Job-shop scheduling with limited capacity buffers

In this paper we investigate job-shop problems where limited capacity buffers to store jobs in non-processing periods are present. In such a problem setting, after finishing processing on a machine, a job either directly has to be processed on the following machine or it has to be stored in a prespecified buffer. If the buffer is completely occupied the job may wait on its current machine but blocks this machine for other jobs. Besides a general buffer model, also specific configurations are considered. The aim of this paper is to find a compact representation of solutions for the job-shop problem with buffers. In contrast to the classical job-shop problem, where a solution may be given by the sequences of the jobs on the machines, now also the buffers have to be incorporated in the solution representation. In a first part, two such representations are proposed, one which is achieved by adapting the alternative graph model and a second which is based on the disjunctive graph model. In a second part, it is investigated whether the given solution representation can be simplified for specific buffer configurations. For the general buffer configuration it is shown that an incorporation of the buffers in the solution representation is necessary, whereas for specific buffer configurations possible simplifications are presented

A new neighborhood and tabu search for the blocking job shop

The Blocking Job Shop is a version of the job shop scheduling problem with no intermediate buffers, where a job has to wait on a machine until being processed on the next machine. We study a generalization of this problem which takes into account transfer operations between machines and sequence-dependent setup times. After formulating the problem in a generalized disjunctive graph, we develop a neighborhood for local search. In contrast to the classical job shop, there is no easy mechanism for generating feasible neighbor solutions. We establish two structural properties of the underlying disjunctive graph, the concept of closures and a key result on short cycles, which enable us to construct feasible neighbors by exchanging critical arcs together with some other arcs. Based on this neighborhood, we devise a tabu search algorithm and report on extensive computational experience, showing that our solutions improve most of the benchmark results found in the literature

The flexible blocking job shop with transfer and set-up times

The Flexible Blocking Job Shop (FBJS) considered here is a job shop scheduling problem characterized by the availability of alternative machines for each operation and the absence of buffers. The latter implies that a job, after completing an operation, has to remain on the machine until its next operation starts. Additional features are sequence-dependent transfer and set-up times, the first for passing a job from a machine to the next, the second for change-over on a machine from an operation to the next. The objective is to assign machines and schedule the operations in order to minimize the makespan. We give a problem formulation in a disjunctive graph and develop a heuristic local search approach. A feasible neighborhood is constructed, where typically a critical operation is moved (keeping or changing its machine) together with some other operations whose moves are "implied”. For this purpose, we develop the theoretical framework of job insertion with local flexibility, based on earlier work of Gröflin and Klinkert on insertion. A tabu search that consistently generates feasible neighbor solutions is then proposed and tested on a larger test set. Numerical results support the validity of our approach and establish first benchmarks for the FBJ

Makespan Minimization in Job Shop Scheduling

In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete

Novel approaches to cyclic job-shop problems with transportation

Scheduling problems can be found in almost any field of application in the real world. These problems may not only have different characteristics but they also imply more or less complex requirements. One specific class within this domain is the cyclic job-shop problem. It occurs in various areas reaching from industrial production planning down to the systems architecture of computers. With manufacturers in particular, one can find increasing demand for effective solution methods in order to tackle these scheduling problems efficiently. This thesis will deal with the Cyclic Job-Shop Problem with Blocking and Transportation. It arises in modern manufacturing companies, where the products move automatically between the different workstations, for instance. The problem itself is not new to the research community, but hardly any work has been done in solving it. Within this thesis we will try to close this gap and present some first approaches, discussing the structure of the problem and how it can be solved. As a result, we will provide three different solution methods, including an integer programming formulation, which is solved with a commercial solver, a branch and bound algorithm and a tabu search heuristic. All algorithms are tested on a range of data sets and compared with each other. Additionally, we have worked on a polynomial solvable subproblem, which has gained more interest in the literature. As a result, a new polynomial algorithm, that outperforms the existing ones in theory as well as in empirical tests (except for some special cases) is presented. This thesis concludes with a discussion about ideas of how to improve the presented methods and some other extensions to the investigated problem

Evaluation of Material Shortage Effect on Assembly Systems Considering Flexibility Levels

The global pandemic caused delays in global supply chains, and numerous manufacturing companies are experiencing a lack of materials and components. This material shortage affects assembly systems at various levels: process level (decreasing of the resource efficiency), system level (blocking or s tarvation of production entities), and company level (breaking the deadlines for the supplying of the products to customers or retailers). Flexible assembly systems allow dynamic reactions in such uncertain environments. However, online scheduling algorithms of current research are not considering reactions to material shortages. In the present research, we aim to evaluate the influence of material shortage on the assembly system performance. The paper presents a discrete event simulation of an assembly system. The system architecture, its behavior, the resources, their capacities, and product specific operations are included. The material shortage effect on the assembly system is compensated utilizing different system flexibility levels, characterized by operational and routing flexibility. An online control algorithm determines optimal production operation under material shortage uncertain conditions. With industrial data, different simulation scenarios evaluate the benefits of assembly systems with varying flexibility levels. Consideration of flexibility levels might facilitate exploration of the optimal flexibility level with the lowest production makespan that influence further supply chain, as makespan minimization cause reducing of delays for following supply chain entities

Multi-job production systems: definition, problems, and product-mix performance portrait of serial lines

This paper pursues two goals: (a) Define a class of widely used in practice flexible manufacturing systems, referred to as Multi-Job Production (MJP) and formulate industrially motivated problems related to their performance. (b) Provide initial results concerning some of these problems pertaining to analysis of the throughput and bottlenecks of MJP serial lines as functions of the product-mix. In MJP systems, all job-types are processed by the same sequence of manufacturing operations, but with different processing time at some or all machines. To analyse MJP with unreliable machines, we introduce the work-based model of production systems, which is insensitive to whether single- or multi-job manufacturing takes place. Based on this model, we investigate the performance of MJP lines as a function of the product-mix. We show, in particular, that for the so-called conflicting jobs there exists a range of product-mixes, wherein the throughput of MJP is larger than that of any constituent job-type manufactured in a single-job regime. To characterise the global behaviour of MJP lines, we introduce the Product-Mix Performance Portrait, which represents the system properties for all product-mixes and which can be used for operations management. Finally, we report the results of an application at an automotive assembly plant