Ant colony optimisation with parameterised search space for the job shop scheduling problem

The job-shop scheduling problem (JSSP) is known to be NP-hard. Due to its complexity, many metaheuristic algorithm approaches have arisen. Ant colony metaheuristic algorithm, lately proposed, has successful application to various combinatorial optimisation problems. In this study, an ant colony optimisation algorithm with parameterised search space is developed for JSSP with an objective of minimising makespan. The problem is modelled as a disjunctive graph where arcs connect only pairs of operations related rather than all operations are connected in pairs to mitigate the increase of the spatial complexity. The proposed algorithm is compared with a multiple colony ant algorithm using 20 benchmark problems. The results show that the proposed algorithm is very accurate by generating 12 optimal solutions out of 20 benchmark problems, and mean relative errors of the proposed and the multiple colony ant algorithms to the optimal solutions are 0.93% and 1.24%, respectively.     

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform. The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors. The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors: early delivery date and fulfillment of processing as many orders as possible. The genetic algorithm (GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem (JSSP) by comparing with the real-world data from a textile weaving factory in South Korea. The proposed platform will provide producers with an optimal production schedule, introduce new producers to buyers, and eventually foster relationships and mutual economic interests.     

Minimizing production flow time in a process and assembly job shop

Scheduling is one of the most important issues in the planning and operation of production systems, but in medium to large shops, the generation of consistently good schedules has proven to be extremely difficult. The problem is that optimal scheduling solutions involve costly and impractical enumeration procedures. In the literature, most scheduling problems only address jobs with serial or sequential operations. Rarely do they consider jobs in which machining and assembly operations are simultaneously involved. This lack of attention to scheduling problems that involve both machining and assembly goes against what one would normally find in most job shops. In this paper, the problem of scheduling a set of N final products on M machines in a job shop environment that involve both machining and assembly operations is addressed. The objective pursued is the minimization of production flow time (makespan). A mathematical model is developed in an effort to obtain optimal solutions. Because this type of model grows exponentially as the size of the problems increases, an heuristic solution approach is developed to solve the problems more efficiently. The models are tested and compared on several test problems.     

Dynamic scheduling of FMS using a real-time genetic algorithm

The paper presents a genetic algorithm capable of generating optimised production plans in flexible manufacturing systems. The ability of the system to generate alternative plans following part-flow changes and unforeseen situations is particularly stressed (dynamic scheduling). Two contrasting objectives represented by the reduction of machine idle-times, thanks to dynamic scheduling computation and the reduction of the makespan, are taken into account by the proposed system. The key-point is the real-time response obtained by an optimised evolutionary strategy capable of minimising the number of genetic operations needed to reach the optimal schedule in complex manufacturing systems.     

Real-time scheduling in manufacturing system with machining and assembly operations: a state of art

Manufacturing systems producing multiple products are common in many industries, where products are made from several parts and/or sub-assemblies that require machining operations in first stage and assembly operations at later stage. Several scheduling techniques are proposed in the literature for such manufacturing system to develop near optimal schedule. A disruption in the manufacturing necessitates adjusting previously planned schedule which is known as real-time scheduling. This paper presents a comparative evaluation of different scheduling methods proposed by different investigators for dealing such situations. The literature indicates that real-time scheduling of manufacturing system with machining and assembly operations is hardly attempted. The paper offers a framework for developing rescheduling methodologies for such manufacturing situations.     

Adaptive production scheduling of virtual production systems using object-oriented Petri nets with changeable structure

Virtual Production Systems (VPSs) are logically constructed by organizing production resources belonging to one or more physical manufacturing systems. VPSs can enhance the agility of manufacturing systems. However, an effective scheduling approach is required to cope with disturbance and changes to these systems. An adaptive production scheduling method is proposed. Object-oriented Petri nets with changeable structure (OPNs-CS) formulate the scheduling problem of VPSs. To resolve resource constraints in a VPS, the OPNs-CS is modified by introducing limited token available time and by revising the enabling and firing rules. The artificial intelligent heuristic search (A*) algorithm is modified and applied to generate the optimal or near optimal schedule. When a VPS encounters any disturbance, an estimate of the effects of the disturbance can be estimated by simulation on the OPNs-CS model. If the scheduling target (completion time) is not affected, rescheduling is not required. Whenever there is a change to the VPS, the TOPNs-CS model is updated to refresh VPS schedule. A case study is presented to demonstrate the procedures for applying the proposed scheduling approach. The given case study shows that the proposed approach is capable of scheduling a VPS dynamically in response to disturbances and changes are involved.     

A hybrid scheduling approach for automated flowshops with material handling and time constraints

Flowshop scheduling problems have been extensively studied by several authors using different approaches. A typical flowshop process consists of successive manufacturing stages arranged in a single production line where different jobs have to be processed following a predefined production recipe. In this work, the scheduling of a complex flowshop process involving automated wet-etch station from semiconductor manufacturing systems requires a proper synchronisation of processing and transport operations, due to stringent storage policies and fixed transfer times between stages. Robust hybrid solution strategies based on mixed integer linear programming formulations and heuristic-based approaches, such as aggregation and decomposition methods, are proposed and illustrated on industrial-scale problems. The results show significant improvements in solution quality coupled with a reduced computational effort compared to other existing methodologies.     

Design of predictable production scheduling model using control theoretic approach

As one of the most important planning and operational issues in manufacturing systems, production scheduling generally deals with allocating a set of resources over time to perform a set of tasks. Recently, control theoretic approaches based on nonlinear dynamics of continuous variables have been proposed to solve production scheduling problems as an alternative to traditional production scheduling methods that deal with decision-making components in discrete nature. The major goal of this paper is to improve predictability and performance of an existing scheduling model that employs the control theoretic approach, called distributed arrival time controller (DATC), to manage arrival times of parts using an integral controller. In this paper, we first review and investigate unique dynamic characteristics of the DATC in regards to convergence and chattering of arrival times. We then propose a new arrival time controller for the DATC that can improve predictability and performance in production scheduling. We call the new mechanism the double integral arrival-time controller (DIAC). We analyse unique characteristics of the DIAC such as oscillatory trajectory of arrival times, their oscillation frequency, and sequence visiting mechanism. In addition, we compare scheduling performance of the DIAC to the existing DATC model through computational experiments. The results show that the proposed system can be used as a mathematical and simulation model for designing adaptable manufacturing systems in the future.     

Scenario-based multi-objective robust scheduling for a semiconductor production line

Scheduling problems of semiconductor manufacturing systems (SMS) with the goal of optimising some classical performance indices (NP-hard), tend to be increasingly complicated due to stochastic uncertainties. This paper targets the robust scheduling problem of an SMS with uncertain processing times. A three-stage multi-objective robust optimisation (MORO) approach is proposed, that can collaboratively optimise the performance indices and their robustness measures. In the first stage, this paper studies the scheduling problem in the deterministic environment and obtains feasible scheduling strategies that perform well in four performance indices (the average cycle time (CT), the on-time delivery rate (ODR), the throughput (TP), and the total movement amount of wafers (MOV)). Then, in the second stage, the uncertainties are introduced into the production system. In the third stage, this paper proposes a hybrid method consisting of scenario planning, discrete simulation, and multi-objective optimisation to obtain an approximately and more robust optimal solution from the feasible scheduling strategy set. The proposed MORO approach is tested in a semiconductor experiment production line and makes a full analysis to illustrate the effectiveness of our method. The results show that our MORO is superior concerning the total robustness with multi-objective.     

Petri nets and genetic algorithms for complex manufacturing systems scheduling

In this paper we propose the GAPN (genetic algorithms and Petri nets) approach, which combines the modelling power of Petri nets with the optimisation capability of genetic algorithms (GAs) for manufacturing systems scheduling. This approach uses both Petri nets to formulate the scheduling problem and GAs for scheduling. Its primary advantage is its ability to model a wide variety of manufacturing systems with no modifications either in the net structure or in the chromosomal representation. In this paper we tested the performance on both classical scheduling problems and on a real life setting of a manufacturer of car seat covers. In particular, such a manufacturing system involves features such as complex project-like routings, assembly operations, and workstations with unrelated parallel machines. The implementation of the algorithm at the company is also discussed. Experiments show the validity of the proposed approach.     

An energy-efficient scheduling and rescheduling method for production and logistics systems†

Scheduling can be defined as the allocation of available resources over time while optimising a set of criteria like early completion time of task, holding inventory, etc. The complexity of the scheduling problem, already known to be high, increases if dynamic events and disruptions are considered. In addition, in production and logistics, designers of scheduling systems must consider sustainability-related expectations. This paper presents an energy-efficient scheduling and rescheduling method (named Green Rescheduling Method, GRM). GRM aims at the solving of the dynamic scheduling problem under the condition of a certain level of routing flexibility enabling the reassignment of tasks to new resources. The key performance indicators integrated into the proposed GRM are effectiveness and efficiency-oriented. Applications concern the domains of production and logistics. In order to assess the proposed approach, experimentations have been made and results illustrate the applicability of GRM to build efficient and effective scheduling and rescheduling both for flexible manufacturing systems and inventory distribution systems in a physical internet network. A mathematical formulation for flexible job shop problem with energy consumption is also proposed using mixed Integer programming to evaluate the performance of the predictive part of GRM.     

A fuzzy logic approach to an integrated maintenance/production scheduling algorithm

Scheduling algorithms play an important role in manufacturing systems as a means of meeting customer demands. On the other hand, fuzzy logic, which has been successfully implemented in many engineering applications, including the recent work of Vanegas and Labib (2001a,b), has an ability to produce a more gradual transition. This paper presents an algorithm for transforming maintenance data to shop floor information. These shop floor data are then used via a fuzzy-logic based scheduling algorithm to determine optimal production systems control policies. The frequency of breakdowns and the mean number of parts required are used as inputs to the fuzzy logic controller. These inputs are transformed to the mean part arrival rate. The output is then fed to the scheduling algorithm. Finally, the optimal batch size is calculated. The algorithm is demonstrated with simulation.     

An integrative heuristic method for detailed operations scheduling in assembly job shop systems

In this paper, we describe a new heuristic method for simulating and supporting the operations scheduling process in assembly job shop systems. The method is based on the assumption that the improvement in operations synchronisation at fabrication and assembly stations brings forth better achievement of due dates. The method implements two scheduling approaches: a backward approach satisfying due date completely and a forward approach satisfying capacity restrictions completely. The two approaches work iteratively within two different simulation models of the production system – one deterministic and the other probabilistic – in searching for operations synchronisation improvement and due date achievement. The method intends to be integrative, i.e., to be able to integrate effectively three fundamental enterprise systems: order processing, production scheduling, and manufacturing activity control. An experimental study was conceived to evaluate the suitability of the method to support scheduling decision making. As results demonstrate, the method proves to be suitable for this objective. As a co-product, results show the method is better than the single-pass procedure/rules tested on average and is as good as the best single-pass procedure/rule tested.     

Modeling the planning and scheduling across the outsourcing supply chain: a Chaos-based fast Tabu–SA approach

Planning and Scheduling are the interrelated manufacturing functions and should be solved simultaneously to achieve the real motives of integration in manufacturing. In this paper, we have addressed the advanced integrated planning and scheduling problem in a rapidly changing environment, where the selection of outsourcing machine/operation, meeting the customers (single or multiple) due date, minimizing the makespan are the main objectives while satisfying several technological constraints. We developed a mixed integer programming model for integrated planning and scheduling across the outsourcing supply chain and showed how such models can be used to make strategic decisions. It is a computationally complex and mathematically intractable problem to solve. In this paper, a Chaos-based fast Tabu-simulated annealing (CFTSA) incorporating the features of SA, Tabu and Chaos theory is proposed and applied to solve a large number of problems with increased complexity. In CFTSA algorithm, five types of perturbation schemes are developed and Cauchy probability function is used to escape from local minima and achieve the optimal/near optimal solution in a lesser number of iterations. An intensive comparative study shows the robustness of proposed algorithm. Percentage Heuristic gap is used to show the effectiveness and two ANOVA analyses are carried out to show the consistency and accuracy of the proposed approach.     

Two-level hedging point control of a manufacturing system with multiple product-types and uncertain demands

This research is motivated by the co-operative production process of networked manufacturing systems (NMS). Manufacturing resource sharing and flexible production scheduling are two features of NMS. For an individual manufacturing system in an NMS, ‘flexible production scheduling’ means that it can produce multiple product-types and the switching of products is quick enough to respond to the demand fluctuation. ‘Manufacturing resource sharing’ means the utilisation of extra production capacity from other manufacturing systems in the NMS. Of course, that will bring extra cost. This paper focuses on the optimal production control problem of such a situation: one manufacturing system, multiple product-types, and uncertain demands. Here, it is assumed that there are two demand-levels for each product-type: the lower one and the higher one. The total normal production capacity is larger than the total lower demands and smaller than the total higher demands. If the total demands cannot be satisfied and the work-in-process (WIP) of all product-types decrease to a certain level, e.g. zero WIP, the extra production capacity may be utilised. For such a system, a new two-level hedging point policy is proposed, in which two hedging points (a higher one and a lower one) are given for each product-type. Different from the prioritised hedging point (PHP) policy which is usually applied to one-machine and multiple part-type systems, our control policy considers all part-types at the same prioritised level and keeps the work-in-process states of all product-types on a straight line in the state space. Thus, the total costs for WIP inventory and the occupation of extra capacity can be obtained in a closed form, which is a function with respect to the hedging points. Then the method for optimising the hedging points is proposed and the special structure of the optimal hedging point is obtained. Numerical experiments verify the optimality and the special structure of the hedging point obtained by our method.     

A heuristic algorithm to minimise the control places of a Petri net for the control of shared machines

This paper proposes a design methodology of a controller based on a Petri net for the shared machines of manufacturing systems. A conflict occurs when several manufacturing systems require the same shared machines at the same time. In this case, we have two issues; the scheduling of jobs on shared machines and the construction of a control procedure for scheduling. The scheduling of production on machines has been extensively studied over the past years by researchers. In this paper, our concern is not the scheduling problem but the construction of a control procedure for the production schedule. We propose a design of a Petri net based controller for the shared machines of manufacturing systems such that the number of control places in the Petri net is minimised. The experimental results show that the proposed algorithm performs better than an upper bound in terms of optimality. Also, the proposed algorithm is computationally more efficient than the optimal algorithm. Finally, we present the application of the proposed algorithm to a realistic batch process system shown in the literature.     

Investigating the value of integrated operations planning: A case-based approach from automotive industry

During the last decade, many researchers have focused on joint consideration of various operations planning aspects like production scheduling, maintenance scheduling, inventory control, etc. Such joint considerations are becoming increasingly important from the point of view of current advancement in intelligent manufacturing, also known as Industry 4.0. Under the concept of Industry 4.0, advanced data analytics aim to remove human intervention in decision-making. Thus, the managerial level coordination of decisions taken independently by various departments will be out of trend. Therefore, developing an approach that optimises various operations planning decisions simultaneously is essential. Available literature on such joint considerations is more of the exploratory in nature and is limited to simplistic production environments. This necessitates the investigations of value of integrated operations planning for wide range of manufacturing scenarios. Present paper adopts a case-oriented approach to investigate the value of integrated operations planning. First, an integrated approach for simultaneously determining job sequencing, batch-sizing, inventory levels and preventive maintenance schedule is developed. The approach is tested in a complex production environment of an automotive plant and substantial economic improvement was realised. Second, a comprehensive evaluation is performed to study the robustness and implications of proposed approach for various production scenarios. Results of such pervasive performance investigations confirm the value of proposed approach over conventional approaches.     

A neural network decision-making model for job-shop scheduling

Scheduling in a job-shop system is a challenging task. Simulation modelling is a well-known approach for evaluating the scheduling plans of a job-shop system; however, it is costly and time-consuming, and developing a model and interpreting the results requires expertise. As an alternative, we have developed a neural network (NN) model focused on detailed scheduling that provides a versatile job-shop scheduling analysis framework for management to easily evaluate different possible scheduling scenarios based on internal or external constraints. A new approach is also proposed to enhance the quality of training data for better performance. Previous NN models in scheduling focus mainly on job sequencing and simple operations flow, and may not consider the complexities of real-world operations. The proposed model’s output proved statistically equivalent to the results of the simulation model. The study was accomplished using sensitivity analysis to measure the effectiveness of the input variables of the NN model and their impact on the output, revealing that the batch size variable had a significant impact on the scheduling results in comparison with other variables.     

Scheduling job shop associated with multiple routings with genetic and ant colony heuristics

The general job shop problem is one of the well known machine scheduling problems, in which the operation sequence of jobs are fixed that correspond to their optimal process plans and/or resource availability. Scheduling and sequencing problems, in general, are very difficult to solve to optimality and are well known as combinatorial optimisation problems. The existence of multiple job routings makes such problems more cumbersome and complicated. This paper addresses a job shop scheduling problem associated with multiple job routings, which belongs to the class of NP hard problems. To solve such NP-hard problems, metaheuristics have emerged as a promising alternative to the traditional mathematical approaches. Two metaheuristic approaches, a genetic algorithm and an ant colony algorithm are proposed for the optimal allocation of operations to the machines for minimum makespan time criterion. ILOG Solver, a scheduler package, is used to evaluate the performance of the proposed algorithms. The comparison reveals that both the algorithms are capable of providing solutions better than the solution obtained with ILOG Solver.     

Minimising makespan on a single batch processing machine with dynamic job arrivals and non-identical job sizes

Batch scheduling is a prevalent policy in many industries such as burn-in operations in semiconductor manufacturing and heat treatment operations in metalworking. In this paper, we consider the problem of minimising makespan on a single batch processing machine in the presence of dynamic job arrivals and non-identical job sizes. The problem under study is NP-hard. Consequently, we develop a number of efficient construction heuristics. The performance of the proposed heuristics is evaluated by comparing their results to two lower bounds, and other solution approaches published in the literature, namely the first-fit longest processing time-earliest release time (FFLPT-ERT) heuristic, hybrid genetic algorithm (HGA), joint genetic algorithm and dynamic programming (GA+DP) approach and ant colony optimisation (ACO) algorithm. The computational experiments demonstrate the superiority of the proposed heuristics with respect to solution quality, especially for the problems with small size jobs. Moreover, the computational costs of the proposed heuristics are very low.