Complex Scheduling Problems Research Articles

Complex scheduling problems using an optimization methodology

Scheduling operations problems arise in diverse areas such as flexible manufacturing, production planning and scheduling, logistics, supply chain problem, etc. A common feature of many of these pro...

A simulated annealing hyperheuristic for construction resource levelling

Resource levelling techniques aim to minimize the fluctuation from one time period to another in resource usage. Except for small‐sized problems, though, computational optimization procedures are inefficient when solving construction resource levelling problems. Consequently, heuristic and metaheuristic approaches are used to get an acceptable, but not necessarily optimal, solution. A simulated annealing hyperheuristic to generate better‐levelled resource profiles is proposed. Unlike traditional heuristic algorithms, a hyperheuristic operates in the ‘low level’ heuristics domain rather than in the solutions domain. A low level heuristic, on the other hand, works in the current solution neighbourhood. The algorithm has been programmed within a commercial project management software system to improve its performance. The low level heuristics operate on the priority levels that the software uses for resource levelling. An illustrative example and the computational analysis demonstrate the potential of the procedure in solving complex scheduling problems.

Genetic algorithms with different representation schemes for complex hybrid flexible flow line problems

This paper introduces some advanced genetic algorithms for a complex hybrid flexible flow line problem with a makespan objective that was recently formulated. General precedence constraints among jobs are taken into account, as are machine release dates, time lags and sequence dependent set-up times; both anticipatory and non-anticipatory. This combination of constraints captures many real world industrial problems; among those is the ceramic tile production that served as our inspiration. The introduced algorithms employ solution representation schemes with different degrees of directness. Several new machine assignment rules are introduced and implemented in some proposed genetic algorithms. The different genetic algorithms are compared among each other and to some heuristics as well. The results indicate that simple solution representation schemes result in the best performance, even for complex scheduling problems and that the genetic algorithms lead to a better solution quality than all tested heuristics.

Nurse scheduling using fuzzy modeling approach

Nurse scheduling is a complex scheduling problem and involves generating a schedule for each nurse that consists of shift duties and days off within a short-term planning period. In real world applications, multiple sources of uncertainties are needed to be treated in providing higher quality schedules. This paper presents a seminal research on the application of fuzzy set theory to the nurse scheduling problem (NSP) to treat uncertainties in the target values of the hospital management and nurses’ preferences. More specifically, a new multi-objective integer programming model for the NSP is developed. Then, based on this model three fuzzy goal programming models are developed using different fuzzy solution approaches. A real world application is presented to confirm the viability of the proposed models. Also, to provide the decision maker for a more confident solution set for policy decision making, a sensitivity analysis is performed. Additionally, to show the efficiency of the proposed model, it is applied to several problem instances. The paper contributes to the literature by revealing that fuzzy modeling approaches can effectively be used in the NSP in providing schedules which are more personalized and equitable for nurses, and more satisfying for hospital management.

Optimal gamma based fixed head hydrothermal scheduling using genetic algorithm

The short-term hydrothermal scheduling is a daily planning proposition in power system operation, a task which is usually more complex than the scheduling of all-thermal generation system. The traditional methods have become inadequate to handle large scheduling problems and tend to be ineffective in terms of their computational speed, robustness and accuracy. Alternative strategies have thus become an imminent necessity and intelligent techniques appear to suit the complex scheduling problems. This paper presents a novel optimal gamma based scheduling algorithm for fixed head hydrothermal problems using genetic algorithm. It includes the simulation results of four test cases with a view to highlight its superior performance and suitability for easy implementation, irrespective of the problem size.

A Component-Based Heuristic Search Method with Evolutionary Eliminations for Hospital Personnel Scheduling

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e., the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and the natural mutation process on these components, respectively, to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated for them to remain there. This demonstration employs an evaluation function that evaluates how well each component contributes toward the final objective. Two elimination steps are then applied: the first elimination removes a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.

An Efficient Mixed-Integer Linear Programming Scheduling Framework for Addressing Sequence-Dependent Setup Issues in Batch Plants

Setup times and/or costs appear in a plethora of industrial and service applications. Sequence-dependent setup times and costs entail significantly complex scheduling problems. In this paper, a new continuous time precedence-based mixed-integer linear programming scheduling framework is developed for dealing with sequence-dependent setup time and/or cost issues. This model is based on the unit-specific general precedence concept, which is introduced in this paper. Several case studies are carried out in order to highlight the efficiency and the special features of the proposed model. Comparisons were made with the existing precedence-based formulations. Medium-sized scheduling problems can be solved by the proposed framework with relatively low computational effort.

Rolling Operation Algorithm for Solving Complex Scheduling Problems

Large-scale scheduling problems have remained difficult to solve despite the intense research in the area and many successful algorithms. In this paper, a new decomposition algorithm is presented which finds a good feasible solution even for large-scale problems in a reasonable time. In each iteration, a maximum allowed number of operations is first assigned to available units, then the algorithm alternates between a production quantity maximization step and a production time minimization step in each iteration. The performance of the “rolling operation” algorithm is illustrated using an industrial example.

Analysis, modeling and solution of the concrete delivery problem

This paper describes a specific local search approach to solve a problem arising in logistics which we prove to be NP-hard. The problem is a complex scheduling or vehicle routing problem where we have to schedule the tours of concrete mixer vehicles over a working day from concrete-producing depots to concrete-demanding customers and vice versa. We give a general mixed integer programming model which is too hard to solve for state of the art mixed integer programming optimizers in the case of the usually huge problem instances coming from practice. Therefore we present a certain local search approach to be able to handle huge practical problem instances.

Performance optimization of a leagility inspired supply chain model: a CFGTSA algorithm based approach

Lean and agile principles have attracted considerable interest in the past few decades. Industrial sectors throughout the world are upgrading to these principles to enhance their performance, since they have been proven to be efficient in handling supply chains. However, the present market trend demands a more robust strategy incorporating the salient features of both lean and agile principles. Inspired by these, the leagility principle has emerged, encapsulating both lean and agile features. The present work proposes a leagile supply chain based model for manufacturing industries. The paper emphasizes the various aspects of leagile supply chain modeling and implementation and proposes a new Hybrid Chaos-based Fast Genetic Tabu Simulated Annealing (CFGTSA) algorithm to solve the complex scheduling problem prevailing in the leagile environment. The proposed CFGTSA algorithm is compared with the GA, SA, TS and Hybrid Tabu SA algorithms to demonstrate its efficacy in handling complex scheduling problems.

Heuristic algorithms for a complex parallel machine scheduling problem

In this work we present a new scheduling model for parallel machines, which extends the multiprocessor scheduling problem with release times for minimizing the total tardiness, and also extends the problem of vehicle routing with time windows. This new model is motivated by a resource allocation problem, which appears in the service sector. We present two class of heuristic algorithms for the solution of the problem, the first class is a class of greedy algorithms, the second class is based on the solutions of linear assignment problems. Furthermore we give a rescheduling algorithm, which improves a given feasible solution of the problem.

Scheduling inbound and outbound trucks at cross docking terminals

At cross docking terminals, shipments from inbound trucks are unloaded, sorted and moved to dispatch points where they are directly loaded onto outbound trucks for an immediate delivery elsewhere in the distribution system. This warehouse management concept aims at realizing economies in transportation cost by consolidating divergent shipments to full truckloads without requiring excessive inventory at the cross dock. The efficient operation of such a system requires an appropriate coordination of inbound and outbound trucks, e.g. by computerized scheduling procedures.This work introduces a base model for scheduling trucks at cross docking terminals, which relies on a set of simplifying assumptions in order to derive fundamental insights into the underlying problem’s structure, i.e. its complexity, and to develop a building block solution procedure, which might be employed to solve more complex real-world truck scheduling problems.

Analyzing scheduling in the food-processing industry: structure and tasks

Production scheduling has been widely studied in several research areas, resulting in a large number of methods, prescriptions, and approaches. However, the impact on scheduling practice seems relatively low. This is also the case in the food-processing industry, where industry-specific characteristics induce specific and complex scheduling problems. Based on ideas about decomposition of the scheduling task and the production process, we develop an analysis methodology for scheduling problems in food processing. This combines an analysis of structural (technological) elements of the production process with an analysis of the tasks of the scheduler. This helps to understand, describe, and structure scheduling problems in food processing, and forms a basis for improving scheduling and applying methods developed in literature. It also helps in evaluating the organisational structures and information flows related to scheduling.

Effective search space control for large and/or complex driver scheduling problems

For real life bus and train driver scheduling instances, the number of columns in terms of the set covering/partitioning ILP model could run into billions making the problem very difficult. Column generation approaches have the drawback that the sub-problems for generating the columns would be computationally expensive in such situations. This paper proposes a hybrid solution method, called PowerSolver, of using an iterative heuristic to derive a series of small refined sub-problem instances fed into an existing efficient set covering ILP based solver. In each iteration, the minimum set of relief opportunities that guarantees a solution no worse than the current best is retained. Controlled by a user-defined strategy, a small number of the banned relief opportunities would be reactivated and some soft constraints may be relaxed before the new sub-problem instance is solved. PowerSolver is proving successful by many transport operators who are now routinely using it. Test results from some large scale real-life exercises will be reported.

Solving a concrete sleepers production scheduling by genetic algorithms

PRECON S.A. is a manufacturing company devoted to produce prefabricated concrete parts for several industries as railway transportation and agricultural industries. Recently, PRECON S.A. signed a contract with RENFE, the Spanish National Railway Company, to manufacture pre-stressed concrete sleepers for the sidings of the new railways of the high speed train (AVE). The scheduling problem associated with the manufacturing process of the sleepers is very complex, since this involves several constraints and objectives. These constraints are related to production capacity, the quantity of available moulds, demand satisfaction and other operational constraints. The two main objectives are related to the way to maximize the utilization of manufacturing resources and minimize mould movements. We developed a deterministic crowding genetic algorithm for this multiobjective problem. The algorithm has proved to be a powerful and flexible tool to solve large-scale instances of this real and complex scheduling problem.

Choquet integral for criteria aggregation in the flexible job-shop scheduling problems

Most complex scheduling problems are combinatorial problems and difficult to solve. That is why, several methods focus on the optimization according to a single criterion such as makespan, workloads of machines, waiting times, etc. In this paper, the Choquet integral is introduced as a general tool for dealing with multiple criteria decision making and used in optimization flexible job-shop scheduling problems. The considered optimization problem is based of the Genetic Algorithm (GA) used as objective function the Choquet integral for criteria aggregation. Then lower bounds are defined for each criterion. Presented examples illustrate theoretical considerations and show the efficiency of the proposed approach.

On scheduling a multifunction radar

Among several other tasks, the radar of a fighter has to search, track and identify potential targets. The waveforms used by the radar for each of these tasks are most often incompatible and hence, cannot be processed simultaneously. Moreover, these tasks are repeated several times in a cyclic fashion. Altogether, this defines a complex scheduling problem that impacts a lot on the quality of the radar's output. In this paper, we define a formal framework for this real time scheduling problem and we introduce several techniques to compute efficient schedules for the radar. Experimental results are provided.

A HYBRID HOPFIELD NEURAL NETWORKS BASED SIMULATION APPROACH FOR OPTIMISATION OF MANUFACTURING GROUP SCHEDULING

ABSTRACT A hybrid Neural Networks have been developed and the simulation techniques have been used to solve complex group scheduling problems. The objectives are to find the best performance criterion optimum for a given number of jobs and machines in order to satisfy different production constraints. The solution methods proposed in this paper, have their roots in both operational research and artificial intelligence. The use of combining Hopfield Neural Networks (HNN) and Tabu (local search) approach to define optimal groups of operations which will facilitate the generation of the route sheet has been discussed and presented in this paper. For each one of the operations, the (HNN) will be used to generate the feasible manufacturing alternatives, and the Tabu search technique will be applied to identify the best schedule. The proposed approach, Hybrid Hopfield Neural Networks (HHNN) and the simulation techniques, save time and minimise unbalanced workloads among the operations. The potential ability of the proposed approach is shown using a numerical case example of a big size of group scheduling problem.

Development of a Profit-Based Air Cargo Loading Information System

In today's competitive logistics business environment, airfreight forwarders need to optimize every aspect of their logistics operations. However, forwarders still heavily rely on human brain and working experiences for calculating complex cargo packing and scheduling problems. Although recent research studies related to cargo packing and scheduling problems have resulted in the development of a number of advanced techniques of cargo planning, it can be seen that most of the research work is focused on the optimization of space in order to achieve the maximum possible amount of cargo to be packed in the minimum of space. After numerous site evaluation and end-user feedbacks, it is found that space optimization does not necessarily cause profit optimization, which is the ultimate aim of logistics providers. A study of contemporary research publications indicates that there are inadequate research studies related to profit-based optimization in cargo packing areas. This paper presents a profit-based air cargo loading information system (ACLIS) that embeds an innovative technology known as heuristics iterative reasoning technology (HIRT) that supports loading plan generation, focusing on maximization of the profit margin. In general, the proposed system is meant to maximize the profit in the airfreight forwarding business. It adopts an objective function governed by a list of constraints together with rule-based reasoning to provide expert advice to support the generation of appropriate loading plans

A Component Based Heuristic Search Method with Adaptive Perturbations for Hospital Personnel Scheduling

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with adaptive perturbations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then mimic a natural evolutionary process on these components to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs a dynamic evaluation function which evaluates how well each component contributes towards the final objective. Two perturbation steps are then applied: the first perturbation eliminates a number of components that are deemed not worthy to stay in the current schedule; the second perturbation may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.