Open Shop Scheduling Research Articles

Practical approaches to chemical tanker scheduling in ports: a case study on the Port of Houston

The objective of this study was to develop practical scheduling solutions for chemical tankers visiting the Port of Houston (PoH). Chemical tanker movements represent approximately 42% of the Houston Ship Channel traffic. Historically, chemical tanker scheduling has been problematic and has resulted in long waiting times for tankers. Scheduling is difficult because chemical tankers carry several liquid cargoes and must visit multiple terminals for loading and unloading. Physical constraints (layout of the port and draft) and commercial constrains (such as terminal and personnel readiness for cargo handling operations, tank cleaning processes, and inspection requirements) create a complex scheduling problem, long waiting times, and unnecessary tanker movements in the port. These problems cause an increase in the business costs for shipowners, risk of collisions and allisions, production of additional air emissions, and decreases in the operating capacity of terminals. The recent expansion decisions for chemical and petrochemical plants in Houston, Texas, will exacerbate the problem. Significant benefits could thus be gained even for small scheduling improvements. Currently, the scheduling practice of loading/unloading activities in the PoH involves primarily the manual and de-centralized use of the “first come, first served” (FCFS) rule, which results in inefficiencies such as long waiting times and poor resource utilization. We propose two mathematical methods to address the tanker scheduling problem in the port: a mixed-integer programming (MIP) method, and a constraint programming (CP) method. The two methods are formulated as open-shop scheduling problems with sequence-dependent post-setup times. MIP yields optimum results that minimize makespan. However, computation time increases significantly as the number of tankers, or the number of terminals, increases. CP achieves better makespan results in a shorter run time, compared to MIP, for medium to large-scale problems including the problem considered in this case study. Overall, the results show that MIP is more suitable for real-time scheduling tools (hourly and daily), whereas CP is the better option for longer-horizon scheduling problems (weekly or monthly). Our models gave good alternative schedules under short optimization run times. Hence, they can afford decision makers sufficient time to complete multiple optimization scenarios and implementation setups.

Extended open shop scheduling with resource constraints: Appointment scheduling for integrated practice units

An Integrated Practice Unit (IPU) is a new approach to outpatient care in which a co-located multidisciplinary team of clinicians, technicians, and staff provide treatment in a single patient visit. This article presents a new integer programming model for an extended open shop problem with application to clinic appointment scheduling for IPUs. The advantages of the new model are discussed and several valid inequalities are introduced to tighten the linear programming relaxation. The objective of the problem is to minimize a combination of makespan and total job processing time, or in terms of an IPU, to minimize a combination of closing time and total patient waiting time. Feasible solutions are obtained with a two-step heuristic, which also provides a lower bound that is used to judge solution quality. Next, a two-stage stochastic optimization model is presented for a joint pain IPU. The expected value solution is used to generate two different patient arrival templates. Extensive computations are performed to evaluate the solutions obtained with these templates and several others found in the literature. Comparisons with the expected value solution and the wait-and-see solution are also included. For the templates derived from the expected value solution, the results show that the average gap between the feasible solution and lower bound provided by the two-step heuristic is 2% for 14 patients. They also show that either of the two templates derived from the expected value solution is a good candidate for assigning appointment times when either the clinic closing time or the patient waiting time is the more important consideration. Sensitivity analysis confirmed that the optimality gap and clinic statistics are stable for marginal changes in key resources.

Adia: Achieving High Link Utilization with Coflow-Aware Scheduling in Data Center Networks

Link utilization has received extensive attention since data centers become the most pervasive platform for data-parallel applications. A specific job of such applications involves communication among multiple machines. The recently proposed coflow abstraction depicts such communication through a group of parallel flows, and captures application performance through corresponding communication requirements. Existing techniques to improve link utilization, however, either restrict themselves to achieving work conservation, or merely focus on flow-level metrics and ignore coflow-level performance. In this paper, we address the coflow-aware scheduling problem with the objective of maximizing link utilization. Through theoretic analyses, we formulate the coflow-aware scheduling problem as a NP-hard open shop scheduling problem with heterogeneous concurrency. We design Adia, a hierarchical scheduling framework to conduct both inter- and intra- link scheduling. The design of Adia leverages priority-based scheduling while guarantees work-conserving and starvation-free bandwidth allocation at the same time. We also prove Adia's algorithm is two-approximate in terms of link utilization. Extensive simulation results on ns3 further show that Adia outperforms both per-flow mechanisms coflow schemes in terms of link utilization, and achieves similar coflow performance in comparison with the state-of-art coflow scheduling schemes.

Minimising the makespan in open shop scheduling problems using variants of discrete firefly algorithm

Minimising makespan in open shop scheduling problem is the aim of this research work. The scheduling problems consist of 'n' jobs and 'm' machines, in which each job has 'O' operations. The processing time for 50 open shop scheduling problems was generated using a linear congruential random number generator. A non-traditional algorithm called discrete firefly algorithm is proposed to minimise the makespan of open shop scheduling problems and this method is referred as A1. Discrete firefly algorithm is also hybridised along with other heuristic algorithms in two ways: 1) firefly algorithm is hybridised with longest total remaining processing on other machines and local search algorithms which is referred as A2; 2) firefly algorithm is hybridised with simulated annealing and referred as A3. Novelty of this work focuses on the application of open shop scheduling problems in quality control of a production industry.

A novel complex network based dynamic rule selection approach for open shop scheduling problem with release dates.

In the open shop scheduling problem, resources and tasks are required to be allocated in an optimized manner, but when the arrival of tasks is dynamic, the problem becomes much more difficult. To solve large scale open shop scheduling problem with release dates, heuristic algorithms are more promising compared with metaheuristic algorithms. In this paper, a framework of general scheduling object is developed, under which open shop scheduling problem is described. Then, a complex scheduling network model for open shop scheduling problem is established, and the problem is transformed into reasonably arranging the node traversal order with the goal of traversing all nodes in the network as quickly as possible, on condition that each node has a traversal time and only disconnected nodes can be traversed simultaneously. Considering that network structural features and local time attributes of nodes can provide heuristic information, six single heuristic rules are raised and a novel complex network based dynamic rule selection approach is proposed to solve dynamic open shop problem by switching dynamically the scheduling rules based on real-time production status. Finally, two experiments are carried out and the experimental results show that the proposed heuristic rules have acceptable performance, and the proposed complex network based dynamic rule selection approach is feasible.

A survey of intelligent algorithms for open shop scheduling problem

In the open shop scheduling problem, two processing orders named operations within jobs and operations on machines need to be decided. When the problem size becomes large, the existing exact method can hardly solve it. Therefore, intelligent algorithms are introduced to solve the problem. The aim of this study is to find out effective intelligent algorithm which can be used in the open shop scheduling problem. Four intelligent algorithms, namely genetic algorithm, particle swarm optimization, cuckoo search algorithm and ant colony optimization have been proposed and modified for this specific problem and two experiments have been carried out to compare the effectiveness of these algorithms. The results show that these intelligent algorithms have an acceptable performance and can be used for large scale problems, which cannot be solved by the exact method. Among these algorithms, cuckoo search algorithm is the best one and can solve problems effectively, due to the levy flight and other features. Further research is still required to further improve the performance of these algorithms.

A Heuristic Rule Based on Complex Network for Open Shop Scheduling Problem With Sequence-Dependent Setup Times and Delivery Times

In the real industrial scenario, the setup times and delivery times are two non-negligible factors, but only few studies have considered the open shop scheduling problem with sequence-dependent setup times and delivery times (OSSP-STDT). In this paper, a mixed integer linear programming model is formulated firstly to accurately solve small-size problems, but it will fail when the size of the problem increases. Then, a complex scheduling network model is developed to characterize OSSP-STDT. After comprehensively considering the local topological features and time attributes in the complex network model, an effective heuristic rule based on complex network is established for solving large-size problems. Finally, an actual warehouse scheduling problem is converted into the aforementioned problem and used as one typical application scenario. Experiments have been conducted and computational results show that compared with exact solutions and meta-heuristics, the proposed algorithm can solve the large-size open shop scheduling problem with sequence-dependent setup times and delivery times more effectively and efficiently.

An improved artificial bee colony algorithm for solving open shop scheduling problem with two sequence-dependent setup times

In the literature on open shop scheduling problems, only few studies have considered the scheduling problem with sequence-dependent setup time and transportation time, and the latter can be considered as another kind of setup time. In order to be closer to real industry scenes, the open shop scheduling problem with two sequence-dependent setup times is investigated. Firstly, the mixed integer linear programming model is established. Then, an improved artificial bee colony algorithm is introduced and compared with other existing algorithms, namely genetic algorithm, particle swarm optimization, ant colony optimization, and cuckoo search algorithm. Two experiments have been conducted and the computational results show that the proposed artificial bee colony algorithm can achieve the best results in large-scale problems.

Minimising the makespan in open shop scheduling problems using variants of discrete firefly algorithm

Minimising the makespan in open shop scheduling problems using variants of discrete firefly algorithm

A chemical tanker scheduling problem: Port of Houston case study

The objective of this study was to develop scheduling solutions for chemical vessels in a port. Chemical tankers carry multiple liquid cargos and have to visit multiple terminals to load/unload these cargos. The practice to date in scheduling these loading/unloading activities for the Port of Houston (PoH) has primarily involved manual methods, which result in several inefficiencies such as long waiting times and inefficient resource utilisation. This paper proposes three methods for tanker-scheduling problems, formulated as an open-shop scheduling problem with sequence-dependent post-set-up times: a mixed-integer programming (MIP); a constraint programming (CP); and a dispatching rule, longest processing time (LPT) scheduling heuristic. Overall, the results showed the MIP method to be a more suitable tool for hourly and daily scheduling, whereas the CP and the LPT heuristic proved suitable for longer-horizon scheduling problems (weekly, monthly, or semi-annual) and for use as real-time scheduling tools.

An hybrid SA-DATC Approach for JIT Open-Shop Scheduling Problem with Earliness and Tardiness Penalties

In this paper, we propose an innovative hybrid integrated approach between simulated annealing (SA) and control theoretic based distributed arrival time control (DATC), named SA-DATC. The two approaches have been combined to achieve the objective of Justin-Time (JIT) scheduling in an open-shop system. This approach makes the two algorithms work together aimed with combining the main advantages of each of them, to obtain a reactive control strategy. The SA algorithm plays a role as the machine-route explorer, while the DATC algorithm is inserted into the SA loop, allowing searching for the best arrival times of jobs to satisfy the JIT objective wanted. The performance of this new hybrid SA-DATC approach is evaluated with quadratic linear program solutions to test its relative performance in a static environment. Computational results show that the algorithm performs well on the most of the test problems generated randomly in this paper with an interesting computational time, proving that our approach is favorable for an open-shop scheduling problem.

Retraction Note to: OSGA: genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises

The Editor-in-Chief has retracted this article because validity of the content of this article cannot be verified.

Multi-objective open shop scheduling by considering human error and preventive maintenance

This study presents an open shop scheduling model by considering human error and preventive maintenance. The proposed mathematical model takes into account conflicting objective functions including makespan, human error and machine availability. In order to find the optimum scheduling, human error, maintenance and production factors are considered, simultaneously. Human error is measured by Human Error Assessment and Reduction Technique (HEART). Three metaheuristic methods including non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization (MOPSO) and strength Pareto evolutionary algorithm II (SPEA-II) are developed to find near-optimal solution. The Taguchi method is applied by adjusting parameters of metaheuristic algorithms. Several illustrative examples and a real case study (auto spare parts manufacturer) are applied to show the applicability of the multi-objective mixed integer nonlinear programming model. The proposed approach of this study may be used for similar open shop problems with minor modifications.

Cat swarm optimization for solving the open shop scheduling problem

This paper aims to prove the efficiency of an adapted computationally intelligence-based behavior of cats called the cat swarm optimization algorithm, that solves the open shop scheduling problem, classified as NP-hard which its importance appears in several industrial and manufacturing applications. The cat swarm optimization algorithm was applied to solve some benchmark instances from the literature. The computational results, and the comparison of the relative percentage deviation of the proposed metaheuristic with other’s existing in the literature, show that the cat swarm optimization algorithm yields good results in reasonable execution time.

Review and classification of hybrid shop scheduling

Hybrid shop scheduling has gained popularity due to the rapid rise of market demand and development of production technology. It is a combination of more than one classical shop scheduling, such as flow shop scheduling, job shop scheduling, open shop scheduling, parallel machine scheduling, and multiprocessor task scheduling. In this paper, a classification of hybrid shop scheduling problem based on the criterion of machine environment is proposed. The problem is classified into hybrid shop scheduling with parallel machines, hybrid shop scheduling with multiprocessor task, and other hybrid shop scheduling such as the mixed shop scheduling. The citation analysis method is used to give a brief review of hybrid flow shop and job shop with parallel machines. At the same time, for hybrid shop scheduling with multiprocessor task and other hybrid shop scheduling, a detailed overview is given because of its relatively few researches. Finally, some research directions for the hybrid shop scheduling are also discussed.

Petri Nets and Deadlock-Free Scheduling of Open Shop Manufacturing Systems

In this paper, we study the open shop scheduling problem with blocking and deadlocks. First, we develop a new Petri net class that extends the well-known $\text{S}^{3}\text{R}$ nets to handle the features of open shop systems. Next, we establish necessary conditions for deadlock-free operation based on the properties of a particular structural siphon. Then, we implement a graph search algorithm that intelligently explores the net reachability graph and uses a search strategy based on a double filtering mechanism and new evaluation functions. We conducted computational tests on a set of classical instances adapted from the literature. The quality of the solutions was established with a lower bound calculated with the aforementioned siphon. The results show the validity of the approach: the proposed algorithm found solutions with values that were very close to the lower bound in most cases.

Two-machine flow shop and open shop scheduling problems with a single maintenance window

The paper considers the two-machine flow shop and open shop scheduling problems to minimize the makespan, provided that one of the machines is subject to maintenance, which has to start within a prescribed time window. In the case of the flow shop, maintenance is performed on the second machine. A non-resumable setting is considered, i.e., if a job cannot be completed prior to the maintenance, it must restart from scratch after the maintenance. For each of these NP-hard problems we develop a 3/2–approximation algorithm.

Extended Genetic Algorithm for solving open-shop scheduling problem

Open-shop scheduling problem (OSSP) is a well-known topic with vast industrial applications which belongs to one of the most important issues in the field of engineering. OSSP is a kind of NP problems and has a wider solution space than other basic scheduling problems, i.e., Job-shop and flow-shop scheduling. Due to this fact, this problem has attracted many researchers over the past decades and numerous algorithms have been proposed for that. This paper investigates the effects of crossover and mutation operator selection in Genetic Algorithms (GA) for solving OSSP. The proposed algorithm, which is called EGA_OS, is evaluated and compared with other existing algorithms. Computational results show that selection of genetic operation type has a great influence on the quality of solutions, and the proposed algorithm could generate better solutions compared to other developed algorithms in terms of computational times and objective values.

An effective heuristic algorithm for the partial shop scheduling problem

In a partial shop scheduling problem the operations of each job have to respect a partial order, which can be different for each job. We study the problem of finding a solution of minimal makespan in partial shops. This problem generalizes many problems which have been studied independently in the literature, such as the group shop scheduling problem, the mixed shop scheduling problem, and the open shop scheduling problem. In this paper we propose an algorithm which is able to find solutions for the partial shop scheduling problem. In computational experiments we find that the proposed single heuristic can compete with the state-of-the-art heuristics for the partial shop, group shop, mixed shop, and open shop, and in many cases, improves the state of the art. The main contribution of this paper is the demonstration that a single algorithm can solve effectively many special cases of the partial shop without taking into consideration their particular structure. We highlight the contribution of the main novel components of the algorithm, namely the initial solution generator, neighbourhood structure, and the lower bound for new solutions generated by such neighbourhood.

Large-scale medical examination scheduling technology based on intelligent optimization

In recent years, the scale of the health examination business has increased rapidly, and research on the combinatorial optimization of medical examinations has become more important. In this context, a special large-scale flexible open shop scheduling problem (FOSP) is introduced based on the idea of the multi-processor open shop scheduling problem. A mixed integer programming model is developed for the FOSP, which regards client satisfaction as the most important objective. As the FOSP is particularly complex, three different intelligent optimization algorithms are examined, namely a genetic algorithm, hybrid particle swarm optimization, and simulated annealing. According to the medical examination preferences of the clients, a group of large-scale test problems are created on the basis of benchmark instances of the flexible job shop problem, and these are used to evaluate the performance of each algorithm. The experimental results show that the genetic algorithm outperforms both simulated annealing and hybrid particle swarm optimization, especially in large-scale problems.