Scheduling jobs with values dependent on their completion times

The paper concerns two scheduling problems with job values and losses of job values (costs) dependent on job completion times. In the first problem, we consider scheduling jobs with stepwise values in parallel processor environment. In the stepwise value, there is given a number of moments at which the job value decreases and between them the job value is constant (thus, the value deteriorates over time). The maximized criterion is the total job value. We prove strong NP-hardness of a single processor case of the problem and construct a pseudo-polynomial time algorithm for a special case with fixed number of unrelated parallel processors and fixed number of common moments of job value changes. Additionally, for uniform and unrelated parallel processors we construct and experimentally test several heuristic algorithms based on the list strategy. The second problem is a single processor one with piecewise linear losses of job values (the loss increases over time). The minimized criterion is the total loss of job value. We prove strong NP-hardness of the problem and existence of a pseudo-polynomial time exact algorithm for its special case. We also construct some heuristic algorithms for this problem and verify experimentally their efficiency.     

Heuristics for operational fixed job scheduling problems with working and spread time constraints

Operational fixed job scheduling problems select a set of jobs having fixed ready and processing times and schedule the selected jobs on parallel machines so as to maximize the total weight. In this study, we consider working time and spread time constrained versions of the operational fixed job scheduling problems. The working time constraints limit the total processing load on each machine. The spread time constraints limit the time between the start of the first job and the finish of the last job on each machine. For the working time constrained problem, we present a filtered beam search algorithm that evaluates the promising nodes of the branch and bound tree. For the spread time constrained problem we propose a two phase algorithm that defines the promising sets for the first jobs and finds a solution for each promising set. The results of our computational tests reveal that our heuristic algorithms perform very well in terms of both solution quality and time.     

Scheduling a maintenance activity and due-window assignment based on common flow allowance

We study a due-window assignment problem on a single machine. The job-dependent due-windows are obtained by the common flow allowance criterion. The scheduler has the option to perform a maintenance activity which is rate modifying, i.e., improves the processing times of the following jobs. We consider a number of versions of this setting: (i) The maintenance requires a constant time, (ii) The maintenance duration is an increasing function of its starting time (linear deterioration), and (iii) The maintenance duration is position-dependent (general deterioration). We study the standard setting of regular job processing times, and investigate also the extension to position-dependent processing times. The set of potential optimal positions for the maintenance activity is fully characterized. Consequently, the problems based on all the combinations of these settings are shown to be solved in polynomial time.     

Minimizing mean absolute deviation of completion time about a common due window subject to maximum tardiness for a single machine

This study deals with the problem of scheduling jobs on a single machine to minimize the mean absolute deviation of the job completion time about a large common due window subject to the maximum tardiness constraint. Using the well-known three-field notation, the problem is identified as MAD/large DueWindow/T_max. The common due window is set to be large enough to allow idle time prior to the beginning of a schedule to investigate the effect of the T_max constraint. Penalties arise if a job is completed outside the due window. A branch and bound algorithm and a heuristic are proposed. Many properties of the solutions and precedence relationships are identified. Our computational results reveal that the branch and bound algorithm is capable of solving problems of up to 50 jobs and the heuristic algorithm yields approximate solutions that are very close to the exact solution.     

Scheduling with processing set restrictions: PTAS results for several variants

We consider multiprocessor scheduling to minimize makespan. Each job has a given processing time and in addition, a subset of machines associated with it, also called its processing set. Each job has to be assigned to one machine in its set, thus contributing to the load of this machine. We study two variants of this problem on identical machines, the case of nested processing sets, and the case of tree-hierarchical processing sets. In addition, we consider uniformly related machines with a special case of inclusive processing sets, which has a clear motivation. We design polynomial time approximation schemes for these three variants. The first case resolves one of the open problems stated in the survey by Leung and Li (2008).     

Two-machine flowshop scheduling with a truncated learning function to minimize the makespan

Scheduling with learning effects has continued to attract the attention of scheduling researchers. However, the majority of the research on this topic has been focused on the single-machine setting. Moreover, under the commonly adopted learning model in scheduling, the actual processing time of a job drops to zero precipitously as the number of jobs increases, which is at odds with reality. To address these issues, we study a two-machine flowshop scheduling problem with a truncated learning function in which the actual processing time of a job is a function of the job's position in a schedule and the learning truncation parameter. The objective is to minimize the makespan. We propose a branch-and-bound and three crossover-based genetic algorithms (GAs) to find the optimal and approximate solutions, respectively, for the problem. We perform extensive computational experiments to evaluate the performance of all the proposed algorithms under different experimental conditions. The results show that the GAs perform quite well in terms of both efficiency and solution quality.     

A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry

This paper studies a solar cell industry scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage could be determined. However, the challenge in solar cell manufacturing is the number of machines can be dynamically adjusted to complete the job within the shortest possible time. Therefore, the paper addresses a multi-stage HFS scheduling problem with characteristics of parallel processing, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. The objective is to schedule the job production sequence, number of sublots, and dynamically allocate sublots to parallel machines such that the makespan time is minimized. The problem is formulated as a mixed integer linear programming (MILP) model. A hybrid approach based on the variable neighborhood search and particle swarm optimization (VNPSO) is developed to obtain the near-optimal solution. Preliminary computational study indicates that the developed VNPSO not only provides good quality solutions within a reasonable amount of time but also outperforms the classic branch and bound method and the current industry heuristic practiced by the case company.     

A hybrid artificial bee colony algorithm for the job shop scheduling problem

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. In terms of the objective function, most existing research has been focused on the makespan criterion (i.e., minimizing the overall completion time). However, for contemporary manufacturing firms, the due date related performance is usually more important because it is crucial for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a novel artificial bee colony (ABC) algorithm is proposed for solving the problem. A neighborhood property of the problem is discovered, and then a tree search algorithm is devised to enhance the exploitation capability of ABC. According to extensive computational tests, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness criterion.     

A note on “minimizing a weighted quadratic function of job lateness in the single machine system”

A set of jobs is to be scheduled on a single machine where an idle time is allowed to be inserted before the processing of the first job begins. The objective is to find an optimal sequence that minimizes the weighted sum of a quadratic function of job lateness. Sen et al. [Sen, T., Dileepan, P., Lind, M.R., 1995. Minimizing a weighted quadratic function of job lateness in the single machine system. International Journal of Production Economics 42(3), 237–243] presented a branch-and-bound algorithm for the problem; however, as shown in this note, their algorithm does not work because the branching rule for adjacent jobs is not applicable to non-adjacent jobs.     

A unified solution approach for the due date assignment problem with tardy jobs

We analyze a number of due date assignment problems with the weighted number of tardy jobs objective and show that these problems can be solved in O(n²) time by dynamic programming. We show that the effects of learning or the effects of past-sequence-dependent setup times can be incorporated into the problem formulation at no additional computational cost. We also show that some single-machine due date assignment problems can be extended to an identical parallel machine setting. Finally, we improve the complexity of the solution algorithms for two other due date assignment problems.     

Scheduling multiple robots in a no-wait re-entrant robotic flowshop

No-wait re-entrant robotic flowshops are widely used in the electronic industry, such as PCB and semiconductor manufacturing. In such an industry, cyclic production policy is often used due to large lot size and simplicity of implementation. This paper addresses cyclic scheduling of a no-wait re-entrant robotic flowshop with multiple robots for material handling. We formulate the problem and propose a polynomial algorithm to find the minimum number of robots for all feasible cycle times. Consequently, the minimum cycle time for any given number of robots can be obtained with the proposed algorithm. The algorithm runs in O(N⁵) time in the worst case, where N is the number of machines in the robotic flowshop.     

Order acceptance and scheduling in a two-machine flowshop

We study the order acceptance and scheduling problem in a two-machine flowshop. The firm receives a pool of orders before a planning period, each of which is characterized by revenue, processing times on machines 1 and 2, a due date, and a tardiness penalty. The firm seeks to decide on the orders to accept and schedule the accepted orders so as to maximize the total net revenue. We formulate the problem as mixed-integer linear programming models, and develop a heuristic and a branch-and-bound (B&B) algorithm based on some derived dominance rules and relaxation techniques. We assess the performance of the B&B algorithm and the heuristic via computational experiments. The computational results show that the B&B algorithm can solve problem instances with up to 20 jobs within a reasonable time while the heuristic is efficient in approximately solving large instances of the problem.     

Supply chain scheduling and coordination with dual delivery modes and inventory storage cost

We study a two-echelon supply chain scheduling problem in which a manufacturer acquires supplies from an upstream supplier and processes orders from the downstream retailers. The supply chain sells a single short-life product in a single season. We consider the scenario where the manufacturer can only accept some of the orders from the retailers due to its supplier's common production time window and its own two common production and delivery time windows. The upstream supplier processes materials and delivers the semi-finished products to the manufacturer within its time window. Then the manufacturer further processes these products to produce finished products and delivers them to the retailers within its two time windows, where one window is for production and normal delivery, and the other is for production and express delivery. Having to store the materials before processing them, the supplier incurs a storage cost, which depends on the order size and storage time. The manufacturer pays the transportation cost for delivering the finished products to the retailers. Due to double marginalization, the performance of the supply chain is sub-optimal. We model the supply chain problem as a flow shop scheduling problem with multiple common time windows. We derive some dominance properties and establish some theorems that help solve the sequencing problems for the orders and eliminate the idle time among the orders. Based on these results, we develop fast pseudo-polynomial dynamic algorithms to optimally solve the problem. We prove that the problem is NP-hard in the ordinary sense only. We develop two practically relevant and robust methods for the supply chain to achieve optimal profit-making performance through channel coordination.     

Permutation flowshop scheduling to minimize the total tardiness with learning effects

Scheduling with learning effects has received considerable attention recently. Often, numbers of operations have to be done on every job in many manufacturing and assembly facilities. However, it is seldom discussed in the general multiple-machine setting, especially without the assumptions of identical processing time on all the machines or dominant machines. With the current emphasis of customer service and meeting the promised delivery dates, we consider a permutation flowshop scheduling problem with learning effects where the objective is to minimize the total tardiness. A branch-and-bound algorithm and two heuristic algorithms are established to search for the optimal and near-optimal solutions. Computational experiments are also given to evaluate the performance of the algorithms.     

Economic optimization of technical-scientific research plans

Abstract . The criterion according to which the plans of a research organization can be optimized with respect to modelling R & D management consists in maximizing such profits as the envisaged results of research investigations may practically achieve throughout their application. The constraints are the available time and financial resources of the research organization, any investment outlays to be made by the beneficiaries and the time and money expenditures required for the solution of each individual theme competing for inclusion in the investigation plan. Mathematically it is a linear programming problem with bivalent variables. Because of the reduced size of the system a dynamic programming approach is also possible.     

Multiple part-type scheduling in flexible robotic cells

This paper considers the scheduling problem arising in two-machine manufacturing cells which repeatedly produce a set of multiple part-types, and where transportation of the parts between the machines is performed by a robot. The cycle time of the cell depends on the robot move sequence as well as the processing times of the parts on the machines. For highly flexible CNC machines, the processing times can be adjusted. To this end, this study tries to find the robot move sequence as well as the processing times of the parts on each machine that jointly minimize the cycle time. The problem of determining the best cycle in a 2-machine cell is first modeled as a traveling salesman problem. Then, an efficient 2-stage heuristic algorithm is constructed and compared with the most common heuristic approach of longest processing time (LPT).     

Logistics scheduling to minimize inventory and transport costs

We study a logistics scheduling problem where a manufacturer receives raw materials from a supplier, manufactures products in a factory, and delivers the finished products to a customer. The supplier, factory and customer are located at three different sites. The objective is to minimize the sum of work-in-process inventory cost and transport cost, which includes both supply and delivery costs. For the special case of the problem where all the jobs have identical processing times, we show that the inventory cost function can be unified into a common expression for various batching schemes. Based on this characteristic and other optimal properties, we develop an O(n) algorithm to solve this case. For the general problem, we examine several special cases, identify their optimal properties, and develop polynomial-time algorithms to solve them optimally.     

A binary solution method for the multi-product newsboy problem with budget constraint

Multi-product newsboy problem (MPNP) with budget constraint is a classical inventory control/management problem. However, solution methods for MPNP under general demand distributions are limited in the current literature. In this paper, by analyzing properties of the optimal solution to the MPNP with a budget constraint, we develop a solution algorithm for the constrained MPNP. The proposed algorithm is binary in nature, and is applicable to general types of demand distribution functions, discrete as well as continuous. For continuous demand distribution function, our approach can obtain the optimal or near optimal solution to the constrained MPNP with polynomial computation complexity of the o(n) order. On the other hand, for discrete demand distribution functions, it can effectively provide good approximate solution. Numerical experiments are presented to show the performance of our method.     

A note: Multi-machine scheduling with general position-based deterioration to minimize total load

The majority of the papers dealing with scheduling deteriorating jobs ignores general deterioration forms, and considers only special cases. Moreover, most of these papers consider deterioration, most of these papers consider deterioration based on job starting times, and only a few study position-based deterioration. Finally, very few researchers focus on the measure of total load, which becomes important in a setting of deterioration on multi-machines. In this note, we study general, non-decreasing, job-dependent and position-dependent deterioration function. The machine setting is parallel identical machines, and the objective function is total load. We introduce a polynomial time solution for this problem.     

Modeling and optimization of a container drayage problem with resource constraints

This paper investigates the problem faced by firms that transport containers by truck in an environment with resource constraints. The considered area is export-dominant. As a result, there are three types of container movements as inbound full, outbound full, and inbound empty movements. Both the time windows at the terminal and at the customers’ places and the operation times are considered. Empty containers are also regarded as separate transportation resources besides trucks. The total operating time including waiting time of all the trucks in operation is minimized. The problem is first formulated as a directed graph and then mathematically modeled based on the graph. It falls into a multiple traveling salesman problem with time windows (m-TSPTW) with resource constraints. An algorithm based on reactive tabu search (RTS) is developed to solve the problem. A number of randomly generated examples indicate that the algorithm can be applied to the real world.