Generating subproblems in branch and bound algorithms for parallel machines scheduling problem

A branch and bound algorithm (B&B) has been widely used in various discrete and combinatorial optimization fields. To obtain optimal solutions as soon as possible for scheduling problems, three tools, which are branching, bounding and dominance rules, have been developed in the B&B algorithm. One of these tools, a branching is a method for generating subproblems and directly determines size of solution to be searched in the B&B algorithm. Therefore, it is very important to devise effective branching scheme for the problem.In this note, a survey of branching schemes is performed for parallel machines scheduling (PMS) problems with n independent jobs and m machines and new branching schemes that can be used for identical and unrelated PMS problems, respectively, are suggested. The suggested branching methods show that numbers of generated subproblems are much smaller than that of other methods developed earlier and therefore, it is expected that they help to reduce a lot of CPU time required to obtain optimal solutions in the B&B algorithm.     

Dominance rules for the parallel machine total weighted tardiness scheduling problem with release dates

We address the parallel machine total weighted tardiness scheduling problem with release dates. We describe dominance rules and filtering methods for this problem. Most of them are adaptations of dominance rules based on solution methods for the single-machine problem. We show how it is possible to deduce whether or not certain jobs can be processed by a particular machine in a particular context and we describe techniques that use this information to improve the dominance rules. On the basis of these techniques we describe an enumeration procedure and we provide experimental results to determine the effectiveness of the dominance rules.     

A theoretical development for the total tardiness problem and its application in branch and bound algorithms

This paper deals with the single machine total tardiness problem, and proves that if the job sequences produced by two heuristics, named as Time Forward and Time Backward algorithms, have the same starting and ending job subsequences, then there exists an optimal job sequence with the starting and ending job subsequences. The computation experiments show that there is a significant improvement of the running time of a branch and bound algorithm with the incorporation of the new property.     

A branch-and-bound algorithm for single-machine scheduling with batch delivery and job release times

This paper addresses scheduling a set of jobs with specified release times on a single machine for delivery in batches to customers or to other machines for further processing. This problem is a natural extension of minimizing the sum of flow times in the presence of release time by considering the possibility of delivering jobs in batches and introducing batch delivery costs. The scheduling objective adopted is that of minimizing the sum of flow times and delivery costs. The extended problem arises in the context of coordination between machine scheduling and a distribution system in a supply chain network. Structural properties of the problem are investigated and used to devise a branch-and-bound solution scheme. Computational experiments show significant improvement over an existing dynamic programming algorithm.     

Parallel machine scheduling with splitting jobs by a hybrid differential evolution algorithm

The problem of parallel machine scheduling for minimizing the makespan is an open scheduling problem with extensive practical relevance. It has been proved to be non-deterministic polynomial hard. Considering a job’s batch size greater than one in the real manufacturing environment, this paper investigates into the parallel machine scheduling with splitting jobs. Differential evolution is employed as a solution approach due to its distinctive feature, and a new crossover method and a new mutation method are brought forward in the global search procedure, according to the job splitting constraint. A specific local search method is further designed to gain a better performance, based on the analytical result from the single product problem. Numerical experiments on the performance of the proposed hybrid DE on parallel machine scheduling problems with splitting jobs covering identical and unrelated machine kinds and a realistic problem are performed, and the results indicate that the algorithm is feasible and efficient.     

A branch-and-price algorithm for the general case of scheduling parallel machines with a single server

We consider the strongly NP-hard problem of scheduling two-operation non-preemptable jobs on two identical parallel machines. A single server, that can handle at most one job at a time, is available to carry out the first (or setup) operation. The second operation, to be carried out on the same machine but without the server, must be executed immediately after the setup. The objective is to minimize the makespan. We apply a column generation method to a population of partial schedules, in turn generated by some well known heuristics, to achieve effective and efficient solutions. We compare the performance of this method with those proposed earlier and also suggest future work.     

A branch-and-bound algorithm to minimize total weighted completion time on identical parallel machines with job release dates

In this paper, we consider an identical parallel machine scheduling problem with release dates. The objective is to minimize the total weighted completion time. This problem is known to be strongly NP-hard. We propose some dominance properties and two lower bounds. We also present an efficient heuristic. A branch-and-bound algorithm, in which the heuristic, the lower bounds and the dominance properties are incorporated, is proposed and tested on a large set of randomly generated instances.     

An efficient branch and bound algorithm for assembly line balancing problems with parallel multi-manned workstations

In the event that big-sized complex products (containing a large number of assembly tasks most of which have long task times) are produced in simple or two-sided assembly lines, hundreds of stations are essentially required. Long product flow time, a large area for establishment of the line, a high budget for the investment of equipment, and tools in stations and several work-in-process are also required for these kinds of products. In order to avoid these disadvantages, assembly lines with parallel multi-manned workstations can be utilized. In this paper, these lines and one of their balancing problems are addressed, and a branch and bound algorithm is proposed. The algorithm is composed of a branching scheme, some efficient dominance and feasibility criteria based on a problem-specific knowledge. A heuristic-based guidance for enumeration process is included as an efficient component of the algorithm as well. VWSolver algorithm proposed for a special version of the problem in the literature has been modified and compared with the proposed algorithm. Results show that proposed algorithm outperforms VWSolver in terms of both CPU times and quality of feasible solutions found.     

A hybrid genetic algorithm for the single machine scheduling problem with sequence-dependent setup times

This paper presents a hybrid approach based on the integration between a genetic algorithm (GA) and concepts from constraint programming, multi-objective evolutionary algorithms and ant colony optimization for solving a scheduling problem. The main contributions are the integration of these concepts in a GA crossover operator. The proposed methodology is applied to a single machine scheduling problem with sequence-dependent setup times for the objective of minimizing the total tardiness. A sensitivity analysis of the hybrid approach is carried out to compare the performance of the GA and the hybrid genetic algorithm (HGA) approaches on different benchmarks from the literature. The numerical experiments demonstrate the HGA efficiency and effectiveness which generates solutions that approach those of the known reference sets and improves several lower bounds.     

MIP models and hybrid algorithm for minimizing the makespan of parallel machines scheduling problem with a single server

This paper considers the identical parallel machines scheduling problem (PMSP) with a single server in charge of job setups. A job can be processed with a precedent setup by a server on one of the machines. The setup can be processed at only one machine at any time. In this paper, the problem P, S1|s_j|C_max with a general job set is formulated using mixed integer programming in two ways. The first one is developed by taking into account the characteristics of the single server problem, and the second one is developed by adding the concept of the server waiting time suggested by Abdekhodaee and Wirth (2002) [3]. Abdekhodaee and Wirth (2002) [3] define the equation of the server waiting time applied to only the special case with two machines and a regular job set. The general model for several machines is studied in this paper by developing the properties on the server waiting time. The hybrid heuristic algorithm is developed for the practical use, which can yield a near-optimal schedule in a reasonable computational time. The performance of algorithm is evaluated by comparing with the results of MIP models and heuristics appearing in the literature.     

An improved approximation algorithm for single machine scheduling with job delivery

In single machine scheduling with release times and job delivery, jobs are processed on a single machine and then delivered by a capacitated vehicle to a single customer. Only one vehicle is employed to deliver these jobs. The vehicle can deliver at most c jobs in a shipment. The delivery completion time of a job is defined as the time in which the delivery batch containing the job is delivered to the customer and the vehicle returns to the machine. The objective is to minimize the makespan, i.e., the maximum delivery completion time of the jobs. We provide an approximation algorithm for this problem which is better than that given in the literature, improving the performance ratio from 5/3 to 3/2.     

Variable neighborhood search approaches for scheduling jobs on parallel machines with sequence-dependent setup times, precedence constraints, and ready times

In this paper, we discuss a scheduling problem for jobs on identical parallel machines. Ready times of the jobs, precedence constraints, and sequence-dependent setup times are considered. We are interested in minimizing the performance measure total weighted tardiness that is important for achieving good on-time delivery performance. Scheduling problems of this type appear as subproblems in decomposition approaches for large scale job shops with automated transport of the jobs as, for example, in semiconductor manufacturing. We suggest several variants of variable neighborhood search (VNS) schemes for this scheduling problem and compare their performance with the performance of a list based scheduling approach based on the Apparent Tardiness Cost with Setups and Ready Times (ATCSR) dispatching rule. Based on extensive computational experiments with randomly generated test instances we are able to show that the VNS approach clearly outperforms heuristics based on the ATCSR dispatching rule in many situations with respect to solution quality. When using the schedule obtained by ATCSR as an initial solution for VNS, then the entire scheme is also fast and can be used as a subproblem solution procedure for complex job shop decomposition approaches.     

A branch and bound algorithm for the cyclic job-shop problem with transportation

The cyclic job-shop problem with transportation can be used to describe optimization problems in fully automated manufacturing systems or assembly lines. We study the problem where the machines have no buffers, which rapidly decreases the number of feasible solutions and, therefore, makes it a lot harder to find those feasible solutions. After formulating the problem, we will characterize feasible solutions based on the route of the robot and their properties. With the aim of minimizing the cycle time, we have developed a tree search method to construct feasible solutions and combined it with a bounding procedure. Computational results are reported and compared to those gained by solving the problem with an LP solver.     

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.     

A hybrid genetic heuristic for scheduling parallel batch processing machines with arbitrary job sizes

This paper investigates the scheduling problem of parallel identical batch processing machines in which each machine can process a group of jobs simultaneously as a batch. Each job is characterized by its size and processing time. The processing time of a batch is given by the longest processing time among all jobs in the batch. Based on developing heuristic approaches, we proposed a hybrid genetic heuristic (HGH) to minimize makespan objective. To verify the performance of our algorithm, comparisons are made through using a simulated annealing (SA) approach addressed in the literature as a comparator algorithm. Computational experiments reveal that affording the knowledge of problem through using heuristic procedures, gives HGH the ability of finding optimal or near optimal solutions in a reasonable time.     

Minimizing earliness and tardiness subject to total completion time in an identical parallel machine system

This study addresses the identical parallel machine scheduling problem in which the total earliness and tardiness about a common due date are minimized subject to minimum total flowtime, P∥(E+T)/∑_Ci$P\parallel (E+T)/\sum C_i$. The problem is demonstrated to be transformed into a simplified version of the parallel machine problem with the objective of minimizing makespan subject to minimum total flowtime, P∥_Cmax/∑_Ci$P\parallel C_{\max }/\sum C_i$. Several properties are considered to solve optimally the restricted version of the problem. A streamlined binary integer programming model is developed to solve the P∥_Cmax/∑_Ci$P\parallel C_{\max }/\sum C_i$ problem and the P∥(E+T)/∑_Ci$P\parallel (E+T)/\sum C_i$ problem. Computational experiments indicate that the binary integer programming model is superior to the existing optimization algorithm for the P∥_Cmax/∑_Ci$P\parallel C_{\max }/\sum C_i$ problem in the literature.     

A parallel hybrid greedy branch and bound scheme for the maximum distance-2 matching problem

We present a new highly parallel algorithm for fast determination of near-optimal solutions to the NP-hard problem of identifying a maximum distance-2 matching in arbitrary graphs. This problem, known as D2EMIS, has important applications such as determining the maximum capacity of the media access (MAC) layer in wireless ad-hoc networks [1]. It can also be seen as a maximum 2-packing problem [2] on the edge-to-vertex dual graph of the original graph. Our algorithm extends the GRASP [3] philosophy in that partial solutions are constructed by adding in a greedy adaptive manner the “best” nodes that can be found; however, when there are multiple alternatives that can be selected in an iteration, the algorithm branches into as many paths as there are (greedy) alternatives. The algorithm, using appropriate bounds to prune partial solutions that cannot be optimal, produces very fast near-optimal solutions that compare very well against other distributed algorithms and random greedy heuristics proposed before or variants thereof, or exact methods (Integer Programming or Maximum Satisfiability state-of-the-art solvers).     

Tabu search for scheduling on identical parallel machines to minimize mean tardiness

This paper presents a tabu search approach for scheduling jobs on identical parallel machines with the objective of minimizing the mean tardiness. Initially, we consider a basic tabu search that uses short term memory only. Local search is performed on a neighborhood defined by two types of moves. Insert moves consist of transferring each job from one machine to another and swap moves are those obtained by exchanging each pair of jobs between two machines. Next, we analyze the incorporation of two diversification strategies with the aim of exploring unvisited regions of the solution space. The first strategy uses long term memory to store the frequency of the moves executed throughout the search and the second makes use of influential moves. Computational tests are performed on problems with up to 10 machines and 150 jobs. The heuristic performance is evaluated through a lower bound given by Lagrangean relaxation. A comparison is also made with respect to the best constructive heuristic reported in the literature.     

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.