On scheduling around large restrictive common due windows

This paper deals with the problem of scheduling a number of jobs on a single machine around a large, restrictive common due window. We consider individual earliness and tardiness penalties for the jobs. The objective is to find an optimal schedule which jointly minimizes the sum of the earliness and tardiness penalties. This problem is intractable and hence no efficient procedure for solving large instances is expected to be found. For this reason we first introduced a mapping of the problem which takes advantage of the structural properties inherent to optimal solutions. Secondly we solved the problem under study by using this mapping and applying three meta-heuristics, namely evolutionary strategy, simulated annealing and threshold accepting. To validate the quality of these approaches, altogether 250 benchmark problems with different window sizes and positions of up to 200 jobs are examined. Furthermore small instances are solved to optimality by a mixed integer programming formulation.     

Lower Bounds for Scheduling a Single Robot in a Job-Shop Environment

We consider a single-machine scheduling problem which arises as a subproblem in a job-shop environment where the jobs have to be transported between the machines by a single transport robot. The robot scheduling problem may be regarded as a generalization of the traveling salesman problem with time windows, where additionally generalized precedence constraints (minimal time-lags) have to be respected. The objective is to determine a sequence of all nodes and corresponding starting times in the given time windows in such a way that all generalized precedence relations are respected and the sum of all traveling and waiting times is minimized.We calculate lower bounds for this problem using constraint propagation techniques and a linear programming formulation which is solved by a column generation procedure. Computational results are presented for test data arising from job-shop instances with a single transport robot and some modified traveling salesman instances.     

Tabu search for discrete–continuous scheduling problems with heuristic continuous resource allocation

Problems of scheduling non-preemptable, independent jobs on parallel identical machines under an additional continuous renewable resource to minimize the makespan are considered. Each job simultaneously requires for its processing a machine and an amount (unknown in advance) of the continuous resource. The processing rate of a job depends on the amount of the resource allotted to this job at a time. The problem is to find a sequence of jobs on machines and, simultaneously, a continuous resource allocation that minimize the makespan. A heuristic procedure for allocating the continuous resource is used. The tabu search metaheuristic to solve the considered problem is presented. The results produced by tabu search are compared with optimal solutions for small instances, as well as with the results generated by simple search methods – multi-start iterative improvement and random sampling for larger instances.     

Stable multi-skill workforce assignments

This paper analyzes stability in multi-skill workforce assignments of technicians and jobs. In our stability analysis, we extend the notion of blocking pairs as stated in the Marriage model of Gale-Shapley to the multi-skill workforce assignment. It is shown that finding stable assignments is NP-hard. A special case turns out to be solvable in polynomial time. For the general case, we give a characterization of the set of stable assignments by means of linear inequalities involving binary variables. We propose an integer programming (IP) model to construct optimal stable assignments with several objectives. In the computational results, we observe that it is easier to attain stability in instances with easy jobs and we consider a range of instances to show how fast the solution time increases. Open questions and further directions are discussed in the conclusion section.     

Sevast'yanov's algorithm for the flow-shop scheduling problem

We consider the flow-shop scheduling problem. The objective is to schedule the jobs on the machines so that we minimize the time by which all jobs are completed. We studied and implemented different versions of the algorithm of Sevast'yanov based on linear programming to solve this problem. Using CPLEX, we did computational tests with random instances having up to 1000 jobs and 100 machines. If the size of the flow-shop scheduling problem is small or if the running time is not a critical factor, the Nawaz-Enscore-Ham approximation algorithm still performs better. But if the running time is an important factor, Sevast'yanov's algorithm can be a very good alternative especially in presence of very large scale instances with a relatively small number of machines.     

Minimising makespan in the two-machine flow-shop with release times

This paper considers the two-machine flow-shop problem with the objective of minimising the makespan subject to different release times. In view of the strongly NP-hard nature of this problem, five lower bounds and two new dominance criteria are proposed together with a decomposition procedure that reduces the problem size by setting jobs at the beginning of the sequence. Several branch and bound procedures are described by applying different lower bounds and branching schemes. A detailed computational campaign has been performed on different kinds of instances testing problems with size up to 200 jobs.     

Tabu search algorithms for job-shop problems with a single transport robot

We consider a generalized job-shop problem where the jobs additionally have to be transported between the machines by a single transport robot. Besides transportation times for the jobs, empty moving times for the robot are taken into account. The objective is to determine a schedule with minimal makespan.We present local search algorithms for this problem where appropriate neighborhood structures are defined using problem-specific properties. An one-stage procedure is compared with a two-stage approach and a combination of both. Computational results are presented for test data arising from job-shop benchmark instances enlarged by transportation and empty moving times.     

Heuristics for a project management problem with incompatibility and assignment costs

Consider a project which consists of a set of jobs to be performed, assuming each job has a duration of at most one time period. We assume that the project manager provides a set of possible durations (in time periods) for the whole project. When a job is assigned to a specific time period, an assignment cost is encountered. In addition, for some pairs of jobs, an incompatibility cost is encountered if they are performed at the same time period. Both types of cost depend on the duration of the whole project, which also has to be determined. The goal is to assign a time period to each job while minimizing the costs. We propose a tabu search heuristic, as well as an adaptive memory algorithm, and compare them with other heuristics on large instances, and with an exact method on small instances. Variations of the problems are also discussed     

Algorithms for parallel machine scheduling: a case study of the tracking and data relay satellite system

This paper presents two algorithms for scheduling a set of jobs with multiple priorities on non-homogeneous, parallel machines. The application of interest involves the tracking and data relay satellite system run by the US National Aeronautics and Space Administration. This system acts as a relay platform for Earth-orbiting vehicles that wish to communicate periodically with ground stations. The problem is introduced and then compared to other more common scheduling and routing problems. Next, a mixed-integer linear programming formulation is given but was found to be too difficult to solve for instances of realistic size. This led to the development of a dynamic programming-like heuristic and a greedy randomized adaptive search procedure. Each is described in some detail and then compared using data from a typical busy day scenario.     

A novel ant colony optimization approach for on-line scheduling and due date determination

This paper aims to develop an on-line Ant Colony Optimization (ACO) framework, where jobs arrive over time, and at any time we lack knowledge concerning future jobs. A due date is determined upon job arrival, and jobs are sequenced on the machine to optimize the sum of weighted lead times with all due dates met. We propose that each ant is associated with a sequence of waiting jobs with quoted due dates. This waiting sequence is constantly updated over time (whenever a job is selected to be processed or a new job arrives). The on-line schedule is constructed by selecting the first job in the waiting list of the “best” ant to process (along with its due date) as the machine becomes available. However, for the ant where this job is not the first one in the list, processing it pushes back the waiting jobs positioned before it. If such push back results in a due date violation, this ant will be eliminated. Further, our ACO framework does not include the iterative procedure due to the characteristics of the on-line problem; this is one difference from the traditional ACO framework besides ant elimination. The computational testing on generated instances shows that our ACO algorithm outperforms an existing effective on-line algorithm in the literature. Also, with local search incorporated using the EDD (Earliest Due Date) rule, improvements can be obtained in both computational outcome and time.     

Minimizing the number of machines with limited workload capacity for scheduling jobs with interval constraints

In this paper, we consider a parallel machine scheduling problem in which machines have a limited workload capacity and jobs have deadlines and release dates. The problem is motivated by the operation of energy storage management systems for microgrids under emergency conditions and generalizes some problems that have already been studied in the literature for their theoretical value. In this work, we propose heuristic and exact algorithms to solve the problem. The heuristics are adaptations of classical bin packing heuristics in which additional conditions on the feasibility of a solution are imposed, whereas the exact method is a branch-and-price approach. The results show that the branch-and-price approach is able to optimally solve random instances with up to 250 jobs within a time limit of one hour, while the heuristic procedures provide near optimal solution within reduced running times. Finally, we also provide additional complexity results for a special case of the problem.     

On a New Class of Parallel Sequencing Situations and Related Games

This paper considers a special class of sequencing situations with two parallel machines in which each agent has precisely two jobs to be processed, one on each machine. The costs of an agent depend linearly on the final completion time of his jobs. We describe a procedure that provides an optimal processing order of the jobs for some particular classes. Furthermore, we study cooperative games arising from these sequencing situations. Our main result will be on the balancedness of these games.     

Optimal Scheduling of a Two-stage Hybrid Flow Shop

We present an exact branch-and-bound algorithm for the two-stage hybrid flow shop problem with multiple identical machines in each stage. The objective is to schedule a set of jobs so as to minimize the makespan. This is the first exact procedure which has been specifically designed for this strongly -hard problem. Among other features, our algorithm is based on the exact solution of identical parallel machine scheduling problems with heads and tails. We report the results of extensive computational experiments on instances which show that the proposed algorithm solves large-scale instances in moderate CPU time.     

Group-shop scheduling with sequence-dependent set-up and transportation times

This paper considers a group-shop scheduling problem (GSSP) with sequence-dependent set-up times (SDSTs) and transportation times. The GSSP provides a general formulation including the job-shop and the open-shop scheduling problems. The consideration of set-up and transportation times is among the most realistic assumptions made in the field of scheduling. In this paper, we study the GSSP with transportation and anticipatory SDSTs, where jobs are released at different times and there are several transporters to carry jobs. The objective is to find a job schedule that minimizes the makespan, that is, the time at which all jobs are completed and transported to the warehouse (or to the customer). The problem is formulated as a disjunctive programming problem and then prepared in a form of mixed integer linear programming (MILP). Due to the non-deterministic polynomial-time hardness (NP-hardness) of the GSSP, large instances cannot be optimally solved in a reasonable amount of time. Therefore, a genetic algorithm (GA) hybridized with an active schedule generator is proposed to tackle large-sized instances. Both Baldwinian and Lamarckian versions of the proposed hybrid algorithm are then implemented and evaluated through computational experiments.     

Crane scheduling with non-crossing constraint

In this paper, we examine crane scheduling for ports. This important component of port operations management is studied when the non-crossing spatial constraint, which is common to crane operations, is considered. We assume that ships can be divided into holds and that cranes can move from hold to hold but jobs are not pre-emptive, so that only one crane can work on one hold or job to complete it. Our objective is to minimize the latest completion time for all jobs. We formulate this problem as an integer programming problem. We provide the proof that this problem is NP-complete and design a branch-and-bound algorithm to obtain optimal solutions. A simulated annealing meta-heuristic with effective neighbourhood search is designed to find good solutions in larger size instances. The elaborate experimental results show that the branch-and-bound algorithm runs much faster than CPLEX and the simulated annealing approach can obtain near optimal solutions for instances of various sizes.     

Exact Algorithms for Large-Scale Unconstrained Two and Three Staged Cutting Problems

In this paper we propose two exact algorithms for solving both two-staged and three staged unconstrained (un)weighted cutting problems. The two-staged problem is solved by applying a dynamic programming procedure originally developed by Gilmore and Gomory [Gilmore and Gomory, Operations Research, vol. 13, pp. 94–119, 1965]. The three-staged problem is solved by using a top-down approach combined with a dynamic programming procedure. The performance of the exact algorithms are evaluated on some problem instances of the literature and other hard randomly-generated problem instances (a total of 53 problem instances). A parallel implementation is an important feature of the algorithm used for solving the three-staged version.     

Multi-coloring and job-scheduling with assignment and incompatibility costs

Consider a scheduling problem (P) which consists of a set of jobs to be performed within a limited number of time periods. For each job, we know its duration as an integer number of time periods, and preemptions are allowed. The goal is to assign the required number of time periods to each job while minimizing the assignment and incompatibility costs. When a job is performed within a time period, an assignment cost is encountered, which depends on the involved job and on the considered time period. In addition, for some pairs of jobs, incompatibility costs are encountered if they are performed within common time periods. (P) can be seen as an extension of the multi-coloring problem. We propose various solution methods for (P) (namely a greedy algorithm, a descent method, a tabu search and a genetic local search), as well as an exact approach. All these methods are compared on different types of instances.     

Minimizing total tardiness in parallel machine scheduling with setup times: An adaptive memory-based GRASP approach

This paper deals with the problem of scheduling jobs in uniform parallel machines with sequence-dependent setup times in order to minimize the total tardiness relative to job due dates. We propose GRASP versions that incorporate adaptive memory principles for solving this problem. Long-term memory is used in the construction of an initial solution and in a post-optimization procedure which connects high quality local optima by means of path relinking. Computational tests are carried out on a set of benchmark instances and the proposed GRASP versions are compared with heuristic methods from the literature.     

A branch and bound algorithm to minimize total tardiness of jobs in a two identical-parallel-machine scheduling problem with a machine availability constraint

This research focuses on the problem of scheduling jobs on two identical parallel machines that are not continuously available with the objective of minimizing total tardiness. After processing a given number of jobs, each machine requires a preventive maintenance task, during which the machine cannot process jobs. We present dominance properties and lower bounds, and develop a branch and bound algorithm using these properties and lower bounds as well as an upper bound obtained from a heuristic algorithm. Performance of the algorithm is evaluated through a series of computational experiments on randomly generated instances and results are reported.     

Single-machine scheduling with common due-window assignment for deteriorating jobs

In this paper, we consider a single-machine common due-window assignment scheduling problem with deteriorating jobs. Jobs’ processing times are defined by function of their starting times and job-dependent deterioration rates that are related to jobs and are not all equal. The objective is to determine an optimal combination of sequence and common due-window location so as to minimize the weighted sum of earliness, tardiness and due-window location penalties. We propose an O(n² log n) time algorithm to solve the problem and discuss several instances to illustrate it.