Integrated Process Planning And Scheduling Problem Research Articles

Improved genetic algorithm based on multi-layer encoding approach for integrated process planning and scheduling problem

Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly, considering the interaction ability between the two subsystems and the multi-flexibility characteristics of the IPPS problem, a new multi-layer integrated encoding method is designed. The encoding method includes feature layer, operation layer, machine layer and scheduling layer, which respectively correspond to the four sub-problems of IPPS problem, which provides a premise for a more flexible and deeper exploration in the solution space. Then, based on the coupling characteristics of process planning and shop scheduling, six evolutionary operators are designed to change the four-layer coding interdependently and independently. Two crossover operators change the population coding in the unit of jobs, and search the solution space globally. The four mutation operators change the population coding in the unit of gene and search the solution space locally. The six operators are used in series and iteratively optimized to ensure a fine balance between the global exploration ability and the local exploitation ability of the algorithm. Finally, performance of IGA-ML is verified by testing on 44 examples of 14 benchmarks. The experimental results show that the proposed algorithm can find better solutions (better than the optimal solutions found so far) on some problems, and it is an effective method to solve the IPPS problem with the maximum completion time as the optimization goal.

A multi-population co-evolutionary algorithm for green integrated process planning and scheduling considering logistics system

Nowadays, green manufacturing is an increasingly significant theme in the worldwide industry fields. System integration can fully exploit the potential of subsystems to achieve satisfying green and low-carbon manufacturing. In particular, integrated process planning and scheduling (IPPS) can obtain better process routes and scheduling schemes to realize more efficient and less energy-consuming production by utilizing the complementary attributes of the process and scheduling subsystems. Additional consideration of the shop logistics system including task assignment of automated guided vehicles (AGVs) can improve shop productivity while ensuring the smooth running of the whole manufacturing system. Taking into account AGV transportation, this paper studies a green multi-objective IPPS problem considering logistics system (MO_IPPS_LS) to minimize the maximum completion time and energy consumption simultaneously. A multi-population co-evolutionary algorithm (MPCEA) is proposed with a novel integrated encoding method. The co-evolutionary framework can ensure the diversity of the populations through a backtracking mechanism and different evolutionary operators including modified critical-path based local searching. The proposed MPCEA is tested on the open benchmarks with different proportions of transport resources. The comparative experimental results show the effectiveness and superiority of MPCEA for solving MO_IPPS_LS problem.

Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling

Integrated process planning and scheduling (IPPS) is important for modern manufacturing companies to achieve manufacturing efficiency and improve resource utilization. Meanwhile, multiple objectives need to be considered in the realistic decision-making process for manufacturing systems. Based on the above realistic manufacturing system requirements, it becomes increasingly important to develop effective methods to deal with multi-objective IPPS problems. Therefore, an improved NSGA-II (INSGA-II) algorithm is proposed in this research, which uses the fast non-dominated ranking method for multiple optimization objectives as an assignment scheme for fitness. A multi-layer integrated coding method is adopted to address the characteristics of the integrated optimization model, which involves many optimization parameters and interactions. Elite and mutation strategies are employed during the evolutionary process to enhance population diversity and the quality of solutions. An external archive is also used to store and update the Pareto solution. The experimental results on the Kim test set demonstrate the effectiveness of the proposed INSGA-II algorithm.

An improved genetic algorithm with modified critical path-based searching for integrated process planning and scheduling problem considering automated guided vehicle transportation task

Integrated process planning and scheduling (IPPS) can take advantage of the complementary attributes of process planning and shop scheduling to obtain better production schemes and process routes improving the whole performance of the manufacturing system. Additional consideration of the shop logistics system including task assignment of automated guided vehicles (AGVs) can improve shop productivity while ensuring the smooth running of the whole manufacturing system. This paper investigates an IPPS problem considering AGV transportation task (IPPS_T). Compared with the original IPPS, IPPS_T addresses not only the process selection, operation sequencing, and machine selection but also the transportation task assignment of the AGVs. Therefore, it is much more difficult than the IPPS problem which has already been proven to be NP-hard. The paper proposes an integrated encoding method to improve the integration of the manufacturing system by representing the process route, shop scheduling scheme, and transportation task assignment plan simultaneously in one individual. This paper designs an improved genetic algorithm (IGA) combining a critical path-based neighborhood searching strategy which can ensure the effectiveness of local search on both AGVs and machines. The numerical experiments with different numbers of AGVs are conducted on the open instances which are extended from the well-known Kim benchmark. The results obtained by the IGA show significant advantages proving the effectiveness of the proposed encoding method and critical path-searching strategy.

An energy-efficient multi-objective integrated process planning and scheduling for a flexible job-shop-type remanufacturing system

This study considers an energy-efficient multi-objective integrated process planning and scheduling (IPPS) problem for the remanufacturing system (RMS) integrating parallel disassembly, flexible job-shop-type reprocessing, and parallel reassembly shops with the goal of realizing the minimization of both energy cost and completion time. The multi-objective mixed-integer programming model is first constructed with consideration of operation, sequence, and process flexibilities in the RMS for identifying this scheduling issue mathematically. An improved spider monkey optimization algorithm (ISMO) with a global criterion multi-objective method is developed to address the proposed problem. By embedding dynamic adaptive inertia weight and various local neighborhood searching strategies in ISMO, its global and local search capabilities are improved significantly. A set of simulation experiments are systematically designed and conducted for evaluating ISMO’s performance. Finally, a case study from the real-world remanufacturing scenario is adopted to assess ISMO’s ability to handle the realistic remanufacturing IPPS problem. Simulation results demonstrate ISMO’s superiority compared to other baseline algorithms when tackling the energy-aware IPPS problem regarding solution accuracy, computing speed, solution stability, and convergence behavior. Meanwhile, the case study results validate ISMO’s supremacy in solving the real-world remanufacturing IPPS problem with relatively lower energy usage and time cost.

Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem

The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches.

Two novel MILP models with different flexibilities for solving integrated process planning and scheduling problems

Process planning and shop scheduling are considered two independent subsystems in traditional flexible manufacturing systems. For correlation and complementarity, integrated process planning and scheduling (IPPS), which has become the main focus of production research, is investigated. The commonly used approaches, intelligent algorithms and their variants, can efficiently find high-quality solutions but cannot guarantee their optimality and stability. To address these shortcomings, based on the OR-nodes of a process network, this paper establishes a mixed-integer linear programming (MILP) model with full-flexibility to solve small-scale IPPS problems. Additionally, for larger-scale problems, another model with semi-flexibility is generated by decomposing the flexibilities into two layers to simplify the original solution space of IPPS. Based on the semi-finished process routes generated by level-1 models, level-2 can search and obtain satisfactory results. The two proposed MILP models are coded in the optimisation programming language and solved by the linear solver CPLEX on 35 benchmark problems with different degrees of flexibility. Extensive experimental results successfully show the superiority of the two proposed models to the other state-of-the-art algorithms and MILP models.

A Genetic Programming-Based Iterative Approach for the Integrated Process Planning and Scheduling Problem

The integrated process planning and scheduling (IPPS) problem is studied in this article, in which operation sequencing, process plan selection, and machine selection are decided simultaneously. For different scenarios, three mixed-integer linear programming (MILP) models are designed. Then, in view of the workload of machines and processing times of jobs, two machine selection techniques are introduced to simplify the optimization of these MILP models. By exploring the structural properties of the MILP models, we put forward a novel lower bound to act as a measurement for the performance of the related algorithms. Considering the real-time requirement and complexity of instances in practice, we design a hybrid greedy heuristic based on a new decision structure of the problem and dispatching rules. Furthermore, in order to create effective dispatching rules to improve the hybrid greedy heuristic, enhanced genetic programming (GP)-based iterative approach is proposed. Experimental results indicate that our approaches are better than other available approaches for the IPPS problem and can reduce the computational time while providing high-quality solutions. <i>Note to Practitioners</i>&#x2014;Due to the widespread use of computer numerical control machines and flexible manufacturing systems in the modern manufacturing industry, the IPPS problem has been widely recognized among both academia and industry over the years. So far, many scholars have put forward several approaches to solve this problem. However, for the instances in practical applications, good solutions are still difficult to get in a reasonable time. The rule-based heuristics, on the other hand, have the advantages of practicability and high efficiency, are much more preferred in practice. Nonetheless, it not only takes plenty of time but requires domain expertise to construct valid dispatching rules for complicated production problems. As an evolution-based machine learning approach, GP has been widely utilized to create dispatching rules for certain scheduling problems. Therefore, in this study, an enhanced GP-based iterative approach is proposed to create dispatching rules for the rule-based heuristic. The computational effectiveness and efficiency of the heuristic based on dispatching rules created by GP are verified in experimental results, and it opens the way for handling the IPPS problem in dynamic environments and multiobjective systems in practice.

Type-2 integrated process-planning and scheduling problem: Reformulation and solution algorithms

We study the type-2 integrated process-planning and scheduling (IPPS) problem where each job is represented by a directed network graph. To the best of our knowledge, there is only one mathematical model in the literature implementing the type-2 IPPS partially, and the solution methods available for this problem are all based on heuristics and metaheuristics. We introduce three properties that enable us to fully formulate all aspects of the type-2 IPPS problem with a mathematical programming model for the first time. To solve our model, we develop a logic-based Benders decomposition method hybridized with constraint programming. We decompose the problem into two smaller ones such that we can use the best solution technique for each one, master problem and subproblem. To enhance our solution approach, we incorporate a combinatorial relaxation of subproblem into the master problem. We evaluate our method using a well-known benchmark including 24 instances and compare its performance with six existing solution methods solving the same benchmark. We solve all the 24 instances of this benchmark to optimality where seven of these 24 instances are solved to optimality for the first time. We also generate a new set of 144 larger instances to further evaluate our solution methods and provide insights on when each method performs better.

Sustainable Integrated Process Planning and Scheduling (IPPS) in RMS: Past, Present and Future

Sustainable manufacturing (SM) is becoming a prominent manufacturing paradigm in the academic and industrial domains as users pay greater attention to the environment and resources. For industrial companies, SM recommends more economic, environmental, and social sustainability standards. Through new technology, the fourth industrial revolution, also known as Industry 4.0, provides greater prospects for SM. In the late 1990s, the reconfigurable manufacturing system (RMS) was presented as an advanced manufacturing paradigm in response to quick market changes. RMS's strong characteristics make it a better fit for SM implementation. Moreover, integrated process planning and scheduling (IPPS) is an important aspect of RMS design since it has the ability to incorporate sustainability, high productivity, low manufacturing costs, and time all at the same time. Nevertheless, it is not an easy task. This paper reviews the state of the art of the IPPS problems in a sustainable RMS context and aims to address the corresponding gaps and challenges for future research directions.

Solving the integrated process planning and scheduling problem using an enhanced constraint programming-based approach

Due to various factors of flexibility introduced into manufacturing systems, researchers have gradually shifted their focus to the integrated process planning and scheduling (IPPS) problem to improve productivity. The previous literature rarely associates IPPS with constraint programming, even though constraint programming has achieved success in the scheduling field. Furthermore, existing approaches are usually customized to certain types of IPPS problems and cannot handle the general problem. In this paper, with a view to obtaining the optimal AND/OR graph automatically, a depth first search generating algorithm is designed to convert the type-1 IPPS problem into our approach's standard input format. Moreover, we propose an approach based on enhanced constraint programming to cope with the general problem, employing advanced schemes to enhance the constraint propagation and improve the search efficiency. Our approach is implemented on ORTOOLS, and its superiority is verified by testing on 15 benchmarks with 50 instances. Experimental results indicate that 41 instances are solved optimally, among which the optimality of the solutions for 20 instances is newly confirmed, and the solutions of six instances are improved. Our approach is the first method to reach the overall optimum in the most influential benchmark with 24 instances.

A modified integer and categorical PSO algorithm for solving integrated process planning, dynamic scheduling and due date assignment problem

Particle Swarm Optimization (PSO) has many successful applications on solving continuous optimization problems. It has been adapted to solve discrete optimization problems using different variants, such as integer PSO (IPSO), discrete PSO (DPSO) and integer and categorical PSO (ICPSO). ICPSO, a recent PSO variant, uses probability distributions instead of the solution values. In this study, we applied ICPSO algorithm to solve dynamic integrated process planning, scheduling and due date assignment (DIPPSDDA) problem which is a higher integration level of well-known problems which are integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Briefly, due date assignment function is integrated to IPPS problem as the third manufacturing function in DIPPSDDA. Furthermore, DIPPSDDA performs scheduling function in a dynamic environment in where jobs arrive to shop floor in any time. The objective of DIPPSDDA problem is to minimize the earliness, tardiness and given due dates length. Since the experimental results show that ICPSO does not find better solutions, crossover and mutation operators used in genetic algorithm were implemented to ICPSO, namely modified ICPSO (MICPSO). Finally, experimental results indicate that the proposed MICPSO provides better performance as compared to genetic algorithm, ICPSO and modified discrete PSO.

Type-2 Integrated Process-Planning and Scheduling Problem: Reformulation and Solution Algorithms

We study the type-2 integrated process-planning and scheduling (IPPS) problem where each job is represented by a directed network graph. To the best of our knowledge, there is only one mathematical model in the literature implementing the type-2 IPPS partially, and the solution methods available for this problem are all based on heuristics and metaheuristics. We introduce three properties that enable us to fully formulate all aspects of the type-2 IPPS problem with a mathematical programming model for the first time. To solve our model, we propose a logic-based Benders decomposition hybridized with constraint programming. We decompose the problem into two smaller problems in such a way that we can use the best solution technique for each part. To enhance our solution approach, we develop a relaxation. We evaluate our proposed method using a well-known benchmark including 24 instances and compare its performance with eight existing solution methods solving the same benchmark. We solve all the 24 instances of this benchmark to optimality where seven of these 24 instances are solved to optimality for the first time. We also generate a set of new 144 larger instances to further evaluate our solution methods and provide insights on when each method performs better.

Multi-Objective Optimization of Integrated Process Planning and Scheduling Considering Energy Savings

With the emergence of the concept of green manufacturing, more manufacturers have attached importance to energy consumption indicators. The process planning and shop scheduling procedures involved in manufacturing processes can both independently achieve energy savings, however independent optimization approaches limit the optimization space. In order to achieve a better optimization effect, the optimization of energy savings for integrated process planning and scheduling (IPPS) was studied in this paper. A mathematical model for multi-objective optimization of IPPS was established to minimize the total energy consumption, makespan, and peak power of the job shop. A hierarchical multi-strategy genetic algorithm based on non-dominated sorting (NSHMSGA) was proposed to solve the problem. This algorithm was based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) framework, in which an improved hierarchical coding method is used, containing a variety of genetic operators with different strategies, and in which a population degradation mechanism based on crowding distance is adopted. The results from the case study in this paper showed that the proposed method reduced the energy consumption by approximately 15% for two different scheduling schemes with the same makespan. The computational results for NSHMSGA and NSGA-Ⅱ approaches were evaluated quantitatively in the case study. The C-metric values for NSHMSGA and NSGA-Ⅱ were 0.78 and 0, the spacing metric values were 0.4724 and 0.5775, and the maximum spread values were 1.6404 and 1.3351, respectively. The evaluation indexes showed that the NSHMSGA approach could obtain a better non-dominated solution set than the NSGA-Ⅱ approach in order to solve the multi-objective IPPS problem proposed in this paper.

Multi-layer collaborative optimization method for solving fuzzy multi-objective integrated process planning and scheduling

Research on integrated process planning and scheduling (IPPS) is of great significance to the improvement of the overall quality of machinery manufacturing system. In the actual manufacturing process, the manufacturing system is often accompanied by some unpredictable uncertain disturbance factors, for instance uncertain processing time of jobs and changes of due date, etc. These uncertain disturbance events will ultimately affect production efficiency and customer satisfaction. Consequently, this paper considers the multi-objective IPPS problem with uncertain processing time and uncertain due date. A multi-layer collaborative optimization (MLCO) method is designed for the fuzzy multi-objective IPPS (FMOIPPS) problem, including three layers. For the process planning layer, the basic genetic algorithm is used to provide various near optimal process plans for the process selection system. For the process selection layer, a multi-objective genetic algorithm (MOGA) is designed to optimize the process selection population. A sharing function method is introduced to maintain population diversity. An individual comprehensive evaluation method is introduced to evaluate non-dominated solutions. The crowded distance, fast non-dominated sorting and elite strategy based on NSGAII is adopted in the proposed MOGA. The external archive method is employed to preserve the non-dominated solutions generated during population evolution. For the scheduling layer, a MOGA with a boundary search strategy is proposed. The boundary search strategy is designed to improve the search ability of boundary solutions. Three optimization objectives are minimizing the spread of fuzzy makespan, minimizing fuzzy makespan and maximizing average customer satisfaction simultaneously. The target of scheduling layer is to make scheduling arrangements for the process information obtained by process selection layer. Through mutual cooperation among each layer, guide the overall optimization process, and finally get satisfactory solutions. Different problem examples of various scales are employed to verify feasibility and effectiveness of the MLCO method. The experimental results indicate that the MLCO method can effectively address FMOIPPS problem.

Hierarchical multistrategy genetic algorithm for integrated process planning and scheduling

To adapt to the flexibility characteristics of modern manufacturing enterprises and the dynamics of manufacturing subsystems, promote collaboration in manufacturing functions, and allocate production resources in a reasonable manner, a mathematical model of integrated process planning and scheduling (IPPS) problems was developed to optimize the global performance of manufacturing systems. To solve IPPS problems, a hierarchical multistrategy genetic algorithm was developed. To address the multidimensional flexibility of IPPS problems, a chromosome coding method was designed to include a scheduling layer, a process layer, a machine layer, and a logic layer. Multiple crossover operators and mutation operators with polytypic global or local optimization strategies were used during the genetic operation stage to expand the algorithm’s search dimension and maintain the population’s diversity, thereby addressing the problems of population evolution stagnation and premature convergence. The effectiveness of the algorithm was verified by benchmark testing in the example simulation process. The test data show that if the makespan is taken as the optimization target, the proposed genetic algorithm performs better in solving IPPS problems with high complexity. The use of multistrategy genetic operators and logic layer coding makes a significant contribution to the improved performance of the algorithm reported in this paper.

A Neutrosophic Number-Based Memetic Algorithm for the Integrated Process Planning and Scheduling Problem With Uncertain Processing Times

Process planning and scheduling are two crucial components in a flexible manufacturing system. Lots of novel meta-heuristics have been applied to the integrated process planning and scheduling (IPPS) problem for an efficient utilization of manufacturing resources; nevertheless, the tricky part in real life stems from the uncertainty in processing times. Existing publications regarding IPPS problems mainly focus on cases with nominal or fixed processing times; nevertheless, processing time fluctuations will certainly result in an intolerable deviation between the actual makespan and the nominal one. This research focuses on the IPPS problem with uncertain processing times to hedge against the uncertainty in makespan. The novel neutrosophic numbers are first introduced to model the uncertain processing times. A neutrosophic number based mixed integer linear programming (MILP) model is established; due to the non-deterministic polynomial (NP)-hardness and the complexity in solving the model, a variable neighborhood search (VNS) incorporated memetic algorithm (MA) is then developed to facilitate more robust solutions. In the proposed algorithm, the nominal makespan criterion and the deviation (robustness) criterion have been considered in a weighted sum manner. The well-known Kim's benchmark is adopted to test the performance of the proposed algorithm and different degrees of fluctuations are also defined in experiments. Computational results reveal that the VNS based local search method is powerful in capturing promising solutions; competitive solutions with superior nominal makespan and robustness have been obtained. This research presents a novel perspective or methodology to seek more robust solutions for the uncertain IPPS problem.

A New Process Quality-based Multi-objective Multi-part Approach for the Integrated Process Planning and Scheduling (IPPS) Problem in Reconfigurable Manufacturing Environment

Abstract This paper addresses the so-called integrated process planning and scheduling (IPPS) problem in a reconfigurable manufacturing environment. Process planning and scheduling are two important and complex functions in manufacturing. To reduce the problem complexity, they are considered sequentially by traditional approaches. In this paper, we consider the simultaneous integration of both functions by developing a heuristic approach to solve the IPPS problem in a reconfigurable environment. Reconfigurable manufacturing systems (RMSs) comprise of a set of machines distinguished by multiple working configurations and tools. Each machine can perform a certain number of operations based on its configurations and their availability. The purpose of the proposed heuristic approach is to find the best assignment of operations to machines while considering process-quality. Finally, to demonstrate the approach applicability, an illustrative numerical example is presented and the results discussed.

Particle swarm optimization hybridized with genetic algorithm for uncertain integrated process planning and scheduling with interval processing time

Integrated process planning and scheduling (IPPS) is a hot research topic on providing a blueprint of efficient manufacturing system. Most existing IPPS models and methods focus on the static machining shop status. However, in the real-world production, the machining shop status changes dynamically because of external and internal fluctuations. The uncertain IPPS can better model the practical machining shop environment but is rarely researched because of its complexity (including the difficulties of modelling and algorithm design). To deal with the uncertain IPPS problem, this paper presents a new uncertain IPPS model with uncertain processing time represented by the interval number. A new probability and preference-ratio based interval ranking method is proposed for precise interval computation. Particle swarm optimization (PSO) algorithm hybridizing with genetic algorithm (GA) is designed to achieve the good solution. To improve the search capability of the hybrid algorithm, the special genetic operators are adopted corresponding to the characteristics of uncertain IPPS problem. Some strategies are designed to prevent the particles from trapping into a local optimum. Six experiments which are adopted from some famous IPPS benchmark problems have been used to evaluate the performance of the proposed algorithm. The experimental results illustrate that the proposed algorithm has achieved good improvement and is effective for uncertain IPPS problem.

Decomposition algorithms for the integrated process planning and scheduling problem

There are several algorithms to solve the integrated process planning and scheduling (IPPS) problem (i.e., flexible job shop scheduling with process plan flexibility) in the literature. All the existing algorithms for IPPS are heuristic-based search methods and no research has investigated the use of exact solution methods for this problem. We develop several decomposition approaches based on the logic-based Benders decomposition (LBBD) algorithm. Our LBBD algorithm allows us to partition the decision variables in the IPPS problem into two models, master-problem and sub-problem. The master-problem determines process plan and operation-machine assignment, while the sub-problem optimizes sequencing and scheduling decisions. To achieve faster convergence, we develop two relaxations for the optimal makespan objective function and incorporate them into the master-problem. We analyze the performance and further enhance the algorithm with two ideas, a Benders optimality cut based on the critical path and a faster heuristic way to solve the sub-problem. 16 standard benchmark instances available in the literature are solved to evaluate and compare the performances of our algorithms with those of the state-of-the-art methods in the literature. The proposed algorithm either results in the optimal solution or improves the best-known solutions in all the existing instances, demonstrating its superiority to the existing state-of-the-art methods in literature.