Optimization of inland shipping

In this paper, we explore problems and algorithms related to the optimisation of locks, as used in inland shipping. We define several optimisation problems associated with inland shipping. We prove that the problem of scheduling a lock is NP-hard if one allows multiple ships to go through in the same lock operation. The single-ship lock optimization problem can, however, be solved in polynomial time and a novel deterministic scheduling algorithm for solving this problem is presented in this paper.     

A quay crane dynamic scheduling problem by hybrid evolutionary algorithm for berth allocation planning

A considerable growth in worldwide container transportation needs essential optimization of terminal operations. An operation schedule for berth and quay cranes can significantly affect turnaround time of ships, which is an important objective of all schedules in a port. This paper addresses the problem of determining the berthing position and time of each ship as well as the number of quay cranes assigned to each ship. The objective of the problem is to minimize the sum of the handling time, waiting time and the delay time for every ship. We introduce a formulation for the simultaneous berth and quay crane scheduling problem. Next, we combine genetic algorithm with heuristic to find an approximate solution for the problem. Computational experiments show that the proposed approaches are applicable to solve this difficult but essential terminal operation problem.     

Biased random key genetic algorithm for the Tactical Berth Allocation Problem

The Tactical Berth Allocation Problem (TBAP) aims to allocate incoming ships to berthing positions and assign quay crane profiles to them (i.e. number of quay cranes per time step). The goals of the TBAP are both the minimization of the housekeeping costs derived from the transshipment container flows between ships, and the maximization of the total value of the quay crane profiles assigned to the ships. In order to obtain good quality solutions with considerably short computational effort, this paper proposes a biased random key genetic algorithm for solving this problem. The computational experiments and the comparison with other solutions approaches presented in the related literature for tackling the TBAP show that the proposed algorithm is applicable to efficiently solve this difficult and essential container terminal problem. The problem instances used in this paper are composed of both, those reported in the literature and a new benchmark suite proposed in this work for taking into consideration other realistic scenarios.     

A hybrid Lagrangian-simulated annealing-based heuristic for the parallel-machine capacitated lot-sizing and scheduling problem with sequence-dependent setup times

This paper examines the parallel-machine capacitated lot-sizing and scheduling problem with sequence-dependent setup times, time windows, machine eligibility and preference constraints. Such problems are quite common in the semiconductor manufacturing industry. In particular, this paper pays special attention to the chipset production in the semiconductor Assembly and Test Manufacturing (ATM) factory and constructs a Mixed Integer Programming (MIP) model for the problem. The primal problem is decomposed into a lot-sizing subproblem and a set of single-machine scheduling subproblems by Lagrangian decomposition. A Lagrangian-based heuristic algorithm, which incorporates the simulated annealing algorithm aimed at searching for a better solution during the feasibility construction stage, is proposed. Computational experiments show that the proposed hybrid algorithm outperforms other heuristic algorithms and meets the practical requirement for the tested ATM factory.     

基于作业链的泊位与岸桥协同调度研究

针对泊位与岸桥协同调度问题,引入“链式优化”思路,用作业链的方法分析集装箱装卸作业过程,首先将泊位计划作为开始链单元,采用资源节点优化策略进行分析,以最小化船舶在港总成本为目标建立模型;然后将岸桥卸船作业作为结束链单元,采用任务节点优化策略进行分析,以最小化岸桥最大完工时间为目标建立模型。考虑到作业链的整体性能,设计嵌套循环算法进行求解,内循环中用遗传算法分别求解泊位岸桥分配模型和岸桥调度模型,外循环中用岸桥数量作为公用变量对两个模型进行传递和反馈,寻找协同调度最优解。与单独调度进行对比,结果表明协同调度的优化效果更好;与粒子群算法、蚁群算法和蜂群算法的求解结果进行比较,表明遗传算法在求解质量和效率方面都更优,证明了提出的模型和算法能够有效解决此问题。     

基于量子遗传混合算法的泊位联合调度

为了提高集装箱港口服务效率,减少船舶服务的拖期费用,针对港口硬件（泊位、拖轮、岸桥）既定条件下的拖轮-泊位联合调度问题,新建了以最小化总体船舶在港时间和总拖期时间为目标的数学模型,设计了一种混合算法进行求解。首先,分析确定了将量子遗传算法（QGA）和禁忌搜索（TS）算法进行串行混合的策略;然后,依据该联合调度问题特点,在解决算法实施中的关键技术问题（染色体结构设计和测量、遗传操作、种群更新等）的同时,采用了动态量子旋转门更新机制;最后,用生产实例验证了算法的可行性及有效性。算法实验结果表明,与人工调度结果相比,混合算法的总体船舶在港时间和总拖期时间分别减少了24%和42.7%;与遗传算法结果相比,分别减少了10.9%和22.5%。所提模型及算法不仅能为港口船舶的入泊、离泊和装卸作业环节提供优化作业方案,而且能增强港口竞争力。     

A case study of scheduling storage tanks using a hybrid geneticalgorithm

This paper proposes the application of a hybrid genetic algorithm (GA) for scheduling storage tanks. The proposed approach integrates GAs and heuristic rule-based techniques, decomposing the complex mixed-integer optimization problem into integer and real-number subproblems. The GA string considers the integer problem and the heuristic approach solves the real-number problems within the GA framework. The algorithm is demonstrated for three test scenarios of a water treatment facility at a port and has been found to be robust and to give a significantly better schedule than those generated using a random search and a heuristic-based approach     

单船装卸作业的岸桥调度

岸桥作为港口的一种重要资源,其利用率直接影响整个码头的效率。对单船装卸作业的岸桥调度问题进行了研究分析,给出了一种改进的启发式算法NEW GRASP。最后通过实验与原始算法进行了比较。     

A fast heuristic for quay crane scheduling with interference constraints

This paper considers the problem of scheduling quay cranes which are used at sea port container terminals to load and unload containers. This problem is studied intensively in a recent stream of research but still lacks a correct treatment of crane interference constraints. We present a revised optimization model for the scheduling of quay cranes and propose a heuristic solution procedure. At its core a Branch-and-Bound algorithm is applied for searching a subset of above average quality schedules. The heuristic takes advantage from efficient criteria for branching and bounding the search with respect to the impact of crane interference. Although the used techniques are quite standard, the new heuristic produces much better solutions in considerably shorter run times than all algorithms known from the literature.     

Minimizing weighted earliness–tardiness on parallel machines using hybrid metaheuristics

We consider the problem of scheduling a set of jobs on a set of identical parallel machines where the objective is to minimize the total weighted earliness and tardiness penalties with respect to a common due date. We propose a hybrid heuristic algorithm for constructing good solutions, combining priority rules for assigning jobs to machines and a local search with exact procedures for solving the one-machine subproblems. These solutions are then used in two metaheuristic frameworks, Path Relinking and Scatter Search, to obtain high quality solutions for the problem.The algorithms are tested on a large number of test instances to assess the efficiency of the proposed strategies.The results show that our algorithms consistently outperform the best reported results for this problem.     

A hybrid scheduling approach for a two-stage flexible flow shop with batch processing machines

In this paper, we discuss a flexible flow shop scheduling problem with batch processing machines at each stage and with jobs that have unequal ready times. Scheduling problems of this type can be found in semiconductor wafer fabrication facilities (wafer fabs). We are interested in minimizing the total weighted tardiness of the jobs. We present a mixed integer programming formulation. The batch scheduling problem is NP-hard. Therefore, an iterative stage-based decomposition approach is proposed that is hybridized with neighborhood search techniques. The decomposition scheme provides internal due dates and ready times for the jobs on the first and second stage, respectively. Each of the resulting parallel machine batch scheduling problems is solved by variable neighborhood search in each iteration. Based on the schedules of the subproblems, the internal due dates and ready times are updated. We present the results of designed computational experiments that also consider the number of machines assigned to each stage as a design factor. It turns out that the proposed hybrid approach outperforms an iterative decomposition scheme where a fairly simple heuristic based on time window decomposition and the apparent tardiness cost dispatching rule is used to solve the subproblems. Recommendations for the design of the two stages with respect to the number of parallel machines on each stage are given.     

Hybrid evolutionary computation methods for quay crane scheduling problems

Quay crane scheduling is one of the most important operations in seaport terminals. The effectiveness of this operation can directly influence the overall performance as well as the competitive advantages of the terminal. This paper develops a new priority-based schedule construction procedure to generate quay crane schedules. From this procedure, two new hybrid evolutionary computation methods based on genetic algorithm (GA) and genetic programming (GP) are developed. The key difference between the two methods is their representations which decide how priorities of tasks are determined. While GA employs a permutation representation to decide the priorities of tasks, GP represents its individuals as a priority function which is used to calculate the priorities of tasks. A local search heuristic is also proposed to improve the quality of solutions obtained by GA and GP. The proposed hybrid evolutionary computation methods are tested on a large set of benchmark instances and the computational results show that they are competitive and efficient as compared to the existing methods. Many new best known solutions for the benchmark instances are discovered by using these methods. In addition, the proposed methods also show their flexibility when applied to generate robust solutions for quay crane scheduling problems under uncertainty. The results show that the obtained robust solutions are better than those obtained from the deterministic inputs.     

An adaptive large neighborhood search for the discrete and continuous Berth allocation problem

The Berth Allocation Problem (BAP) consists of assigning ships to berthing positions along a quay in a port. The choice of where and when the ships should move is the main decision to be made in this problem. Considering the berthing positions, there are restrictions related to the water depth and the size of the ships among others. There are also restrictions related to the berthing time of the ships which are modeled as time windows. In this work the ships are represented as rectangles to be placed into a space ×time area, avoiding overlaps and satisfying time window constraints. We consider discrete and continuous models for the BAP and we propose an Adaptive Large Neighborhood Search heuristic to solve the problem. Computational experiments indicate that the proposed algorithm is capable of generating high-quality solutions and outperforms competing algorithms for the same problem. In most cases the improvements are statistically significant.     

A multi-objective genetic algorithm for yard crane scheduling problem with multiple work lines

Due to increasing ships and quay cranes, container terminals operations become more and more busy. The traditional handling based on work line is converted into pool strategy, namely loading and unloading containers with multiple work lines are operating simultaneously. In the paper we discuss the yard crane scheduling problem with multiple work lines in container terminals. We develop a multi-objective 0-1 integer programming model considering the minimum total completion time of all yard cranes and the maximization balanced distribution of the completion time at the same time. With the application of adaptive weight GA approach, the problem can be solved by a multi-objective hybrid genetic algorithm and the Pareto solutions can be finally got. Using the compromised approach, the nearest feasible solution to ideal solution is chosen to be the best compromised Pareto optimal solution of the multi-objective model. The numerical example proves the applicability and effectiveness of the proposed method to the multi-objective yard crane scheduling problem.     

Dynamic rolling strategy for multi-vessel quay crane scheduling

This paper focuses on the container loading and unloading problem with dynamic ship arrival times. Using a determined berth plan, in combination with the reality of a container terminal production scheduling environment, this paper proposes a scheduling method for quay cranes that can be used for multiple vessels in a container terminal, based on a dynamic rolling-horizon strategy. The goal of this method is to minimize the operation time of all ships at port and obtain operation equilibrium of quay cranes by establishing a mathematical model and using a genetic algorithm to solve the model. Numerical simulations are applied to calculate the optimal loading and unloading order and the completion time of container tasks on a ship. By comparing this result with the traditional method of quay crane loading and unloading, the paper verifies that the quay crane scheduling method for multiple vessels based on a dynamic rolling-horizon strategy can provide a positive contribution to improve the efficiency of container terminal quay crane loading and unloading and reduce resource wastage.     

A linear programming-based method for job shop scheduling

We present a decomposition heuristic for a large class of job shop scheduling problems. This heuristic utilizes information from the linear programming formulation of the associated optimal timing problem to solve subproblems, can be used for any objective function whose associated optimal timing problem can be expressed as a linear program (LP), and is particularly effective for objectives that include a component that is a function of individual operation completion times. Using the proposed heuristic framework, we address job shop scheduling problems with a variety of objectives where intermediate holding costs need to be explicitly considered. In computational testing, we demonstrate the performance of our proposed solution approach.     

SOLVING THE OPEN SHOP SCHEDULING PROBLEM VIA A HYBRID GENETIC-VARIABLE NEIGHBORHOOD SEARCH ALGORITHM

In this article, a hybrid metaheuristic method for solving the open shop scheduling problem (OSSP) is proposed. The optimization criterion is the minimization of makespan and the solution method consists of four components: a randomized initial population generation, a heuristic solution included in the initial population acquired by a Nawaz-Enscore-Ham (NEH)-based heuristic for the flow shop scheduling problem, and two interconnected metaheuristic algorithms: a variable neighborhood search and a genetic algorithm. To our knowledge, this is the first hybrid application of genetic algorithm (GA) and variable neighborhood search (VNS) for the open shop scheduling problem. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches a high quality solution in short computational times. Moreover, 12 new hard, large-scale open shop benchmark instances are proposed that simulate realistic industrial cases.     

Production capacity planning and scheduling in a no-wait environment with controllable processing times: An integrated modeling approach

This paper develops an integrated model between a production capacity planning and an operational scheduling decision making process in which a no-wait job shop (NWJS) scheduling problem is considered incorporating with controllable processing times. The duration of any operations are assumed to be controllable variables based on the amount of capacity allocated to them, whereas in classical NWJS it is assumed that the machine capacity and hence processing times are fixed and known in advance. The suggested problem which is entitled no-wait job shop crashing (NWJSC) problem is decomposed into the crashing, sequencing and timetabling subproblems. To tackle the addressed NWJSC problem, an improved hybrid timetabling procedure is suggested by employing the concept of both non-delay and enhanced algorithms which provides better solution than each one separately. Furthermore, an effective two-phase genetic algorithm approach is devised integrating with hybrid timetabling to deal with the crashing and sequencing components. The results obtained from experimental evaluations support the outstanding performance of the proposed approach.     

Hybrid heuristic search approach for deadlock-free scheduling of flexible manufacturing systems using Petri nets

Based on the Petri net models of flexible manufacturing systems (FMSs), this paper focuses on deadlock-free scheduling problem with the objective of minimizing the makespan. Two hybrid heuristic search algorithms for solving such scheduling problems of FMSs are proposed. To avoid deadlocks, the deadlock control policy is embedded into heuristic search strategies. The proposed algorithms combine the heuristic best-first strategy with the controlled backtracking strategy based on the execution of the Petri nets. The scheduling problem is transformed into a heuristic search problem in the reachability graph of the Petri net, and a schedule is a transition sequence from the initial marking to the final marking in the reachability graph. By using the one-step look-ahead method in the deadlock control policy, the safety of a state in the reachability graph is checked, and hence, deadlock is avoided. Experimental results are provided and indicate the effectiveness of the proposed hybrid heuristic search algorithms in solving deadlock-free scheduling problems of FMSs. Especially, the comparison against previous work shows that both new algorithms are promising in terms of solution quality and computing times.     

A branch-and-bound algorithm for the three-machine flowshop scheduling problem with bicriteria of makespan andtotal flowtime

This paper addresses the three‐machine flowshop scheduling problem with a bicriteria of minimizing a weighted sum of makespan and total flowtime. Three lower bounds, an upper bound, and several dominance relations are developed. The upper bound is developed using a two‐phase hybrid heuristic method. A branch‐and‐bound algorithm, incorporating the developed bounds and dominance relations, is presented. An extensive computational analysis on randomly generated problems is conducted. The analysis indicates that the proposed bounds, dominance relations, and branch‐and‐bound algorithm are efficient.