Job Shop

The first comprehensive survey paper on scheduling problems with separate setup times or costs was conducted by Allahverdi et al. (1999), who reviewed the literature since the mid-1960s. Since the appearance of that survey paper, there has been an increasing interest in scheduling problems with setup times (costs) with an average of more than 40 papers per year being added to the literature. The objective of this paper is to provide an extensive review of the scheduling literature on models with setup times (costs) from then to date covering more than 300 papers. Given that so many papers have appeared in a short time, there are cases where different researchers addressed the same problem independently, and sometimes by using even the same technique, e.g., genetic algorithm. Throughout the paper we identify such areas where independently developed techniques need to be compared. The paper classifies scheduling problems into those with batching and non-batching considerations, and with sequence-independent and sequence-dependent setup times. It further categorizes the literature according to shop environments, including single-machine, parallel machines, flow shop, no-wait flow shop, flexible flow shop, job shop, open shop, and others. This is the Pre-Published Version.

Due to the stubborn nature of the deterministic job-shop scheduling problem many solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem specific strategies. We seek to assess a subclass of this problem in which the objective is minimising makespan, by providing an overview of the history, the techniques used and the researchers involved. The sense and direction of their work is evaluated by assessing the reported results of their techniques on the available benchmark problems. From these results the current situation and pointers for future work are provided. of manufacturing it involves finding a sequential allocation of competing resources that optimises a particular objective function. The deterministic job-shop scheduling problem, hereinafter referred to as Π J , is the most general of the classical scheduling problems. Π J consists of a finite set J of n jobs { } J i i n =1 to be processed on a finite set M of m machines { } . M k k m =1 Each job J i must be processed on every machine and consists of a chain or complex of m i operations O i1 , O i2 , …, O im i , which have to be scheduled in a predetermined given order (precedence constraint). There are N operations in total, N = m i i n .

We attempt to present in this paper a critical review of a particular segment of scheduling research in which the due date assignment decision is of primary interest. The literature is classified into the static and dynamic job shop situations. The static job shop is analyzed from two different perspectives: the due date is constrained to be greater than or equal to makespan, and the optimal due date and optimal sequence are to be determined when the method of assigning due dates is specified. The literature on dynamic job shops is also reviewed under two broad categories. First, we discuss all the literature concerned with comparative and investigative studies to identify the most desirable due date assignment method. Second, we discuss the literature dealing with determination of optimal due dates. We note that computer simulation and analytical methods are two common approaches for the second type of problems. We observe that while the static single-machine problem with constant or common due dates has been well researched, very little or no work has been done on the dynamic multi-machine problem with sophisticated due date assignment methods. Finally, we identify and suggest some worthwhile areas for future research.

In this paper we introduce a genetic algorithm whose peculiarities are the introduction of an encoding based on preference rules and an updating step which speeds up the evolutionary process. This method improves on the results gained previously with Genetic Algorithms and has shown itself to be competitive with other heuristics. The same algorithm has been applied to flow shop problems, revealing itself to be considerably more effective than Branch and Bound techniques.

Scope and Purpose-Machine scheduling problems arise in diverse areas such as flexible manufacturing, production planning, computer design, logistics, communication, etc. A common feature of many of these problems is that no efficient solution algorithms are known that solve each instance to optimality in a time bounded polynomially in the size of the problem. Approximation algorithms can help to overcome these difficulties at the cost of a quality loss of the solutions. Local decision rules are mostly the backbone of such approximation algorithms, i.e. the latter try to find the best sequence of local decisions by deterministic or probabilistic learning. The outcome of such an approach heavily depends on the underlying meta-strategy. In this paper we provide a probabilistic learning strategy based on principles of evolution. We shall indicate how the method can be extended to consider local decision rules of any number and complexity in order that the resulting program will do better than the deterministic learning approach. Abstraet-A class of approximation algorithms is described for solving the minimum makespan problem of job shop scheduling. A common basis of these algorithms is the underlying genetic algorithm that serves as a meta-strategy to guide an optimal design of local decision rule sequences. We consider sequences of dispatching rules for job assignment as well as sequences of one machine solutions in the sense of the shifting bottleneck procedure of Adams et al. Computational experiments show that our algorithm can find shorter makespans than the shifting bottleneck heuristic or a simulated annealing approach with the same running time. INTRODUCTION-THE JOB SHOP SCHEDULING PROBLEM A job shop consists of a set of different machines (like lathes, milling machines, drills etc.) that perform operations on jobs. Each job has a specified processing order through the machines, i.e. a job is composed of an ordered list of operations each of which is determined by the machine required and the processing time on it. There are several constraints on jobs and machines: (i) There are no precedence constraints among operations of different jobs. (ii) Operations can not be interrupted (non-preemption) and each machine can handle only one job at a time. Consequently, each job can be performed on only one machine at a time. The operation sequences on the machines are unknown and have to be determined in order to minimize the makespan, i.e. the time required to complete all jobs. It is well known that the problem is NP-hard [1] and belongs to the most intractable problems considered [2]. A huge amount of literature has been published within the last 30 years, among others the books by Muth and Thompson [3], Conway ?Ulrich Dorndorf is a scientific consultant at the Institute for Operations Resarch and Management in Aachen. He studied at the University of Technology in Darmstadt and at the University of Illinois at Urbana Champaign and graduated in Mechanical Engineering and Business Administration. His research interests are in the area of Decision Support Systems, the integration and application of OR and AI techniques in industrial practice, and Transportation and Production Planning and Scheduling. He has published in engineering and computer science journals, including ORSA Journal on Computing. ~;El"win Pesch is an Assistant Professor at the Faculty of Economics at the University of Limburg in Maastricht. He graduated in Mathematics and Computer Science, obtained his Ph.D. in Mathematics from the University of Technology in Darmstadt, and worked for some time on the problem of software quality control in a banking environment. His research fields are in Artificial Intelligence, Decision Support Systems, Production Planning and Scheduling. He has published in many international journals, including

In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique-mathematically known as "permutation with repetition" is presented. The main advantage of this single chromosome representation is-in analogy to the permutation scheme of the traveling salesman problem (TSP)-that it cannot produce illegal operation sequences. As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually the GOX operator for permutations with repetition arises from a Generalisation of OX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of genetic search for scheduling problems strongly. Znsammenfassung. Im vorliegenden Beitrag wird ein neuer Ansatz zur genetischen Repr/isentation des Maschinenbelegungsproblems vorgestellt. Der Ansatz basiert auf dem bekannten Konzept der Repr/isentation von Rundreiseproblemen durch Permutationen. In Erweiterung dieses Konzepts werden ,,Permutationen mit Wiederholung" zur Kodierung von L6sungen eingesetzt. Die zentrale Schwierigkeit vorangegangener Ans/itze, n/imlich die Behandlung unzul/issiger Genotypen, entf'~illt vollst/indig. Permutationen mit Wiederholung reprfisentieren grunds/itzlich zul/issige Ltsungen des betrachteten Problems. Aus einer Generalisierung der permutationsbewahrenden Order-Crossover Technik (OX) wird der Kreuzungsoperator GOX (Generalisiertes OX) ftir Permutationen mit Wiederholung abgeleitet. In einer Testreihe wird die mit dem neuen Konzept zu erreichende Ltsungsgfite untersucht. Es zeigt sich, dab die koopera-Supported by the Deutsche Forschungsgemeinschaft (Project Parnet) five Komponente der genetischen Suche durch die Repr/isentation und ihren Kreuzungsoperator verstfirkt wird. Ein einfacher genetischer Algorithmus erzielt L6sungsgfiten, die zuvor nur von stark hybriden Algorithmen erreicht wurden.

In the UK the gender pay gap on entry to the labour market is approximately zero but after ten years after labour market entry, there is a gender wage gap of almost 25 log points. This paper explores the reason for this gender gap in early-career wage growth, considering three main hypotheses -human capital, job-shopping and 'psychological' theories. Human capital factors can explain about 12 log points, job-shopping about 1.5 log points and the psychological theories about half a log point. But a substantial unexplained gap remains: women who have continuous full-time employment, have had no children and express no desire to have them earn about 12 log points less than equivalent men after 10 years in the labour market.

The vast majority of the research efforts in project scheduling over the past several years has concentrated on the development of exact and suboptimal procedures for the generation of a baseline schedule assuming complete information and a deterministic environment. During execution, however, projects may be the subject of considerable uncertainty, which may lead to numerous schedule disruptions. Predictive-reactive scheduling refers to the process where a baseline schedule is developed prior to the start of the project and updated if necessary during project execution. It is the objective of this paper to review possible procedures for the generation of proactive (robust) schedules, which are as well as possible protected against schedule disruptions, and for the deployment of reactive scheduling procedures that may be used to revise or re-optimize the baseline schedule when unexpected events occur. We also offer a framework that should allow project management to identify the proper scheduling methodology for different project scheduling environments. Finally, we survey the basics of Critical Chain scheduling and indicate in which environments it is useful.

In this paper, we study the reactive scheduling problems in a stochastic manufacturing environment. Speci®cally, we test the several scheduling policies under machine breakdowns in a classical job shop system. In addition, we measure the eect of system size and type of work allocation (uniform and bottleneck) on the system performance. The performance of the system is measured for the mean tardiness and makespan criteria. We also investigate a partial scheduling scheme under both deterministic and stochastic environments for several system con®gurations.

This article addresses the flexible job-shop scheduling problem (FJSP) to minimize makespan. The FJSP is strongly NP-hard and consists of two sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on all machines. To solve this problem, an artificial immune algorithm (AIA) based on integrated approach is proposed. This algorithm uses several strategies for generating the initial population and selecting the individuals for reproduction. Different mutation operators are also utilized for reproducing new individuals. To show the effectiveness of the proposed method, numerical experiments by using benchmark problems are conducted. Consequently, the computational results validate the quality of the proposed approach.

In recent years, the interaction between evolution and learning has received much attention from the research commu-12 nity. Some recent studies on machine learning have shown that it can significantly improve the efficiency of problem solv-13 ing when using evolutionary algorithms. This paper proposes an architecture for learning and evolving of flexible job-shop 14 schedules called LEarnable genetic architecture (LEGA). LEGA provides an effective integration between evolution and 15 learning within a random search process. Unlike the canonical evolution algorithm, where random elitist selection and 16 mutational genetics are assumed; through LEGA, the knowledge extracted from previous generation by its schemata learn-17 ing module is used to influence the diversity and quality of offsprings. In addition, the architecture specifies a population 18 generator module that generates the initial population of schedules and also trains the schemata learning module. A large 19 range of benchmark data taken from literature and some generated by ourselves are used to analyze the efficacy of LEGA. 20 Experimental results indicate that an instantiation of LEGA called GENACE outperforms current approaches using 21 canonical EAs in computational time and quality of schedules. 22 43 involves n jobs and m machines. Each job is to 44 be processed on each machine in a pre-defined 45 sequence. Each operation of a job is to be processed 46 only on one machine at a time. In practice, the shop-47 floor setup typically consists of multiple copies of 48 the most critical machines so that bottlenecks due 49 to long operations or busy machines can be reduced. 50 Therefore, an operation may be processed on more 51 than one machine having the same function. This 52 leads to a more complex problem known as the flex-53 ible job-shop scheduling problem (FJSP). With the 54 new extension, two decisions have to be made: 55 assignment of an operation to an appropriate 56 machine and sequencing the operations on each 57 machine. In addition, for complex manufacturing 58 systems, a job can visit a machine more than once 59 (called recirculation). These three features of the 60 FJSP significantly increase the complexity of finding 61 even approximately optimal solutions [7]. 62

In many job shop environments production orders arrive continuously to the production system over time. If the Order Review and Release (ORR) techniques are applied, the arrival itself does not necessarily involve the release of a job to the shop floor: in facts, the ORR activities determine which jobs have to be selectively dispatched to the shop floor and when job release is to take place, so as to improve job shop management and performances.

Beam Search is a heuristic method for solving optimization problems. It is an adaptation of the branch and bound method in which only some nodes are evaluated in the search tree. At any level, only the promising nodes are kept for further branching and remaining nodes are pruned o permanently. In this paper, we develop a beam search based scheduling algorithm for the job shop problem. Both the makespan and mean tardiness are used as the performance measures. The proposed algorithm is also compared with other well known search methods and dispatching rules for a wide variety of problems. The results indicate that the beam search technique is a very competitive and promising tool which deserves further research in the scheduling literature.

Several order release methods have been developed for workload control in job shop production. The release methods of the traditional workload control concepts di!er in how they deal with the #ow of work to each station. Previous research has pointed at strengths and weaknesses of each method. Till now the choice of the appropriate method for a particular situation has hardly received attention. This research shows that shop characteristics are an important factor to this choice. A simulation study indicates that the relative performance of the release methods changes completely with for instance the presence or absence of a dominant #ow direction in the shop. Adjustments to the traditional release methods are suggested which prove to make these methods more robust.

This paper presents two new dynamic due-date assignment rules which utilize shop congestion information. The new rules estimate job flowtime based on a sampling of recently completed jobs. These rules are compared with other established flowtime estimate models on the criterion of due-date performance via computer simulation. To evaluate the robustness of the rules, an experimental design with three different queue sequencing heuristics and two different shop balance levels was used. The results of this investigation clearly indicate that flowtimes from recently completed jobs provide very useful information for establishing effective due-dates in a job shop environment. In addition it isshown how the useof particular sequencing rules greatly increases the precision of flowtime estimates.

The "job-shop scheduling problem" is known to be NP-complete. The version of interest m this paper concerns an assembly line designed to produce various cars, each of which reqmres a (possibly different) set of options. The combinatorlcs of the problem preclude seeking a maximal solution. Nevertheless, because of the underlying economic considerations, an approach that yields a "good' sequence of cars, given the specific required options for each, would be most valuable In this paper, we focus on an environment for seeking, studying, and evaluating approaches for yielding good sequences The environment we discuss rehes on the automated reasoning program ITP. Automated reasoning programs of this type offer a wide variety of ways to reason, strategies for controlling the reasoning, and auxfl,ary procedures that contribute to the effective study of problems of this k,nd. We view the study presented m this paper as a prototype of what can be accomplished with the assistance of an automated reasoning program.

In today's fast-paced Just-In-Time and mass customization manufacturing in a sequence-dependent setup environment, the challenge of making production schedules to meet due-date requirements is becoming a more complex problem. Unfortunately, much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper considers the problem of minimizing tardiness, a common measure of due-date performance, in a sequence-dependent setup environment. Simulated annealing was used to solve the sequencing problem, and its performance was compared with random search. Our experimental results show that the algorithm can find a good solution fairly quickly, and thus can rework schedules frequently to react to variations in the schedule. The algorithm is invaluable for 'on-line' production scheduling and 'last-minute' changes to production schedule. The results of this research also suggest ways in which more complex and realistic job shop environments, such as multiple machines with a higher number of jobs in the sequence, and other scheduling objectives can be modeled. This research also investigates computational aspects of simulated annealing in solving complex scheduling problems.

Developing dispatching rules for manufacturing systems is a tedious process, which is time-and cost-consuming. Since there is no good general rule for different scenarios and objectives automatic rule search mechanism are investigated. In this paper an approach using Genetic Programming (GP) is presented. The priority rules generated by GP are evaluated on dynamic job shop scenarios from literature and compared with manually developed rules yielding very promising results also interesting for Simulation Optimization in general.

This paper studies a flexible job shop problem considering dynamic events such as stochastic job arrivals, uncertain processing times, and unexpected machine breakdowns. Also, the considered job shop problem has routing flexibility and process flexibility. A multi-agent scheduling system has been developed for solution with good quality and robustness. A pheromone-based approach is proposed for coordination among agents. The proposed multi-agent approach is compared with five dispatching rules from literature via simulation experiments to statistical analysis. The simulation experiments are performed under various experimental settings such as shop utilization level, due date tightness, breakdown level, and mean time to repair. The results show that the proposed agent-based approach performs well under all problem settings.

During several decades, research in production scheduling mainly concerns a single criterion to optimize. However, the analysis of the performance of a schedule often involves more than one aspect and therefore requires multi-objective analysis. Such situation appears in the real case study considered here.

We present and compare a number of branch and bound algorithms for minimizing the total weighted tardiness in job shops. There are basically two types of branching schemes. The ®rst one inserts operations in a partial schedule, while the second one ®xes arcs in the disjunctive graph formulation of the problem. The bounding schemes are based on the analysis of precedence constraints, and on the solution of nonpreemptive single machine subproblems that are subject to so-called delayed precedence constraints. We obtain optimal solutions for all the instances with ten jobs and ten machines that we consider, including three tardiness versions of a well-known 10 Â 10 instance introduced by Muth and Thompson [1] in 1963.

We examine the performance of Shifting Bottleneck (SB) heuristics for shop scheduling problems where the performance measure to be minimized is makespan (C max) or maximum lateness (L max). Extensive computational experiments are conducted on benchmark problems from the literature as well as several thousand randomly generated test problems with three different routing structures and up to 1000 operations. Several different versions of SB are examined to determine the effect on solution quality and time of different subproblem solution procedures, reoptimization procedures and bottleneck selection criteria. Results show that the performance of SB is significantly affected by job routings, and that SB with optimal subproblem solutions and full reoptimization at each iteration consistently outperforms dispatching rules, but requires high computation times for large problems. High quality subproblem solutions and reoptimization procedures are essential to obtaining good solutions. We also show that schedules developed by SB to minimize L max perform well with respect to several other performance measures, rendering them more attractive for practical use.

In High Dose Rate (HDR) brachytherapy the conventional dose optimization algorithms consider the multiple objectives in form of an aggregate function which combines individual objectives into a single utility value. As a result, the optimization problem becomes single objective, prior to optimization. Up to 300 parameters must be optimized satisfying objectives which are often competing. We use multiobjective dose optimization methods where the objectives are expressed in terms of quantities derived from dose-volume histograms or in terms of statistical parameters of dose distributions from a small number of sampling points. For the last approach we compare the optimization results of evolutionary multiobjective algorithms with deterministic optimization methods. The deterministic algorithms are very efficient and produce the best results. The performance of the multiobjective evolutionary algorithms is improved if a small part of the population is initialized by deterministic algorithms.

We explore use of data mining for lead time estimation in make-to-order manufacturing. The regression tree approach is chosen as the specific data mining method. Training and test data are generated from variations of a job shop simulation model. Starting with a large set of job and shop attributes, a reasonably small subset is selected based on their contribution to estimation performance. Data mining with the selected attributes is compared with linear regression and three other lead time estimation methods from the literature. Empirical results indicate that our data mining approach coupled with the attribute selection scheme outperforms these methods.

In this paper, we consider a modified shifting bottleneck heuristic for complex job shops. The considered job shop environment contains parallel batching machines, machines with sequence-dependent setup times and reentrant process flows. Semiconductor wafer fabrication facilities (Wafer Fabs) are typical examples for manufacturing systems with these characteristics. Our primary performance measure is total weighted tardiness (TWT). The shifting bottleneck heuristic uses a disjunctive graph to decompose the overall scheduling into scheduling problems for single tool groups. The scheduling algorithms for these scheduling problems are called subproblem solution procedures (SSPs). In previous research, only subproblem solution procedures based on dispatching rules have been considered. In this paper, we are interested in how much we can gain in terms of TWT if we apply more sophisticated subproblem solution procedures like genetic algorithms for parallel machine scheduling. We conduct simulation experiments in a dynamic job shop environment in order to assess the performance of the suggested subproblem solution procedures. It turns out that using near to optimal subproblem solution procedures leads in many situations to improved results compared to dispatching-based subproblem solution procedures.

Cellular manufacturing (CM) is an approach that includes both flexibility of job shops and high production rate of flow lines. Although CM provides many benefits in reducing throughput times, setup times, workin-process inventories but the design of CM is complex and NP complete problem. The cell formation problem based on operation sequence (ordinal data) is rarely reported in the literature. The objective of the present paper is to propose a visual clustering approach for machine-part cell formation using Self Organizing Map (SOM) algorithm an unsupervised neural network to achieve better group technology efficiency measure of cell formation as well as measure of SOM quality. The work also has established the criteria of choosing an optimum SOM map size based on results of quantization error, topography error, and average distortion measure during SOM training which have generated the best clustering and preservation of topology. To evaluate the performance of the proposed algorithm, we tested the several benchmark problems available in the literature. The results show that the proposed approach not only generates the best and accurate solution as any of the results reported, so far, in literature but also, in some instances the results produced are even better than the previously reported results. The effectiveness of the proposed approach is also statistically verified.

Order Review/Release (ORR) activities have mostly been ignored in past job shop research. In most previous studies, arriving jobs are immediately released to the shop #oor without considering any information about the system or job characteristics. In practice however, these jobs are often "rst collected in a pool and then released to the system according to a speci"c criterion. Although practitioners often observe the bene"ts of ORR, researchers have found limited support for the use of these input reglation policies. One objective of this paper is to examine this research paradox in a capacitated system. We also o!er a new classi"cation framework for existing research work. Finally, for the "rst time in this paper, both periodic and continuous ORR methods are compared simultaneously under various experimental conditions against di!erent performance measures. The results of simulation experiments and statistical tests are also presented in the paper.

Although a significantamount of research has been carried out in the schedulingof flexible manufacturing systems(FMSs),it has generallybeen focused on developing intelligent schedulingsystems. Most of these systems use simplescheduling rules as a part of their decisionprocess.While these schedulingrules have been investigated extensively for ajob shop environment, there is little guidance in the literature as to their performance in an FMS environment. This paper attempts to investigate the performances of machine and AGV scheduling rules against the mean flow-time criterion. The scheduling rulesare tested under a variety ofexperimental conditions by using an FMS simulation model.

In this paper we deal with the heuristic solution of the classical job shop problem. Both the constructive and the iterative phase of our algorithm apply insertion techniques combined with beam search. In the first phase we successively insert the operations into feasible partial schedules. In the iterative phase we generate paths in a particular neighbourhood graph instead of investigating the neighbourhood completely. To select "interesting" neighbours, we use the combinatorial path structure of feasible solutions of the job shop problem. The results of our algorithm are compared with those from other well-known methods on benchmark problems. 0166-218X/95/%09.50 0 1995-Elsevier Science B.V. All rights reserved SSDl 0166-218X(93)E0127-K 3.09

In this paper, we develop a tabu search procedure for solving the uniform graph partitioning problem. Tabu search, an abstract heuristic search method, has been shown to have promise in solving several NP-hard problems, such as job shop and flow shop scheduling, vehicle routing, quadratic assignment, and maximum satisfiability. We compare tabu search to other heuristic procedures for graph partitioning, and demonstrate that tabu search is superior to other solution approaches for the uniform graph partitioning problem both with respect to solution quality and computational requirements.

An analysis of heuristics in a dynamic job shop with weighted tardiness objectives E. KUTANOGLU ² and I. SABUNCUOGLU * Meeting due dates as a re¯ection of customer satisfaction is one of the scheduling criteria that is frequently encountered in today's manufacturing environments. The natural quanti® cation of this qualitative goal involves tardiness related measures. In this study, we consider the dynamic job shop scheduling problem with the weighted tardiness criterion. After we present a comprehensive literature survey on the topic, we measure the long-run performances of more than 20 single-pass dispatching rules under various experimental conditions. In this study, we pay special attention to recently proposed dispatching heuristics such as CEXSPT, CR+ SPT, S/RPT+ SPT, and Bottleneck Dynamics (BD). We also investigate the e ects of six resource pricing schemes proposed recently for BD. Moreover, we extend the earlier versions of inserted idleness and identif y the conditions in which these techniques can be applied without incurring too much computational cost. Future research directions are also outlined in light of the computational results.

Production Scheduling problems are typically named bases on the processing routes of their jobs on different processors and also the number of processors in each stage. In this paper, we consider the problem of scheduling a job shop (JSS) where set-up times are sequence-dependent (SDST) to minimize the maximum completion times of operations or makespan. Our problem is generally formulated as J/STsd/C max . To tackle such an NP-hard problem, a recent effective metaheuristic algorithm known as variable neighborhood search (VNS) is employed. VNS algorithms have shown excellent capability to solve scheduling problems to optimal or near-optimal schedule. Our proposed VNS is readily intelligible yet is a robust solution technique for the problem of SDST JSS. VNS is categorized as a local search-based algorithm armed with systematic neighborhood search structures. Our proposed VNS obviates the notorious myopic behavior of local search-based metaheuristic algorithms by the means of several systematic insertion neighborhood search structures. An experimental design based on Taillard's benchmark is conducted to evaluate the efficiency and effectiveness of our proposed algorithm against some effective algorithms in the literature. The obtained results strongly support the high performance of our proposed algorithm with respect to other well-known heuristic and metaheuristic algorithms.

In this paper, flexible job shop scheduling problem with a new approach, overlapping in operations, is discussed. In many flexible job shops, a customer demand can be released more than one for each job, where demand determines the quantity of each finished job ordered by a customer. In these models each job has a demand more than one. This assumption is an important and practical issue for many flexible job shops such as petrochemical industries. To consider this assumption, we use a new approach, named overlapping in operations. In this approach, embedded operations of each job can be performed due to overlap considerations in which each operation may be overlapped with the others because of its nature. The overlapping is limited by structural constraints, such as the dimensions of the box to be packed or the capacity of the container used to move the pieces from one machine to the next. Since this problem is well known as NP-Hard class, a hierarchical approach used simulated annealing algorithm is developed to solve large problem instances. Moreover, a mixed integer linear programming (MILP) method is presented. To evaluate the validity of the proposed SA algorithm, the results are compared with the optimal solution obtained with the traditional optimization technique (The Branch and Bound method). The computational results validate the efficiency and effectiveness of the proposed algorithm. Also the computational results show that the overlapping considering can improve the makespan and machines utilization measures. So the proposed algorithm can be applied easily in real factory conditions and for the large size problems and it should thus be useful to both practitioners and researchers.

The objective of this study is to develop dynamic scheduling heuristics for cellular manufacturing environments (group scheduling or family heuristics) and compare them with existing family heuristics under various shop floor conditions. The proposed family heuristics stress good due date performance while reducing overall set-up time. Computer simulation is used to test three queue selection rules in conjunction with three dispatching rules under eight experimental conditions in a job shop cell. The results indicate that several of the proposed heuristics substantially improve the performance of the cell over the best previously suggested family heuristic under all experimental conditions.

A life-cycle energy consumption analysis of a Bridgeport manual mill and a Mori Seiki DuraVertical 5060 has been conducted. The use phase incorporated three manufacturing environments: a community shop, a job shop, and a commercial facility. The CO2-equivalent emissions were presented per machined part. While the use phase comprised the majority of the overall emissions, the manufacturing phase emissions were significant especially for the job shop, which is not as efficient as the other facilities due to its inherent need for flexibility. Since the Mori Seiki is heavier, the manufacturing phase of this machine tool had a greater impact on emissions than the Bridgeport. Transportation was small relative to the use phase, which was dominated by cutting, HVAC, and lighting. These results highlight areas for energy reductions in machine tool design as well as the importance of facility type to the manufacture of any product.

Due date setting is a difficult task for make-to-order companies and requires efficient capacity and lead time management. In the job-shop literature, production planning for make-to-order companies is tackled by using shop floor scheduling algorithms coupled with simulation. However, capacity management cannot be realized in the most detailed phase of planning and should be carried out at a higher decision level so that work loads are smoothed over time.

We describe the design and implementation of a scheduling system in a glass factory. The factory produces a large variety of manufactured glass objects in a complex process ranging from melting the glass in the furnaces and blowing it automatically or manually to decorating, assembling and packing it. The structure basically corresponds to a flexible job-shop scheduling problem with some special characteristics. On the one hand, dealing with hot liquid glass imposes no-wait constraints on some operations. On the other hand, skilled workers performing some manual tasks are modelled as special machines. The system produces approximate solutions in very short computing times, trying to minimize a non-regular criterion defined by the user and based on due dates. It can be used to establish delivery dates for new customer orders, taking into account current machine workloads, or to schedule a set of orders, trying to meet given customer due dates.

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop problem in which each operation must be processed on a given machine chosen among a finite subset of candidate machines. The aim is to find an allocation for each operation and to define the sequence of operations on each machine, so that the resulting schedule has a minimal completion time. We propose a variant of the climbing discrepancy search approach for solving this problem. We also present various neighborhood structures related to assignment and sequencing problems. We report the results of extensive computational experiments carried out on well-known benchmarks for flexible job shop scheduling. The results demonstrate that the proposed approach outperforms the best-known algorithms for the FJSP on some types of benchmarks and remains comparable with them on other ones.

This paper deals with the problem of scheduling n jobs on m machines in order to minimize the maximum completion time or mean flow time of jobs. We extend the results obtained in Sotskov (1989, 1990, 1991) on the complexity of shop-scheduling problems with n = 3. The main result of this paper is an NP-hardness proof for scheduling 3 jobs on 3 machines, whether preemptions of operations are allowed or forbidden.

In this paper, the performance of hybrid cellular manufacturing (CM) systems is compared with that of functional layouts that use traditional job shop procedures as well as part family scheduling rules, under a variety of shop operating conditions. Unlike much of past research in group technology (GT), this work examines the entire shop floor, in which the CM systems comprise cells and a remainder shop organized as a functional layout. The experimental factors investigated include: the system (two functional layouts and five hybrid systems); setup factor, a surrogate for the degree of part family similarities; scheduling rule, which includes both job shop and part family-oriented rules; and lot size. The performance measures used were flow time, work-in-process inventory, machine utilization and flow ratio. Several findings emerged that form a useful addition to the literature. While part family-oriented scheduling rules significantly outperformed job shop rules, little difference was detected among the former. The results also indicated that the performance of the remainder shop deteriorated with increasing conversion, due to erosion of pooling synergy.

This paper concerns the mathematical formulation and optimal solutions for the Flexible Manufacturing Systems Scheduling Problem (FMSSP) with one vehicle. This linear formulation differs from the previously published ones as it takes into account the maximum number of jobs allowed in the system, limited input/output buffer capacities, empty vehicle trips and no-move-ahead trips simultaneously. Our objective is to propose optimal solutions for small and medium-sized instances and to examine a number of commonly used assumptions and heuristics. Computational experiments are carried out on instances adapted from Bilge and Ulusoy [Bilge, Ü., Ulusoy, G., 1995. A time window approach to simultaneous scheduling of machines and material handling system in an FMS. Operations Research 43, 1058-1070] and the following heuristics are evaluated: FIFO (First In First Out) rules for input/output buffer management; and FIFO, SPT (Shortest Processing Time), STT (Shortest Travel Time) and MOQS (Maximum Outgoing Queue Size) rules concerning the vehicle. The consequences of classical assumptions are also studied: ignoring empty trips, ignoring no-move-ahead constraints, and ignoring vehicle-disjunction constraints. The numerical experiments provide a set of optimal solutions and allow to evaluate the performances of heuristic search schemes.

We consider large volume job shop scheduling problems, in which there is a fixed number of machines, a bounded number of activities per job, and a large number of jobs. In large volume job shops it makes sense to solve a fluid problem and to schedule the jobs in such a way as to track the fluid solution. There have been several papers which used this idea to propose approximate solutions which are asymptoticaly optimal as the volume increases. We survey some of these results here. In most of these papers it is assumed that the problem consists of many identical copies of a fixed set of jobs. Our contribution in this paper is to extend the results to the far more general situation in which the many jobs are all different. We propose a very simple heuristic which can schedule such problems. We discuss asymptoitc optimality of this heuristic, under a wide range of previously unexplored situations. We provide a software package to explore the performance of our policy, and present extensive computational evidence for its effectiveness.