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The food processing industry is growing with retail and catering supply chains. With the rising complexity of food products and the need to address food customization expectations, food processing systems are progressively shifting from production line to job-shops that are characterized by high flexibility and high complexity. A food job-shop system processes multiple items (i.e. raw ingredients, toppings, dressings) according to their working cycles in a typical resource and capacity constrained environment. Given the complexity of such systems, there are divergent goals of process cost optimization and of food quality and safety preservation. These goals deserve integration at both an operational and a strategic decisional perspective. The twofold purpose of this paper is to design a simulation model for food job-shop processing and to build understanding of the extant relationships between food flows and processing equipment through a real case study from the catering industry.

The authors designed a simulation tool enabling the analysis of food job-shop processing systems. A methodology based on discrete event simulation is developed to study the dynamics and behaviour of the processing systems according to an event-driven approach. The proposed conceptual model builds upon a comprehensive set of variables and key performance indicators (KPIs) that describe and measure the dynamics of the food job-shop according to a multi-disciplinary perspective.

This simulation identifies the job-shop bottlenecks and investigates the utilization of the working centres and product queuing through the system. This approach helps to characterize how costs are allocated in a flow-driven approach and identifies the trade-off between investments in equipment and operative costs.

The primary purpose of the proposed model relies on the definition of standard resources and operating patterns that can meet the behaviour of a wide variety of food processing equipment and tasks, thereby addressing the complexity of a food job-shop. The proposed methodology enables the integration of strategic and operative decisions between several company departments. The KPIs enable identification of the benchmark system, tracking the system performance via multi-scenario what-if simulations, and suggesting improvements through short-term (e.g. tasks scheduling, dispatching rules), mid-term (e.g. recipes review), or long-term (e.g. re-layout, working centres number) levers.

The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness.

A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation.

The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop.

Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines.

The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error.

Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

In today's very competitive, dynamic and unpredictable manufacturing environments it is critical to improve manufacturing performance in order to be able to compete. Responsiveness and agility become important characteristics of manufacturing systems and organizations. Manufacturing systems must be designed optimally by taking into account responsiveness and agility related measures in order to improve effectiveness and performance. One of the important enablers of performance improvement is flexibility. It is a known fact that flexibility has a positive effect on the manufacturing system performance if it is properly utilized by the control system (usually scheduling). However, the relationship between flexibility and manufacturing system performance through scheduling is not entirely explored in the previous literature. The purpose of this paper is to investigate the effects of process plan and machine flexibilities on the scheduling performance of manufacturing job‐shops.

Effects of process plan and machine flexibilities on the scheduling performance of manufacturing job‐shops are analyzed at different flexibility levels by using the grammar‐based flexible job shop scheduling system that is developed by Baykasoğlu et al.. Three different flexibility levels are defined for process plans and machines. Four different problem sizes are evaluated according to “makespan” “machine load balance” and “mean waiting times of jobs”. Performance differences among “process plan” and “machine flexibility” levels are determined and statistically analyzed through Taguchi experimental design methodology.

It is found out after detailed analysis that the effect of machine flexibility on job shop performance is higher than the process plan flexibility. It is also figured out that after a certain level of machine flexibility, the speed of scheduling performance improvement decreases considerably.

The paper presents the interaction between flexibility and scheduling performance of manufacturing job‐shops. The findings should be taken into account while designing scheduling systems for job shops that have flexible processing capabilities.

Presents a microcomputer‐based interactive decision support system (DSS) to assist managers to improve decision making for machine scheduling in a job shop environment. The system downloads data from a factory information system, and schedules work into the appropriate work centres. The system produces sequences using a rule set that incorporates set‐up time reduction, shortest processing time (SPT) sequencing, downstream requirements, and job due dates. The system automatically expedites where required, and provides an interactive interface to the decision maker. A SLAM II‐based computer simulation model guides system development and provides a test bed for policy decisions with regard to implementation of the DSS.

The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops.

An analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model.

Significant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s.

Considerable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints.

Two-stage production systems including a processing shop and an assembly stage are widely used in various manufacturing industries. These two stages are usually studied independently which may not lead to ideal results. This paper aims to deal with a two-stage production system including a job shop and an assembly stage.

Some exact methods are proposed based on branch and bound (B&B) approach to minimize the total completion time of products. As B&B approaches are usually time-consuming, three efficient lower bounds are developed for the problem and variable neighborhood search is used to provide proper upper bound of the solution in each branch. In addition, to create branches and search new nodes, two strategies are applied including the best-first search and the depth-first search (DFS). Another feature of the proposed algorithms is that the search space is reduced by releasing the precedence constraint. In this case, the problem becomes equivalent to a parallel machine scheduling problem, and the redundant branches that do not consider the precedence constraint are removed. Therefore, the number of nodes and computational time are significantly reduced without eliminating the optimal solution.

Some numerical examples are used to evaluate the performance of the proposed methods. Comparison result to mathematical model (mixed-integer linear programming) validates the performance accuracy and efficiency of the proposed methods. In addition, computational results indicate the superiority of the DFS strategy with regard to CPU time.

Studies about the scheduling problems for two-stage production systems including job shop followed by an assembly stage traditionally present approximate method and metaheuristic algorithms to solve the problem. This is the first study that introduces exact methods based on (B&B) approach.

The purpose of this paper is to optimise the job shop scheduling problem using simulation and genetic algorithm.

The 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.

Simulation‐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.

The 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.

Proposes a conceptual interpretative framework as a reading key to management differences in the two principal manufacturing contexts – intermittent manufacturing and repetitive manufacturing – within the three basic operations management subsystems: planning, inventory control and shopfloor control.

To compare the performance of a new hybrid manufacturing system (HMS) with a conventional cellular manufacturing system (CMS). The hybrid system is a combination of the cellular manufacturing and job shop.

A hypothetical manufacturing facility with eight machines and 20 parts is used as a case. Simulation models are developed for two manufacturing systems. A multi‐factor comparison is carried out to test the performance of the systems under different scenarios.

It was found that group scheduling rules (GSR) and the manufacturing system design factors have significant impact on the performance of the system. In particular, the hybrid system shows its best performance when the MSSPT GSR is applied, whereas the cellular system is superior when DDSI is implemented. The results also demonstrate that, by adding non‐family parts to the production schedule of the HMS, significant benefits in the performance measures can be attained.

The conclusion cannot be generalized, as the result is dependent upon the input data and the size of the problem.

The application may be limited to certain industry sectors. Further studies may be needed to identify the appropriate industry.

While the majority of the literature focuses on either a job shop or a pure CMS, this paper has a distinctive approach that allows the combined use of both systems. This could be a useful transitional approach from one system to the other.

In the current competitive environment, each company faces a number of challenges: quick response to customers’ demands, high quality of products or services, customers’ satisfaction, reliable delivery dates, high efficiency, and others. As a result, during the last five years many firms have proceeded to the adoption of enterprise resource planning (ERP) solutions. ERP is a packaged software system, which enables the integration of operations, business processes and functions, through common data‐processing and communications protocols. However, the majority, if not all, of these systems do not support the production scheduling process that is of crucial importance in today’s manufacturing and service industries. In this paper, the authors propose a knowledge‐based system for production‐scheduling that could be incorporated as a custom module in an ERP system. This system uses the prevailing conditions in the industrial environment in order to select dynamically and propose the most appropriate scheduling algorithm from a library of many candidate algorithms.