Approach For Job Shop Scheduling Research Articles

An efficient hybrid optimization method for Fuzzy Flexible Job-Shop Scheduling Problem: Steady-state performance and analysis

Due to the uncertainty pervasive in manufacturing and production systems, a crisp processing time is no longer appropriate. Thus, the flexible job-shop scheduling issue comes with fuzzy processing times. Fuzzy numbers are used to depict the uncertainty associated with processing times. This paper proposes a Hybrid Fuzzy Flexible job-shop Scheduling Approach (HFFSA) for addressing the Fuzzy Flexible Job-Shop Scheduling Problem (FFJSSP). An encoding-and-decoding scheme is employed for initializing the population. HFFSA incorporates four operators to enhance the quality of solutions: the Different Positions Shuffling (DPS) operator, the Randomly Selected Positions Shuffling (RSPS) operator, the Block Shuffling (BS) operator and the Inversion Mutation (IM) operator. The DPS operator shuffles the different positions between a randomly generated solution and the current one. On the other hand, the RSPS shuffles random positions to maintain population diversity. The BS and IM work on a subset of positions from the current solution to obtain near-optimal solutions. Furthermore, we integrate the pair-wise local search strategy with HFFSA to improve the best solution. HFFSA is compared to twenty-five algorithms using Lei and remanufacturing benchmarks. Several statistical measures are employed, like the CPU time, best, average and worst fuzzy makespan values, boxplots and the Wilcoxon signed-rank test. The statistical analyses affirm the superiority of the proposed HFFSA.

Responsiveness to sudden disturbances in manufacturing through dynamic job shop scheduling using Quantum Annealing

An efficient production planning and control is inevitable for the economic operation of a manufacturing system. As an essential part of production planning and control, process scheduling aims to assign processes to the available resources of a manufacturing system under consideration of its objectives. This forms an optimization problem also known as job shop scheduling which can be solved with computer aided techniques. Exact solution methods are only practical up to a certain number of functional units and processes, therefore, approximation methods are used in industry. However, as the problem size increases, the computational solving time increases significantly and the solution quality decreases in equal measure. In order to react to the current effects of global crises, such as disruptions in supply chains, approaches for fast and efficient rescheduling are needed. A dynamic shop job scheduling approach using Quantum Annealing bears the potential to close this research gap. Previous work has shown that Quantum Annealing is able to solve static job shop scheduling problems within seconds while finding good solutions. However, in a flexible environment such as a manufacturing system, the static approach is not suitable for process scheduling. Therefore, a dynamic Quantum Annealing based approach for job shop scheduling with consideration of machine breakdowns and new job arrivals is proposed. The approach monitors a manufacturing system and reacts to changes in the job pool or availabilities in functional units with rescheduling. The method is tested with several use cases involving small and large-scale problems and is compared with a simulated annealing approach. Thereby, the Quantum Annealing based computations show better results regarding solution quality and computing time. Moreover, the dynamic approach bears the potential for industrial application, especially as a supplement to a conventional advanced planning system.

Optimal Train Rescheduling in Oslo Central Station

Real-time train dispatching (i.e., rescheduling and replatforming) in passenger railway stations is a very important and very challenging task. In most major stations, this task is carried out by hand by highly trained dispatchers who use their extensive experience to find near-optimal solutions under most conditions. With several simultaneous deviations from the timetable, however, the traffic situation may become too complex for any human to handle it far beyond finding feasible solutions. As part of a prototype for a dispatching support tool developed in collaboration with Bane NOR (Norwegian rail manager), we develop an approach for Optimal Train Rescheduling in large passenger stations. To allow for replatforming, we extend the standard job-shop scheduling approach to train-scheduling, and we develop and compare different MILP formulations for this extended approach. With this approach, we can find, in just a few seconds, optimal plans for our realistic instances from Oslo Central Station, the largest passenger train hub in Norway. The prototype will be tested by dispatchers in the greater Oslo area, starting from the fall of 2021.

Application of job shop scheduling approach in green patient flow optimization using a hybrid swarm intelligence

With the increasing demand for hospital services amidst the COVID-19 pandemic, allocation of limited public resources and management of healthcare services are of paramount importance. In the field of patient flow scheduling, previous research primarily focused on classical-based objective functions, while ignoring environmental-based objective functions. This study presents a flexible job shop scheduling problem to optimize patient flow and, thereby, minimize the total carbon footprint, as the sustainability-based objective function. Since flexible job shop scheduling is an NP-hard problem, a metaheuristic optimization algorithm, called Chaotic Salp Swarm Algorithm Enhanced with Opposition-Based Learning and Sine Cosine (CSSAOS), was developed. The proposed algorithm integrates the Salp Swarm Algorithm (SSA) with chaotic maps to update the position of followers, the sine cosine algorithm to update the leader position, and opposition-based learning for a better exploration of the search space. generating more accurate solutions. The proposed method was successfully applied in a real-world case study and demonstrated better performance than other well-known metaheuristic algorithms, including differential evolution, genetic algorithm, grasshopper optimization algorithm, SSA based on opposition-based learning, quantum evolutionary SSA, and whale optimization algorithm. In addition, it was found that the proposed method is scalable to different sizes and complexities.

An Innovative Formulation Tightening Approach for Job-Shop Scheduling

Job shops are an important production environment for low-volume high-variety manufacturing. Its scheduling has recently been formulated as an integer linear programming (ILP) problem to take advantages of popular mixed-integer linear programming (MILP) methods, e.g., branch-and-cut. When considering a large number of parts, MILP methods may experience difficulties. To address this, a critical but much overlooked issue is formulation tightening. The idea is that if problem constraints can be transformed to directly delineate the problem convex hull in the data preprocessing stage, then a solution can be obtained by using linear programming (LP) methods without combinatorial difficulties. The tightening process, however, is fundamentally challenging because of the existence of integer variables. In this article, an innovative and systematic approach is established for the first time to tighten the formulations of individual parts, each with multiple operations, in the data preprocessing stage. It is a major advancement of our previous work on problems with binary and continuous variables to integer variables. The idea is to first link integer variables to binary variables by innovatively combining constraints so that the integer variables are uniquely determined by the binary variables. With binary and continuous variables only, it is proved that the vertices of the convex hull can be obtained based on vertices of the LP problem after relaxing binary requirements. These vertices are then converted to tightened constraints for general use. This approach significantly improves our previous results on tightening individual operations. Numerical results demonstrate significant benefits on solution quality and computational efficiency. This approach also applies to other complex ILP and MILP problems with similar characteristics and fundamentally changes the way how such problems are formulated and solved. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Scheduling is an important but difficult problem in planning and operation of job shops. The problem has been recently formulated in an integer linear programming (ILP) form to take advantage of popular mixed-integer linear programming methods. Given an ILP problem, there must exist a linear programming (LP) formulation so that all of its vertices are also the vertices to the ILP problem. If such an LP problem can be found in the data preprocessing stage, then the corresponding ILP problem is tight and can be solved by using an LP method without difficulties. In this article, an innovative and systematic approach is established to tighten the formulations of individual parts, each with one or multiple operations. It is a major advancement of our previous work on problems with binary and continuous variables by novel exploitation of the relationship between integer and binary variables in job-shop scheduling. The resulting tightened constraints are characterized by part parameters and the length of the scheduling horizon and can be easily adjusted for other data sets. Results demonstrate significant benefits on solution quality and computational efficiency. This approach also applies to other complex ILP and MILP problems with similar characteristics and fundamentally changes the way how such problems are formulated and solved.

A flexible job shop scheduling approach with operators for coal export terminals – A mature approach

This article presents an improved scheduling approach for optimising the activities of a coal export terminal (CET) and includes for the first time, new methodology for handling blending, concurrency, de-ballasting delays, dual reclaiming and through loading. The proposed methodology can be applied to bulk materials import and export terminals and other very complex scheduling scenarios whose activities require multiple resources to be present and operating. It is also adaptable to industries with similarly complex scheduling environments with concurrency requirements and pre-emption. This scheduling problem is highly intricate and is far more challenging than traditional problems. As such it has been necessary to develop an improved meta-heuristic algorithm, with additional resource assignment chromosome and improved perturbation operators for pre-emption handling and resource assignment. A comprehensive suite of test problems is solved to demonstrate the efficacy of our approach and the capability to solve scenarios with the aforementioned features.

An integrated approach for remanufacturing job shop scheduling with routing alternatives.

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. However, the stochastic natures inherent in the remanufacturing processes complicate its scheduling. This paper undertakes the challenge and presents a remanufacturing job shop scheduling approach by integrating alternative routing assignment and machine resource dispatching. A colored timed Petri net is introduced to model the dynamics of remanufacturing process, such as various process routings, uncertain operation times for cores, and machine resource conflicts. With the color attributes in Petri nets, two types of decision points, recovery routing selection and resource dispatching, are introduced and linked with places in CTPN model. With time attributes in Petri nets, the temporal aspect of recovery operations for cores as well as the evolution dynamics in cores' operational stages is mathematically analyzed. A hybrid meta-heuristic algorithm embedded scheduling strategy over CTPN is proposed to search for the optimal recovery routings for worn cores and their recovery operation sequences on workstations, in minimizing the total production cost. The approach is demonstrated through the remanufacturing of used machine tool and its effectiveness is compared against another two cases: baseline case with fixed recovery process routings and case 2 using standard SA/MST.

A flexible job shop scheduling approach with operators for coal export terminals

Operating a coal export terminal (CET) is a challenging task. The throughput of the terminal and the delays incurred by ships and trains is greatly affected by where material is stacked and reclaimed, and which machinery is used to perform those tasks. To improve the operation of these terminals, and to make the aforementioned decisions, a novel optimisation approach has been developed. In that approach the CET is modelled as a flexible job shop with operators (FJSOP). The optimization problem is then solved using an advanced meta-heuristic algorithm that incorporates a variety of sophisticated perturbation techniques, local improvement algorithms and pre-emption handling procedures. That level of intricacy is rarely required for more traditional scheduling problems but is a necessity for this one. A key component used in the optimisation process is a priority ordering of the activities. The priority ordering is used to create the required activity sequences, for each piece of machinery. This list dictates the order in which activities are scheduled and can be perturbed quickly and in many different ways, leading to the identification of improved solutions. The optimization approach is applied to an Australian coal terminal to demonstrate its viability and effectiveness. Numerical testing shows that problems of the size encountered by existing terminals can be handled and high quality solutions can be obtained with reasonable computing effort.

A semantics-based dispatching rule selection approach for job shop scheduling

Dispatching rules are commonly used for job shop scheduling in industries because they are easy to implement, and they yield reasonable solutions within a very short computational time. Many dispatching rules have been developed but they can only perform well in specific scenarios. This is because a dispatching rule or a combination of dispatching rules always pursues a single or multiple fixed production objectives. A lot of approaches (e.g. simulation based or machine learning based approaches) have been published in the literatures attempted to solve the problem of selecting the proper dispatching rules for a given production objective. To select a combination of dispatching rules per randomly selected combination of objectives, this paper investigates a novel semantics-based dispatching rule selection system. Each of the dispatching rules and production objectives relates to a set of scheduling parameters like processing time, remaining work, total work, due date, release date, tardiness, etc. These parameters are semantically interrelated so that a dispatching rule and a production objective can also be semantically related through their semantic expressions. A semantic similarity value can be calculated by comparing their semantic expressions. Based on this idea, a semantics-based dispatching rule selection system for job shop scheduling is developed to generate a combination of dispatching rules given randomly selected combination of production objectives. A proof-of-concept verification process is provided at the end of the paper.

The Dynamic Job Shop Scheduling Approach Based on Data-Driven Genetic Algorithm

Rapid development of the Internet of Things not only provides large amounts of data to the job-shop scheduling, but also proposes a great challenge for dynamic job shop scheduling. A dynamic job shop scheduling approach is proposed based on the data-driven genetic algorithm. Application examples suggest that this approach is correct, feasible and available. This approach can provide the technical support for the long-term development of enterprises in the field of intelligent production.

Emergency local searching approach for job shop scheduling

Existing methods of local search mostly focus on how to reach optimal solution. However, in some emergency situations, search time is the hard constraint for job shop scheduling problem while optimal solution is not necessary. In this situation, the existing method of local search is not fast enough. This paper presents an emergency local search(ELS) approach which can reach feasible and nearly optimal solution in limited search time. The ELS approach is desirable for the aforementioned emergency situations where search time is limited and a nearly optimal solution is sufficient, which consists of three phases. Firstly, in order to reach a feasible and nearly optimal solution, infeasible solutions are repaired and a repair technique named group repair is proposed. Secondly, in order to save time, the amount of local search moves need to be reduced and this is achieved by a quickly search method named critical path search(CPS). Finally, CPS sometimes stops at a solution far from the optimal one. In order to jump out the search dilemma of CPS, a jump technique based on critical part is used to improve CPS. Furthermore, the schedule system based on ELS has been developed and experiments based on this system completed on the computer of Intel Pentium(R) 2.93 GHz. The experimental result shows that the optimal solutions of small scale instances are reached in 2 s, and the nearly optimal solutions of large scale instances are reached in 4 s. The proposed ELS approach can stably reach nearly optimal solutions with manageable search time, and can be applied on some emergency situations.

Job-shop Scheduling Approach to Order-picking Problem

In recent years, a large-scale logistic center plays an important role in mail-order business with Internet. In the logistic center, the efficient managing is required to deliver products to customers as soon as possible. Researches to efficiently control the logistic center have been done in the various approaches. This study proposed a new method for the order-picking problem considering worker’s jamming at the same shelf in the logistic center. In the proposed method, we formulate worker’s scheduling in the logistic center as Job-shop Scheduling Problem and optimize this problem. Numerical experiments show the proposed method improve worker’s scheduling compared with rule-based scheduling.

A genetic algorithm‐based approach for job shop scheduling

PurposeThe purpose of this paper is to optimise the job shop scheduling problem using simulation and genetic algorithm.Design/methodology/approachThe paper presents a simulation‐based genetic algorithm approach for the job shop scheduling problem. In total, three cases have been considered to access the performance of the job shop, with an objective to minimise mean tardiness and makespan. A restart scheme is embedded into regular genetic algorithm in order to avoid premature convergence.FindingsSimulation‐based genetic algorithm can be used for job shop scheduling problems. Moreover, a restart scheme embedded into a regular genetic algorithm results in improvement in the fitness value. Single process plans selected on the basis of minimum production time criterion results in improved shop performance, as compared to single process plans selected randomly. Moreover, availability of multiple process plans during scheduling improves system performance measures.Originality/valueThe paper presents a simulation‐based genetic algorithm approach for job shop scheduling problem, with and without restart scheme. In this paper the effect of multiple process plans over single process plans, as well as criterion for selection of single process plans, are studied. The findings should be taken into account while designing scheduling systems for job shop environments.

A three-fold approach for job shop problems: A divide-and-integrate strategy with immune algorithm

This paper presents a novel divide-and-integrate strategy based approach for solving large scale job-shop scheduling problems. The proposed approach works in three phases. First, in contrast to traditional job-shop scheduling approaches where optimization algorithms are used directly regardless of problem size, priority rules are deployed to decrease problem scale. These priority rules are developed with slack due dates and mean processing time of jobs. Thereafter, immune algorithm is applied to solve each small individual scheduling module. In last phase, integration scheme is employed to amalgamate the small modules to get gross schedule with minimum makespan. This integration is carried out in dynamic fashion by continuously checking the preceding module's machine ideal time and feasible slots (satisfying all the constraint). In this way, the proposed approach will increase the machine utilization and decrease the makespan of gross schedule. Efficacy of the proposed approach has been tested with extremely hard standard test instances of job-shop scheduling problems. Implementation results clearly show effectiveness of the proposed approach.

A job-shop scheduling approach for optimising sugarcane rail operations

The sugarcane transport system is very complex and uses a daily schedule, consisting of a set of locomotives runs, to satisfy the requirements of the mill and harvesters. The total cost of sugarcane transport operations is very high; over 35% of the total cost of sugarcane production in Australia is incurred in cane transport. Producing efficient schedules for sugarcane transport can reduce the cost and limit the negative effects that this system can have on the raw sugar production system. In this paper, the sugarcane rail operations are formulated as a blocking job shop scheduling problem. A mixed integer programming approach is used to formulate the shop job scheduling problem. Mixed integer programming and constraint programming search techniques are integrated for solving the problem. A case study is solved to test the approach.

Multi Criteria Job Shop Schedule Using Fuzzy Logic Control for Multiple Machines Multiple Jobs

  Abstract—In the present scenario the modern engineering and industrial manufacturing units are facing lot of problems in many aspects such as machining time, raw material, man power, electricity, and customer's constraints. Such a complex problem of vagueness and uncertainty can be handled by the theory of fuzzy logic based in using a membership function to solve a fuzzy mix product selection. We use the amalgamation of fuzzy job shop scheduling approach to find profits and customer satisfaction using a fuzzy -JSP approach (1).

Scheduling on Unrelated Machines under Tree-Like Precedence Constraints

We present polylogarithmic approximations for the R|prec|C max and R|prec|∑ j w j C j problems, when the precedence constraints are “treelike”—i.e., when the undirected graph underlying the precedences is a forest. These are the first non-trivial generalizations of the job shop scheduling problem to scheduling with precedence constraints that are not just chains. These are also the first non-trivial results for the weighted completion time objective on unrelated machines with precedence constraints of any kind. We obtain improved bounds for the weighted completion time and flow time for the case of chains with restricted assignment—this generalizes the job shop problem to these objective functions. We use the same lower bound of “congestion + dilation”, as in other job shop scheduling approaches (e.g. Shmoys, Stein and Wein, SIAM J. Comput. 23, 617–632, 1994). The first step in our algorithm for the R|prec|C max problem with treelike precedences involves using the algorithm of Lenstra, Shmoys and Tardos to obtain a processor assignment with the congestion + dilation value within a constant factor of the optimal. We then show how to generalize the random-delays technique of Leighton, Maggs and Rao to the case of trees. For the special case of chains, we show a dependent rounding technique which leads to a bicriteria approximation algorithm for minimizing the flow time, a notoriously hard objective function.

ON-LINE JOB-SHOP SCHEDULING OF A MANUFACTURING SYSTEM BASED ON A VIRTUAL SUPERVISOR CONCEPT

The controls for reconfigurable manufacturing systems have to be capable not only of identifying exceptions on-line, but also simultaneously developing on-line strategies for unpredictable customer orders or inaccurate estimates of processing times. This paper presents an approach for job-shop scheduling with uncertain arrival times. The approach exploits Virtual Supervisor (VS) concept, which provides access to all system information during program execution and thus can readily monitor the overall system performance. The goal is to minimize expected part tardiness and earliness cost. A solution methodology based on a combined Lagrangian relaxation, VS-Patterns, Maxwell equations and temporal difference is developed to obtain a dual solution for on-line implementation.

Holonic Job Shop Scheduling Using a Multiagent System

Manufacturing job shop scheduling is a notoriously difficult problem that lends itself to various approaches - from optimal algorithms to suboptimal heuristics. We combined popular heuristic job shop-scheduling approaches with emerging AI techniques to create a dynamic and responsive scheduler. We fashioned our job shop scheduler's architecture around recent holonic manufacturing systems architectures and implemented our system using multiagent systems. Our scheduling approach is based on evolutionary algorithms but differs from common approaches by evolving the scheduler rather than the schedule. A holonic, multiagent systems approach to manufacturing job shop scheduling evolves the schedule creation rules rather than the schedule itself. The authors test their approach using a benchmark agent-based scheduling problem and compare performance results with other heuristic-scheduling approaches.

An alternative framework to Lagrangian relaxation approach for job shop scheduling

A new Lagrangian relaxation (LR) approach is developed for job shop scheduling problems. In the approach, operation precedence constraints rather than machine capacity constraints are relaxed. The relaxed problem is decomposed into single or parallel machine scheduling subproblems. These subproblems, which are NP-complete in general, are approximately solved by using fast heuristic algorithms. The dual problem is solved by using a recently developed “surrogate subgradient method” that allows approximate optimization of the subproblems. Since the algorithms for subproblems do not depend on the time horizon of the scheduling problems and are very fast, our new LR approach is efficient, particularly for large problems with long time horizons. For these problems, the machine decomposition-based LR approach requires much less memory and computation time as compared to a part decomposition-based approach as demonstrated by numerical testing.