Adaptation of Shift Sequence Based Method for High Number in Shifts Rostering Problem for Health Care Workers

Purpose—is to investigate a shift sequence-based approach efficiency then problem consisting of a high number of shifts. Research objectives:• Solve health care workers rostering problem using a shift sequence based method.• Measure its efficiency then number of shifts increases. Design/methodology/approach—Usually rostering problems are highly constrained.Constraints are classified to soft and hard constraints. Soft and hard constraints of the problem are additionally classified to: sequence constraints, schedule constraints and roster constraints. Sequence constraints are considered when constructing shift sequences. Schedule constraints are considered when constructing a schedule. Roster constraints are applied, then constructing overall solution, i.e. combining all schedules.Shift sequence based approach consists of two stages:• Shift sequences construction,• The construction of schedules.In the shift sequences construction stage, the shift sequences are constructed for each set of health care workers of different skill, considering sequence constraints. Shifts sequences are ranked by their penalties for easier retrieval in later stage.In schedules construction stage, schedules for each health care worker are constructed iteratively, using the shift sequences produced in stage 1. Shift sequence based method is an adaptive iterative method where health care workers who received the highest schedule penalties in the last iteration are scheduled first at the current iteration. During the roster construction, and after a schedule has been generated for the current health care worker, an improvement method based on an efficient greedy local search is carried out on the partial roster. It simply swaps any pair of shifts between two health care workers in the (partial) roster, as long as the swaps satisfy hard constraints and decrease the roster penalty.Findings—Using shift sequence method for solving health care workers rostering problem is inefficient, because of large amount of shifts sequences (feasible shifts sequences are approximately 260 thousands).In order to speed up roster construction process shifts are grouped to four groups: morning shifts, day shifts, night shifts and duty shifts. There are only 64 feasible shifts sequences, in this case.After roster construction shift groups are replaced with the one of shift belonging to that group of shifts.When all shifts are added to roster, computation of workload for each schedule is performed. If computed workload is equal to the one defined in working contract, then this schedule is complete, else begin shifts revision process. During revision process those schedules are considered which do not meet work contract requirements.If computed workload is larger than the one defined in working contract, each shift is replaced with the shift, if it’s possible, with lesser duration time. If computed workload is lesser than the one defined in working contract, each shift is replaced with the shift, if it’s possible, with larger duration time.This process continues while schedule does not meet workload requirement defined in working contract or no further improvement can be made.Research limitations/implications—Problem dimension: 27 health care workers, 15 shifts, over 20 soft constraints, rostering period—one calendar month.Practical implications – modifications made to shift sequence based approach allows to construct a roster for one of the major Lithuania’s hospitals personnel in shorter time.Originality/Value—modification of shift sequence based approach is proposed

Size-reduction heuristics for the unrelated parallel machines scheduling problem

[EN] In this paper we study the unrelated parallel machines problem where n independent jobs must be assigned to one out of m parallel machines and the processing time of each job differs from machine to machine. We deal with the objective of the minimisation of the maximum completion time of the jobs, usually referred to as makespan or Cmax. This is a type of assignment problem that has been frequently studied in the scienti¿c literature due to its many potential applications. We propose a set of metaheuristics based on a size-reduction of the original assignment problem that produce solutions of very good quality in a short amount of time. The underlying idea is to consider only a few of the best possible machine assignments for the jobs and not all of them. The results are simple, yet powerful methods. We test the proposed algorithms with a large benchmark of instances and compare them with current state-of-the-art methods. In most cases, the proposed size-reduction algorithms produce results that are statistically proven to be better by a signi¿cant margin. & 2010 Elsevier Ltd. All rights reservedThis work is partially funded by the Spanish Ministry of Science and Innovation, under the project ‘‘SMPA—Advanced Parallel Multiobjective Sequencing: Practical and Theoretical Advances’’ with references number DPI2008-03511/DPI. The authors should also thank the IMPIVA—Institute for the Small and Medium Valencian Enterprise, for the project OSC with reference IMIDIC/2008/137 and the Polytechnic University of Valencia, for the project PPAR with reference 3147.Fanjul Peyró, L.; Ruiz García, R. (2011). Size-reduction heuristics for the unrelated parallel machines scheduling problem. Computers and Operations Research. 38(1):301-309. https://doi.org/10.1016/j.cor.2010.05.005S30130938

A speed-up procedure for the hybrid flow shop scheduling problem

Article number 115903During the last decades, hundreds of approximate algorithms have been proposed in the literature addressing flow-shop-based scheduling problems. In the race for finding the best proposals to solve these problems, speedup procedures to compute objective functions represent a key factor in the efficiency of the algorithms. This is the case of the well-known Taillard’s accelerations proposed for the traditional flow shop with makespan minimisation or several other accelerations proposed for related scheduling problems. Despite the interest in proposing such methods to improve the efficiency of approximate algorithms, to the best of our knowledge, no speed-up procedure has been proposed so far in the hybrid flow shop literature. To tackle this challenge, we propose in this paper a speed-up procedure for makespan minimisation, which can be incorporate in insertion-based neighbourhoods using a complete representation of the solutions. This procedure is embedded in the traditional iterated greedy algorithm. The computational experience shows that even incorporating the proposed speed-up procedure in this simple metaheuristic results in outperforming the best metaheuristic for the problem under consideration.Junta de Andalucía(España) US-1264511Ministerio de Ciencia e Innovación (España) PID2019-108756RB-I0

Randomized heuristics for the Capacitated Clustering Problem

In this paper, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving this NP-hard problem. The former is a memory-less approach that constructs independent solutions, while the latter is a memory-based method that constructs linked solutions, obtained by partially rebuilding previous ones. Both are based on the combination of greediness and randomization in the constructive process, and coupled with a subsequent local search phase. We propose these two multi-start methods and their hybridization and compare their performance on the CCP. Additionally, we propose a heuristic based on the mathematical programming formulation of this problem, which constitutes a so-called matheuristic. We also implement a classical randomized method based on simulated annealing to complete the picture of randomized heuristics. Our extensive experimentation reveals that Iterated Greedy performs better than GRASP in this problem, and improved outcomes are obtained when both methods are hybridized and coupled with the matheuristic. In fact, the hybridization is able to outperform the best approaches previously published for the CCP. This study shows that memory-based construction is an effective mechanism within multi-start heuristic search techniques

Review on unrelated parallel machine scheduling problem with additional resources

This study deals with an unrelated parallel machine scheduling problem with additional resources (UPMR). That is one of the important sub-problems in the scheduling. UPMR consists of scheduling a set of jobs on unrelated machines. In addition to that, a number of one or more additional resources are needed. UPMR is very important and its importance comes from the wealth of applications; they are applicable to engineering and scientific situations and manufacturing systems such as industrial robots, nurses, machine operators, bus drivers, tools, assembly plant machines, fixtures, pallets, electricity, mechanics, dies, automated guided vehicles, fuel, and more. The importance also comes from the concern about the limitation of resources that are dedicated for the production process. Therefore, researchers and decision makers are still working on UPMR problem to get an optimum schedule for all instances which have not been obtained to this day. The optimum schedule is able to increase the profits and decrease the costs whilst satisfying the customers’ needs. This research aims to review and discuss studies related to unrelated parallel machines and additional resources. Overall, the review demonstrates the criticality of resolving the UPMR problem. Metaheuristic techniques exhibit significant effectiveness in generating results and surpassing other algorithms. Nevertheless, continued improvement is essential to satisfy the evolving requirements of UPMR, which are subject to operational changes based on customer demand

Sequence-dependent group scheduling problem on unrelated-parallel machines

In this research we address a sequence-dependent group scheduling problem on a set of unrelated-parallel machines where the run time of each job differs on different machines. To benefit both producer and customers we attempt to minimize a linear combination of total weighted completion time and total weighted tardiness. Since the problem is shown to be NP-hard, meta-heuristic algorithms based on tabu search are developed to find the optimal/near optimal solution. For some small size yet complex problems, the results from these algorithms are compared to the optimal solutions found by CPLEX. The result obtained in all of these problems is that the tabu search algorithms could find solutions at least as good as CPLEX but in drastically shorter computational time, thus signifying the high degree of efficiency and efficacy attained by the former.Keywords: Bi-criteria, Group scheduling, Sequence-dependent setup time, Mixed-integer linear programming, Unrelated-parallel machines, Tabu searc