Mixed integer goal programming models for the flexible job-shop scheduling problems with separable and non-separable sequence dependent setup times

The optimization problem addressed in this paper is an advanced form of the flexible job shop scheduling problem (FJSP) which also covers process plan flexibility and separable/non-separable sequence dependent setup times in addition to routing flexibility. Upon transforming the problem into an equivalent network problem, two mixed integer goal programming models are formulated. In the first model (Model A) the sequence dependent setup times are non-separable. In the second one (Model B) they are separable. Model B is obtained from Model A with a minor modification. The formulation of the models is described on a small sized numerical example and the solutions are interpreted. Finally, computational results are obtained on test problems.     

Production planning with load dependent lead times

Lead times impact the performance of the supply chain significantly. Although there is a large literature concerning queuing models for the analysis of the relationship between capacity utilization and lead times, and there is a substantial literature concerning control and order release policies that take lead times into consideration, there have been only few papers describing models at the aggregate planning level that recognize the relationship between the planned utilization of capacity and lead times. In this paper we provide an in-depth discussion of the state-of-the art in this literature, with particular attention to those models that are appropriate at the aggregate planning level. Received: September 2005 / Revised version: November 2005 AMS classification: 46N10 All correspondence to: Stefan Vo?     

An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives

Iterated Greedy (IG) algorithms are based on a very simple principle, are easy to implement and can show excellent performance. In this paper, we propose two new IG algorithms for a complex flowshop problem that results from the consideration of sequence dependent setup times on machines, a characteristic that is often found in industrial settings. The first IG algorithm is a straightforward adaption of the IG principle, while the second incorporates a simple descent local search. Furthermore, we consider two different optimization objectives, the minimization of the maximum completion time or makespan and the minimization of the total weighted tardiness. Extensive experiments and statistical analyses demonstrate that, despite their simplicity, the IG algorithms are new state-of-the-art methods for both objectives.     

Production planning with load dependent lead times: an update of research

Lead times impact the performance of the supply chain significantly. Although there is a large body of literature concerning queuing models for the analysis of the relationship between capacity utilization and lead times, and another body of work on control and order release policies that take lead times into consideration, there have been relatively few aggregate planning models that recognize the (nonlinear) relationship between the planned utilization of capacity and lead times. In this paper we provide an in-depth discussion of the state-of-the art in this area, with particular attention to those models that are appropriate at the aggregate planning level. An earlier version of this paper appeared in 4OR 3, 257–302, 2005.     

Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times

This paper presents an enhanced migrating bird optimization (MBO) algorithm and a new heuristic for solving a scheduling problem. The proposed approaches are applied to a permutation flowshop with sequence dependent setup times and the objective of minimizing the makespan. In order to augment the MBOs intensification capacity, an original problem specific heuristic is introduced. An adapted neighborhood, a tabu list, a restart mechanism and an original process for selecting a new leader also improved the MBO’s behavior. Using benchmarks from the literature, the resulting enhanced MBO (EMBO) gives state-of-the-art results when compared with other algorithms reference. A statistical analysis of the numerical experiments confirms the relative efficiency and effectiveness of both EMBO and the new heuristic.     

Reduction of the bilevel stochastic optimization problem with quantile objective function to a mixed‐integer problem

The paper is devoted to the stochastic optimistic bilevel optimization problem with quantile criterion in the upper level problem. If the probability distribution is finite, the problem can be transformed into a mixed‐integer nonlinear optimization problem. We formulate assumptions guaranteeing that an optimal solution exists. A production planning problem is used to illustrate usefulness of the model. Copyright © 2017 John Wiley & Sons, Ltd.     

Minimizing total tardiness in a scheduling problem with a learning effect

Minimizing of total tardiness is one of the most studied topics on single machine problems. Researchers develop a number of optimizing and heuristic methods to solve this NP-hard problem. In this paper, the problem of minimizing total tardiness is examined in a learning effect situation. The concept of learning effects describes the reduction of processing times arising from process repetition. A 0–1 integer programming model is developed to solve the problem. Also, a random search, the tabu search and the simulated annealing-based methods are proposed for the problem and the solutions of the large size problems with up to 1000 jobs are found by these methods. To the best of our knowledge, no works exists on the total tardiness problem with a learning effect tackled in this paper.     

Comparison of two algorithms for solving a two‐stage bilinear stochastic programming problem with quantile criterion

The paper is devoted to solving the two‐stage problem of stochastic programming with quantile criterion. It is assumed that the loss function is bilinear in random parameters and strategies, and the random vector has a normal distribution. Two algorithms are suggested to solve the problem, and they are compared. The first algorithm is based on the reduction of the original stochastic problem to a mixed integer linear programming problem. The second algorithm is based on the reduction of the problem to a sequence of convex programming problems. Performance characteristics of both the algorithms are illustrated by an example. A modification of both the algorithms is suggested to reduce the computing time. The new algorithm uses the solution obtained by the second algorithm as a starting point for the first algorithm. Copyright © 2015 John Wiley & Sons, Ltd.