Variable Sublots Research Articles

Lot streaming in workforce scheduling problem for seru production system under Shojinka philosophy

Drawing upon efficiency, flexibility, and responsiveness, this study attempts to explore the impact of lot streaming and worker assignment strategies in the workforce scheduling problem for the seru production system (SPS), which has emerged as an alternative to traditional assembly lines. The addressed problem involves decisions related to employee timetabling, lot splitting, and sublot scheduling. While scheduling problems in SPS have been extensively researched in recent years, the workforce scheduling problem has not received much attention in the literature. Moreover, to our best knowledge, it has not been investigated with the sublot division methodologies within the context of SPS so far. To bridge this gap, a generic novel optimization model is developed with the objective of minimizing average flow time (AFT) by integrating lot streaming and the Shojinka philosophy through modules within the model. Additionally, the structural properties of the problem are examined, and lower and upper bound formulations are developed based on these properties. Given the NP-hard nature of the problem, customized approaches based on the genetic algorithm (GA) are proposed for solving large-sized problems, considering the Shojinka and division methodologies. The computational results clarify that achieving Shojinka with variable sublots division methodology significantly reduces the AFT objective. Furthermore, the findings confirm that adapting variable sublots in operational scenarios results in a drastic improvement in system performance, regardless of the implemented worker assignment strategy.

A novel collaborative iterative greedy algorithm for hybrid flowshop scheduling problem with batch processing machines and variable sublots

ABSTRACT Lot streaming technology enables continuous overlapping operations, which is of great significance in shortening production cycles, reducing unnecessary waiting time, and increasing production capacity. However, the capacity constraint of batch processing machines may lead to inevitable variations in sublots. Therefore, the key focus of our research is to control variations of sublot for maximising benefits. In view of this, we investigate a hybrid flowshop scheduling problem (HFSP) with batch processing machines and variable sublots (HFSP-BVS) integrating sequence-dependent setup times and transportation times. To address HFSP-BVS, a MILP model is first established, and a novel collaborative iterative greedy (NCIG) algorithm is proposed to optimise the cumulative payoffs associated with delivery dates. In NCIG, a collaborative initialisation method by extracting good information from an archive is proposed, and a specific destruction-reconfiguration strategy is designed to control the variations of sublots in the batch processing stage. Furthermore, a dynamic acceptance criterion is designed to balance the algorithm's exploitation and exploration capabilities. Lastly, we conduct comparisons between the NCIG algorithm and five other metaheuristic algorithms on 100 test instances. The results show that NCIG outperforms them by 1.89% and 61.42% on average in terms of the total penalty and RPI values, respectively.

A decomposition-based multi-objective Jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting

This study examined the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which has rarely been considered in the literature and is beneficial to real industry settings. Upon variable sublots and intermingling setting, a multi-objective mixed-integer linear programming model (MILP) is formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. Considering the NP-hard characteristic of the problem, the MILP model would get restricted in solving medium and large scale instances. Thus building an efficient multi-objective optimization algorithm is desirable. Motivated by the Jaya algorithm and decomposition idea, we propose a new algorithm framework called a decomposition based multi-objective Jaya algorithm (MOJA/D) to achieve a balance between exploration and exploitation. This MOJA/D framework can provide some guidance in algorithm construction for other multi-objective optimization problems. Following the MOJA/D framework, a specific MOJA/D metaheuristic is further designed for coping with the proposed problem. First, a novel forward and backward decoding strategy is designed to implement coevolution among different sub-problems and enhance the population diversity. Second, the Jaya updating mechanism is designed based on problem knowledge to guide the solutions moving towards the best solution and away from the worst solution. Moreover, considering the tradeoff between two conflicting objectives, the problem-specific local search strategies are designed and integrated with the neighborhood based local search strategies to enhance exploitation in the search process. The influence of parameter setting on MOJA/D is investigated by using design of experiments. The statistical results from extensive computational experiments demonstrate the effectiveness of the specific algorithm designs and show that the MOJA/D metaheuristic is superior to the existing algorithms in solving the proposed problem.

Guided shuffled frog-leaping algorithm for flexible job shop scheduling problem with variable sublots and overlapping in operations

In this study, a guided shuffled frog-leaping algorithm (GSFLA) is proposed to minimise the makespan of the flexible job shop scheduling problem with variable sublots and overlapping in operations (FJSP-VSOO). In order to express the solution space more efficiently and completely for the co-optimization of batching and scheduling problems, an operation-level batching encoding considering sub-batches splitting is designed. Considering the characteristics of overlapping in operations, an active decoding method based on the unit assignment and right-shift operation is developed. To ensure the quality and diversity of the initial population, a hybrid initialization method based on dynamic load balancing is proposed. In addition, to balance the global and local search capabilities of the GSFLA, a sublot disturbance based local search algorithm is embedded in the process of the priority guide memeplex evolution, which can adaptively adjust the search tendency of the algorithm through the priority guide operation. Finally, the effectiveness of the proposed methods is verified in extensive experiments based on instances of FJSP with lot streaming (LS) on different scales. Moreover, compared with some advanced algorithms, the results show that the GSFLA has superiority in terms of optimization ability and solution efficiency.

Research on discrete intelligent workshop lot-streaming scheduling with variable sublots under engineer to order

Engineer to order is a highly customized production pattern. A flexible job shop scheduling problem with lot streaming is introduced in the discrete intelligent workshop, which considers setup time, changeover time, and single-piece flowing workpieces under engineer to order. For lot streaming, the advantages of variable sublots are analyzed by comparing different lot splitting strategies. Upon variable sublots, a mixed-integer linear programming model is formulated, but gets restricted in solving large-size problems. Then a hyper-heuristic improved genetic algorithm consisting of high and low levels is proposed. An improved niche genetic algorithm is employed for the high level due to its excellent global search performance, while the low level encapsulates the particle swarm optimization as its perturbation operator. Furthermore, several problem-oriented components are designed to enhance its performance, for example, a high-low level coordination mechanism with clearly global–local division, a decoding method based on greedy and saving rules, a self-adaptive earliest start time, and a population initialization and resetting method. Finally, experiments are organized, and the result shows the proposed algorithm performs up to 24.46% better than the reference group in solving benchmark instances, for which several components are tested to distinguish their main contributions. The general computational performance is also examined through the CEC’17 test suite. For enterprise examples with medium to large sizes, an average 99.52% operation rate demonstrates its high practicality.

Improved Multi-Objective Artificial Bee Colony Algorithm for Parallel Machine Lot-Streaming Scheduling Problem with Limited and Variable Sub-Lots

Improved Multi-Objective Artificial Bee Colony Algorithm for Parallel Machine Lot-Streaming Scheduling Problem with Limited and Variable Sub-Lots

Efficient multi-objective algorithm for the lot-streaming hybrid flowshop with variable sub-lots

Abstract Recent years, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been researched and applied for numerous optimization problems. In this study, we propose an improved version of MOEA/D with problem-specific heuristics, named PH-MOEAD, to solve the hybrid flowshop scheduling (HFS) lot-streaming problems, where the variable sub-lots constraint is considered to minimize four objectives, i.e., the penalty caused by the average sojourn time, the energy consumption in the last stage, as well as the earliness and the tardiness values. For solving this complex scheduling problem, each solution is coded by a two-vector-based solution representation, i.e., a sub-lot vector and a scheduling vector. Then, a novel mutation heuristic considering the permutations in the sub-lots is proposed, which can improve the exploitation abilities. Next, a problem-specific crossover heuristic is developed, which considered solutions with different sub-lot size, and therefore can make a solution feasible and enhance the exploration abilities of the algorithm as well. Moreover, several problem-specific lemmas are proposed and a right-shift heuristic based on them is subsequently developed, which can further improve the performance of the algorithm. Lastly, a population initialization mechanism is embedded that can assign a fit reference vector for each solution. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several presented algorithms, both in solution quality and population diversity.

Two-stage hybrid flow shop batching and lot streaming with variable sublots and sequence-dependent setups

A paint manufacturing firm's customers typically place orders for two or more products simultaneously. Each product belongs to a family that denotes batching compatibility during manufacturing. Further, products can be split into several sublots to allow overlapping production in a two-stage hybrid flow shop wherein various identical, capacitated machines operate in parallel at each stage. We present a mixed-integer linear program (MILP) for this integrated batching and lot streaming problem with variable sublots, incompatible job families, and sequence-dependent setup times. The model determines the number and size of sublots for each product and the production sequencing for each sublot such that the total weighted completion time is minimised. To promote practical implementation, we develop and evaluate heuristics to efficiently solve this problem.

Lot Streaming Flow Shop with a Heterogeneous Machine

It is possible to obtain greater productivity of a production system by overlapping the operations required to process a manufacturing order. This methodology, known as lot streaming, requires dividing the production order (lot) into smaller sublots. In this article, we study production systems that include machines that operate in batch mode (processing a group of units at the same time) and single processing machines (processing one unit at a time) arranged in a flow shop configuration, that is all jobs must go through the same production stages in the same order. The obtained results show that addressing the problem with consistent sublots (a common sublot size used for the whole process) is inefficient. On the other hand, addressing the problem considering the sizing of sublots for each machine (variable sublots) greatly improves the quality of the solution but is computationally intensive (limiting the size of the problem that can be solved). Therefore, a decomposition procedure is proposed on the decision of sublots sizing. This procedure greatly improves the solution obtained using consistent sublots and does so with lower computational requirements than the variable sublots approach.

Flexible job shop scheduling with lot streaming and sublot size optimisation

Models and optimisation approaches are developed for a flexible job shop scheduling problem with lot streaming and lot sizing of the variable sublots. A two-stage optimisation procedure is proposed. First, the makespan value is minimised with the smallest sublots defined for the problem instance. This makes it possible to shorten the makespan significantly, because each sublot is transferred separately to the next operation of a job. In the second stage, the sizes of the sublots are maximised without increasing the obtained makespan value. In this way, the quantity of sublots and transport activities is limited together with the related manufacturing cost. Two objectives are defined for the second stage. The first one is the maximisation of the sum of the sublot sizes of all operations, the second one is the maximisation of the number of the operations which do not need to be split at all. Mixed-integer linear programming, constraint programming and graph-based models are implemented for the problem. Two optimisation approaches are developed and compared in computational experiments for each stage and objective, one approach is based on a third-party solver, and the second one on an independent own implementation, namely a tabu search and a greedy constructive heuristic.

Off-Line and Dynamic Production Scheduling – A Comparative Case Study

Abstract A comprehensive case study of manufacturing scheduling solutions development is given. It includes highly generalized scheduling problem as well as a few scheduling modes, methods and problem models. The considered problem combines flexible job shop structure, lot streaming with variable sublots, transport times, setup times, and machine calendars. Tabu search metaheuristic and constraint programming methods have been used for the off-line scheduling. Two dynamic scheduling methods have also been implemented, i.e., dispatching rules for the completely reactive scheduling and a multi-agent system for the predictivereactive scheduling. In these implementations three distinct models of the problem have been used, based on: graph representation, optimal constraint satisfaction, and Petri net formalism. Each of these solutions has been verified in computational experiments. The results are compared and some findings about advantages, disadvantages, and suggestions on using the solutions are formulated.

Comparison of Improved Sheep Flock Heredity Algorithm and Artificial Bee Colony Algorithm for lot Streaming in m-Machine Flow Shop Scheduling

Lot streaming is a technique used to split the processing of lots into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production time (makespan). The objective of this paper is to minimize the makespan and total flow time of n-job, m-machine lot-streaming problem in a flow shop with variable size sublots and also to determine the optimal sublot size. In recent times researchers are concentrating and applying intelligent heuristics to solve Flow shop problems with lot streaming. In this research, Improved Sheep Flock Heredity algorithm (ISFHA) and Artificial Bee Colony (ABC) algorithms are used to solve the problem. The results obtained by the proposed algorithms are also compared with the performance of other worked out traditional heuristics. The computational results show that the identified algorithms are more efficient, effective and better than the algorithms already tested for this problem.

A genetic algorithm for one-job m-machine flowshop lot streaming with variable sublots

Lot streaming is a technique used to split the processing of lots (batches) into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production makespan. In this technique, a production lot may be split into equal, consistent or variable sublots. Recent literature shows that, when production setup time is considered, significant lead time improvement is possible if variable sublots are used. In this research, however, we noted that lot streaming problems with variable sublots are difficult to solve using off-shelf optimisation packages even for problems of smaller sizes. Thus, efficient solution procedures are needed for solving such problems. In this paper, we develop a hybrid genetic algorithm for a model that appeared in recent literature for one -job m -machine lot streaming problems with variable sublots and setup. Computational results showed that the performance of the proposed genetic algorithm is encouraging.

A hybrid genetic algorithm for flowshop lot streaming with setups and variable sublots

Lot streaming is the process of splitting a given lot or job to allow the overlapping of successive operations in flowshops or multi-stage manufacturing systems to reduce manufacturing lead time. Recent literature shows that significant lead time improvement is possible if variable sublots, instead of equal or consistent sublots, are used when production setup time is considered. However, lot streaming problems with variable sublots are difficult to solve to optimality using off-shelf optimisation packages even for problems of small and experimental sizes. Thus, efficient solution procedures are needed for solving such problems for practical applications. In this paper, we develop a mathematical programming model and a hybrid genetic algorithm for solving n-job m-machine lot streaming problems with variable sublots considering setup times. The preliminary computational results are encouraging.

Lot streaming with variable sublots: an integer programming formulation

This paper deals with the question of how to split a given lot into sublots so as to allow their overlapping production in a flow shop environment. The size of each sublot may vary over the stages. We consider an arbitrary number of stages and assume sublot availability, that is, only completed sublots are allowed to be transferred to the next stage. A mixed integer programming formulation is presented, which enables us to find optimal solutions for medium sized instances. The model is extended further to deal with different settings and objectives. Computational results confirm that the exploitation of variable sublots is advantageous and may lead to a significant increase in productivity.

A heuristic method for discrete lot streaming with variable sublots in a flow shop

Lot streaming is the process of splitting a given lot or job to allow the overlapping of successive operations in multi-stage manufacturing systems, thereby reducing the makespan of the corresponding schedule. In this research, we propose a heuristic method for discrete lot streaming with variable sublots. This method consists of two procedures: (1) determining a continuous solution (sublots with real values) for variable lot streaming and (2) deriving a discrete solution by rounding up the continuous solution. One sample description is presented for demonstration purposes. The proposed method is then tested and evaluated via simulation. The results show that the proposed method outperforms other existing heuristic methods such as discrete lot streaming with consistent sublots, and simulated annealing (SA).

MODEL STRUCTURE AND SOLUTION PROCEDURES FOR VARIABLE SUBLOTS OF A SINGLE PRODUCT IN A FLOW SHOP

ABSTRACT Variable sublot models have received a lot of attentions [4,23]. In this research, a model structure for variable sublots is proposed and analyzed for a single product in a multi-stage flow shop environment. The structure contains three issues as follows: (1)Determine transferring machines, (2)Determine the corresponding reallocated lot streaming ranges, and (3)Determine the optimal lot streaming. Based on the three issues mentioned above, optimal lot streaming is discussed. For computational efficiency, a heuristic solution procedure for the variable lot streaming is proposed. The proposed heuristic procedure is tested and evaluated via simulation. The results show that this heuristic procedure is better than other existing variable lot streaming procedures.

LOT STREAMING WITH VARIABLE SUBLOTS FOR SINGLE PRODUCT IN A FLOW SHOP

ABSTRACT Lot-streaming is the process of splitting a given lot or job to allow the overlapping of successive operations in multi-stage production system, thereby reducing the makespan of the corresponding schedule. In the past, the concept of consistent transfer batches is always discussed and applied in the lot-splitting problem. However, the drawback for consistent transfer batches may increase the makespan. In this research, we propose a model with the concept of variable transfer batches for a single job in a multi-stage flow shop environment with the objective of minimizing makespan. The results show the proposed model, through flexibly allocating transfer batch sizes on each machine, outperforms others based on the concept of consistent transfer batches in terms of makespan and time complexity.

Basic Techniques for Lot Streaming

We present an overview of basic models and solution algorithms for the lot streaming problem. We include models with continuous and discrete sublot sizes, models with and without intermittent idling of machines, and models with consistent and variable sublots. We also introduce a model with limited transporter capacity. First we present solutions for two machines, then generalize to three machines and, where possible, to several machines. We synthesize previous research and introduce several new results.