Mixed-model Sequencing Research Articles

A bi-objective mixed-model assembly line sequencing problem considering customer satisfaction and customer buying behaviour

ABSTRACTCustomer satisfaction and focusing on the most profitable customers are the key elements to achieving a sustainable competitive advantage in the current business environment. The mixed-model assembly line (MMAL) is an efficient type of manufacturing system that has been used increasingly over recent years to satisfy diverse customer demands. Product sequencing in an MMAL system has a huge effect on the performance of the firm in meeting customer demand and, therefore, on customer satisfaction. This study aims to address a sequencing problem with respect to recency, frequency and monetary factors. A two-stage framework is developed to achieve this goal. First, the prioritized orders based on the preference ranking organization method for enrichment of evaluations (PROMETHEE) are categorized into high-priority and normal-priority orders. Then, a bi-objective mathematical programming model is developed to determine the optimum sequence of products to minimize the total cost, as well as to maximize levels of customer satisfaction. After validation of the proposed model, owing to the NP-hard nature of this problem, two multi-objective metaheuristics are applied to tackle the second stage of this study. Several test problems are presented to evaluate the performance of these algorithms. The results show the superiority of one solution approach over the other.

An imperialist competitive algorithm in mixed-model assembly line sequencing problem to minimise unfinished works

ABSTRACTMixed-model assembly lines (MMALs) are a type of production lines where a variety of product models similar to product characteristics are assembled in a just-in-time production 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 downstream 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. There is a set of criteria on which to judge sequences of product models in terms of the effective utilisation of these lines. In this paper, the sequence of models for minimising the total unfinished work within their work zone is discussed. A novel imperialist competitive algorithm (ICA) is developed for solving this problem in small-, medium- and large-scale problems. The solutions obtained via ICA are compared against solutions obtained via B&B, a heuristic procedure, GRASP and BDP-2 in small problems, and also against a proposed genetic algorithm and a simulated annealing in small, medium and large problems. Experimental results show that this algorithm provides reasonably good solutions with low computational costs.

Free and regular mixed-model sequences by a linear program-assisted hybrid algorithm GRASP-LP

A linear program-assisted hybrid algorithm (GRASP-LP) is presented to solve a mixed-model sequencing problem in an assembly line. The issue of the problem is to obtain manufacturing sequences of product models with the minimum work overload, allowing the free interruption of operations at workstations and preserving the production mix. The implemented GRASP-LP is compared with other procedures through a case study linked with the Nissan’ Engine Plant from Barcelona.

Multi-level assembly process complexity analysis and its application for mixed-model assembly sequencing

This research aims at understanding the process complexity in assembly system, and the complexity is defined to describe the complexity for the production activities and their sequences. Four primary integer layers and other fractal layers are decomposed from the whole assembly process according to the idea of fractal theory. The four kinds of complexities are station operation complexity, assembly flow complexity, production sequence complexity and production cycle complexity; they are proposed to present the complexity characteristic for different integer layers. The information entropy is adopted to measure these process complexities, and two different measurements are proposed for the pull and push production models respectively. For conquering the two contradictory problems, high operation failure rate and decrease of working emotion, which are caused by inappropriate product similarity distribution, the optimisation objective minimising the diversity of the assembly flow complexity is exploited. Then the multi-objective genetic algorithm is adopted to modelling the mixed-model assembly sequencing problem with two optimisation objectives, and a case study is implemented to demonstrate the approach.

Modelos y métricas para la versión robusta del Car Sequencing Problem con Flotas de vehículos especiales

Partiendo del Car Sequencing Problem (CSP), introducimos el concepto de demanda parcial incierta, incorporando Flotas de vehículos especiales en un plan de demanda. Tras establecer las hipótesis de trabajo con Flotas, proponemos un modelo de programación lineal entera mixta (r-CSP) para satisfacer el máximo número de restricciones del CSP. Posteriormente, definimos multi-secuencia de producción y algunas métricas para evaluar su robustez. El r-CSP considera diversos escenarios de demanda y funciones para medir el requerimiento excesivo de opciones en programas de producción. Dichas funciones son válidas como objetivo en problemas de optimización y como métricas de robustez de multi-secuencias de producción.

A hybrid dynamic programming for solving a mixed-model sequencing problem with production mix restriction and free interruptions

In this article, we propose a hybrid procedure based on bounded dynamic programming assisted by linear programming to solve the mixed-model sequencing problem with workload minimization with serial workstations, free interruption of the operations and with production mix restrictions. We performed a computational experiment with 23 instances related to a case study of the Nissan powertrain plant located in Barcelona. The results of our proposal are compared with those obtained by mixed integer linear programming.

Heuristic approaches for mixed-model sequencing problem with stochastic processing times

Despite many pioneering efforts and works over the past decades, stochastic events have not been studied extensively in mixed-model assembly lines thus far. For a mixed-model sequencing problem with stochastic processing times, this paper aims to minimise expected total work overload. It also focuses on the most critical workstation of the line. In practice, this assumption is useful when the whole or a big portion of the assembly line is considered as a single station. In order to tackle the problem, a dynamic programming (DP) algorithm as well as two greedy heuristics from the literature is employed. However, it is realised that the DP cannot guarantee the optimal sequence neither for stochastic nor deterministic problems. It is because the calculation of work overload is involved in a recursive procedure that affects the states’ value functions. Therefore, by the use of network representation, the problem is modelled as a shortest path problem and a new heuristic, inspired by Dijkstra’s algorithm is developed to deal with it. Numerical results show that the proposed method outperforms other algorithms strongly. Finally, some discussion is provided about why one should consider stochastic parameters and why the proposed heuristic performs well in this regard.

Sequencing Mixed-Model Assembly Line in Electronic Industries Company (EIC)

Mixed-model assembly line has great importance because of its variability. This type of assembly line includes two important problems, the first is the mixed-model line balancing problem and the second is the model sequencing problem. The mixed-model line balancing problem is the problem of assigning tasks to stations in a way to get an effective performance measures, and the model sequencing problem is the problem of deciding the sequence in which different models should be released to the same assembly line. This paper focused on sequencing problem that dependent on the results of balancing problem. There are several methods could be used for solving the sequencing problem, we described the Goal chasing 1 method. This method is applied on the assembly plant of Electronic Industries Company (EIC) that assembles different models of automatic changeover (ACH) which is dependent on the results of (MMSLO) program with a heuristic balancing method.

A note on ‘A bit-wise mutation algorithm for mixed-model sequencing in JIT production systems’ (Nazar & Pillai, 2015, IJPR, 53:19, 5931-5947)

This note discusses three points about the problem of sequencing units on a mixed-model assembly line in ‘A bit-wise mutation algorithm for mixed-model sequencing in JIT production systems’ (Nazar & Pillai, 2015, IJPR, 53:19, 5931-5947). Specifically, the mixed-integer quadratic model, the bit-wise mutation algorithm and the bi-objective problem to optimise both product rate variation and makespan. The conclusion of the discussion is that the three alleged contributions are not valid or are outperformed by those presented in some previously published papers.

Erratum to: GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity

Erratum to: GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity

GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity

A GRASP algorithm is presented for solving a sequencing problem in a mixed-model assembly line. The problem is focused on obtaining a manufacturing sequence that completes the greatest possible amount of required work and fulfils the production regularity property. The implemented GRASP algorithm is compared with other resolution procedures by means of instances from a case study linked to the Nissan’s engine plant in Barcelona.

Modeling of Order Oriented Planning for Printed Circuit Board Assembly Line

Printed circuit board (PCB) assembly planning problem has been given lot of attention to efficiently manage the orders of different PCB products. However, little effort has been paid to describe its comprehensive model which can demonstrate all the planning problems collectively considering in the surface mount technology (SMT) lines. A mixed integer model for PCB assembly line which includes the integration of different levels of the planning problems and considered component allocation problem (line balancing), PCB model sequencing problem (mixed model sequencing problem), feeder arrangement problem and component placement sequencing problem on different SMT machines collectively. Current research is significant to solve different planning problems of the PCB assembly line simultaneously to get the overall global solution of the problem in future.

Consideration of human resources in the Mixed-model Sequencing Problem with Work Overload Minimization: Legal provisions and productivity improvement

Beginning with a variation of the sequencing problem in a mixed-products line (MMSP-W: Mixed-Model Sequencing Problem with Workload Minimization), we propose two new models that incorporate a set of working conditions in regard with human resources of workstations on the line. These conditions come from collective agreements and therefore must be respected by both company and labor unions. The first model takes into account the saturation limit of the workstations, and the second model also includes the activation of the operators throughout the working day. Two computational experiments were carried out using a case study of the Nissan motor plant in Barcelona with two main objectives: (1) to study the repercussions of the saturation limit on the decrease in productivity on the line and (2) to evaluate the recovery of productivity on the line via both activation of operators, while maintaining the same quality in working conditions achieved by limiting the saturation, and auxiliary processors. By results we state that saturation limitation leads an important increase of work overload, which means average economic losses of 28,731.8 Euros/day. However, the productivity reduction may be counteracted by the work pace factor increase, at certain moments of workday, and/or by the incorporation of auxiliary processors into the line.

Hierarchical approach for paced mixed-model assembly line balancing and sequencing with jolly operators

In order to increase flexibility and reduce costs, several companies adopt mixed-model assembly lines whose output products are variations of the same basic model with specific and distinctive attributes. Unfortunately, such attributes typically lead to variations in the task process times. In the case of un-paced buffered assembly lines, these variations are smoothed by buffers with consequences in terms of work-in-progress, costs, space utilisation and lower productivity control. To face such weaknesses, some companies adopt paced un-buffered assembly lines where the cycle time is controlled by the continuous/synchronous moving of the products from the first to the last assembly station. In such contexts, the possibility of assembling different models with different assembly times can be managed through the use of supplementary flexible workforce. This article introduces an innovative balancing and sequencing hierarchical approach for paced mixed-model assembly lines using supplementary flexible workforce called ‘jolly operators’. The goals are to minimise the number of jolly operators and to limit the occurrence of work-overloads, which typically result in out-of-the-line assembly completion. The proposed approach is preliminary validated and applied to a case study from an Italian company assembling industrial air-dryers.

A bit-wise mutation algorithm for mixed-model sequencing in JIT production systems

Product sequencing in a mixed-model production system is an operational level problem and is computationally hard. This necessitates the use of non-traditional optimisation methods to solve it in a short time. In the first phase, this study addresses the mixed-model sequencing problem with the objective of minimising production rates variation (PRV) which is a just-in-time objective. We formulated a mixed integer quadratic assignment model for this problem. LINGO 14.0 solver could solve this model for small- and medium-size problems. The study presents a bit-wise mutation algorithm to solve the sequencing problem with the same objective. This algorithm uses a single unary operator which makes it simpler and faster. Computational results show that the algorithm succeeds in solving large-size PRV problems in a reasonable time, for which the algorithms reported in the literature could only get near-optimal solutions. In the second phase, we consider both PRV and one of the system performance measures, makespan as objectives for the mixed-model sequencing problem. A mathematical description of the underlying model is provided. The bit-wise mutation algorithm is modified to generate a set of non-dominated solutions which provide the decision-maker with the opportunity to trade-off between the two objectives.

Two-stage approach to the mixed-model sequencing problem

This paper proposes a method to address a mixed–model sequencing problem that currently exists in automobile manufacturing. From the standpoint of supply chain management, we proposed a new sequencing method that aims at: 1) levelling the workloads for each process on an assembly line; 2) maintaining a constant rate of production for all the parts in the production process; 3) meeting delivery dates of distributors when the final products are delivered by specialised shipping vessels. In order to simultaneously satisfy these three goals, a two–stage approach is proposed. First, by dividing the production period into shorter periods, the production volume of each product is determined for each of these shorter periods to maintain a constant rate of parts usage and meet the delivery date to distributors. Next, a production schedule is established for each period to realise smoother production on the assembly line.

JIT Mixed-Model Sequencing Rules: Is There a Best One?

This research effort compares four sequencing rules intended to smooth production scheduling for mixed-model production systems in a Just-in-Time/Lean manufacturing environment (“JIT” hereafter). Each rule intends to schedule mixed-model production in such a way that manufacturing flexibility is optimized in terms of system utilization, units completed, average in-process inventory, average queue length, and average waiting time. A simulation experiment, where the various sequencing rules are tested against each other in terms of the above production measures, shows that three of the sequencing rules essentially offer the same performance, whereas one of them shows more variation.

Sequencing mixed-model assembly lines operating with a heterogeneous workforce

We study the problem of sequencing mixed-model assembly lines operating with a heterogeneous workforce. The practical motivation for this study comes from the context of managing assembly lines in sheltered work centres for the disabled. We propose a general framework in which task execution times are both worker and model dependent. Within this framework, the problem is defined and mathematical mixed-integer models and heuristic procedures are proposed. These include a set of fast constructive heuristics, two local search procedures based on approximate measures using either a solution upper bound or the solution of a linear program and a GRASP metaheuristic. Computational tests with instances adapted from commonly used literature databases are used to validate the proposed approaches. These tests give insight on the quality of the different techniques, which prove to be very efficient both in terms of computational effort and solution quality when compared to other strategies such as a random sampling or the solution of the MIP models using a commercial solver.

Modeling and solving the mixed-model sequencing problem to improve productivity

In this paper, it is presented an extension of the mixed-model sequencing problem with work overload minimization (MMSP-W) for production lines with serial workstations, parallel homogeneous processors, and variable operation processing times. This extension is intended to consider that the processing times of the operations can be prolonged or shrunk with respect to the established standard processing times depending on the work pace of the workers. To do this, the activity of workers is set by means of functions which take into account the periods of adaptation and fatigue of the beginning and end of the workday, respectively. Thus, two mathematical models and four functions for the work pace factor are presented and their performances are analyzed through a case study of the Nissan powertrain plant in Barcelona, using the Gurobi solver. The results show that the work overload can completely be either eliminated with an increase of the activity of operators of 5% over their normal work pace or reduced by 88% with an increase of 3.33%. Consequently, the losses due to the uncompleted work or the hiring costs of auxiliary operators can be avoided by demanding a greater effort to workers at certain moments of their workday, but always respecting the limits set by collective agreement.

Simultaneous order scheduling and mixed-model sequencing in assemble-to-order production environment: a multi-objective hybrid artificial bee colony algorithm

In today's competitive manufacturing market, effective production planning and scheduling are crucial to streamline production and increase profit. Successful production planning can achieve efficient capacity utilization and fulfill customer demand in a timely manner. For assemble-to-order companies, the assembly production planning is mainly driven by customer orders. In literature, the master production schedule which assigns production orders of individual models to production intervals is generally treated independently from the product sequencing, which might lead to local optimization for the final assembly schedule. In this paper, both order scheduling and mixed-model sequencing are taken into account simultaneously to formulate the final assembly schedule. Three objectives are concurrently considered including, maximizing net profit earned from orders, reducing sequence-dependent setup time between different models and leveling material usage. A novel multi-objective hybrid artificial bee colony (MHABC) algorithm combined with some steps of genetic algorithm and the Pareto optimality is developed to solve the current problem. Experiments are conducted and performance of the proposed MHABC algorithm is examined with the improved strength Pareto evolutionary algorithm (SPEA2). The results indicate that the proposed MHABC performs better as compared to the SPEA2 and gives better Pareto optimal solutions. Finally, a practical case problem from an engineering machinery company is solved with the proposed approach for simultaneous order scheduling and mixed-model sequencing.