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Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms. [ABSTRACT FROM AUTHOR]

Fitness landscape analysis is a tool that can help us gain insight into a problem, determine how hard it is to solve a problem using a given algorithm, choose an algorithm for solving a given problem, or choose good algorithm parameters for solving the problem. In this paper, fitness landscape analysis of hyperheuristics is used for clustering instances of three scheduling problems. After that, good parameters for tree-based genetic programming that can solve a given scheduling problem are calculated automatically for every cluster. Additionally, we introduce tree editing operators which help in the calculation of fitness landscape features in tree based genetic programming. A heuristic is proposed based on introduced operators, and it calculates the distance between any two trees. The results show that the proposed approach can obtain parameters that offer better performance compared to manual parameter selection. [ABSTRACT FROM AUTHOR]

We consider a variant of the NP-hard problem of assigning jobs to machines to minimize the completion time of the last job. Usually, precedence constraints are given by a partial order on the set of jobs, and each job requires all its predecessors to be completed before it can start. In this paper, we consider a different type of precedence relation that has not been discussed as extensively and is called OR-precedence. In order for a job to start, we require that at least one of its predecessors is completed—in contrast to all its predecessors. Additionally, we assume that each job has a release date before which it must not start. We prove that a simple List Scheduling algorithm due to Graham (Bell Syst Tech J 45(9):1563–1581, 1966) has an approximation guarantee of 2 and show that obtaining an approximation factor of 4 / 3 - ε is NP-hard. Further, we present a polynomial-time algorithm that solves the problem to optimality if preemptions are allowed. The latter result is in contrast to classical precedence constraints where the preemptive variant is already NP-hard. Our algorithm generalizes previous results for unit processing time jobs subject to OR-precedence constraints, but without release dates. The running time of our algorithm is O (n 2) for arbitrary processing times and it can be reduced to O(n) for unit processing times, where n is the number of jobs. The performance guarantees presented here match the best-known ones for special cases where classical precedence constraints and OR-precedence constraints coincide. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two parallel machine problem where each machine has a fixed and known unavailable period. The objective is to minimize the makespan. In the literature, the problem has been considered for the case in which there is only one unavailable period on either one of the machines. This paper extends the results to two unavailable periods, one for each machine. For each of the different cases of the problem, an algorithm is developed to find the optimal solution. Although all the proposed algorithms have exponential time complexities, they are quite efficient in solving large problems. Computational results for problems with up to 1000 jobs are reported. [ABSTRACT FROM AUTHOR]

The m-machine no-wait flowshop scheduling problem, with the objective of minimizing total completion time subject to the constraint that makespan has to be less than or equal to a certain value, is addressed in this paper. First, an algorithm is proposed to find an upper bound on the makespan in case the upper bound is not given or unknown. Given the upper bound on makespan, a proposed algorithm ( PAL) with five versions L (1, 5, 10, 15, and 20) and a genetic algorithm ( GA) are utilized for solving the problem. Furthermore, a dominance relation is established for the case of four machines. The five versions of PAL and GA are evaluated on randomly generated problems with different number of jobs and number of machines. Computational experiments show that the errors of PA10, PA15, and PA20 are much smaller than that of GA while the CPU times of PA10, PA15, and PA20 are significantly smaller than that of GA. Therefore, the algorithms PA10, PA15, and PA20 are superior to the GA algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the single machine scheduling problem with a new version of time-dependent processing times. The processing time of a job is defined as a piecewise linear function of its start time. It is preferred that the processing of each job be started at a specific time which means that processing the job before or after that time implies additional effort to accomplish the job. The job-processing time is a nonmonotonic function of its start time. The increasing rate of processing times is job independent and the objective is to minimize the cycle time. We show that the optimal schedule is V shaped and propose an optimal polynomial time algorithm for the problem. [ABSTRACT FROM AUTHOR]

In order to obtain efficiency and flexibility, assignment of machines' layout and determination of jobs' schedule on each machine are among the most important decisions. These decisions are interrelated and may impact each other but they are often treated separately or as a sequential decision in prior researches. In this paper, we propose a new approach to concurrently make the layout and scheduling decisions in a job shop environment. In other words, we consider an extension of the well-known job shop scheduling problem with transportation delay in which in addition to decisions made in the classic problem, the locations of machines have to be selected among possible sites. The only goal of the problem is the minimization of the makespan. A hybrid metaheuristic approach based on the scatter search algorithm is developed to tackle this problem. Using 43 randomly generated benchmark instances, the performance of the scatter search and its components are evaluated. We also applied our procedure to the classic job shop scheduling problems. Computational results show that our procedure is efficient. [ABSTRACT FROM AUTHOR]

In this paper, we focus on real-life settings that require the development of new models of flowshop scheduling problems, where job processing times can increase with the number of processed jobs due to the aging effect and decrease by the allocation of additional resource. We analyse the makespan minimization flowshop problem with such model and also with the aging effect only. We prove that the considered problems and their special cases are still polynomially solvable under given conditions, and on their basis, we provide optimal polynomial time solution algorithms. [ABSTRACT FROM AUTHOR]

In the past decades, resource parameters have been introduced in project scheduling literature to measure the scarceness of resources of a project instance. In this paper, we incorporate these resource scarceness parameters in the search process to solve the multi-mode resource constrained project scheduling problem, in which multiple execution modes are available for each activity in the project. Therefore, we propose a scatter search algorithm, which is executed with different improvement methods, each tailored to the specific characteristics of different renewable and nonrenewable resource scarceness values. Computational results prove the effectiveness of the improvement methods and reveal that the procedure is among the best performing competitive algorithms in the open literature. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Generating schedules such that all operations are repeated every constant period of time is as important as generating schedules with minimum delays in all cases where a known discipline is desired or obligated by stakeholders. In this paper, a periodic job shop scheduling problem (PJSSP) based on the periodic event scheduling problem (PESP) is presented, which deviates from the cyclic scheduling. The PESP schedules a number of recurring events as such that each pair of event fulfills certain constraints during a given fixed time period. To solve such a hard PJSS problem, we propose a hybrid algorithm, namely PSO-SA, based on particle swarm optimization (PSO) and simulated annealing (SA) algorithms. To evaluate this proposed PSO-SA, we carry out some randomly constructed instances by which the related results are compared with the proposed SA and PSO algorithms as well as a branch-and-bound algorithm. In addition, we compare the results with a hybrid algorithm embedded with electromagnetic-like mechanism and SA. Moreover, three lower bounds (LBs) are studied, and the gap between the found LBs and the best found solutions are reported. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the PJSSP. [ABSTRACT FROM AUTHOR]

This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem for the comprehensive description of disruption management problems. This conceptual framework employs the concept of alternative activities to consider both the temporal shift of activities or the reallocation of resources and switches from one valid process variant to another one. Activities can be serialized or parallelized, process steps can be inserted or removed and durations as well as resource requirements can be modified dynamically during optimization. Focusing on the domain of the aircraft turnaround as the most important airport ground process, we illustrate how the Extended RCPSP (x-RCPSP) can be applied for decision support. A specific evolutionary algorithm is presented that identifies good-quality solutions to relatively large disruption management problems within only a few seconds. The results of the evaluation illustrate fast convergence on good or optimal schedules and serve as a basis for the development of future problem solving algorithms. [ABSTRACT FROM AUTHOR]

This paper considers group scheduling problem in hybrid flexible flow shop with sequence-dependent setup times to minimize makespan. Group scheduling problem consists of two levels, namely scheduling of groups and jobs within each group. In order to solve problems with this context, two new metaheuristics based on simulated annealing (SA) and genetic algorithm (GA) are developed. A design procedure is developed to specify and adjust significant parameters for SA- and GA-based metaheuristics. The proposed procedure is based on the response surface methodology and two types of objective function are considered to develop multiple-objective decision making model. For comparing metaheuristics, makespan and elapsed time to obtain it are considered as two response variables representing effectiveness and efficiency of algorithms. Based on obtained results in the aspect of makespan, GA-based metaheuristic is recommended for solving group scheduling problems in hybrid flexible flow shop in all sizes and for elapsed time SA-based metaheuristic has better results. [ABSTRACT FROM AUTHOR]

This paper presents a set of satisfiability tests and time-bound adjustment algorithms that can be applied to cumulative scheduling problems. An instance of the cumulative scheduling problem (CuSP) consists of (1) one resource with a given capacity, and (2) a set of activities, each having a release date, a deadline, a processing time and a resource capacity requirement. The problem is to decide whether there exists a start time assignment to all activities such that at no point in time the capacity of the resource is exceeded and all timing constraints are satisfied. The cumulative scheduling problem can be seen as a relaxation of the decision variant of the resource-constrained project scheduling problem. We present three necessary conditions for the existence of a feasible schedule. Two of them are obtained by polynomial relaxations of the CuSP. The third is based on energetic reasoning. We show that the second condition is closely related to the subset bound, a well-known lower bound of the m-machine problem. We also present three algorithms, based on the previously mentioned necessary conditions, to adjust release dates and deadlines of activities. These algorithms extend the time-bound adjustment techniques developed for the one-machine problem. They have been incorporated in a branch and bound procedure to solve the resource-constrained project scheduling problem. Computational results are reported. [ABSTRACT FROM AUTHOR]