Real-world Scheduling Problems Research Articles

Exact algorithms for a parallel machine scheduling problem with workforce and contiguity constraints

We address a real-world scheduling problem where the objective is to allocate a set of tasks to a set of machines and to a set of workers in such a way that the total weighted tardiness is minimized. Our case study encompasses four types of constraints: precedence, resource, eligibility, and contiguity. While the first three constraints are common in the scheduling literature, contiguity constraints, which can be defined as a form of precedence constraints that requires both a predecessor and its successor to be processed on the same machine with no intermediate jobs in-between (but idle time is allowed), have never been studied in the literature. We present four exact methods to solve the problem: two methods use integer linear programming, one uses constraint programming, and one uses a combinatorial Benders’ decomposition. We introduce method-specific strategies to model the contiguity constraints for each of the proposed methods. We empirically evaluate, through an extensive set of computational experiments, the performance of the four methods on a heterogeneous dataset composed of real, realistic, and random instances, and outline that every method offers a competitive advantage on a targeted subset of instances. We also show that our algorithms can be generalized to solve related scheduling problems with contiguity constraints.

Supporting scheduling decisions by using genetic algorithm based on tourists’ preferences

As the tourism industry switches from mass to personalized solutions, tourists’ preferences should be considered. This study aims to optimize tourists’ activity chains based on their preferences and 10 realistic parameters. A method using activity-based modeling and genetic algorithm is developed. The method handles flexible touristic activities with time window restrictions. Then scenarios including private vehicles (PV) and public transport (PT) are created. The results show high priority for security and provide significant reduction in journey times, particularly in case of flexible activities. The outcomes confirm that the proposed method provides an optimized solution to generate personalized travel itineraries that enhance tourists’ travel experience. To evaluate the performance of the algorithm, Wilcoxon Signed-Ranks Test is applied. The statistical results confirm that the performance of the GA significantly differs in the various scenarios. Travelers can benefit from the proposed approach since it offers a solution to complex real-world scheduling problems. In addition, travel patterns can be derived thus supporting the work of planners and decision-makers in the tourism industry.

Optimal scheduling algorithm for residential building distributed energy source systems using Levy flight and chaos-assisted artificial rabbits optimizer

The increase in demand for MicroGrids (MGs) is a significant factor in the provision of electricity in the future, mainly due to the use of renewable energy sources, which reduces the release of hazardous gases. The grid-connected MG operation is the most cost-effective and reliable because it actively involves the grid buying and selling power, lowering the electricity cost of the MG. This study describes a residential thermal/electrical home energy system comprising a battery energy storage system and a combined heat and power fuel cell. The optimal planning of various energy resources is scheduled by a new optimization algorithm called Levy Flight and Chaos-assisted Artificial Rabbits Optimization (LFCARO), resulting in the lowest operational cost of this combined power system. The operating cost of a residential building is reduced by using a day-ahead scheduling process for controlling multiple energy sources to create a reliable look-up table that estimates the best schedule for the distributed energy sources at each time frame. The impact of various electricity prices for obtaining energy from the primary grid on the system’s operating costs is examined. The efficiency of LFCARO is compared with other algorithms, and the results show that LFCARO performs better than other algorithms. The execution time of the proposed LFCARO is less than 1 sec. for 10 numerical problems and less than 1.5 sec. for the resource scheduling of residential distribution systems. Based on the average Friedman’s ranking test values, the proposed algorithm stands first with 1.82 for numerical and real-world scheduling problems.

Simulation of Job Sequencing for Stochastic Scheduling with a Genetic Algorithm

Sequencing is done to determine the order in which the jobs are to be processed. Extensive research has been carried out with an aim to tackle real-world scheduling problems. In industries, experimentation is performed before an ultimate choice is made to know the optimal priority sequencing rule. Therefore, an extensive approach to selecting the correct choice is necessary for the management decision-making perspective. In this research, the genetic algorithm (GA) and working of a simulation environment are explained, in which a scheduling operator, under any given circumstances, can obtain the appropriate sequence for job scheduling in a shop. The paper also explains the stochastic based linguistic, scenarios and probabilistic approaches to solve sequencing problem. The simulation environment allows the operator to select the tardiness and non-tardiness related performance measures. The simulator takes input values such as number of jobs, processing time and due date and discovers a near-optimal sequence for scheduling of jobs that minimizes the performance measures selected by the operator as per requirement. The case study considered is solved using scenarios based stochastic scheduling approach and results are shown. The results are compared with the classical method used in the company and observed that the proposed approach gives a better result.

A GRASP for a real-world scheduling problem with unrelated parallel print machines and sequence-dependent setup times

We consider a real-world scheduling problem arising in the colour printing industry. The problem consists in assigning print jobs to a heterogeneous set of flexographic printer machines and finding a processing sequence for the jobs assigned to each machine. The machines are characterised by a limited sequence of colour groups and can equip additional components (e.g. embossing rollers and perforating rolls) to process jobs that require specific treatments. The process to equip a machine with an additional component or to clean a colour group takes a long time, with the effect of significantly raising the setup times. The aim is to minimise a weighted sum of total weighted tardiness and total setup time. The problem derives from the activities of an Italian food packaging company. To solve it, we developed a greedy randomised adaptive search procedure equipped with several local search procedures. The excellent performance of the algorithm is proved by extensive computational experiments on real-world instances, for which it produced good-quality solutions within a limited computing time. The algorithm is currently in use at the company to support their weekly scheduling decisions.

NICE: Robust Scheduling through Reinforcement Learning-Guided Integer Programming

Integer programs provide a powerful abstraction for representing a wide range of real-world scheduling problems. Despite their ability to model general scheduling problems, solving large-scale integer programs (IP) remains a computational challenge in practice. The incorporation of more complex objectives such as robustness to disruptions further exacerbates the computational challenge. We present NICE (Neural network IP Coefficient Extraction), a novel technique that combines reinforcement learning and integer programming to tackle the problem of robust scheduling. More specifically, NICE uses reinforcement learning to approximately represent complex objectives in an integer programming formulation. We use NICE to determine assignments of pilots to a flight crew schedule so as to reduce the impact of disruptions. We compare NICE with (1) a baseline integer programming formulation that produces a feasible crew schedule, and (2) a robust integer programming formulation that explicitly tries to minimize the impact of disruptions. Our experiments show that, across a variety of scenarios, NICE produces schedules resulting in 33% to 48% fewer disruptions than the baseline formulation. Moreover, in more severely constrained scheduling scenarios in which the robust integer program fails to produce a schedule within 90 minutes, NICE is able to build robust schedules in less than 2 seconds on average.

Knowledge representation in Industry 4.0 scheduling problems

ABSTRACT Industry 4.0 raises the need for a closer integration of management systems in manufacturing companies. Such process is driven by Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Starting from the potential of these technologies, a knowledge architecture aimed at addressing scheduling problems is proposed. Scheduling-support systems generally do not solve real-world scheduling problems, being instead only capable of solving simplified versions, producing solutions that human schedulers adapt to real problems. The architecture aims to record and consolidate the empirical knowledge generated by the solutions of actual scheduling problems. In this way, it summarizes the implicit criteria used by human schedulers. The architecture presented here records this knowledge in data structures compatible with the structure of scheduling problems. In further iterations this knowledge crystallizes into a sound and smart structure.

Local search approaches for the test laboratory scheduling problem with variable task grouping

The Test Laboratory Scheduling Problem (TLSP) is a real-world scheduling problem that extends the well-known Resource-Constrained Project Scheduling Problem (RCPSP) by several new constraints. Most importantly, the jobs have to be assembled out of several smaller tasks by the solver, before they can be scheduled. In this paper, we introduce different metaheuristic solution approaches for this problem. We propose four new neighborhoods that modify the grouping of tasks. In combination with neighborhoods for scheduling, they are used by our metaheuristics to produce high-quality solutions for both randomly generated and real-world instances. In particular, Simulated Annealing managed to find solutions that are competitive with the best known results and improve upon the state-of-the-art for larger instances. The algorithm is currently used for the daily planning of a large real-world laboratory.

Cable tree wiring - benchmarking solvers on a real-world scheduling problem with a variety of precedence constraints

Cable trees are used in industrial products to transmit energy and information between different product parts. To this date, they are mostly assembled by humans and only few automated manufacturing solutions exist using complex robotic machines. For these machines, the wiring plan has to be translated into a wiring sequence of cable plugging operations to be followed by the machine. In this paper, we study and formalize the problem of deriving the optimal wiring sequence for a given layout of a cable tree. We summarize our investigations to model this cable tree wiring problem (CTW). as a traveling salesman problem with atomic, soft atomic, and disjunctive precedence constraints as well as tour-dependent edge costs such that it can be solved by state-of-the-art constraint programming (CP), Optimization Modulo Theories (OMT), and mixed-integer programming (MIP). solvers. It is further shown, how the CTW problem can be viewed as a soft version of the coupled tasks scheduling problem. We discuss various modeling variants for the problem, prove its NP-hardness, and empirically compare CP, OMT, and MIP solvers on a benchmark set of 278 instances. The complete benchmark set with all models and instance data is available on github and was included in the MiniZinc challenge 2020.

Lot-Sizing and Scheduling for the Plastic Injection Molding Industry—A Hybrid Optimization Approach

The management of industrial systems is done through different levels, ranging from strategic (designing the system), to tactical (planning the activities and assigning the resources) and operational (scheduling the activities). In this paper, we focus on the latter level by considering a real-world scheduling problem from a plastic injection company, where the production process combines parallel machines and a set of resources. We present a scheduling algorithm that combines a metaheuristic and a list algorithm. Two metaheuristics are tested and compared when used in the proposed scheduling approach: the stochastic descent and the simulated annealing. The method’s performances are analyzed through an experimental study and the obtained results show that its outcomes outperform those of the scheduling policy conducted in a case-study company. Moreover, besides being able to solve large real-world problems in a reasonable amount of time, the proposed approach has a structure that makes it flexible and easily adaptable to several different planning and scheduling problems. Indeed, since it is composed by a reusable generic part, the metaheuristic, it is only required to develop a list algorithm adapted to the objective function and constraints of the new problem to be solved.

A Novel Maximin-Based Multi-Objective Evolutionary Algorithm Using One-by-One Update Scheme for Multi-Robot Scheduling Optimization

With the continuous development of E-commerce, warehouse logistics is also facing emerging challenges, including more batches of orders and shorter order processing cycles. When more orders need to be processed simultaneously, some existing task scheduling methods may not be able to give a suitable plan, which delays order processing and reduces the efficiency of the warehouse. Therefore, the intelligent warehouse system that uses autonomous robots for automated storage and intelligent order scheduling is becoming mainstream. Based on this concept, we propose a multi-robot cooperative scheduling system in the intelligent warehouse. The aim of the multi-robot cooperative scheduling system of the intelligent storage is to drive many robots in an intelligent warehouse to perform the distributed tasks in an optimal (e.g., time-saving and energy-conserved) way. In this paper, we propose a multi-robot cooperative task scheduling model in the intelligent warehouse. For this model, we design a maximin-based multi-objective algorithm, which uses a one-by-one update scheme to select individuals. In this algorithm, two indicators are devised to discriminate the equivalent individuals with the same maximin fitness value in the environmental selection process. The results on benchmark test suite show that our algorithm is indeed a useful optimizer. Then it is applied to settle the multi-robot scheduling problem in the intelligence warehouse. Simulation experiment results demonstrate the efficiency of the proposed algorithm on the real-world scheduling problem.

Metaheuristics for the online printing shop scheduling problem

In this work, the online printing shop scheduling problem is considered. This challenging real-world scheduling problem, that emerged in the present-day printing industry, corresponds to a flexible job shop scheduling problem with sequencing flexibility; and it presents several complicating requirements such as resumable operations, periods of unavailability of the machines, sequence-dependent setup times, partial overlapping between operations with precedence constraints, and fixed operations, among others. A local search strategy and metaheuristics are proposed and evaluated. Based on a common representation scheme, trajectory and populational metaheuristics are considered. Extensive numerical experiments on large-sized instances show that the proposed methods are suitable for solving practical instances of the problem; and that they outperform a half-heuristic-half-exact off-the-shelf solver by a large extent. In addition, numerical experiments on classical instances of the flexible job shop scheduling problem show that the proposed methods are also competitive when applied to this particular case.

A Self-Adaptive Parameter Control Method Based on Entropy and Security Market Line for Scheduling Problems

Scheduling problems, as one of the classic combinatorial optimization problems are essential issues in many fields. Various meta-heuristic algorithms have been adopted to solve scheduling problems. However, parameter control problem is still crucial to the performance of algorithms. In this paper, we propose a self-adaptive parameter control method based on entropy and security market line, fully considering the characteristics scheduling problems. It consists of two key parts: locus-entropy strategy and parameter-control strategy. Firstly, the entropy on each genetic locus is calculated to accurately evaluate the population status of scheduling algorithms. Then, a parameter-control strategy based on the conception of security market line is proposed to address the issue that the nature of multipeak in scheduling problems makes algorithms fall into local optimal solutions. The strategy maintains the solutions of good quality and eliminate the solutions of poor quality by using locus-entropy as feedback. Through our method, the balance between exploitation and exploration is kept in algorithms to perform well in scheduling problems with different dimensions and characteristics. These strategies are tightly linked to adjust parameters adaptively without introducing new parameters, so that meta-heuristic algorithms equipped with the proposed approach are able to find a better solution. Moreover, our parameter control method is universal for meta-heuristic algorithms. The proposed approach is hybrid with genetic algorithm and particle swarm optimization. The hybrid algorithms are first compared with the standard algorithms in multipeak benchmark functions, then with other variants of the standard algorithms in real-world single and multi-objective scheduling problems. The results demonstrate that the proposed approach is valid for different kinds of algorithms to enhance the performance of solving a variety of scheduling problems.

Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm

In intelligent manufacturing, it is important to schedule orders from customers efficiently. Make-to-order companies may have to reject or postpone orders when the production capacity does not meet the demand. Many such real-world scheduling problems are characterised by processing times being dependent on the start time (time dependency) or on the preceding orders (sequence dependency), and typically have an earliest and latest possible start time. We introduce and analyze four algorithmic ideas for this class of time/sequence-dependent over-subscribed scheduling problems with time windows: a novel hybridization of adaptive large neighbourhood search (ALNS) and tabu search (TS), a new randomization strategy for neighbourhood operators, a partial sequence dominance heuristic, and a fast insertion strategy. Through factor analysis, we demonstrate the performance of these new algorithmic features on problem domains with varying properties. Evaluation of the resulting general purpose algorithm on three domains—an order acceptance and scheduling problem, a real-world multi-orbit agile Earth observation satellite scheduling problem, and a time-dependent orienteering problem with time windows—shows that our hybrid algorithm robustly outperforms general algorithms including a mixed integer programming method, a constraint programming method, recent state-of-the-art problem-dependent meta-heuristic methods, and a two-stage hybridization of ALNS and TS.

A Genetic Algorithm for Scheduling a Semi-Continuous Process Industry: A Case Study

In today’s competitive industry, scheduling plays a significant role in improving the efficiency of manufacturing systems. Hence, many scholars and practitioners have been researching to enhance the quality of scheduling methods. In this research, the focus is on solving a real-world scheduling problem in a food industry which was previously dealt with a very time-consuming manual method without high-quality solutions. The problem is to find the best schedule for producing multiple products on multiple machines in a semi-continuous manufacturing system. Having a continuous section in the system makes scheduling too complicated and very different from typical scheduling problems found in literature. In this paper, metaheuristics based on genetic algorithm is proposed to solve the problem.

Heuristic algorithms for scheduling intrees on m machines with non-availability constraints

Many real-world scheduling problems are solved to obtain optimal solutions in terms of processing time, cost, and quality as optimization objectives. Parallel computing systems promise higher performance for computationally intensive applications. Since programs for parallel systems consist of tasks that can be executed simultaneously, task scheduling becomes crucial for the performance of these applications. This problem has also its own application in the industry where machines may not always be available due to machine breakdowns or preventive maintenance during the scheduling period. Given dependence constraints between tasks and limited resources available for execution, optimal task scheduling is considered as an NP-hard problem. Thus, the development of heuristic methods that provide high-quality solutions with computational efficiency are the motivating aspects for the development of this research. This paper proposes a heuristic for scheduling n tasks subject to intree-precedence constraints on m identical machines subject to non-availability constraints. We also present a tabu search implementation and identify a polynomial algorithm to schedule a chain in the same environment. The tabu search algorithm along with the heuristic are compared via simulation to a proposed lower bound.

Creating resident shift schedules under multiple objectives by generating and evaluating the Pareto frontier

Creating shift schedules for medical residents is challenging, not only because of the large number of conflicting rules and requirements needed to ensure both adequate patient care and resident educational opportunities, but also because there is no one clear, well-defined single objective function to optimize. Instead, many factors should be taken into account when selecting the “best” schedule. In our practical experience, it is impossible for the scheduler (typically, a Chief Resident) to accurately determine weights that would allow these factors to be captured in a mathematical objective function that truly represented their preferences. We therefore propose to instead provide the Chief with a set of Pareto-dominant schedules from which to select. We present an integer programming-based approach embedded within a recursive algorithm to generate these schedules. We then present both computational results to assess the tractability of our approach and a case study, based on a real-world scheduling problem at the University of Michigan Pediatric Emergency Department, to study how a Chief Resident would evaluate the Pareto set.

Rostering Air Traffic Controllers

Many, if not most, real world scheduling problems fall into the class of NP. Classical, mathematically exact methods when applied to these problems often suffer from scaling issues, that prevent the computation of a solution in reasonable time. Similarly, real world problems can often be characterised by the requirement to dynamically change any prebuilt roster in reaction to unforeseen changes to the resources available, such as short notice staff absence or a change in the requirements of the task to be fulfilled. Metaheuristic methods have been successfully used to make approximations to optimal solutions which are good enough for practical use. In this paper an example of a novel and complex employee scheduling, or rostering, problem will be discussed and a simple metaheuristic method demonstrated to solve the problem.

A two-step multi-objectivization method for improved evolutionary optimization of industrial problems

Multi-objectivization means that helper objectives are added to an optimization problem with the purpose of altering the search space in a way that improves the progress of the optimization algorithm. In this paper, a new method for multi-objectivization is proposed that is based on a two-step process. In the first step, a helper objective that conflicts with the main objective is added, and in the second step a helper objective that is in harmony with, but subservient to, the main objective is added. In contrast to existing methods for multi-objectivization, the proposed method aims at obtaining improved results in real-world optimizations by focusing on three aspects: (a) adding as little extra complexity to the problem as possible, (b) achieving an optimal balance between exploration and exploitation in order to promote an efficient search, and (c) ensuring that the main objective, which is of main interest to the user, is always prioritized. Results from evaluating the proposed method on a complex real-world scheduling problem and a theoretical benchmark problem show that the method outperforms both a traditional single-objective approach and the prevailing method for multi-objectivization. Besides describing the proposed method, the paper also outlines interesting aspects of multi-objectivization to investigate in the future.

\u201cYou have to get wet to learn how to swim\u201d applied to bridging the gap between research into personnel scheduling and its implementation in practice

Personnel scheduling problems have attracted research interests for several decades. They have been considerably changed over time, accommodating a variety of constraints related to legal and organisation requirements, part-time staff, flexible hours of staff, staff preferences, etc. This led to a myriad of approaches developed for solving personnel scheduling problems including optimisation, meta-heuristics, artificial intelligence, decision-support, and also hybrids of these approaches. However, this still does not imply that this research has a large impact on practice and that state-of-the art models and algorithms are widely in use in organisations. One can find a reasonably large number of software packages that aim to assist in personnel scheduling. A classification of this software based on its purpose will be proposed, accompanied with a discussion about the level of support that this software offers to schedulers. A general conclusion is that the available software, with some exceptions, does not benefit from the wealth of developed models and methods. The remaining of the paper will provide insights into some characteristics of real-world scheduling problems that, in the author’s opinion, have not been given a due attention in the personnel scheduling research community yet and which could contribute to the enhancement of the implementation of research results in practice. Concluding remarks are that in order to bridge the gap that still exists between research into personnel scheduling and practice, we need to engage more with schedulers in practice and also with software developers; one may say we need to get wet if we want to learn how to swim.