Production Sequencing Problem Research Articles

Production scheduling problem with assembly flow shop systems: mathematical optimisation models

This work presents four mixed integer linear programming (MILP) models for the assembly flow shop problem in order to minimize the makespan. This production environment has two stages: production and assembly. The first stage consists of different machines designed to manufacture parts of a product. The second stage is intended for a final assembly. The performance measure considered is highly essential for industries from different segments, as it focuses on the best use of the time available for production. Statistical analysis with different tools was used to assess the performance and efficiency of mathematical models, emphasizing the analysis of performance profiles. Results showed that mathematical models are efficient, and the position-based model presented the best results for small and large instances during computational experimentation. All mathematical models can be used as direct tools in decision-making for the production sequencing problem in the approached environment.

On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach

Mixed-model straight/U-shaped assembly line has been recognized as a relevant component of Just-In-Time (JIT) production line system. For this system, “Heijunka” design is also challenged as both the task assignment and the production sequence affect the workload imbalance among workstations. In this context and recognizing uncertain task time environment that is often observed in actual manufacturing scene, this research addresses the Line Balancing Problem (LBP) and the Product Sequencing Problem (PSP) jointly and proposes a mathematical model with stochastic task time which is subjected to normal distribution. The objectives of this model are to maximize line efficiency and to minimize the variation of work overload time. A Multi-objective Genetic Algorithm (MOGA) and an Ameliorative Structure of Multi-objective Genetic Algorithm (ASMOGA) with Priority-based Chromosome (PBC) are applied to solve this problem. At last, this research conducts an experimental simulation on a set of benchmark problems to verify the outperformance of the proposed algorithm.

Master production scheduling and sequencing at mixed-model assembly lines in the automotive industry

The customization of final products in the automotive industry involves a large number of optional parts and leads to a huge variety of operation times at the various stations of the assembly line. The master production scheduling problem (MPS) for high-variant mixed-model assembly lines is to assign the individual customer-defined models of a basic product type to short-term production periods while anticipating the negative impacts of an unbalanced model sequence at the lower planning level. We propose a mathematical model formulation for the MPS and develop heuristic solution procedures that attempt to minimize the workload variability. Specifically, these procedures anticipate decisions on the mixed-model sequence and the resulting work overload at stations which has to be balanced by the assignment of utility workers. Furthermore, an integrated planning approach for solving the MPS and the production sequencing problem is proposed.

Optimal production sequencing problem to minimise line stoppage time in a mixed-model assembly line

Mixed-model assembly line is utilised to assemble many product variants on a single line in automobile and other industries. In just-in-time production system, the automation (Jidoka) allows workers to stop a conveyor line whenever they fail to complete their assembly operations within predetermined process times. Given this situation, it becomes important to determine the production sequence to minimise the total conveyor stoppage time in the mixed-model assembly line. In this article, we propose an explicit and complete formulation of the production sequencing problem for the mixed-model line, in which the objective function is to minimise the total line stoppage time. Some important properties are derived and by relaxing the integer constraints in the formulation, a branch and bound algorithm is developed. The performance of a commercial mathematical programming package is discussed by solving several numerical examples using the formulation.

GRASP with path relinking for a multiple objective sequencing problem for a mixed-model assembly line

This research presents a new application of greedy randomised adaptive search procedure (GRASP) to address a production sequencing problem for mixed-model assembly line in a just-in-time (JIT) production system in two different cases. In the former case, small size sequencing problems are considered and two objectives are presented; minimisation of setups and optimisation of stability of material usage rates. These two objectives are inversely correlated with each other, so simultaneous optimisation of both is challenging. This type of problem is NP-hard. The GRASP, with path relinking, searches for efficient frontier where simultaneous optimisation of number of setups and usage rates is desired. Several test problems are solved via GRASP and its performance is compared to solutions obtained via complete enumeration and simulated annealing (SA), tabu search (TS) and genetic algorithms (GA) approaches from the literature. Experimental results reveal that the GRASP with path relinking provides near-optimal solutions in terms of the two objectives and its ‘average inferiority%’ and ‘average percentile’ performances are superior to that of other heuristics. In the latter case, the goal is to explore varying the emphasis of these two conflicting objectives. Larger sequencing problems are considered and solved via GRASP with path relinking. Its objective function values are compared to the solutions obtained via a SA approach from the literature. Experimental results show that GRASP also provides good performance on large size problems and its percentage improvement is better than that of SA. Overall results also show, however, that the GRASP performs poorly with regard to CPU time.

Formulation of an Optimal Production Sequencing Problem in a Complex Free Flow Assembly Line

Formulation of an Optimal Production Sequencing Problem in a Complex Free Flow Assembly Line

Product sequencing problem in Mixed-Model Assembly Line to minimize unfinished works

This study is concerned about how to optimize the input sequence of product models with sequence-dependent setup time in Mixed-Model Assembly Line (MMAL) using conveyor system. Usually MMAL consists of a number of stations linked by a conveyor belt and each station has a work zone limited by upstream and down stream boundaries. To avoid improper interference between operators in the adjacent stations and excess of machine moving range, operators are forced to complete their operations within their predetermined work zone. In this study, our goal is to determine the sequence of models for minimizing the total unfinished work within their work zone. A generalized formulation of the product sequencing problem in MMAL is presented. We developed an optimal procedure using Branch & Bound technique and a heuristic procedure using lower bound and local search. Experimental results show that the heuristic procedure provides reasonably good solutions with low computational costs.

Intra-cell manpower transfers and cell loading in labor-intensive manufacturing cells

Labor-intensive manufacturing cells consist of simple machines and equipment that require continuous operator attendance and involvement. Operators are often re-assigned to different machines when a new product is released to the cell. The main reason for this re-assignment is to maximize the output rate of the cell by balancing the flow of products through several machines with varying capacities. In this paper, first a product-sequencing problem with the objective of minimizing the total intra-cell manpower transfers is introduced. A three-phase hierarchical methodology is proposed to solve the problem optimally. Next, manpower transfer matrix values are modified considering the distances traveled among machines. In the second part of the paper, a machine-level-based similarity coefficient that uses the number of machines as a similarity measure is discussed. Later, these coefficients are used during the cell loading process to minimize makespan and also machine and space requirements. Manpower allocation decisions are made along with scheduling decisions that are critical in most labor-intensive manufacturing cells and both approaches are illustrated with an example problem.

Group sequencing a PCB assembly system via an expected sequence dependent setup heuristic

In circuit board manufacturing, the production sequencing problem with sequence dependent setups is complicated by the fact that the amount of setup required depends on not only the setup directly before the current setup, but can depend on all of the preceding setups. We present a case study that examines the use of a heuristic for estimating the expected number of setups from the sequence dependent setups. Our method is based on estimating the expected number of setups which may occur given a board-feeder setup configuration. Once estimates for the sequence dependent setups are established, they can be used to measure the similarity between boards in clustering algorithms and in nearest neighbor heuristics for group sequencing. The method is tested on an actual printed circuit board assembly system. Then, using a simulation of the assembly system, we compare the sequences generated using the expected number of setups distance measure and the Hamming distance measure to optimal sequences. Our results indicate that grouped sequences generated by using the expected number of setups had significantly better makespan performance when compared to sequences based on the more traditional Hamming distance for the particular system under study. The significant gains in makespan resulted in only moderate increases in work in process and slight increases in manual station utilization.

Production sequencing problem with re-entrant work flows and sequence dependent setup times

This paper addresses a shop scheduling problem where a set of n jobs needs to be scheduled on two machines for the side frame press shop in a truck manufacturing company. A most unusual aspect of the problem is that the setup times required for a job in the first machine depend not on the immediately preceding job but on the job which is two steps prior to it. Redefining the job elements, the problem is formulated into a general two machine flow shop problem which can be solved by dynamic programming with the objective of the minimum makespan. An optimal schedule is found utilizing the sequence dominance conditions and the decisiondelay scheme. Since the computational requirements of dynamic programming are impracticably demanding for large-sized problems, a genetic algorithm is developed and its performance is examined through a comparative study.

Production sequencing problem with reentrant work flows and sequence dependent setup times

This paper addresses a shop scheduling problem for the side frame press shop in a truck manufacturing company. In the problem, a set of n jobs to be scheduled on two machines. All the jobs require processing by the first machine more than once in their operation sequences with reentrant work flows. An unusual aspect of the problem is that the setup times required for a job in the first machine depend not on the immediately preceding job but on the job which is two steps prior to it. Redefining the job elements, the problem is formulated into a general two machine flow shop problem which has a set of job-element precedence constraints. The problem is solved with a modified dynamic programming with the objective of the minimum makespan. An optimal schedule is found utilizing the sequence dominance condition and a decision-delay scheme. A numerical example is presented for the illustration purpose.

Problema de programação da produção um esquema de classificação

Muitas vezes, não é simples encontrar uma classificação exata para os problemas de programação, não somente porque existem diferentes versões para um dado problema, mas, porque vários procedimentos para uma questão particular, são caracterizados por premissas diferentes e limitações de aplicação dos modelos desenvolvidos. O objetivo deste artigo é delinear uma classificação ampla que permita estabelecer o sentido, direção e perspectiva de pesquisas conduzidas na área. O trabalho não tem a intenção de dar um levantamento exaustivo da literatura de programação da produção, que pode ser encontrado em vários outros trabalhos de revisão.

Inter-process synchronization in steel production

SUMMARY The development of strategies for sequencing slabs through the reheat furnace and rolling mill of a modern steel production facility is complicated by multiple, and often conflicting production objectives. Optimal energy efficiency through the reheat furnace may lead to inefficient rolling sequences or less than desirable product delivery schedules. Not only is model formulation complicated, but the combinatorial nature of the problem precludes optimal solution. A heuristic procedure for solving this multi-objective product sequencing problem has been developed and tested under production conditions in a Danish steel mill. The methodology, which is based on a modified ‘greedy’ algorithm is presented and results using actual production data are discussed

A dynamic programming formulation of a production sequencing problem

A dynamic programming algorithm is presented for determining an optimal solution to a problem where n jobs are to be sequenced in a two-stage production environment. Conditions are that the job sequence is the same in both stages; each job requires setups at each stage; setup times are sequence dependent; the second stage setups may overlap first stage production; and the optimal solutions is that with minimum overall duration. The recursion relation is developed, and computational schemes are described for obtaining the optimal solution or solutions. Computational experience is reported. The recursion relation is also extended to the case of any number of stages.

A dynamic programming formulation for a two-stage production sequencing problem with setup times

A dynamic programming formulation for a two-stage production sequencing problem with setup times

A Branch-and-Bound Algorithm for Two-Stage Production-Sequencing Problems

This paper presents an algorithm for determining an optimum solution to a two-stage production sequencing problem with these characteristics: There are n jobs to be sequenced in a two-stage production environment; each production stage is equipped with a single facility; jobs to be sequenced are subject to due-date constraints; facilities in both stages require setup prior to processing each job; setup times in both stages are sequence dependent, and setup cost is assumed to be directly proportional to setup time; and the optimal solution is one that minimizes the total setup cost (of stages I and II) without violating job-due dates. The algorithm employs controlled enumeration through branch-and-bound procedures, together with feasibility tests.