A multi-objective mixed-model assembly line sequencing problem in order to minimize total costs in a Make-To-Order environment,considering order priority

Nowadays, mixed-model assembly line is used increasingly as a result of customers’ demand diversification. An important problem in this field is determining the sequence of products for entering the line. Before determining the best sequence of products, a new procedure is introduced to choose important orders for entering the shop floor. Thus the orders are sorted using an analytical hierarchy process (AHP) approach based on three criteria: critical ratio of each order (CR_o), Significance degree of customer and innovation in a product, while the last one is presented for the first time. In this research, six objective functions are presented: minimizing total utility work cost, total setup cost and total production rate variation cost are the objectives which were presented previously, another objective is minimizing total idle cost, meanwhile two other new objectives regarding minimizing total operator error cost and total tardiness cost are presented for the first time. The total tardiness cost tries to choose a sequence of products that minimizes the tardiness cost for customers with high priority. First, to check the feasibility of the model, GAMS software is used. In this case, GAMS software could not search all of the solution space, so it is tried in two stages and because this problem is NP-hard, particle swarm optimization (PSO) and simulated annealing (SA) algorithms are used. For small sized problems, to compare exact method with proposed algorithms, the problem must be solved using meta-heuristic algorithms in two stages as GAMS software, whereas for large sized problems, the problem can be solved in two ways (one stage and two stages) by using proposed algorithms; the computational results and pairwise comparisons (based on sign test) show GAMS is a proper software to solve small sized problems, whereas for a large sized problem the objective function is better when solved in one stage than two stages; therefore it is proposed to solve the problem in one stage for large sized problems. Also PSO algorithm is better than SA algorithm based on objective function and pairwise comparisons.     

Integrated Set Parts Supply system in a mixed-model assembly line

The mixed-model assembly line (MMAL) is a type of assembly line in which a variety of product models are assembled on the same line. The use of highly variant parts on the assembly line need to be considered carefully to enable satisfactory material flow control and allow for smooth production. To increase the quality of parts supply and parts assembly in MMAL, Toyota has introduced an innovation system known as Set Parts Supply (SPS). In this paper, we investigate the parts supply issues in SPS implementation using a case study in the automotive industry. The linkage of parts supply strategies with Manufacturing Execution System (MES) is introduced to improve the SPS implementation which are (i) synchronized parts supply, (ii) e-kanban system and (iii) Synchronized Supply Sheet. From the research findings, the integration with MES has contributed to the Just In Time in parts supply at the supermarket area and assembly line.     

An imperialist competitive algorithm for a mixed-model assembly line sequencing problem

This paper presents a novel imperialist competitive algorithm (ICA) to a just-in-time (JIT) sequencing problem where variations of production rate are to be minimized. This type of problem is NP-hard. Up to now, some heuristic and meta-heuristic approaches are proposed to minimize the production rates variation. This paper presents a novel algorithm for optimization which inspired by imperialistic competition in real world. Sequences of products where minimize the production rates variation is desired. Performance of the proposed ICA was compared against a genetic algorithm (GA) in small, medium and large problems. Experimental results show the ICA performance against GA.     

A multi-objective scatter search for a mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems.     

A batch scheduling scheme for the workcenter that supplies parts to a mixed-model assembly line

The factory considered in this study consists of a mixed-model assembly line and a workcenter. The mixed-model assembly line (main line) simultaneously produces different product models whose assembly parts are provided in batches by the workcenter. The main purpose of this study is to develop a batch scheduling scheme for the workcenter. The objective function of the scheduling is to provide parts for the main line without delays. The problems that make the scheduling challengeable are as follows: (1) different product models being simultaneously produced on the main line require different parts and (2) space for part inventory in the workcenter is limited. This study presents two batch scheduling approaches used to build a real system for the workcenter.     

Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines

In spite of many studies, investigating balancing and sequencing problems in Mixed-Model Assembly Line (MMAL) individually, this paper solves them simultaneously aiming to minimize total utility work. A new Mixed-Integer Linear Programming (MILP) model is developed to provide the exact solution of the problem with station-dependent assembly times. Because of NP-hardness, a Simulated Annealing (SA) is applied and compared to the Co-evolutionary Genetic Algorithm (Co-GA) from the literature. To strengthen the search process, two main hypotheses, namely simultaneous search and feasible search, are developed contrasting Co-GA. Various parameters of SA are reviewed to calibrate the algorithm by means of Taguchi design of experiments. Numerical results statistically show the efficiency and effectiveness of the proposed SA in terms of both the quality of solution and the time of achieving the best solution. Finally, the contribution of each hypothesis in this superiority is analyzed.     

Scheduling a single vehicle in the just-in-time part supply for a mixed-model assembly line

This paper focuses on the scheduling of a single vehicle, which delivers parts from a storage centre to workstations in a mixed-model assembly line. In order to avoid part shortage and to cut down total inventory holding and travelling costs, the destination workstation, the part quantity and the departure time of each delivery have to be specified properly according to predetermined assembly sequences. In this paper, an optimisation model is established for the configuration that only one destination workstation is involved within each delivery. Four specific properties of the problem are deduced, then a backward-backtracking approach and a hybrid GASA (genetic algorithm and simulated annealing) approach are developed based on these properties. Both two approaches are applied to several groups of instances with real-world data, and results show that the GASA approach is efficient even in large instances. Furthermore, the existence of feasible solutions (EOFS) is analysed via instances with different problem settings, which are obtained by an orthodox experimental design (ODE). An analysis of variance (ANOVA) shows that the buffer capacity is the most significant factor influencing the EOFS. Besides this, both the assembly sequence length and distances to workstations also have noticeable impacts.     

A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing

In this paper, a new modified particle swarm optimization algorithm with negative knowledge is proposed to solve the mixed-model two-sided assembly line balancing problem. The proposed approach includes new procedures such as generation procedure which is based on combined selection mechanism and decoding procedure. These new procedures enhance the solution capability of the algorithm while enabling it to search at different points of the solution space, efficiently. Performance of the proposed approach is tested on a set of test problem. The experimental results show that the proposed approach can be acquired distinguished results than the existing solution approaches.     

Optimisation of mixed-model assembly line balancing problem under uncertain demand

Assembly lines play a crucial role in determining the profitability of a company. Market conditions have increased the importance of mixed-model assembly lines. Variations in the demand are frequent in real industrial environments and often leads to failure of the mixed-model assembly line balancing scheme. Decision makers have to take into account this uncertainty. In an assembly line balancing problem, there is a massive amount of research in the literature assuming deterministic environment, and many other works consider uncertain task times. This research utilises the uncertainty theory to model uncertain demand and introduces complexity theory to measure the uncertainty of assembly lines. Scenario probability and triangular fuzzy number are used to describe the uncertain demand. The station complexity was measured based on information entropy and fuzzy entropy to assist in balancing systems with robust performances, considering the influence of multi-model products in the station on the assembly line. Taking minimum station complexity, minimum workload difference within station, maximum productivity as objective functions, a new optimization model for mixed-model assembly line balancing under uncertain demand was established. Then an improved genetic algorithm was applied to solve the model. Finally, the effectiveness of the model was verified by several instances of mixed-model assembly line for automobile engine.     

A sequencing problem for a mixed-model assembly line in a JIT production system

In an assembly line of a just-in-time (JIT) production system, workers have the power and the responsibility to stop the line when they fail to complete their operations within their work zones. This paper deals with a sequencing problem for the mixed-model assembly conveyor line in the JIT production system. In some environment, the most important criterion is the line stoppage rather than the variation of production rates. The problem is to find an optimal sequence of units that minimizes the total line stoppage time. Lower and upper bounds of the total line stoppage time are derived and the branch-and-bound method is applied to the problem. A numerical example is given.     

A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Mixed-model assembly lines allow for the simultaneous assembly of a set of similar models of a product, which may be launched in the assembly line in any order and mix. As current markets are characterized by a growing trend for higher product variability, mixed-model assembly lines are preferred over the traditional single-model assembly lines.

This paper presents a mathematical programming model and an iterative genetic algorithm-based procedure for the mixed-model assembly line balancing problem (MALBP) with parallel workstations, in which the goal is to maximise the production rate of the line for a pre-determined number of operators.

The addressed problem accounts for some relevant issues that reflect the operating conditions of real-world assembly lines, like zoning constraints and workload balancing and also allows the decision maker to control the generation of parallel workstations.     

A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.     

A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem

The sequencing of products for mixed-model assembly line in Just-in-Time manufacturing systems is sometimes based on multiple criteria. In this paper, three major goals are to be simultaneously minimized: total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Due to the NP-hardness of the problem, a new multi-objective particle swarm (MOPS) is designed to search locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms (MOGAs), i.e. PS-NC GA, NSGA-II, and SPEA-II. Comparison shows that MOPS provides superior results to MOGAs.     

A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.     

The two-stage assembly scheduling problem to minimize total completion time with setup times

We address the two-stage assembly scheduling problem where there are m machines at the first stage and an assembly machine at the second stage. The objective is to schedule the available n jobs so that total completion time of all n jobs is minimized. Setup times are treated as separate from processing times. This problem is NP-hard, and therefore we present a dominance relation and propose three heuristics. The heuristics are evaluated based on randomly generated data. One of the proposed heuristics is known to be the best heuristic for the case of zero setup times while another heuristic is known to perform well for such problems. A new version of the latter heuristic, which utilizes the dominance relation, is proposed and shown to perform much better than the other two heuristics.     

An efficient heuristic for a two-stage assembly scheduling problem with batch setup times to minimize makespan

This paper considers a two-stage assembly scheduling problem of N products with setup times to minimize the makespan. In this problem, there is a machining machine which produces components in the first stage. When the required components are available, a single assembly machine can assemble these components into products in the second stage. A setup time is needed whenever the machining machine starts processing components, or the item of component is switched on the machine. The problem is formulated as a mixed integer programming model, and several properties for finding optimal solutions are developed. Moreover, an efficient heuristic based on these optimal properties is proposed. A lower bound is derived to evaluate the performance of the proposed heuristic. Computational results show that the proposed heuristic can obtain a near optimal solution in almost zero time and the average percentage deviation is only 0.478.     

Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks

This paper presents a new hybrid algorithm, which executes ant colony optimization in combination with genetic algorithm (ACO-GA), for type I mixed-model assembly line balancing problem (MMALBP-I) with some particular features of real world problems such as parallel workstations, zoning constraints and sequence dependent setup times between tasks. The proposed ACO-GA algorithm aims at enhancing the performance of ant colony optimization by incorporating genetic algorithm as a local search strategy for MMALBP-I with setups. In the proposed hybrid algorithm ACO is conducted to provide diversification, while GA is conducted to provide intensification. The proposed algorithm is tested on 20 representatives MMALBP-I extended by adding low, medium and high variability of setup times. The results are compared with pure ACO pure GA and hGA in terms of solution quality and computational times. Computational results indicate that the proposed ACO-GA algorithm has superior performance.     

A study on sequencing method for the mixed-model assembly line in just-in-time production systems

This paper deals with the problem of sequencing products on the mixed-model assembly line in a multi-level JIT production system. We consider both Goal(1) which levels the load at each station on the mixed-model assembly line, and Goal(2) which keeps a constant rate of production of every part in the preceding processes which are used on the mixed-model assembly line, together. We propose a mathematical model which determines the number of every product to be scheduled in each withdrawal position to realize making each withdrawal quantity of all the parts, with a fixed interval, as constant as possible. A sequencing method based on the solution stated above is given. Finally, an example is shown.     

Developing a multi-objective genetic optimisation approach for an operational design of a manual mixed-model assembly line with walking workers

A walking worker assembly line (WWAL), in which each cross-trained worker travels along the line to carry out all required tasks, is an example of lean system, specifically designed to respond quickly and economically to the fluctuating nature of market demands. Because of the complexity of WWAL design problems, classical heuristic approaches are not capable of solving problematic design characteristic of WWAL of very large design space. This paper presents a new genetic approach to address the mixed model walking worker manual assembly line optimisation design problem with multiple objectives. The aim is to select a set of operational variables to perform to the required demand for two product models. The goal is to produce the required models at the lowest cost possible, whilst keeping within an ergonomically balanced operation. Genetic algorithms are developed to tackle this problem. This paper describes the fundamental structure of this approach, as well as the influence of the crossover probability, the mutation probability and the size of the population on the performance of the genetic algorithm. The paper also presents an application of a developed algorithm to the operational design problem of plastic electrical box assembly line.     

Openshop and flowshop scheduling to minimize sum of completion times

This paper deals with efficiently solvable special cases of openshop and permutation-flowshop scheduling where the objective function is minimum sum of completion times.Two O(mn) algorithms for openshop scheduling where all operations have equal processing times, are presented. The first constructs a no-wait schedule and the second a schedule where both criteria (sum of completion times and schedule length) take on their minimal values.For permutation-flowshop scheduling where processing times satisfy dominancy and/or ordered relations, SPT rules are proved to be optimal.