Single-machine scheduling problems with both deteriorating jobs and learning effects

In this paper we consider the single-machine scheduling problems with job-position-based and sum-of-processing-times based processing times. The real processing time of a job is a function of its position and the total processing time of the jobs that are in front of it in the sequence. The objective is to minimize the makespan, and to minimize the mean finish time. We prove that some special cases are polynomially solvable under some restrictions of the parameters. In addition, for some another special cases of minimization of the mean finish time and the makespan, we show that an optimal schedule is V-shaped with respect to job normal processing times. Then, we propose a heuristic based on the V-shaped property, and show through a computational experiment that it performs efficiently.     

Parallel machine scheduling with nested processing set restrictions

We consider the problem of scheduling a set of n independent jobs on m parallel machines, where each job can only be scheduled on a subset of machines called its processing set. The machines are linearly ordered, and the processing set of job j   is given by two machine indexes _aj$a_j$ and _bj$b_j$; i.e., job j   can only be scheduled on machines _aj,_aj+1,…,_bj$a_j\text{,}{a}_j+1\text{,}\dots \text{,}{b}_j$. Two distinct processing sets are either nested or disjoint. Preemption is not allowed. Our goal is to minimize the makespan. It is known that the problem is strongly NP-hard and that there is a list-type algorithm with a worst-case bound of 2-1/m$2-1/m$. In this paper we give an improved algorithm with a worst-case bound of 7/4. For two and three machines, the algorithm gives a better worst-case bound of 5/4 and 3/2, respectively.     

Heuristic solution methods for the stochastic flow shop problem

We investigate the stochastic flow shop problem with m machines and general distributions for processing times. No analytic method exists for solving this problem, so we looked instead at heuristic methods. We devised three constructive procedures with modest computational requirements, each based on approaches that have been successful at solving the deterministic counterpart. We compared the performance of these procedures experimentally on a set of test problems and found that all of them achieve near-optimal performance.     

Open shop scheduling problem to minimize makespan with release dates

The scheduling problem of open shop to minimize makespan with release dates is investigated in this paper. Unlike the usual researches to confirm the conjecture that the tight worst-case performance ratio of the Dense Schedule (DS) is 2 − 1/m, where m is the number of machines, the asymptotic optimality of the DS is proven when the problem scale tends to infinity. Furthermore, an on-line heuristic based on DS, Dynamic Shortest Processing Time-Dense Schedule, is presented to deal with the off-line and on-line versions of this problem. At the end of the paper, an asymptotically optimal lower bound is provided and the results of numerical experiments show the effectiveness of the heuristic.     

Scheduling with deteriorating jobs and learning effects

This paper studies a single machine scheduling problem simultaneously with deteriorating jobs and learning effects. The objectives are to minimize the makespan and the number of tardy jobs, respectively. Two polynomial time algorithms are proposed to solve these problems optimally.     

A note on the proportionate flow shop with a bottleneck machine

Choi, B.-C., Yoon, S.-H., Chung, S.-J., 2007. Minimizing maximum completion time in a proportionate flow shop with one machine of different speed. European Journal of Operational Research 176, 964–974 consider the proportionate flow shop with a slow bottleneck machine and propose the SLDR heuristic for it. Choi et al. (2007) derive a data-dependent worst-case ratio bound for the SLDR heuristic which is then bounded by two. In this note, we show that the tight worst-case ratio bound of the SLDR heuristic is 3/2.     

Total completion time with makespan constraint in no-wait flowshops with setup times

The m-machine no-wait flowshop scheduling problem with the objective of minimizing total completion time subject to the constraint that the makespan value is not greater than a certain value is addressed in this paper. Setup times are considered non-zero values, and thus, setup times are treated as separate from processing times. Several recent algorithms, an insertion algorithm, two genetic algorithms, three simulated annealing algorithms, two cloud theory-based simulated annealing algorithms, and a differential evolution algorithm are adapted and proposed for the problem. An extensive computational analysis has been conducted for the evaluation of the proposed algorithms. The computational analysis indicates that one of the nine proposed algorithms, one of the simulated annealing algorithms (ISA-2), performs much better than the others under the same computational time. Moreover, the analysis indicates that the algorithm ISA-2 performs significantly better than the earlier existing best algorithm. Specifically, the best performing algorithm, ISA-2, proposed in this paper reduces the error of the existing best algorithm in the literature by at least 90% under the same computational time. All the results have been statistically tested.     

Makespan minimization in open shops: A polynomial time approximation scheme

In this paper, we demonstrate the existence of a polynomial time approximation scheme for makespan minimization in the open shop scheduling problem with an arbitrary fixed numberm of machines. For the variant of the problem where the number of machines is part of the input, it is known that the existence of an approximation scheme would implyP = NP. Hence, our result draws a precise separating line between approximable cases (i.e., withm fixed) and non-approximable cases (i.e., withm part of the input) of this shop problem. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.Supported by the DIMANET/PECO Program of the European Union.Supported by a research fellowship of the Euler Institute for Discrete Mathematics and its Applications. This research was done while Gerhard Woeginger was with the Department of Mathematics and Computing Science, Eindhoven University of Technology, P.O. Box 513, NL-5600 MB Eindhoven, The Netherlands.     

A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal

The scheduling problem in a container terminal is characterized by the coordination of different types of equipment. In this paper, we present an integrated model to schedule the equipment. The objective is to minimize the makespan, or the time it takes to serve a given set of ships. The problem is formulated as a Hybrid Flow Shop Scheduling problem with precedence and Blocking constraints (HFSS-B). A tabu search algorithm is proposed to solve this problem. Certain mechanisms are developed and introduced into the algorithm to assure its quality and efficiency. The performance of the tabu search algorithm is analyzed from the computational point of view.