Flexible Job Shop Manufacturing Research Articles

A decentralized production–distribution scheduling problem: Solution and analysis

In modern production–distribution supply chains, decentralization has increased significantly, due to increasing production network efficiency. This study investigates a production scheduling and vehicle routing problem in a make-to-order context under a decentralized decision-making structure. Specifically, two different decision makers hierarchically decide the production and distribution schedules to minimize their incurred costs and we formulate the problem as a bi-level mixed-integer optimization model as a static Stackelberg game between manufacturer and distributor. At the upper level, the manufacturer decides its best scheduling under a flexible job-shop manufacturing system, and at the lower level, the distributor decides its distribution scheduling (routing) which influences the upper-level decisions. The model derives the best production–distribution scheduling scheme, with the objective of minimizing the cost of the manufacturer (leader) at the lowest possible cost for the distributor (follower). As the lower level represents a mixed-integer programming problem, it is challenging to solve the resulting bi-level model. Therefore, we extend an efficient decomposition algorithm based on Duplication Method and Column Generation. Finally, to discuss the decentralization value, the results of the presented bi-level model are compared with those of the centralized approach.

Energy cost efficient scheduling in flexible job-shop manufacturing systems

This paper studies the problem of determining energy efficient schedules in a flexible job shop. The goal is to minimize the total energy cost, given a time-of-use pricing scheme, while ensuring that the schedule does not violate a maximum makespan. The problem is first formalized as a mixed integer program. Because it is already difficult to solve, the simpler problem with a fixed sequence of operations is then extensively studied. Some properties are derived for the specific problem with a fixed sequence. These properties show that the complexity of the problem depends on the structure of the energy pricing scheme. They are also used to propose two heuristic approaches. Relying on these heuristics, we further develop an iterative tabu search for the general problem. Extensive computational experiments are carried out to evaluate the solution methods and the potential gains on the total energy cost, depending on the flexibility associated to the maximum allowed makespan and on the time-of-use structures.

Robust Shipping Date Predictions in Manufacturing using High-level Petri Nets

ABSTRACT Machine failures, unavailability of the workforce, and missing raw materials are typical, frequent faults in complex manufacturing systems and cause unstable processes. Plans and schedules become volatile, predictability of shipping dates suffers and delayed delivery appears. However, for a customer reliable shipping can often be of higher benefit than promised short-term shipping with significant probability of delay and error. Inserting buffer times stabilises schedules by reducing the potential impact of disruptions. Therefore, computing appropriately sized buffer times for individual jobs in a flexible job shop manufacturing with setup times is a promising yet difficult mission for production research. In this paper, we discuss methods for buffer time calculation that are based on data collected directly from the underlying production processes. We show how a high-level Petri net model can be used to simulate stochastic variations of the manufacturing process. Using this simulation model, we estimate the effect of buffer times and produce predictions for the expected shipping dates together with probabilities for how likely the predicted dates will actually be met.

Production and transport scheduling in flexible job shop manufacturing systems

This paper addresses an extension of the flexible job shop scheduling problem by considering that jobs need to be moved around the shop-floor by a set of vehicles. Thus, this problem involves assigning each production operation to one of the alternative machines, finding the sequence of operations for each machine, assigning each transport task to one of the vehicles, and finding the sequence of transport tasks for each vehicle, simultaneously. Transportation is usually neglected in the literature and when considered, an unlimited number of vehicles is, typically, assumed. Here, we propose the first mixed integer linear programming model for this problem and show its efficiency at solving small-sized instances to optimality. In addition, and due to the NP-hard nature of the problem, we propose a local search based heuristic that the computational experiments show to be effective, efficient, and robust.

A simulated multi-objective model for flexible job shop transportation scheduling

This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The objectives considered in scheduling jobs and transportation tasks in a flexible job shop manufacturing system include makespan, robot travel distance, time difference with due date and critical waiting time. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.

Rugalmas többutas gyártórendszer ütemezési feladatainak megoldása többcélú reaktív módszerrel

A cikk bemutatja a többutas diszkrét gyártórendszerek egy továbbfejlesztett ütemezési modelljét és annak többcélú reaktív megoldási módszerét. A rugalmas gyártórendszerekre jellemző alternatív technológiai útvonalak, korlátozottan rendelkezésre álló gépek, eltérő műveletvégzési és átállítási idők, valamint határidős és eltérő prioritású munkák kezelésére alkalmas ütemezési koncepció kerül bemutatásra. Az erőforrások allokálásának, az operációk időbeli végrehajtásának részletes ütemezésére egy kiterjesztett rugalmas job shop modellt definiáltunk. A cikk ismerteti a vizsgált feladattípus legfontosabb jellemzőit és a kifejlesztett többcélú reaktív megoldási módszer koncepcióját.

Solving a flexible job shop lot sizing problem with shared operations using a self-adaptive COA

This paper deals with lot sizing decisions in a flexible job shop manufacturing system considering dependencies between lot sizing decisions. A novel mathematical model is developed to optimise lot sizing and scheduling simultaneously using a product-oriented approach as opposed to the job-oriented approach which has been prevalently considered in the literature. Moreover, the proposed model considers further underlying assumptions such as assembly operations, sequence-dependent setup times, initial inventory and safety stock levels, as well as lots with unequal and variable sizes. The objective is to minimise the total production, setup, and tardiness penalty costs of the system. To solve the formulated problem, a self-adaptive Cuckoo Optimisation Algorithm (COA) embedded with three new immigration mechanisms is developed. Numerical experiments are conducted to demonstrate the validity of the model and investigate the efficiency and effectiveness of the employed optimisation algorithm.

Sustainable evaluation of environmental and occupational risks scheduling flexible job shop manufacturing systems

Sustainability has become a challenge of relevant importance for organizational management. Companies should not only achieve appropriate levels of economic efficiency, but also of environmental and social efficiency. In this sense, the inclusion of sustainability principles in the scheduling of flexible job shop systems (FJS) has focused on the evaluation of energy consumption and key economic indicators (makespan, total weighted tardiness …). An industrial case (Medellín, Colombia) from the metal-mechanic sector is used to illustrate the simultaneous evaluation of the three sustainability dimensions: economic, environmental and social. The makespan is the indicator associated with the economic performance; CO2 equivalent emissions, water consumption, metal waste, and chrome waste are the indicators to evaluate the environmental performance; and weight manipulated at workstations, noise, ambient temperature, and vibrations are the indicators associated with the social performance. The measurement, normalization, and weighted sum of the previous indicators, allow for the estimation of the performance of each sustainability dimension. The NSGA-II and NSGA-III methods are used in order to carry out the multi-objective evaluation process, based on the values estimated for the three general objectives (i.e., environmental, social, and economic). Consequently, in the industrial case analyzed, the applied methods identify the corresponding structure of the Pareto optimal fronts. The post-optimal analysis of the solutions shows that the solutions can be directly associated with market demand tendencies, and their implementation can be detected in advance. Solutions that balance economic, environmental, and social efficiency, could be potentially selected according to the market trend, reaching a win-win scenario for all actors involved.

Material Flow Optimisation of Flexible Manufacturing System using Real Coded Genetic Algorithm (RCGA)

In flexible jobshop manufacturing system ‘n’ number of jobs are manufactured on ‘m’ machines in the‘s’ stages. Each product has its predefined sequence. Material processing is scheduled by following alternate process plans. Material flow consists of the total completion time which comprises of travel time, waiting time and processing time, etc. Material flow optimisation is the part of production planning stage. So, it is a crucial part in the flexible manufacturing system of decision making to optimise a material flow in the system.In this paper, a mathematical model is modified to optimise material flow optimisation of a flexible manufacturing system. Waiting time due to unavailability of machines is considered for the modification of this mathematical model. A real coded Genetic Algorithm (RCGA) has implemented on flexible jobshop manufacturing system problem to optimise material flow of the system. The results shows that RCGA can be applied to solve problems of flexible jobshop manufacturing system and better as compared to Genetic Algorithm (GA).

Effect of routing flexibility and sequencing rules on performance of stochastic flexible job shop manufacturing system with setup times: Simulation approach

Routing flexibility is a major contributor towards flexibility of a flexible job shop manufacturing system. This article focuses on a simulation-based experimental study on the effect of routing flexibility and sequencing rules on the performance of a stochastic flexible job shop manufacturing system with sequence-dependent setup times while considering dynamic arrival of job types. Six route flexibility levels and six sequencing rules are considered for detailed study. The performance of manufacturing system is evaluated in terms of flow time related and due date–related measures. Results reveal that routing flexibility and sequencing rules have significant impact on system performance, and the performance of a system can be increased by incorporating routing flexibility. Furthermore, the system performance starts deteriorating as the level of route flexibility is increased beyond a particular limit for a specified sequencing rule. The statistical analysis of the results indicates that when flexibility exists, earliest due date rule emerges as a best sequencing rule for maximum flow time, mean tardiness and maximum tardiness performance measures. Furthermore, smallest setup time rule is better than other sequencing rules for mean flow time and number of tardy jobs performance measures. Route flexibility level two provides best performance for all considered measures.

GA approach to optimise material flow and makespan in an AGV-based flexible jobshop manufacturing system

Flexible jobshop scheduling problem (FJSP) is an extended traditional jobshop scheduling problem, which more approximates to practical scheduling problems. This paper presents a genetic algorithm–based (GA) algorithm to solve the multi objective FJSP. Flexible jobshop manufacturing system (FJMS) is a complex network of processing, inspecting, and buffering nodes connected by system of transportation mechanisms. For an FJMS, it is desirable to be capable to increase or decrease the output with the rise and fall of demand. Such specifications show the complexity of decision making in the field of FJMSs and the need for concise and accurate modelling methods. Therefore, in this paper, an AGV–based flexible jobshop automated manufacturing system is considered to optimise the material flow and makespan. The flexibility is on the multishops of the same type and also multiple products that can be produced. An automated guided vehicle is applied for material handling. The objective is to optimise the material flow regarding the demand fluctuations and machine specifications and the makespan. An illustrative example is adopted from the literature to test the validity of the proposed algorithm.

Stability-based Short-term Capacity Requirement Planning under Uncertainty

This paper proposes a new method of short-term capacity requirement planning (CRP) from the viewpoint of “stability”. The term stability refers to the higher predictability of a released capacity requirement plan (or a load plan), and also signifies the robustness of a manufacturing process against internal/external uncertainties. The objective of CRP mentioned in this study is to determine a stable allocation of each manufacturing resource to given operation orders with the aim of minimizing the probability that the resource requirement of each operation order exceeds the capacity of corresponding resource. Through some computational simulations, it is demonstrated that the proposed CRP method is applicable as a short-term CRP method by applying it to a flexible job-shop manufacturing system.

An optimal path in a bi-criteria AGV-based flexible jobshop manufacturing system having uncertain parameters

We propose an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. With rise in demands, advancement in technology and increase in production capacity, the need for more shops persists. Therefore, a flexible jobshop system has more than one shop with the same duty. The difference among shops with the same duty is in their machines with various specifications. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, we consider time to be a triangular fuzzy number and apply an expert system to infer cost. The objective is to find a path minimising both the time and cost criteria, aggregately. Since time and cost have different scales, a normalisation procedure is proposed to remove the scales. The model being bi-objective, the analytical hierarchy process weighing method is applied to construct a single objective. Finally, a dynamic programming approach is presented for computing a shortest path in the network. The efficiency of the proposed approach is illustrated by a numerical example.

Scheduling of Automated Guided Vehicle and Flexible Jobshop using Jumping Genes Genetic Algorithm

Problem statement: Now a day’s many researchers try Genetic algorithm based optimization to find near optimal solution for flexible job shop. It is a global search. In Our study in the GA, some changes are made to search locally and globally by adding jumping genes operation. A typical flexible job shop model is considered for this research study. For that layout, five different example problems are formulated for purpose of evaluation. The material flow time for different shop types, processing times of products, waiting times of products, sequences of products are created and given in tabular form. Approach: The one of best evolutionary approach i.e., genetic algorithm with jumping genes operation is applied in this study, to optimize AGV flow time and the performance measures of Flexible Job shop manufacturing system. The non dominated sorting approach is used. Genetic algorithm with jumping genes operator is used to evaluate the method. Results: The AGV flow sequence is found out. Using this flow sequence make span, flow time of products with AGV, completion of the products is minimized. The position of the shop types are calculated for all products. The effectiveness of the proposed method is proved by comparing with Hamed Fazlollahtabar method. Conclusion: It is found that jumping genes genetic algorithm delivered good solutions as like as other evolutionary algorithms. Jumping genes genetic algorithm may applied to Multi objective optimization techniques in future.

Producer’s behavior analysis in an uncertain bicriteria AGV-based flexible jobshop manufacturing system with expert system

Here, an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost is proposed. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, time is considered to be a triangular fuzzy number and apply an expert system to infer the cost. The expert system based on fuzzy rule backpropagation network to configure the rules for estimating the cost under uncertainty is proposed. A multiple linear regression model is applied to analyze the rules and find the effective rules for cost estimation. The objective is to find a path minimizing an aggregate weighted unscaled time and cost criteria. A fuzzy dynamic programming approach is presented for computing a shortest path in the network. Then, a comprehensive economic and reliability analysis is worked out on the obtained paths to find the optimal producer’s behavior. Finally, an application of the model is illustrated by a numerical example. The results show the effectiveness of our approach for finding an optimal path in a manufacturing system under uncertainty.

Scheduling in flexible job-shop manufacturing system by improved tabu search

Scheduling in flexible job-shop manufacturing system by improved tabu search

Mathematical programming approach to optimize material flow in an AGV-based flexible jobshop manufacturing system with performance analysis

An automated manufacturing system (AMS) is a complex network of processing, inspecting, and buffering nodes connected by system of transportation mechanisms. For an AMS, it is desirable to be capable to increase or decrease the output with the rise and fall of demand. Such specifications show the complexity of decision making in the field of AMSs and the need for concise and accurate modeling methods. Therefore, in this paper, a flexible jobshop automated manufacturing system is proposed to optimize the material flow. The flexibility is on the multi-shops of the same type and also multiple products that can be produced. An automated guided vehicle is applied for material handling. The objective is to optimize the material flow regarding the demand fluctuations and machine specifications. An illustrative example is presented to test the validity of the proposed mathematical model.

Development of a simulation-based decision support system for controlling stochastic flexible job shop manufacturing systems

This paper describes a simulation-based decision support system (DSS) to production control of a stochastic flexible job shop (SFJS) manufacturing system. The controller design approach is built around the theory of supervisory control based on discrete-event simulation with an event–condition–action (ECA) real-time rule-based system. The proposed controller constitutes the framework of an adaptive controller supporting the co-ordination and co-operation relations by integrating a real-time simulator and a rule-based DSS. For implementing SFJS controller, the proposed DSS receives online results from simulator and identifies opportunities for incremental improvement of performance criteria within real-time simulation data exchange (SDX). A bilateral method for multi-performance criteria optimization combines a gradient based method and the DSS to control dynamic state variables of SFJS concurrently. The model is validated by some benchmark test problems.

Scheduling manufacturing systems with blocking: a Petri net approach

This paper introduces a Petri net-based approach for scheduling manufacturing systems with blocking. The modelling of the job routings and the resource and blocking constraints is carried out with the Petri net formalism due to their capability of representing dynamic, concurrent discrete-event dynamic systems. In addition Petri nets can detect deadlocks typically found in systems with blocking constraints. The scheduling task is performed with an algorithm that combines the classical A* search with an aggressive node-pruning strategy. Tests were conducted on a variety of manufacturing systems that included classical job shop, flexible job shop and flexible manufacturing scheduling problems. The optimisation criterion was makespan. The experiments show that the algorithm performed well in all types of problems both in terms of solution quality and computing times.

A Petri Net based algorithm for minimizing total tardiness in flexible manufacturing systems

Petri Nets have been extensively used for modeling and simulating of the dynamics of flexible manufacturing systems. Petri Nets can capture features such as parallel machines, alternative routings, batch sizes, multiplicity of resources, to name but a few. However, Petri Nets have not been very popular for scheduling in manufacturing due to the Petri Net “state explosion” combined with the NP-hard nature of many of such problems. A promising approach for scheduling consists of generating only portions of the Petri Net state space with heuristic search methods. Thus far, most of this scheduling work with Petri Nets has been oriented to minimize makespan. The problem of minimizing total tardiness and other due date-related criteria has received little attention. In this paper, we extend the Beam A* Search algorithm presented in a previous work with capability to handle the total tardiness criterion. Computational tests were conducted on Petri Net models of both flexible job shop and flexible manufacturing systems. The results suggest that the Petri Net approach is also valid to minimize due date related criteria in flexible systems.