Worker Assignment Problem Research Articles

Cross-trained worker assignment and comparative analysis on throughput of divisional and rotating seru

PurposeSeru (cell) manufacturing system has achieved huge success in production. However, related research is limited, especially, the problem of cross-trained worker assignment. The purpose of this paper is to solve this problem for two representative seru types, divisional and rotating seru, and subsequently, compare throughput performance between the two seru types under reasonable worker-task assignment.Design/methodology/approachFor the cross-trained worker assignment problem, this research presents new models aiming at maximum throughput of seru and workload balance of workers under considering skill levels (SLs) and several practical constraints. Furthermore, factorial experiments that involve four factors, the number of tasks (NT), gap of task time, SL and gap of SL, are performed to compare throughput performance between divisional and rotating seru.FindingsFirst, the maximum throughput of the divisional seru is better than that of the rotating seru under suitable worker assignment. Second, in the seru which has less difference of task time, throughput performance of the rotating seru is better than the divisional seru when the NT is close to the number of assigned workers. Moreover, the influence tendency of the four factors on throughput gap between the two seru types is significant.Originality/valueThis research addresses the worker-task assignment for divisional and rotating seru based on their characteristics. Several findings can help decision maker select more applicable seru type according to various production environments from the perspective of optimum throughput.

A multi-skilled worker assignment problem in seru production systems considering the worker heterogeneity

Abstract The flexibility of production systems is becoming more and more important for manufacturers facing various changes. As an effective way to increase the flexibility in a short time with a small investment, multi-skilled workers have received more attention in recent years. In this paper, a multi-skilled worker assignment problem is solved in the context of seru production systems, in which differences in workers’ skill sets and proficiency levels are taken into consideration. Worker grouping, cell loading, and task assignment are solved concurrently in the problem. A mathematical model with objectives of improving the inter-seru and inter-worker workload balance is proposed to solve the problem. In order to verify the proposed model, a numerical example is presented and solved by a sum weighted method. Due to the NP-hard nature of the model, a meta-heuristic algorithm based on NSGA-II is developed. The algorithm is tested by several numerical examples and the impact of differences in workers’ competency on workload balance is analyzed based on the computational results.

Solving the integrated cell formation and worker assignment problem using particle swarm optimization and linear programming

Both cell design and human issues are important factors for successful implementation of cellular manufacturing. To better implement cellular manufacturing, we investigate the integrated cell formation and worker assignment problem (ICFWAP). A comprehensive linear model is developed for the ICFWAP to determine the optimal allocation of machines, parts and workers. Specific characteristics of this model include the simultaneous consideration of production planning, coexistence of alternative process routings, lot splitting, workload balancing between cells and worker over-assignment to multiple cells. Motivated by the inefficiency of exact approaches, this paper proposes a hybrid approach combining combinatorial particle swarm optimization and linear programming (CPSO-LP) to efficiently solve real-sized problems. In CPSO-LP, decision variables corresponding to part routing selection and part operation assignment are fixed and other variables are allowed to be changed. CPLEX is then used to solve the reduced LP problem. Numerical experiments validate the proposed model. Results reveal that worker over-assignment can reduce the number of workers hired and improve labor utilization rate. The better efficiency and effectiveness of CPSO-LP are proved by comparisons with CPLEX, a genetic algorithm (GA), CPSO, and a hybrid approach combining GA and LP.

A bicriterion algorithm for the allocation of cross-trained workers based on operational and human resource objectives

The problem of allocating a pool of cross-trained workers across multiple departments, units, or work centers is important for both manufacturing and service environments. Within the context of services, such as hospital nursing, the problem has commonly been formulated as a nonlinear assignment problem with an operationally-oriented objective function that focuses on the maximization of service utility as a function of deviations from target staffing levels in the departments. However, service managers might also deem it appropriate to consider human resource-oriented goals, such as accommodating worker preferences, avoiding decay of skill productivity, or the provision of training. We present a bicriterion formulation of the nonlinear worker assignment problem that incorporates both operational and human resource objective criteria. An algorithm for generating the entire Pareto efficient set associated with the bicriterion model is subsequently presented. A small numerical example is used to illustrate the bicriterion model and algorithm. A second example based on a test problem from the literature is also contained in the paper, and a third example is provided in an online supplement. In addition, a simulation experiment was conducted to evaluate the sensitivity of the algorithm to a variety of environmental characteristics.

Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem

This paper deals with mixed-model assembly line balancing and worker assignment problem (MMALBWAP). Mixed-model assembly lines allow the simultaneous assemble of a set of products on a single assembly line and have achieved great attention during last decades. The worker assignment problem deals with assigning workers to workstations considering their abilities and operating costs. The proposed model in this paper considers two incoherent objectives. The first objective aims to minimize the total cycle time. From one side different models of product have different operating task times and on the other hand different worker skills make more varieties in operating times, therefore minimizing cycle time in these problems seems so important. Simultaneous with cycle time the operating costs related to workers is the second objective of interest to be minimized. In order to solve this multi-objective problem a goal programming approach is utilized and because of high complexity of the problem, an evolutionary algorithm named imperialist competitive algorithm (ICA) is developed. In order to evaluate the efficiency of proposed algorithm, the experimental results obtained are compared with a genetic algorithm.

Scheduling and worker assignment problems on hybrid flowshop with cost-related objective function

This paper proposes a colonial competitive algorithm which is improved by variable neighborhood search algorithm for the simultaneous effects of learning and deterioration on hybrid flowshop scheduling with sequence-dependent setup times. By the effects of learning and deterioration, the processing time of a job is determined by position in the sequence and its execution start time. In addition, it is assumed that the processing time of any job depends on the number of workers assigned to the job on a particular stage, and the more workers assigned to a stage, the shorter the job processing time. These additional traits that are added to the scheduling problem coexist in many realistic scheduling situations. This problem consists of two basic questions of job scheduling and worker assignment. Minimization of the earliness, tardiness, makespan, and total worker employing costs is considered as the objective function. To evaluate the performance of the hybrid colonial competitive algorithm, the random key genetic algorithm, immune algorithm, variable neighborhood search, and hybrid simulated annealing metaheuristic presented previously are investigated for comparison purposes, and computational experiments are performed on standard test problems. Results show that our proposed algorithm performs better than the other algorithms for various test problems.

A branch-and-bound algorithm for assembly line worker assignment and balancing problems

In this paper, we studied the assembly line worker assignment and balancing problem, which is an extension of the classical assembly line balancing problem in which an optimal partition of the assembly work among the stations is sought along with the assignment of the operators to the stations. The relationship between this problem and several other well-studied problems is explored, and new lower bounds are derived. Additionally, an exact enumeration algorithm, which makes use of the lower bounds, is developed to solve the problem. The algorithm is tested by using a standard benchmark set of instances. The results show that the algorithm improves upon the best-performing methods from the literature in terms of solution quality, and verifies more optimal solutions than the other available exact methods.

Workforce Assignment into Large-Sized Virtual Cells Using Learning Vector Quantization (LVQ) Approach

Much has been reported in literature on virtual cell formation problems while a limited work is reported on worker assignments. Virtual Cellular Manufacturing Systems (VCMS) have come into existence, replacing traditional Cellular Manufacturing Systems (CMS), to meet highly dynamic production conditions in terms of demand, production lots, processing times, product mix and production sequences. Although the problem of worker assignment and flexibility in cell based manufacturing environments has been studied and analyzed in plenty using various heuristics/mathematical models, application of Artificial Neural Networks (ANN), adapted from the biological neural networks, is the recent development in this field exploiting the ability of ANN to work out mathematically-difficult-to-solve problems. In this attempt, the previous work of the author has been further developed and an attempt has been made to apply Learning Vector Quantization (LVQ) approach into worker assignment problems for higher order virtual cells i.e., three cells configurations and analyze the suitability of LVQ approach in terms of successful classification rate and simulation parameters for a number of VCMS periods.

Minimizing flow time for the worker assignment problem in identical parallel machine models using GA

This article proposes a genetic algorithm approach to minimize total flow time of a set of tasks for identical parallel machines and worker assignment to machines. A spreadsheet-based domain independent general purpose genetic algorithm (GA), an add-in to the spreadsheet software, is used to solve the problem. The paper demonstrates an adaptation of the proprietary GA software to the problem of minimizing total flow time for the worker assignment scheduling problem for identical parallel machine models. Two 100 I/P/n/m/W problems taken from Hu (Int J Adv Manuf Technol 25:1046–1052, 2005, 29:753–757, 2006). The performance of GA is superior to SPT-A/LMC approach used by Hu. As compared to Hu, the proposed GA finds optimal solution for all 100 I/P/6/3/10 problems and for 99 problems out of the 100 I/P/12/3/10 problems.

Minimizing total tardiness for the machine scheduling and worker assignment problems in identical parallel machines using genetic algorithms

The concept of parallel machines has been widely used in manufacturing. This article proposes a genetic algorithm (GA) approach to minimize total tardiness of a set of tasks for identical parallel machines and worker assignment to machines. A spreadsheet-based GA approach is presented to solve the problem. A domain-independent general purpose GA is used, which is an add-in to the spreadsheet software. The paper demonstrates an adaptation of the proprietary GA software to the problem of minimizing total tardiness for the worker assignment scheduling problem for identical parallel machine models. Two 100 I/P/n/m/W problems taken from Hu (Int J Adv Manuf Technol 23:383–388, 2004, Int J Adv Manuf Technol 29:165–169, 2006) for a similar study are simulated. The performance of GA is superior to SES-A/LMC approach used by Hu and very close to the Exhaustive search procedure. It is shown that the spreadsheet GA implementation makes it very easy to adapt the problem for any set of objective measures without changing the actual model. Empirical analysis has been carried out to study the effect of GA parameters, namely, crossover rate, mutation rate, and the population size.

Dynamic cell formation and the worker assignment problem: a new model

In this paper a new model is developed to deal with a simultaneous dynamic cell formation and worker assignment problem (SDCWP). Part routing flexibility and machine flexibility and also promotion of workers from one skill level to another are considered. The proposed model is formulated as a single objective nonlinear integer programming which is converted to a linear one. The objective function consists of two separate components. The first part of the objective function is related to machine-based costs such as production cost, intercell material handling cost, machine costs in the planning horizon. The second part is related to human issues and consists of hiring cost, firing cost, training cost and salary. It is the first time that worker assignment and dynamic cell formation are considered simultaneously. To verify the performance of the proposed model, some numerical examples are presented. Computational and sensitivity analysis results imply the significance of SDCWP.