Optimizing blocking flow shop scheduling problem with total completion time criterion

The blocking flow shop scheduling problem has found many applications in manufacturing systems. There are a few exact methods for solving this problem with different criteria. In this paper, efforts will be made to optimize the total completion time criterion for this problem. We present two mixed binary integer programming models, one of which is based on the departure times of jobs from machines, and the other is based on the idle and blocking times of jobs. An initial upper bound generator and some lower bounds and dominance rules are also developed to be used in a branch and bound algorithm. The algorithm solves 17 instances of the Taillard's benchmark problem set in less than 20 min.     

A branch-and-bound algorithm for single machine scheduling with quadratic earliness and tardiness penalties

This paper considers the problem of scheduling a single machine, in which the objective function is to minimize the weighted quadratic earliness and tardiness penalties and no machine idle time is allowed. We develop a branch and bound algorithm involving the implementation of lower and upper bounding procedures as well as some dominance rules. The lower bound is designed based on a lagrangian relaxation method and the upper bound includes two phases, one for constructing initial schedules and the other for improving them. Computational experiments on a set of randomly generated instances show that one of the proposed heuristics, used as an upper bound, has an average gap less than 1.3% for instances optimally solved. The results indicate that both the lower and upper bounds are very tight and the branch-and-bound algorithm is the first algorithm that is able to optimally solve problems with up to 30 jobs in a reasonable amount of time.     

A Truncated Sum of Processing‐Times–Based Learning Model for a Two‐Machine Flowshop Scheduling Problem

Scheduling with learning effects has gained increasing attention in recent years. A well‐known learning model is called “sum‐of‐processing‐times‐based learning” in which the actual processing time of a job is a nonincreasing function of the jobs already processed. However, the actual processing time of a given job drops to zero precipitously when the normal job processing times are large. Moreover, the concept of learning process is relatively unexplored in a flowshop environment. Motivated by these observations, this article addresses a two‐machine flowshop problem with a truncated learning effect. The objective is to find an optimal schedule to minimize the total completion time. First, a branch‐and‐bound algorithm incorporating with a dominance property and four lower bounds is developed to derive the optimal solution. Then three simulated annealing algorithms are also proposed for near‐optimal solution. The experimental results indicated that the branch‐and‐bound algorithm can solve instances up to 18 jobs, and the proposed simulated annealing algorithm performs well in item of CPU time and error percentage. © 2011 Wiley Periodicals, Inc.     

A branch and bound algorithm for minimizing total completion time on a single batch machine with incompatible job families and dynamic arrivals

In this paper, we consider a single batch machine scheduling problem with incompatible job families and dynamic job arrivals. The objective is to minimize the total completion time. This problem is known to be strongly NP-hard. We present several dominance properties and two types of lower bounds, which are incorporated to construct a basic branch and bound algorithm. Furthermore, according to the characteristics of dynamic job arrivals, a decomposed branch and bound algorithm is proposed to improve the efficiency. The proposed algorithms are tested on a large set of randomly generated problem instances.     

修改D-W分解求具有需求时间窗和投机性成本的批量问题

研究多产品具有能力约束、需求时间窗、允许延期交货和投机性成本的批量问题.分析无能力约束凸包极点的特征,采用修正的Dantzig-Wolfe分解对原问题进行等价变换.使用列生成获得下界,同时采用启发式分支定界寻找近优解.对随机算例进行了测试与比较,计算结果表明上界与下界之间的间隙非常小;另外分析了当能力参数和订单规模变化时解的质量和计算时间.     

Single machine scheduling to minimize maximum lateness subject to release dates and precedence constraints

This paper considers the problem of finding a nonpreemptive schedule for a single machine to minimize the maximum lateness with release dates and precedence constraints. A branch and bound algorithm is developed. The algorithm uses four different heuristics to find upper bounds at the initial branch node: early release date heuristic, modified Schrage's heuristic, heuristic BLOCK, and a variable neighborhood descent procedure. At each branch node, two branches evolve from a schedule found by heuristic BLOCK using a binary branching rule based on bottleneck and critical jobs, and a lower bound is obtained by optimally solving the relaxed problem with preemption. The algorithm solves 14,984 out of the 15,000 systematically generated instances with up to 1,000 jobs within 1 minute of CPU time.     

Algorithms for a two‐machine flowshop problem with jobs of two classes

We consider a problem of scheduling jobs of two classes of urgencies in a two‐machine flowshop with the objective of minimizing total tardiness of one class for urgent jobs and the maximum completion time of the other class for non‐urgent jobs. Urgent jobs are an important consideration in the real manufacturing systems, but it has not been studied due to the difficulty of the problem. In this research, a branch‐and‐bound (B&B) algorithm is proposed through developing lower bounds, dominance properties, and heuristic algorithms for obtaining an initial feasible solution. To evaluate the performance of the proposed algorithms, computational experiments on randomly generated instances are performed. Results of the experiments show that the suggested B&B algorithm can solve problems with up to 20 jobs in a reasonable amount of CPU time.     

Heuristic approaches for the family traveling salesman problem

In this article, we address the family traveling salesman problem (FTSP), an NP‐hard problem that may be seen as a generalization of the traveling salesman problem. In the FTSP, the set of cities is partitioned into several families and one wants to find the minimum cost route that visits a given number of cities in each family. We propose two metaheuristics, a population‐based method and a local search method, and a hybrid algorithm, which combines a branch‐and‐cut algorithm with a local search procedure. All the proposed methods improve the best known upper bounds from the literature. The local search method and the hybrid algorithm improve the best known upper bounds for all the benchmark instances with unknown optimal value, while the population‐based method improves the best known upper bounds for all instances, except two. Furthermore, we developed an instance generator to create additional test instances with different visit patterns. These newly generated instances were considered in the computational experiment and, thus, we provide optimal values or upper bounds for each instance. Additionally, we created a website dedicated to the FTSP, where this information is made available to the scientific community ( http://familytsp.rd.ciencias.ulisboa.pt/ ).     

A two-machine flowshop problem with processing time-dependent buffer constraints—An application in multimedia presentations

To have a quality multimedia presentation through networks, its presentation lag needs to be controlled. One way to reduce the lag is to prefetch the media objects before their due dates. This paper explores techniques for optimizing the object sequence in a prefetch-enabled TV-like presentation. An optimal solution is the one with which the presentation lag is minimized. We formulate the problem into a two-machine flowshop scheduling problem with a single chain precedence constraint and a player-side buffer constraint. The player-side buffer is “processing time-dependent” and distinguished from the conventional item-based intermediate buffer constraints discussed in previous flowshop studies. We prove the problem to be strongly NP-hard. A branch and bound algorithm equipped with four lower bounds and an NEH-based upper bound is developed. The simulation results show that the average gaps between the overall lower bounds and the NEH-based upper bound are less than 3% for problems with a large buffer size, and less than 13% for problems with a small buffer size and high density of precedence constraints. For applications where the media objects are delivered through extremely busy servers with which only very restricted CPU resources can be allocated for computation, the CDS-based algorithm provides better sequences than the NEH-based algorithm.     

Some algorithms of solving minimax multiprocessor scheduling problem

A problem of constructing schedules of minimal length without interrupts and switches is considered for a multiprocessor system, in which the job execution time depends on the processor assigned to the job. To solve this problem, the branch and bound method is developed. The method is based on efficient algorithms for calculating lower and upper bounds of minimal length of the schedule. For the particular case when processors are identical, their number is fixed and the directive deadline is given, a pseudo-polynomial algorithm is proposed for constructing the admissible schedule. The number of processors required for efficient parallelizing of the algorithm is found.     

非凸二次规划全局极小问题的新型分枝定界算法

针对求解多面集上二次函数的全局近似最优解问题,利用逐步缩小对偶间隙的处理办法,提出了一个新型分枝定界算法。新算法的主要改进之处是利用了Lagrange 对偶性获取下界。最后,用构造和随机产生的问题实例,对提出的新算法和传统的分枝定界算法做了初步的数值比较实验。计算实验表明算法对求解中大规模非凸二次规划问题的有效性。     

A branch-and-bound algorithm for solving a two-machine flow shop problem with deteriorating jobs

In this paper we consider a two-machine flow shop scheduling problem with deteriorating jobs. By a deteriorating job we mean that the job's processing time is an increasing function of its starting time. We model job deterioration as a function that is proportional to a linear function of time. The objective is to find a sequence that minimizes the total completion time of the jobs. For the general case, we derive several dominance properties, some lower bounds, and an initial upper bound by using a heuristic algorithm, and apply them to speed up the elimination process of a branch-and-bound algorithm developed to solve the problem.     

A fast algorithm for the maximum weight clique problem

We present a branch and bound method which finds a maximum weight clique in an arbitrary weighted graph. The main ingredients are a weighted coloring heuristic which simultaneously produces lower and upper bounds and a branching rule that uses the information obtained in the coloring. The algorithm performs comparable to the fastest method known so far but is much easier to implement.     

同尺寸矩形毛坯排样的连分数分支定界算法

在确定同尺寸矩形毛坯最优排样方式的算法中，连分数算法的时间效率最高，但所生成排样方式的切割工艺复杂．提出连分数分支定界算法，该算法应用连分数法确定毛坯数最优值，采用贴切的上界估计方法；在搜索过程中只保留上界不小于最优值的分支，遇到下界等于最优值的分支时结束搜索．实验结果表明，该算法的时间效率和连分数算法接近，并可以有效地简化切割工艺，生成切割工艺最简单的排样方式．最后，通过实例分析说明该算法的节约材料潜力。     

Minimal spanning tree subject to a side constraint

This paper addresses the problem of finding a minimal spanning tree in a network subject to a knapsack-type constraint. The problem is shown to belong to the class of NP -complete problems. Certain properties of an optimal solution to this problem are established considering a bicriteria spanning tree problem. Based on these, an algorithm is proposed which is mainly a branch and bound scheme branching from the test upper bound and generating efficient frontiers successively. It also uses certain effective heuristics and bounds that are obtained in polynomially bounded operations. The algorithm is validated and a numerical example is included.     

Reduction constraints for the global optimization of NLPs

Convergence of branch‐and‐bound algorithms for the solution of NLPs is obtained by finding ever‐nearer lower and upper bounds to the objective function. The lower bound is calculated by constructing a convex relaxation of the NLP. Reduction constraints are new linear problem constraints which are (a) linearly independent from the existing constraints; (b) redundant with reference to the original NLP formulation; (c) not redundant with reference to its convex relaxation. Thus, they can be successfully employed to reduce the feasible region of the convex relaxation without cutting the feasible region of the original NLP.     

Scheduling algorithms to minimize the number of tardy jobs in two-stage hybrid flow shops

This paper considers a two-stage hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. Each job is processed through the two production stages in series, each of which has multiple identical parallel machines. The problem is to determine the allocation of jobs to the parallel machines as well as the sequence of the jobs assigned to each machine. To solve the problem, a branch and bound algorithm, which incorporates the methods to obtain the lower and upper bounds as well as the dominance properties to reduce the search space, is suggested that gives the optimal solutions. In addition, two-phase heuristic algorithms are suggested to obtain good solutions for large-size problems within a reasonable amount of computation time. To show the performances of the optimal and heuristic algorithms suggested in this paper, computational experiments are done on a number of randomly generated test problems, and the test results are reported.     

The single-machine total weighted tardiness scheduling problem with position-based learning effects

This study addresses the problem of minimizing the total weighted tardiness on a single-machine with a position-based learning effect. Several dominance properties are established to develop branch and bound algorithm and a lower bound is provided to derive the optimal solution. In addition, three heuristic procedures are developed for near-optimal solutions. Computational results are also presented to evaluate the performance of the proposed algorithms.     

Hybrid approaches for the two‐scenario max–min knapsack problem

In this paper, we deal with the two‐scenario max–min knapsack (MNK) problem. First, we consider several formulations of MNK as a mixed integer programming problem. Then, we propose a hybrid method as an alternative to solve the MNK exactly. The approach combines relaxation technique and the temporary setting of variables to improve iteratively two sequences of upper and lower bounds. More precisely, pseudo‐cuts are added to the problem to strengthen the bounds and reduce the gap between the best lower bound and the best upper bound. The algorithm stops when the proof of the optimality of the best solution is found. We also use a reduction technique to set some variables definitively at their optimal values. Numerical experiments demonstrate the robustness of the approach. In particular, our algorithm is efficient to solve large and correlated instances of MNK.     

Semidefinite relaxation for linear programs with equilibrium constraints

In this paper, we present a semidefinite programming (SDP) relaxation for linear programs with equilibrium constraints (LPECs) to be used in a branch‐and‐bound (B&B) algorithm. The procedure utilizes the global optimal solution of LPECs and was motivated by the B&B algorithm proposed by Bard and Moore for linear/quadratic bilevel programs, where complementarities are recursively enforced. We propose the use of the SDP relaxation to generate bounds at the nodes of the B&B tree. Computational results compare the quality of the bounds given by the SDP relaxation with the ones given by conventional linear programming relaxations.