter study. Yang et al. (2015) analysed two kinds of microscopic parameter models to assess the performance of ACT based on automatic stacking cranes (ASCs) or AGVs. The implemented models revealed advantages, such as higher efficiency and stability, effectively reduced labour costs, decreased the probability of personal injury accidents, and proved to be safer for the terminal environment. Automated terminals possess many advantages in terms of labour cost, improved operation efficiency and economic benefit, reduced energy consumption, and improved levels of safe operation and the port’s reputation (Le, Yassine, & Riadh, 2012; Zhang, Ioannou, & Chassiakos, 2006; Martín-Soberón, Monfort, Sapi?a, Monterde, & Calduch, 2014). While an ACT offers such crucial benefits, more research should be conducted to further rationalize its use. The operation mode of automated container terminal can be divided into a loading process and unloading process. In the loading process, containers from the storage location are transferred from the yard by YCs to AGVs, which then transport containers to the port to be loaded onto ships by QCs. In the unloading process, containers are removed by QCs and transferred to AGVs, which transport the containers to the YCs that place containers to the corresponding storage location in the yard (Gharehgozli, Roy, & Koster, 2016). The operation of container terminals is shown in Fig. 1. In this work, we considered actual circumstances in automated terminals where the automatic rail-mounted gantry (ARMG) unloads the container from AGVs to the AGV-mate in the front of a yard or from the AGV-mate to AGVs. The AGV-mate is an auxiliary equipment of AGVs that is used to reduce the waiting times associated with ARMG and AGV, which can optimise the cycle of container loading and unloading and improve the efficiency of ACT. Due to the development of large containers and rising labour costs, many overseas and domestic researchers have investigated the http://doi.org/10.1016/j.cie.2018.10.007 Received 12 July 2018; Received in revised form 2 October 2018; Accepted 4 October 2018 ? Corresponding author. E-mail address: yangys_smu@126.com (Y. Yang). Computers & Industrial Engineering 126 (2018) 482–493 Available online 06 October 2018 0360-8352/ ? 2018 Elsevier Ltd. All rights reserved. T scheduling problem of AGVs for decades. Since the containerthroughput in Chinese ports have taken a great leap forward, the scheduling problem of AGVs has become more urgent. In many studies, AGVs and QCs are considered independently. However, AGVs and QCs work in a close relationship and should be studied dependently for two reasons: (1) AGVs play an important role in the quayside and yard-side operations, which restricts and influences the operation of the other two parts, and (2) the scheduling of AGVs affects the time needed for every container to be handled by AGVs. AGVs, as the carrier to connect QCs with YCs, can influence the operation system of QCs and YCs, which is directly related to the working efficiency of terminals, and affect the capacity of a container terminal. In addition, owing to the high cost of QCs or other factors like the quantitative restriction, an inefficient operation will cause delays in container terminals and increase the transportation cost of containers. Therefore, studying the problem of integrated scheduling of QCs, AGVs, and YCs is the key to automated terminal efficiency. Moreover, a series of problems still exists, including AGV congestion and conflict, especially in the practical operation of automated terminals. Considering that there are many indefinite factors in current running environments, realizing dynamic scheduling based on the conditions of AGV path planning is of great significance in solving the practical problems in automated terminal. In this work, we put forward integrated scheduling of QCs, AGVs, and ARMG and path planning for AGVs to reduce the problems of AGV conflict and congestion, which has yet to be done in previous literature. Therefore, we studied path planning to achieve the integrated scheduling of QCs and AGVs and the ARMG optimisation method in the automation terminal. We also propose a bi-level programming model and Congestion Prevention Rule-based Bi-level Genetic Algorithm (CPR-BGA). This paper is structured as follows. Section 2 provides a review of the existing literature on AGV path planning and handling equipment scheduling. Section 3 presents a bi-level programming model formulated for the problem of interest. Section 4 proposes a bi-level Genetic Algorithm to solve the formulated model handling a large-sized problem. Section 5 shows and analyses the numerical results to show the performance of the formulated model and proposed algorithm. Section 6 presents the conclusion and offers suggestions for further research. 2. Literature review In recent years, there has been numerous researches on the equipment of automation terminals, especially focused on improving the efficiency of QCs. For example, Meisel and Bierwirth (2009, 2013), combined berth allocation with QCs assignment, by the heuristic algorithm to get the solution, and then they build a framework with three stages to finish the integrated planning, which has improved the resource utilization and reduced costs in terminals. Park, Choe, Ok, and Ryu (2010) used the heuristic-based and local-search-based real-time scheduling methods and analysed the reason for the delay when operating QCs and AGVs to better increase QC utilization and reduce costs. Wen, Ek?io?lu, Greenwood, and Zhang (2010) also attempted to minimise the interference between the QCs and improve the utilization of QCs by dividing the task allocation of QCs through the Ant Colony Optimisation Algorithm to increase efficient scheduling of QC projects. Li et al. (2016) considered the interference among QCs and fixed the distance between the QCs, taking full account of the acceleration and deceleration of QCs in real-time operating. The latter research used heuristics and a rolling horizon algorithm to improve the efficiency and reduce the waiting time of QCs, which is a key to improve the throughput of container terminals. While the above-mentioned methods merely focus on QCs in loading or unloading operation mode, we consider the actual port operation mode for loading and unloading of QCs in this work. Previous research that only focused on the QCs does not match the actual situation of automation terminals without considering the AGVs and the yards, which cannot improve the overall operating efficiency. The use of AGVs first appeared in 1950s, and now there are many researches on the AGV scheduling method. Grunow, Günther, and Lehmann (2006) first found that AGVs could handle two 20-ft containers or one large 40-ft container. The randomness of the off-line heuristic model was proposed to improve the efficiency of the

Assignment 2 COMP9021, Term 2, 2021 1. General matter
時間: 2021-08-10 08:55:40

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】1.1. Aims. The purpose of the assignment is to: ? design and implement an interface based on the desired behaviour of an application program; ? practice the use of Python syntax; ? develop problem solving skills. 1.2. Submission. Your program will be stored in a file named sudoku.py. After you have developed and tested your program, upload it using Ed (unless you worked directly in Ed). Assignments can be submitted more than once; the last version is marked. Your assignment is due by August 9, 10:00am. 1.3. Assessment. The assignment is worth 13 marks. It is going to be tested against a number of input files. For each test, the automarking script will let your program run for 30 seconds. Late assignments will be penalised: the mark for a late submission will be the minimum of the awarded mark and 13 minus the number of full and partial days that have elapsed from the due date. The outputs of your programs should be exactly as indicated. 1.4. Reminder on plagiarism policy. You are permitted, indeed encouraged, to discuss ways to solve the assignment with other people. Such discussions must be in terms of algorithms, not code. But you must implement the solution on your own. Submissions are routinely scanned for similarities that occur when students copy and modify other people’s work, or work very closely together on a single implementation. Severe penalties apply. 1 2 2. Background A sudoku grid consists of 9 lines and 9 columns, making up 81 cells, that are grouped in nine 3x3 boxes. In a sudoku puzzle, some but not all of the cells already contain digits between 1 and 9. Here is an example of a sudoku puzzle. 1 9 8 6 8 5 3 7 6 1 3 4 9 5 4 1 4 2 5 7 9 1 8 4 7 7 9 2 Solving a sudoku puzzle means completing the grid so that each digit from 1 to 9 occurs once and only once in every row, once and only one in every column, and once and only once in every box. For instance, the previous puzzle has the following solution. 3 3 4 1 9 2 7 5 6 8 6 9 2 1 8 5 7 3 4 8 5 7 4 6 3 1 9 2 1 3 4 2 9 6 8 7 5 2 7 8 5 3 4 6 1 9 5 6 9 7 1 8 4 2 3 4 2 5 3 7 1 9 8 6 9 1 6 8 4 2 3 5 7 7 8 3 6 5 9 2 4 1 Solving a sudoku puzzle is a very common assignment; it is not difficult and moderately interesting as a “solution” (the completed grid) tells nothing about how the solution was reached. More interesting solvers are logical in the sense that they (possibly partially only) solve the puzzle in steps and at every step, explain how they made some progress; they do so by using some of the well known techniques that most people who solve sudoku puzzles apply. Two remarks are in order. ? Methods that only discover digits in empty cells are fairly limited; most methods need to keep track of the list of possible digits that can go into a given cell, and making progress might mean reducing that list. To apply techniques of the second kind, it is necessary to first mark the grid. ? Often, it is not possible to completely solve a puzzle using exclusively the chosen methods; at some point no progress can be made and then a random guess has to be made to either put a digit into a given empty cell, or to remove a digit from the list of possible digits that can go into a given cell. It might subsequently be necessary to backtrack and make alternative guesses if the earlier guesses turn out to be inconsistent with a solution. For this assignment, you will have to implement two such techniques, based on the notions of forced digits and preemptive sets described in the paper A Pencil-and-Paper Algorithm for Solving Sudoku Puzzles by J. F. Crook, Notices of the AMS, 56(4), pp. 460–468. Before anything else, you should study this paper. The forced digits technique is applied first, followed by the preemptive set technique. When no progress can be made, the forced digits techniques could be applied again, but that might not yield anything; an alternative would be to try and fill some empty cell with one of the possible digits for that cell and apply the preemptive set technique applied again, knowing that that guess might prove wrong and that other possible digits might have to be used instead. In this assignment, we will stop at the point where the preemptive set technique can no longer be applied; hence we can expect that our implementation will only partially solve most puzzles. But the technique is very powerful and as explained in the article, subsumes many of the well known techniques. You will design and implement a program that will read a sudoku grid whose representation is stored in a file filename.txt and create a Sudoku object, with a number of methods: 4 ? a method preassess() that prints out to standard output whether the representation is correct and has no digit that occurs twice on the same row, on the same column or in the same box; ? a method bare_tex_output() that outputs some Latex code to a file, filename_bare.tex, that can be compiled by pdflatex to produce a pictorial representation of the grid; ? a method forced_tex_output() that outputs some Latex code to a file, filename_forced.tex, that can be compiled by pdflatex to produce a pictorial representation of the grid to which the forced digits technique has been applied; ? a method marked_tex_output() that outputs some Latex code to a file, filename_marked.tex, that can be compiled by pdflatex to produce a pictorial representation of the grid to which the forced digits technique has been applied and that has been marked; ? a method worked_tex_output() that outputs some Latex code to a file, filename_worked.tex, that can be compiled by pdflatex to produce a pictorial representation of the grid to which the forced digits technique has been applied, that has been marked, and to which the preemptive set technique has been applied. The input is expected to consist of 9 lines of digits, with possibly lines consisting of spaces only that will be ignored and with possibly spaces anywhere on the lines with digits. If the input is incorrect, that is, does not satisfy the conditions just spelled out, then the program should generate a SudokuError with Incorrect input as message. Here is a possible interaction: $ python3 ... >>> from sudoku import * >>> Sudoku('sudoku_wrong_1.txt') ... sudoku.SudokuError: Incorrect input >>> Sudoku('sudoku_wrong_2.txt') ... sudoku.SudokuError: Incorrect input >>> Sudoku('sudoku_wrong_3.txt') ... sudoku.SudokuError: Incorrect input

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