A two-stage heuristic algorithm for the nurse scheduling problem with fairness objective on weekend workload under different shift designs

The article presents a cognitive approach for system design. This approach is developed at the STADE in collaboration with the LIASC. At the TrefilARBED industry, we treat a complex scheduling problem of wire drawing production. The aim of the industrial project is to design an automatic scheduling system. We use constraint logic programming (CLP) techniques to build this system (R.S. Bisdorff et al., 1995). These techniques have been already tested in complex industrial problems in previous projects at the CRP-CU (R.S. Bisdorff and S. Laurent, 1995). The design of this system is based on a cognitive study of a human agent's task. The use of cognitive artefacts raises several questions. How can we simplify the realistic model? Which aspect has to be treated with the help of a cognitive artefact? What level of detail is needed for each artefact? Who is able to define these artefacts?.

With the rapid increase of mission demands for the tracking and data relay satellite system (TDRSS), the technical issue of high-efficient scheduling has attracted more attention in recent years. Most of previous scheduling algorithms are designed based on the assumption of missions' uniform time- space distribution, which have showed unsatisfactory performance in real scenarios with non-uniform distribution of mission demands. In this paper, we first transform the TDRSS scheduling problem into the heterogeneous inter- satellite link antenna (ILA) pointing route problem. Then, a two-stage heuristic algorithm with hierarchical scheduling strategies is proposed with the consideration of non-uniform time-space distribution of missions. Finally, we employ the TDRSS dataset to verify our proposed algorithm by comparing with the improved Rojanasoonthon's greedy randomized adaptive search procedure (GRASP) algorithm. Experimental results show that our proposed two-stage heuristic algorithm can schedule 2.41%, 4.43% and 6.02% more missions and consume 11.84%, 10.38% and 9.54% less setup times of SA antennas than the improved GRASP algorithm for the mission scale of 200, 400 and 600, respectively. In addition, setup times of SA antennas in those instances with non-uniform distribution in space can be more efficiently compressed by our proposed two-stage heuristic algorithm.

Operating room (OR) planning and scheduling is a highly complex combinatorial optimization problem that involves patient assignment, OR allocation, and surgical sequencing within a constrained planning horizon. This study addresses the OR scheduling problem by incorporating downstream bed availability in both hospital wards and intensive care units (ICUs), aiming to improve operational efficiency and patient outcomes. A mixed-integer programming (MIP) model is developed to minimize the total completion time across all operating rooms during the planning period. Given the NP-hard nature of the problem, a novel Genetic Engineering Algorithm (GEA) is proposed as an advanced extension of the traditional Genetic Algorithm (GA). The GEA incorporates three innovative search strategies inspired by genetic engineering principles, refining the crossover and mutation operators to improve solution quality and convergence speed. To evaluate the effectiveness of the proposed algorithm, GEA and its variants are tested on 18 benchmark instances of varying problem sizes for the proposed OR planning and scheduling problem. A real-world case study from a hospital in Pakistan illustrates the practical applicability of the proposed approach using different real-world criteria. The results demonstrate that GEA consistently outperforms state-of-the-art metaheuristic algorithms. Its robustness and accuracy are further validated using 10 standard mathematical benchmark functions. The results confirm the GEA’s effectiveness and efficiency in addressing complex OR planning and scheduling problems and underscore its potential for advancing metaheuristic algorithm design in combinatorial optimization.

This paper investigates the application of a new class of neighborhood search algorithms—cyclic transfers—to multivehicle routing and scheduling problems. These algorithms exploit the two-faceted decision structure inherent to this problem class: First, assigning demands to vehicles and, second, routing each vehicle through its assigned demand stops. We describe the application of cyclic transfers to vehicle routing and scheduling problems. Then we determine the worst-case performance of these algorithms for several classes of vehicle routing and scheduling problems. Next, we develop computationally efficient methods for finding negative cost cyclic transfers. Finally, we present computational results for three diverse vehicle routing and scheduling problems, which collectively incorporate a variety of constraint and objective function structures. Our results show that cyclic transfer methods are either comparable to or better than the best published heuristic algorithms for several complex and important vehicle routing and scheduling problems. Most importantly, they represent a novel approach to solution improvement which holds promise in many vehicle routing and scheduling problem domains.

In recent years, with the advent of robust solvers such as Cplex and Gurobi, constraint programing (CP) has been widely applied to a variety of scheduling problems. This paper presents CP models for formulating four scheduling problems with minimal makespan and complex constraints: the no‐wait hybrid flow shop scheduling problem, the hybrid flow shop scheduling problem with sequence‐dependent setup times, the flexible job shop scheduling problem with worker flexibility and the semiconductor final testing problem. The advantages of CP method in solving these four complex scheduling problems are explored. Finally, a set of benchmark instances are adopted to demonstrate the effectiveness and efficiency of the CP method. Experiment results show that the proposed CP models outperform existing algorithms; in particular, several best‐known solutions of benchmark instances are improved by our CP method.

The reliable design of a hierarchical multi-modes transportation hub location problems (HMMTHLP) under dynamic network disruption (DND)

Minimizing total earliness and tardiness on a permutation flow shop using VNS and MIP

The resource constrained project scheduling problem is considered as a complex scheduling problem. In order to solve this NP-hard problem, an efficient differential evolution DE algorithm is proposed in this paper. In the algorithm, improved mutation and crossover operators are introduced with an aim to maintain feasibility for generated individuals and hence being able to converge quickly to the optimal solutions. The algorithm is tested on a set of well-known project scheduling problem library PSPLIB, with instances of 30, 60, 90 and 120 activities. The proposed DE is shown to have superior performance in terms of lower average deviations from the optimal solutions compared to some of the state-of-the-art algorithms.

This application is motivated by a complex real-world scheduling problem found in the bottleneck workstation of the production line of an automotive safety glass manufacturing facility. The scheduling problem consists of scheduling jobs (glass parts) on a number of parallel batch processing machines (furnaces), assigning each job to a batch, and sequencing the batches on each machine. The two main objectives are to maximize the utilization of the parallel machines and to minimize the delay in the completion date of each job in relation to a required due date (specific for each job). Aside from the main objectives, the output batches should also produce a balanced workload on the parallel machines, balanced job due dates within each batch, and minimal capacity loss in the batches. The scheduling problem also considers a batch capacity constraint, sequence-dependent processing times, incompatible product families, additional resources, and machine capability. We propose a two-phase heuristic approach that combines exact methods with search heuristics. The first phase comprises a four-stage mixed-integer linear program for building the batches; the second phase is based on a Greedy Randomized Adaptive Search Procedure for sequencing the batches assigned to each machine. We conducted experiments on instances with up to 100 jobs built with real data from the manufacturing facility. The results are encouraging both in terms of computing time--5 min in average--and quality of the solutions--less than 10 % relative gap from the optimal solution in the first phase and less than 5 % in the second phase. Additional experiments were conducted on randomly generated instances of small, medium, and large size.

A branch and bound algorithm for a single-machine scheduling problem with positive and negative time-lags

In this paper, we treat the capacitated location-routing problem (CLRP). The CLRP which combines the facility location problem with the routing problem is one of the most important problems in combinatorial optimisation field. In this study, we propose a new two-stage heuristic (2-SH) algorithm for the large-scale CLRP. In fact, our aim is to find high-quality solutions for very large-scale problems within a short computing time. The proposed method consists of two major stages. In the first, the routing sub-problem is solved using a tabu search. Then, in the second stage, to associate each route to a facility, the location-allocation sub-problem is treated by means of a simulated annealing method. Our proposed approach is evaluated on large-scale randomly generated instances and compared successfully with other two state-of-the-art algorithms from the literature.

The permutation flow shop scheduling problem (PFSP), which is one of the most important scheduling types, is widespread in the modern industries. With the increase of scheduling scale, the difficulty and computation time of solving the problem will increase exponentially. Adding the knowledge to intelligent algorithms is a good way to solve the complex and difficult scheduling problems in reasonable time. To deal with the complex PFSPs, this paper proposes an improved simulated annealing (SA) algorithm based on residual network (SARes). First, this paper defines the neighborhood of the PFSP and divides its key blocks. Second, the Residual Network (ResNet) is used to extract and train the features of key blocks. And, the trained parameters are stored in the SA algorithm to improve its performance. Afterwards, some key operators, including the initial temperature setting and temperature attenuation function of SA algorithm, are also modified. After every new solution is generated, the parameters trained by the ResNet are used for fast ergodic search until the local optimal solution found in the current neighborhood. Finally, the most famous benchmarks including part of TA benchmark are selected to verify the performance of the proposed SARes algorithm, and the comparisons with the-state-of-art methods are also conducted. The experimental results show that the proposed method has achieved good results by comparing with other algorithms. This paper also conducts experiments on network structure design, algorithm parameter selection, CPU time and other problems, and verifies the advantages of SARes algorithm from the aspects of stability and efficiency.

In this paper, we study the problem of considering the internal behavior of complex machines when solving complex job-shop scheduling problems encountered in semiconductor manufacturing. The scheduling problem in the diffusion area is presented, and the complex structures and behaviors of the different machine types in this area are described. Previous related research is reviewed, and our approach to consider the complexity of these machines with batching constraints when scheduling the diffusion area is described. The main part of the paper presents and discusses numerical experiments on industrial instances to show the benefit of choosing a suitable modeling for complex machines.

A dynamic programming optimization procedure for a two-product biomass-to-methane conversion system

In this paper, we study the problem of considering the internal behavior of complex machines when solving complex job-shop scheduling problems encountered in semiconductor manufacturing. The scheduling problem in the diffusion area is presented, and the complex structures and behaviors of the different machine types in this area are described. Previous related research is reviewed, and our approach to consider the complexity of these machines with batching constraints when scheduling the diffusion area is described. The main part of the paper presents and discusses numerical experiments on industrial instances to show the benefit of choosing a suitable modeling for complex machines.