A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

Job shop scheduling to minimize work-in-process, earliness and tardiness costs

Ph.DDOCTOR OF PHILOSOPH

Shop Scheduling In The Presence Of Batching, Sequence-dependent Setups And Incompatible Job Families Minimizing Earliness And Tardiness Penalties

The motivation of this research investigation stems from a particular job shop production environment at a large international communications and information technology company in which electro-mechanical assemblies (EMAs) are produced. The production environment of the EMAs includes the continuous arrivals of the EMAs (generally called jobs), with distinct due dates, degrees of importance and routing sequences through the production workstations, to the job shop. Jobs are processed in batches at the workstations, and there are incompatible families of jobs, where jobs from different product families cannot be processed together in the same batch. In addition, there are sequence-dependent setups between batches at the workstations. Most importantly, it is imperative that all product deliveries arrive on time to their customers (internal and external) within their respective delivery time windows. Delivery is allowed outside a time window, but at the expense of a penalty. Completing a job and delivering the job before the start of its respective time window results in a penalty, i.e., inventory holding cost. Delivering a job after its respective time window also results in a penalty, i.e., delay cost or emergency shipping cost. This presents a unique scheduling problem where an earlinesstardiness composite objective is considered. This research approaches this scheduling problem by decomposing this complex job shop scheduling environment into bottleneck and non-bottleneck resources, with the primary focus on effectively scheduling the bottleneck resource. Specifically, the problem of scheduling jobs with unique due dates on a single workstation under the conditions of batching, sequence-dependent iii setups, incompatible job families in order to minimize weighted earliness and tardiness is formulated as an integer linear program. This scheduling problem, even in its simplest form, is NP-Hard, where no polynomial-time algorithm exists to solve this problem to optimality, especially as the number of jobs increases. As a result, the computational time to arrive at optimal solutions is not of practical use in industrial settings, where production scheduling decisions need to be made quickly. Therefore, this research explores and proposes new heuristic algorithms to solve this unique scheduling problem. The heuristics use order review and release strategies in combination with priority dispatching rules, which is a popular and more commonly-used class of scheduling algorithms in real-world industrial settings. A computational study is conducted to assess the quality of the solutions generated by the proposed heuristics. The computational results show that, in general, the proposed heuristics produce solutions that are competitive to the optimal solutions, yet in a fraction of the time. The results also show that the proposed heuristics are superior in quality to a set of benchmark algorithms within this same class of heuristic

Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource

[EN] In this paper, we study the unrelated parallel machine scheduling problem with one scarce additional resource to minimise the maximum completion time of the jobs or makespan. Several heuristics are proposed following two strategies: the first one is based on the consideration of the resource constraint during the whole solution construction process. The second one starts from several assignment rules without considering the resource constraint, and repairs the non feasible assignments in order to obtain a feasible solution. Several computation experiments are carried out over an extensive benchmark. A comparative evaluation against previously proposed mathematical models and matheuristics (combination of mathematical models and heuristics) is carried out. From the results, we can conclude that our methods outperform the existing ones, and the second strategy performs better, especially for large instances. (C) 2017 Elsevier Ltd. All rights reserved.The authors are supported by the Spanish Ministry of Economy and Competitiveness, under the projects "SCHEYARD - Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) and "OPTEMAC - Optimizacion de Procesos en Terminales Maritimas de Contenedores" (No. DPI2014-53665-P), all of them partially financed with FEDER funds. The authors are also partially supported by the EU Horizon 2020 research and innovation programme under grant agreement no. 731932 "Transforming Transport: Big Data Value in Mobility and Logistics". Interested readers can download contents from http://soa.iti.es, like the instances used and a software for generating further instances. Source codes are available upon justified request from the authors.Villa Juliá, MF.; Vallada Regalado, E.; Fanjul Peyró, L. (2018). Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource. Expert Systems with Applications. 93:28-38. https://doi.org/10.1016/j.eswa.2017.09.054S28389

Dominance-Based Heuristics for One-Machine Total Cost Scheduling Problems

International audienceWe study the one-machine scheduling problem with release dates and we look at several objective functions including total (weighted) tardiness and total (weighted) completion time. We describe dominance rules for these criteria, as well as techniques for using these dominance rules to build heuristic solutions. We use them to improve certain well-known greedy heuristic algorithms from the literature. Finally, we introduce a Tabu Search method with a neighborhood based on our dominance rules. Experiments show the effectiveness of our techniques in obtaining very good solutions for all studied criteria

An exact approach to minimize single machine total weighted tardiness problem with unequal release dates

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 1998.Thesis (Master's) -- Bilkent University, 1998.Includes bibliographical references leaves 117-119In this research, the problem of scheduling a set of jobs on a single machine to minimize total weighted tardiness with unequal release dates is considered. We present a new dominance rule by considering the time depending orderings between each pair of jobs. The proposed rule provides a sufficient condition for local optimality. Therefore, if any sequence violates the dominance rule then switching the violating jobs either lowers the total weighted tardiness or leaves it unchanged. Based on the dominance rule, an algorithm is developed which is compared to a number of heuristics in the literature. Our computational results indicate that the proposed algorithm dominates the competing algorithms in all runs, therefore it can improve the upper bounding scheme and can be used in reducing the number of alternatives in any enumerative algorithm. Furthermore, the proposed dominance rule is incorporated in a branch and bound algorithm in conjunction with lower bounding scheme, branching condition and search strcitegy. To the best of our knowledge, author know of no other published exact approach for l|rj| problem. This enhances contribution of our study in the literature.Özdemir, DenizM.S

Approximate Algorithms for the Combined arrival-Departure Aircraft Sequencing and Reactive Scheduling Problems on Multiple Runways

The problem addressed in this dissertation is the Aircraft Sequencing Problem (ASP) in which a schedule must be developed to determine the assignment of each aircraft to a runway, the appropriate sequence of aircraft on each runway, and their departing or landing times. The dissertation examines the ASP over multiple runways, under mixed mode operations with the objective of minimizing the total weighted tardiness of aircraft landings and departures simultaneously. To prevent the dangers associated with wake-vortex effects, separation times enforced by Aviation Administrations (e.g., FAA) are considered, adding another level of complexity given that such times are sequence-dependent. Due to the problem being NP-hard, it is computationally difficult to solve large scale instances in a reasonable amount of time. Therefore, three greedy algorithms, namely the Adapted Apparent Tardiness Cost with Separation and Ready Times (AATCSR), the Earliest Ready Time (ERT) and the Fast Priority Index (FPI) are proposed. Moreover, metaheuristics including Simulated Annealing (SA) and the Metaheuristic for Randomized Priority Search (Meta-RaPS) are introduced to improve solutions initially constructed by the proposed greedy algorithms. The performance (solution quality and computational time) of the various algorithms is compared to the optimal solutions and to each other. The dissertation also addresses the Aircraft Reactive Scheduling Problem (ARSP) as air traffic systems frequently encounter various disruptions due to unexpected events such as inclement weather, aircraft failures or personnel shortages rendering the initial plan suboptimal or even obsolete in some cases. This research considers disruptions including the arrival of new aircraft, flight cancellations and aircraft delays. ARSP is formulated as a multi-objective optimization problem in which both the schedule\u27s quality and stability are of interest. The objectives consist of the total weighted start times (solution quality), total weighted start time deviation, and total weighted runway deviation (instability measures). Repair and complete regeneration approximate algorithms are developed for each type of disruptive events. The algorithms are tested against difficult benchmark problems and the solutions are compared to optimal solutions in terms of solution quality, schedule stability and computational time