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Main constraints for an assembly line balancing problem (ALBP) are cycle time/number of stations and task precedence relations. However, due to the technological and organizational limitations, several other restrictions can be encountered in real production systems. These restrictions are called as assignment restrictions and can be task assignment, station, resource and distance limitations. The purpose of the study is to evaluate the effects of these restrictions on ALBP using constraint programming (CP) model.

A novel CP model is proposed and compared to mixed-integer programming (MIP) as a benchmark. The objective is to minimize the cycle time for a given number of stations. The authors also provide explicit anthology of the assignment restriction effects on line efficiency, the solution quality and the computation time.

The proposed approach is verified with the literature test instances and a real-life problem from a furniture manufacturing company. Computational experiments show that, despite the fact that additional assignment restrictions are problematic in mathematical solutions, CP is a versatile exact solution alternative in modelling and the solution quality.

Assembly line is a popular manufacturing system in the making of standardized high volume products. The problem of assembly line balancing is a crucial challenge in these settings and consists of assigning tasks to the stations by optimizing one or more objectives. Type-2 AR-ALBP is a specific case with the objective function of minimizing the cycle time for a given number of stations. It further assumes assignment restrictions that can be confronted due to the technological limitations or the strategic decisions of the company management. This is especially encountered in rebalancing lines.

Several solution approaches such as mathematical modelling, heuristic and meta-heuristic are proposed to solve the ALBP in the literature. In this study, a new approach has been presented using CP. Efficient models are developed for Type-2 ALBP with several assignment restrictions. Previous studies have not considered the problem to the presented extent. Furthermore, to the best of the authors’ knowledge, the paper is the first study that solves ALBP with assignment restrictions using CP.

The purpose of this paper is to present the process of balancing a mixed-model assembly line by incorporating unskilled temporary workers who enhance productivity. The authors develop three models to minimize the sum of the workstation costs and the labor costs of skilled and unskilled temporary workers, cycle time and potential work overloads.

This paper deals with the problem of designing an integrated mixed-model assembly line with the assignment of skilled and unskilled temporary workers. Three mathematical models are developed using integer linear programming and mixed integer linear programming. In addition, a hybrid genetic algorithm that minimizes total operation costs is developed.

Computational experiments demonstrate the superiority of the hybrid genetic algorithm over the mathematical model and reveal managerial insights. The experiments show the trade-off between the labor costs of unskilled temporary workers and the operation costs of workstations.

The developed models are based on practical features of a real-world problem, including simultaneous assignments of workers and precedence restrictions for tasks. Special genetic operators and heuristic algorithms are used to ensure the feasibility of solutions and make the hybrid genetic algorithm efficient. Through a case study, the authors demonstrated the validity of employing unskilled temporary workers in an assembly line.

The case provides an opportunity to students to learn about the basic concepts in Project Management using a situation that can be easily understood by all. The case provides the instructor an opportunity to demonstrate to the students as to how precedence relationships are to be generated from assertions made about a project by the project in-charge – a feature that is generally missing in most cases on the subject. The case also provides an opportunity to develop a Linear Programming (LP) model for the project. The teaching note accompanying the case provides a simple, innovative LP formulation and outlines as to how it can be used to identify the critical path and the critical activities. The case can be taught in one session (if LP formulation is not covered). Else, since it is suitable for two sessions, the model can be developed and solved in the class in the second session.

This paper aims to develop a new differential evolution algorithm (DEA) for solving the simple assembly line balancing problem of type 2 (SALBP-2).

Novel approaches of mutation operator and crossover operator are presented. A self-adaptive double mutation scheme is implemented and an elitist strategy is used in the selection operator.

Test and comparison results show that the proposed IDEA obtains better results for SALBP-2.

The presented DEA is called the integer-coded differential evolution algorithm (IDEA), which can directly deal with integer variables of SALBP-2 on a discrete space without any posterior conversion. The proposed IDEA will be an alternative in evolutionary algorithms, especially for various integer/discrete-valued optimization problems.

A survey of project scheduling problems since 1973 limited to work done specifically in the project scheduling area (although several techniques developed for assembly line balancing and job‐shop scheduling can be applicable to project scheduling): the survey includes the work done on fundamental problems such as the resource‐constrained project scheduling problem (RCPSP); time/cost trade‐off problem (TCTP); and payment scheduling problem (PSP). Also discusses some recent research that integrates RCPSP with either TCTP or PSP, and PSP with TCTP. In spite of their practical relevance, very little work has been done on these combined problems to date. The future of the project scheduling literature appears to be developing in the direction of combining the fundamental problems and developing efficient exact and heuristic methods for the resulting problems.

This paper aims to review and discuss four aspects of mixed-model assembly line balancing (MMALB) problem mainly on the optimization angle. MMALB is a non-deterministic polynomial-time hard problem which requires an effective algorithm for solution. This problem has attracted a number of research fields: manufacturing, mathematics and computer science.

This paper review 59 published research works on MMALB from indexed journal. The review includes MMALB problem varieties, optimization algorithm, objective function and constraints in the problem.

Based on research trend, this topic is still growing with the highest publication number observed in 2016 and 2017. The review indicated that the future research direction should focus on human factors and sustainable issues in the problem modeling. As the assembly cost becomes crucial, resource utilization in the assembly line should also be considered. Apart from that, the growth of new optimization algorithms is predicted to influence the MMALB optimization, which currently relies on well-established algorithms.

The originality of this paper is on the research trend in MMALB. It provides the future direction for the researchers in this field.

The value chain of the Norwegian meat production industry has recently been through major structural changes resulting in increased flows and transportation needs at all levels. The purpose of this paper is to present results of the initial stage of a five‐year research project between the Norwegian Meat Research Centre, Norwegian meat companies and Molde University College. The main goal of the project is to develop a decision support system for the transport of live animals to a slaughterhouse to reduce transportation costs while maintaining high level of livestock welfare and meat quality, as these are three main factors for the profitability of both farmers and industry.

The paper presents a mixed integer programming model that combines vehicle routing and inventory control. We introduce the possibility for multiple routes for a given vehicle on a given day in a multiple‐period planning perspective. Arrival times of the loaded vehicles to the slaughterhouse are controlled by production (slaughter) rate and inventory level at the abattoirs so that the supply of animals for slaughter is steady and production breaks are avoided. Livestock welfare is secured by the route duration constraints.

The model has been formulated and tested on small data sets. The major future challenge is to solve real‐life problems from the involved companies.

The main limitation is the present inability to solve large cases.

The model combining transportation and inventory control in a setting of animal welfare constraints is original.

The efficiency of assembly lines is a critical factor for the competitiveness of industries in the global market. The purpose of this paper is to present a line balancing methodology consisting of the combination of a heuristic model and an exact algorithm with intelligent task location or line zone constraints. The objective is to find a minimum cost solution in a feasible computational time with a realistic cost function considering short‐term operation costs, task‐related and workstation capital investment costs, and workstation paralleling.

The methodology is evaluated using different problem sizes, zone sizes and cycle time scenarios. The quality of results is measured by the closeness to the global optimum and the computational time requirements.

The proposed methodology is found to be highly effective with an average percentage difference of −0.63 percent from the optimum solution. In the experimentation, results are compared against the global optimum in 24 of the 36 scenarios tested. In 23 of the 24 (95.8 percent) results, the largest percentage difference is 0.55 percent. In eight of the 12 cases in which the global optimum is not found, the algorithm with zone constraints provided a better solution than the upper bound available when the simulation model pursuing the optimum is stopped. These unfinished runs are stopped after a minimum run time of 24 hours.

The originality of the methodology is on the strategy used to consider workstation paralleling with task‐related capital investment costs. It is the only one with an exact algorithm considering task‐related capital investment costs in combination with workstation paralleling.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

The purpose of this paper is to develop a feasible sequence-oriented new discrete particle swarm optimization (NDPSO) algorithm with novel particles’ updating mechanism for solving simple assembly line balancing problems (SALBPs).

In the NDPSO, a task-oriented representation is adopted to solve type I and type II SALBPs, and a particle directly represents a feasible task sequence (FTS) as a permutation. Then, the particle (permutation) is updated as a whole using the geometric crossover based on the edit distance with swaps for two permutations. Furthermore, the fragment mutation with adaptive mutation probability is incorporated into the NDPSO to improve exploration ability.

Case study illustrates the effectiveness of the NDPSO. Comparative results between the NDPSO and existing real-encoded PSO (CPSO) and direct discrete PSO (DDPSO) against benchmark instances of type I SALBP and type II SALBP show promising higher performance of the proposed NDPSO.

A novel particles’ updating mechanism for FTS-encoded particle is proposed to solve the SALBPs. The comparative results indicate that updating of FTS as a whole seems superior to existing updating of FTS by fragment with respect to exploration ability for solving SALBPs. The novel particles’ updating mechanism is also applicable to generalized assembly line balancing problems.