Integrating Ship Movement Scheduling and Tug Assignment Within a Canal Harbor

We present a bulk ship scheduling problem that is a combined multi-ship pickup and delivery problem with time windows (m-PDPTW) and multi-allocation problem. In contrast to other ship scheduling problems found in the literature, each ship in the fleet is equipped with a flexible cargo hold that can be partitioned into several smaller holds in a given number of ways. Therefore, multiple products can be carried simultaneously by the same ship. The scheduling of the ships constitutes the m-PDPTW, while the partition of the ships' flexible cargo holds and the allocation of cargoes to the smaller holds make the multi-allocation problem. A set partitioning approach consisting of two phases is proposed for the combined ship scheduling and allocation problem. In the first phase, a number of candidate schedules (including allocation of cargoes to the ships' cargo holds) is generated for each ship. In the second phase, we minimise transportation costs by solving a set partitioning problem where the columns are the candidate schedules generated in phase one. The computational results show that the proposed approach works, and optimal solutions are obtained on several cases of a real ship planning problem.

This paper describes a new method for modeling and solving different scheduling and resource assignment problems that are common in high-level synthesis (HLS) and system-level synthesis. It addresses assignment of resources for operations and tasks as well as their static, off-line scheduling. Different heterogeneous constraints are considered for these problems. These constraints can be grouped into two classes: problem-specific constraints and design-oriented constraints. They are uniformly modeled, in our approach, by finite domain (FD) constraints and solved using related constrained programming (CP) techniques. This provides a way to improve quality of final solutions. We have developed in Java a constraint solver engine, JaCoP ( Ja va Co nstraint P rogramming), to evaluate this approach. This solver and a related framework make it possible to model different resource assignment and scheduling problems, and handle them uniformly. The JaCoP prototype system has been extensively evaluated on a number of HLS and system-level synthesis benchmarks. We have been able to obtain optimal results together with related proofs of optimality for all HLS scheduling benchmarks and for all explored design styles (except one functional pipeline design). Many system-level benchmarks can also be solved optimally. For large randomly generated task graphs, we have used heuristic search methods and obtained results that are 1--3% worse than lower bounds or optimal results. These experiments have proved the feasibility of the presented approach.

This dissertation is to illustrate the current state of the art of DNA computing achievements, especially of new approaches to solve theoretical 3-SAT problems and the hitting-set problem. Beginning with Adleman’s breakthrough which is an molecular algorithm for the solution of a NP-complete, combinatorial problem, the directed Hamiltonian path problem (HPP). Today, many researchers all over the world concentrate on proposing new methods to solve engineering or application problems with a DNA computing approach. Satisfiability problem is given a Boolean formula, and decide if a satisfying truth assignment exists. ( ) ( ) … ( ) ( ) is an example of Boolean formula. k-SAT problem means that each clause has exactly k literals. Not-All-Equal (NAE) 3-SAT problem and One-In-Three (1IN3) 3-SAT problem are both NP-complete problems. In this dissertation, we present molecular solutions to find all true assignments (3-SAT problem) and furthermore find Not-All-Equal (NAE) solutions and One-In-Three (1IN3) solutions in DNA-based Supercomputing. Hitting-set problem assume that there exists a collection C of subsets of a finite set S, and a positive integer K  |S|, and we need to know if there is a subset  S with | |  K such that contains at least one element of each subset in C. In other words, is the subset that intersects every subset in C and is called the hitting-set. In this dissertation, a DNA-based algorithm is proposed to solve the small hitting-set problem. A small hitting-set is a hitting-set with the smallest K value, i.e., the hitting-set with the smallest number of elements. Furthermore, an algorithm is introduced to find the number of ones from 2n combinations and minimum numbers of ones represents the small hitting-set since K is expected to be as small as possible. The complexity of all the presented DNA-based algorithms is also discussed. We describe time complexity and volume complexity of three Algorithms, numbers of test tube used and the longest library strand in solution space of all three Algorithms. Finally, the simulated experiment is applied to verify correctness of the proposed DNA-based algorithm for solving the One-In-Three (1IN3) 3-SAT problem, and simulation of Not-All-Equal (NAE) 3-SAT problem is similar. Also, another simulated experiment is applied to our proposed DNA-based algorithm 6-2, in order to solve the well-known hitting-set problem. This research has been motivated by the benefit and the application of DNA computing and gives new methods to solve two 3-SAT problems and the hitting-set problem which are NP-complete.

In the big data processing systems, the amount of data is increasing. At the same time, the real-time requirement of data processing and analysis is higher and higher. Therefore, it is required that the big data processing and analysis systems have better performance. Job scheduling plays an important role in improving the overall system performance in big data processing frameworks. However, job scheduling is a difficult NP-hard problem. There are many factors that need to be considered for job scheduling. For example, jobs have dependencies among stages, therefore we should not allocate resources to tasks that are not ready. Sometimes, there are constraints between jobs. These are a challenge to the scheduling performance of big data processing and analysis systems. In this paper, we try to solve the problem by translating it into Boolean Satisfiability Problem (SAT) which is an exact method. SAT-based scheduling algorithm is not a new approach, but in the past it mainly used to solve the static scheduling problems. For dynamic scheduling system, it requires all problems to be solved within a limited time, which is a challenge for SAT encoding. In this paper, we refer to the previous SAT solution to the Job Shop Scheduling Problem, and adjust the algorithm to meet the requirements of the big data processing system. At the same time, we optimized the coding approach and reduced the number of clauses. Thus, the efficiency of the problem solved is improved to meet the performance requirements. The experimental results show that the number of clauses is reduced by more than 30%, and the processing time of the SAT solver to get the solution can be reduced by more than 50%. To demonstrate its effectiveness, we have also implemented our new job scheduler in Apache Hadoop YARN, and validated its effectiveness

This study investigates patient appointment scheduling and examination room assignment problems involving patients who undergo ultrasound examination with considerations of multiple examination rooms, multiple types of patients, multiple body parts to be examined, and special restrictions. Following are the recommended time intervals based on the findings of three scenarios in this study: In Scenario 1, the time interval recommended for patients’ arrival at the radiology department on the day of the examination is 18 min. In Scenario 2, it is best to assign patients to examination rooms based on weighted cumulative examination points. In Scenario 3, we recommend that three outpatients come to the radiology department every 18 min to undergo ultrasound examinations; the number of inpatients and emergency patients arriving for ultrasound examination is consistent with the original time interval distribution. Simulation optimization may provide solutions to the problems of appointment scheduling and examination room assignment problems to balance the workload of radiological technologists, maintain high equipment utilization rates, and reduce waiting times for patients undergoing ultrasound examination.

Nowadays, many manufacturing companies are trying to improve the performance of their processes using available innovative technologies such as collaborative robots (cobots). Cobots are robots with whom no safety distance is necessary. Through cooperation with human workers, they can help increase the production speed of existing workstations. The well-known job shop scheduling problem is, therefore, extended with the addition of a cobot to the workstation assignment. The considered objective is to maximize the normalized sum of production costs and makespan. To solve this problem, we propose a hybrid genetic algorithm with a biased random-key encoding and a variable neighborhood search. The hybrid method combines the exploration aspects of a genetic algorithm with the exploitation abilities of a variable neighborhood search. The developed algorithm is applied to real-world data and artificially generated data. To demonstrate the performance of this algorithm, a constraint programming model is implemented and the results are compared. Additionally, benchmark instances from a related problem from the cobot assignment and assembly line balancing, have been solved. The results from the real-world data show how much the objective function can be improved by the deployment of additional robots. The normalized objective function could be improved by up to 54% when using five additional cobots. As a methodological contribution, the biased random-key encoding is compared with a typical integer-based encoding. A comparison with a dataset from the literature shows that the developed algorithm can compete with state-of-the-art methods on benchmark instances.

In this work we address the problem of joint link scheduling and power assignment in WSNs. It focuses on finding a feasible schedule and a power assignment scheme such that the schedule length is minimized and the concurrent transmissions have a fair quality in terms of SINR (Signal-to-Interference-and-Noise Ratio). As the problem is shown to be NP-hard we propose a greedy heuristic for the scheduling problem which seeks to minimize the number of time slots for link scheduling. Our goal is to design an algorithm that performs transmission power control in order to guarantee fair SINR link transmissions. This insures that every node that is actively transmitting in the network chooses a transmission power which will minimally affect the other concurrent transmissions. Hence, we opt for a cooperative strategy which intends to maximize the minimum value of SINR. This problem is modeled and solved using an iterative algorithm based on linear programming which provides an optimal solution. The solution of this problem may be crucial for the performance of WSNs as it impacts different network requirements such as lifetime (energy savings), delay (schedule length) and throughput (number of concurrent links in a time slot).

Boolean Satisfiability (SAT) is a classical NP-complete problem with both theoretical and practical interests. This paper presents our work in developing an application-specific processor for SAT based on a commercial configurable processor core. We customize the processor configuration and design new instruction extensions based on the data structure and atomic operations used in SAT. The customized processor has achieved around 24 /spl times/ speedup at a very low hardware cost. The small size of the processor makes it possible to integrate multiple processors and other customized logic into a single chip for an application-specific multiprocessor solution for SAT. Our work shows the strength of application-specific processing in accelerating applications with complex control and dynamic data structures - an area that has traditionally not been targeted by application-specific processing. It also demonstrates that configurable processor cores can be used to cut the development time and cost for designing and building such application-specific processors.

Based on a previously proposed packet scheduling algorithm to control the downlink flow in wireless CDMA networks (C. Makaya et al., July 2003), we investigate the problem of joint packet scheduling and base station assignment to maximize data throughput and fairness among mobile users. The approach is based on a user utility function, and seeks to exploit joint packet scheduling and base station assignment in order to achieve efficient utilization of network resources. In the proposed algorithm, the base station corresponding to a mobile unit with high resource requirements is adaptively selected so as to maximize the system's performance. Simulation results show that the proposed approach allows increasing performance both in terms of throughput and fairness.

In order to improve the port service level and the efficiency of ships entering and leaving the port in restricted two-way channels, the ship scheduling problem under the type of restricted two-way channels is studied; considering the influence of tides, ship avoidance and safety distance of single two-way channels and other factors on the ship scheduling in the port, a mixed-integer planning model is constructed by taking the time of the ship entering and leaving the port as the decision variable, and minimising the total delay cost of the entering and leaving ships as the objective function; based on the features of the ship scheduling problem, the model is solved by using the Gurobi solver. Through numerical experiments, it can be seen that comparing the results of the model with the current scheduling rules, the average optimization rates under different case sizes are 15.57%, 12.15%, and 19.07%, respectively, which can significantly reduce the cost of delay of ships in the port and improve the efficiency of ship services in the port, thus verifying the validity of the scheme.

The purpose of this paper is to present an exact solution method for an important planning problem faced by many shipping companies dealing with transportation of bulk cargoes. A bulk shipping company usually has a set of contract cargoes that it is committed to carry and will try to derive additional revenue from optional spot cargoes. Each cargo, either it is a contract or spot cargo, consists of a given quantity to be picked up in a given loading port and delivered in a given unloading port within specified time windows. The shipping company controls a fixed fleet for the purpose of transporting the cargoes. In most of the literature on ship routing and scheduling problems, a cargo cannot be transported by more than one ship. By introducing split loads, this restriction is removed and each cargo can be transported by several ships. The resulting planning problem can be denoted as a maritime pickup and delivery problem with time windows and split loads (PDPTWSL). We present an arc flow formulation of the PDPTWSL. We also suggest a solution method based on a priori generation of single ship schedules and two alternative path flow models that deal with the selection of ship schedules and assignment of cargo quantities to the schedules. Computational results show that the solution method can provide optimal solutions to small realistic planning instances. We also believe this paper provides an important starting point for research on other exact solution methods for the maritime PDPTWSL.

In earlier work, the problem of generating a preset distinguishing sequence from a finite state machine (FSM) was converted into a Boolean formulae to be fed into a SAT solver, with experiments suggesting that such approaches are required as the size of input alphabet grows. In this paper we extend the approach to the minimum height adaptive distinguishing sequence construction problem for partially specified FSMs (PSFMSs), which is known to be an NP-Hard problem. The results of experiments with randomly generated PSFSMs and case studies from the literature show that SAT solvers can perform better than a previously proposed brute-force algorithm.KeywordsModel based testingFinite state machinesAdaptive distinguishing sequence

It was investigated the Boolean satisfiability (SAT) problem defined as follows: given a Boolean formula, check whether an assignment of Boolean values to the propositional variables in the formula exists, such that the formula evaluates to true. If such an assignment exists, the formula is said to be satisfiable; otherwise, it is unsatisfiable. With using of analytical expressions of multi-valued logic 2SAT boolean satisfiability was formulated as linear programming optimization problem. The same linear programming formulation was extended to find 3SAT and kSAT boolean satisfiability for k greater than 3. So, using new analytic multi-valued logic expressions and linear programming formulation of boolean satisfiability proposition that kSAT is in P and could be solved in linear time was proved.KeywordsBoolean satisfiabilityConjunctive normal formConvex functionLinear programmingMulti-valued logicNP problemTime complexity.

Identical parallel machine scheduling problem for minimizing the makespan is a very important production scheduling problem which has been proven to be NP-hard. The problem further compounds with additional constraints. Genetic algorithms (GA) have shown great advantages in solving the combinatorial optimization problem in view of its characteristic that has high efficiency and that is fit for practical application. In this chapter we present a spreadsheet based GA approach for minimizing the makespan for scheduling of a set of tasks for identical parallel machines and worker assignment to machines. The results obtained from the proposed approach are compared with two sets of benchmark problems consisting of 100 problems each. It has been demonstrated that the performance of proposed approach is superior to the results that have been obtained earlier. The proposed approach produces optimal solution for almost 95% of the problems demonstrating the effectiveness of the proposed approach. An empirical analysis of GA parameters has also been carried out to see the effect on the performance of the proposed algorithm.

This paper develops an efficient variant of a Genetic Algorithm (GA) for a ship routing and scheduling problem (SRSP) with time-window in industrial shipping operation mode. This method addresses the problem of loading shipments for many customers using heterogeneous ships. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and ship capacities. The results of a computational investigation are presented and the solution quality and execution time are explored with respect to problem size. The proposed algorithm is compared, in terms of solution quality and computational time, with an exact method that uses Set Partitioning Problem (SPP). It is found that while the exact method solves small scale problem efficiently, treating large scale problems with the exact method becomes involved due to computational problem, a deficiency that the GA can encounter. Meantime, GA consistently returns better solution than other published work using Tabu Search method in term of solution quality.