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This paper studies the single-machine scheduling problem with truncated learning effect, time-dependent processing time, and past-sequence-dependent delivery time. The delivery time is the time that the job is delivered to the customer after processing is complete. The goal is to determine an optimal job schedule to minimize the total weighted completion time and maximum tardiness. In order to solve the general situation of the problem, we propose a branch-and-bound algorithm and other heuristic algorithms. Computational experiments also prove the effectiveness of the given algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the single-machine group scheduling problem with convex resource allocation and learning effect. The number of jobs in each group is different, and the corresponding common due dates are also different, where the processing time of jobs follows a convex function of resource allocation. Under common due date assignment, the objective is to minimize the weighted sum of earliness, tardiness, common due date, resource consumption, and makespan. To solve the problem, we present the heuristic, simulated annealing, and branch-and-bound algorithms. Computational experiments indicate that the proposed algorithms are effective. [ABSTRACT FROM AUTHOR]

This paper addresses the single-machine scheduling with general truncated learning effects (GTLE) and delivery times that are past sequence dependent (DT psd) . The objective is to determine an optimal job schedule such that makespan, sum of the β th power of job completion times, and total weighted completion time are minimized, where β > 0 is a given constant. We proved that the makespan, the sum of the β th power of job completion times, and a special case of the total weighted completion time can be solved in polynomial time. For the general case of the total weighted completion time, we propose the heuristic, simulated annealing and branch-and-bound algorithms. Extensive numerical experiments validate the efficiency of the proposed solution algorithms on a set of randomly generated instances. Furthermore, the computational experiment is also conducted to show that the simulated annealing performs effectively and efficiently. [ABSTRACT FROM AUTHOR]

In this paper, the single-machine scheduling problems under the common and slack due date assignments are studied, where the actual processing time of the job needs to consider some factors, such as convex resource allocation, maintenance activity, and learning effects. The goal of this study is to find the optimal sequence, maintenance activity location, resource allocation and common due date (flow allowance). The objective function is (1) to minimize the sum of scheduling cost (including the weighted sum of earliness, tardiness and common due date (flow allowance), where the weights are position-dependent weights) and resource consumption cost, and (2) to minimize the scheduling cost under the resource consumption cost which is bounded. We prove that these problems can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

In classical schedule problems, the actual processing time of a job is a fixed constant, but in the actual production process, the processing time of a job is affected by a variety of factors, two of which are the learning effect and resource allocation. In this paper, single-machine scheduling problems with resource allocation and a time-dependent learning effect are investigated. The actual processing time of a job depends on the sum of normal processing times of previous jobs and the allocation of non-renewable resources. With the convex resource consumption function, the goal is to determine the optimal schedule and optimal resource allocation. Three problems arising from two criteria (i.e., the total resource consumption cost and the scheduling cost) are studied. For some special cases of the problems, we prove that they can be solved in polynomial time. More generally, we propose some accurate and intelligent algorithms to solve these problems. [ABSTRACT FROM AUTHOR]

In enterprise management, there are often multiple agents competing for the same products to reduce production cost. On this basis, this paper investigates a two-agent slack due-date single-machine scheduling problem with deteriorating jobs, where the processing time of a job is extended as a function of position-dependent workload, resource allocation and a common deterioration rate. The goal is to find the optimal sequence and resource allocation that minimizes the maximal value of earliness, tardiness, and decision variables of one agent subject to an upper bound on cost value of the second agent. Through theoretical analysis, a polynomial time algorithm with O (N 3) time is proposed for the problem, where N is the maximum number of jobs between the two agents. [ABSTRACT FROM AUTHOR]

This paper investigates the single-machine resource allocation scheduling problem with learning effects and group technology. The objective is to determine the optimal job and group schedules, and resource allocations such that total completion time is minimized subject to limited resource availability. For some special cases, we show that the problem remains polynomially solvable. For general case of the problem, we propose the heuristic algorithm, tabu search algorithm and branch-and-bound algorithm. Numerical experiments are tested to evaluate the performance of the heuristic and branch-and-bound algorithms. [ABSTRACT FROM AUTHOR]

This article addresses how to minimize total completion time on a single machine with a position-weighted learning effect. Under non-zero job release dates, a lower bound on the objective function, a number of dominance properties, and an initial solution (i.e. an NEH-based heuristic) are derived, which are used to expedite the elimination process of a branch-and-bound solution procedure. The performance of the branch-and-bound algorithm and the heuristic algorithm are evaluated on a set of randomly generated problem instances. [ABSTRACT FROM AUTHOR]

In this article, the two-machine no-wait permutation flowshop scheduling problem with common due window assignment, learning effect and resource allocation is considered, wherein learning effect and resource allocation mean that the processing time of a job is a function of its position in a sequence and its amount of resource allocation. According to the common due window assignment method, each job is associated with a common due window, which is a decision variable. The goal is to determine the sequence of jobs, common due window starting time, due window size and resource allocation such that the linear weighted sum of job earliness, tardiness, due window size and resource cost is minimized. This article shows that the scheduling problem can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

This article addresses proportionate flowshop scheduling problems with position-dependent weights wherein the weight is not related to the job but to the position in which the job is scheduled. Common and slack due date assignment models are discussed under a due date assignment framework. The goal is to determine a feasible schedule for all jobs and due dates of all jobs to minimize a total cost function wherein the objective function is of the minsum type. Optimal properties for the problems are proposed, based on which polynomial time algorithms are provided to solve these two problems optimally. [ABSTRACT FROM AUTHOR]

This article addresses the no-wait flowshop scheduling problem with simultaneous consideration of common due date assignment, convex resource allocation and learning effect in a two machine setting. The processing time of each job can be controlled by its position in a sequence and also by allocating extra resource, which is a convex function of the amount of a common continuously divisible resource allocated to the job. The objective is to determine the optimal common due date, the resource allocation and the schedule of jobs such that the total earliness, tardiness and common due date cost (the total resource consumption cost) are minimized under the constraint condition that the total resource consumption cost (the total earliness, tardiness and common due date cost) is limited. Polynomial time algorithms are developed for two versions of the problem. [ABSTRACT FROM AUTHOR]

This paper deals with a single machine bi-criterion scheduling problem with exponentially time-dependent learning effects and non-zero job release times. The goal is to minimize the weighted sum of the makespan and the total completion time. First, a branch-and-bound algorithm incorporating with some dominance properties and three lower bounds is developed for the optimal solutions. Then heuristic and particle swarm optimization algorithms are presented for near-optimal solutions. Finally, computational experiments are conducted to evaluate the performances of the proposed algorithms. Computational results indicate that the algorithms perform well in either solving the problem or efficiently generating near-optimal solutions. [ABSTRACT FROM AUTHOR]

This article considers the no-wait flowshop scheduling with learning effect and resource allocation on two-machine. Our goal is to find the optimal resource allocations and job sequence that minimize the scheduling criterion (the total weighted resource consumption) subject to the constraint that the total weighted resource consumption (the scheduling criterion) is less than or equal to a given constant, where the schedule criteria include weighted makespan, total completion (waiting) time and total absolute differences in completion (waiting) times. We show that these two problems are polynomially solvable respectively. [ABSTRACT FROM AUTHOR]

This paper considers a single-machine scheduling problem with deteriorating jobs and convex resource allocation. We assume that the actual processing time of a job is a function of its convex resource allocation and its starting time. For the multi-objective single-machine scheduling problem, we show that the problem is polynomially solvable in O (n log n) time, where n is the total number of jobs. [ABSTRACT FROM AUTHOR]

In this study we consider the problem of scheduling n jobs on m unrelated parallel processors with deterioration effect. We allow an option for performing a modifying (i.e. deteriorating) maintenance activity on each machine, i.e. the job normal processing times will be changed following this modifying maintenance activity. We need to make a decision on when to schedule the modifying maintenance activities and the sequence of jobs so as to minimize a cost function containing total completion (waiting) time, total absolute differences in completion (waiting) times, and total machine load. We show that the problem remains polynomially solvable under the proposed model. [ABSTRACT FROM AUTHOR]

Abstract: We study a single-machine scheduling and due window assignment problem, in which job processing times are defined by functions of their starting times (deteriorating effect) and positions in the sequence (learning effect). The problem is to determine the optimal due windows and the processing sequence simultaneously to minimize costs for earliness, tardiness, the window location, window size and makespan. We show that the problem remains polynomially solvable under the proposed model for two popular due window assignment methods: The slack due date assignment method (usually referred to as SLK) and the unrestricted due date assignment method (usually referred to as DIF). [Copyright &y& Elsevier]

Abstract: The single-machine scheduling problems with position and sum-of-processing-time based processing times are considered. The actual processing time of a job is defined by function of its scheduled position and total normal processing time of jobs in front of it in the sequence. We provide optimal solutions in polynomial time for some special cases of the makespan minimization and the total completion time minimization. We also show that an optimal schedule to be a V-shaped schedule in terms of the normal processing times of jobs for the total completion time minimization problem and the makespan minimization problem. [Copyright &y& Elsevier]

Abstract: The paper deals with machine scheduling problems with a general learning effect. By the general learning effect, we mean that the actual processing time of a job is not only a non-increasing function of the total weighted normal processing times of the jobs already processed, but also a non-increasing function of the job’s position in the sequence, where the weight is a position-dependent weight. We show that even with the introduction of a general learning effect to job processing times, some single machine scheduling problems are still polynomially solvable under the proposed model. We also show that some special cases of the flow shop scheduling problems can be solved in polynomial time. [Copyright &y& Elsevier]

Abstract: The purpose of this study is to explore the single-machine scheduling with the effects of exponential learning and general deterioration. By the effects of exponential learning and general deterioration, we meant that job processing time is decided by the functions of their starting time and positions in the sequence. Results showed that with the introduction of learning effect and deteriorating jobs to job processing time, single-machine makespan, and sum of completion time (square) minimization problems remained polynomially solvable, respectively. But for the following objective functions: the weighted sum of completion time and the maximum lateness, this paper proved that the weighted smallest basic processing time first (WSPT) rule and the earliest due date first (EDD) rule constructed the optimal sequence under some special cases, respectively. [Copyright &y& Elsevier]

Abstract: This paper deals with single-machine scheduling problems with decreasing linear deterioration, i.e., jobs whose processing times are a decreasing function of their starting times. In addition, the jobs are related by parallel chains and a series–parallel graph precedence constraints, respectively. It is shown that for the problems of minimization of the makespan, polynomial algorithms exist. [Copyright &y& Elsevier]