Multi-objective worker allocation optimisation in a multiple U-line system

Purpose This paper aims to evaluate two operational modes of the worker allocation problem (WAP) in the multiple U-line system (MULS). Five objectives are optimised simultaneously for the most complicated operational modes, i.e. machine-dominant working and fixed-station walking. Besides, the benefits of using multiline workstations (MLWs) are investigated. Design/methodology/approach The elite non-dominated sorting differential evolutionary III (ENSDE III) algorithm is developed as a solution technique. Also, the largest remaining available time heuristic is proposed as a baseline in determining the number and utilisation of workers when the use of MLWs is not allowed. Findings ENSDE III outperforms the cutting-edged multi-objective evolutionary algorithms, i.e. multi-objective evolutionary algorithm based on decomposition and non-dominated sorting differential evolutionary III, under two key Pareto metrics, i.e. generational distance and inverted generational distance, regardless of the problem size. The best-found number of workers from ENSDE III is substantially lower than the upper bound. The MULS with MLWs requires fewer workers than the one without. Research limitations/implications Although this research has extended several issues in the basic model of multiple U-line systems, some assumptions were used to facilitate mathematical computation as follows. The U-line system in this research assumed that all lines were produced only a single product. Besides, all workers were well-trained to gain the same skill. These assumptions could be extended in the future. Practical implications The implication of this research is the benefits of multiline workstations (MLWs) used in the multiple U-line system. Instead of leaving each individual line to operate independently, all lines should be working in parallel through the use of MLWs to gain benefits in terms of worker reduction, balancing worker’s workload, higher system utilisation. Originality/value This research is the first to address the WAP in the MULS with machine-dominant working and fixed-station walking modes. Worker’s fatigue due to standing and walking while working is incorporated into the model. The novel ENSDE III algorithm is developed to optimise the multi-objective WAP in a Pareto sense. The benefits of exploiting MLWs are also illustrated.

Bi-objective unrelated parallel machines scheduling problem with worker allocation and sequence dependent setup times considering machine eligibility and precedence constraints

<p style='text-indent:20px;'>In today's competitive world, scheduling problems are one of the most important and vital issues. In this study, a bi-objective unrelated parallel machine scheduling problem with worker allocation, sequence dependent setup times, precedence constraints, and machine eligibility is presented. The objective functions are to minimize the costs of tardiness and hiring workers. In order to formulate the proposed problem, a mixed-integer quadratic programming model is presented. A strategy called repair is also proposed to implement the precedence constraints. Because the problem is NP-hard, two metaheuristic algorithms, a multi-objective tabu search (MOTS) and a multi-objective simulated annealing (MOSA), are presented to tackle the problem. Furthermore, a hybrid metaheuristic algorithm is also developed. Finally, computational experiments are carried out to evaluate different test problems, and analysis of variance is done to compare the performance of the proposed algorithms. The results show that MOTS is doing better in terms of objective values and mean ideal distance (MID) metric, while the proposed hybrid algorithm outperforms in most cases, considering other employed comparison metrics.</p>

A Simulation-Based Optimization Method for Warehouse Worker Assignment

The general assignment problem is a classical NP-hard (non-deterministic polynomial-time) problem. In a warehouse, the constraints on the equipment and the characteristics of consecutive processes make it even more complicated. To overcome the difficulty in calculating the benefit of an assignment and in finding the optimal assignment plan, a simulation-based optimization method is introduced. We first built a simulation model of the warehouse with the object-oriented discrete-event simulation (O2DES) framework, and then implemented a random neighborhood search method utilizing the simulation output. With this method, the throughput and service level of the warehouse can be improved, while keeping the number of workers constant. Numerical results with real data demonstrate the reduction of discrepancy between inbound and outbound service level performance. With a less than 10% reduction in inbound service level, we can achieve an over 30% increase in outbound service level. The proposed decision support tool assists the warehouse manager in dealing with warehouse worker allocation problem under conditions of random daily workload.