Some single-machine scheduling with both learning and deterioration effects

In this paper we consider the single-machine scheduling problems with both learning and deterioration effects. By the effects of learning and deterioration, we mean that job processing times are defined by functions of their starting times and positions in the sequence. It is shown that even with the introduction of learning effect and deteriorating jobs to job processing times, single-machine makespan minimization problem and the sum of the θth power of job completion times minimization problem remain polynomially solvable, respectively. But for the following objective functions: the weighted sum of completion times and the maximum lateness, this paper proves that the WSPT rule and the EDD rule can construct the optimal sequence under some special cases, respectively.     

Two-agent single-machine scheduling problems under increasing linear deterioration

A new scheduling model in which both two-agent and increasing linear deterioration exist simultaneously is investigated in this paper. The processing time of a job is defined as an increasing linear function of its starting time. Two agents compete to perform their respective jobs on a common single machine and each agent has his own criterion to optimize. We introduce an increasing linear deterioration model into the two-agent single-machine scheduling, where the goal is to minimize the objective function of the first agent with the restriction that the objective function of the second agent cannot exceed a given upper bound. We study two scheduling problems with the different combinations of two agents’ objective functions: makespan, maximum lateness, maximum cost and total completion time. We propose the optimal properties and present the optimal polynomial time algorithms to solve the scheduling problems, respectively.     

Single machine scheduling with resource dependent release times and processing times

We consider the single machine scheduling problem with resource dependent release times and processing times, in which both the release times and processing times are strictly linear decreasing functions of the amount of resources consumed. The objective is to minimize the makespan plus the total resource consumption costs. We propose a heuristic algorithm for the general problem by utilizing some derived optimal properties and analyze its performance bound. For some special cases, we propose another heuristic algorithm that achieves a tighter performance bound.     

Perspectives on practical aspects of truck routing and scheduling

Truck routing and scheduling problems are differentiated from other vehicle routing and scheduling problems and a classification scheme for the former ones is outlined. Many characteristics of practical truck routing and scheduling problems are listed and several aspects are discussed, among them are: soft constraints, demand variability, multiple objectives, complex cost functions, and alternate solution approaches with their potential for solving practical problems. It is suggested that cost-based interactive heuristics coupled with graphical presentation of solutions may be the right method to deal with the more complex practical problems. Some basic generic heuristics are suggested and important software design and acquisition considerations are presented.     

pth Power Lagrangian Method for Integer Programming

When does there exist an optimal generating Lagrangian multiplier vector (that generates an optimal solution of an integer programming problem in a Lagrangian relaxation formulation), and in cases of nonexistence, can we produce the existence in some other equivalent representation space? Under what conditions does there exist an optimal primal-dual pair in integer programming? This paper considers both questions. A theoretical characterization of the perturbation function in integer programming yields a new insight on the existence of an optimal generating Lagrangian multiplier vector, the existence of an optimal primal-dual pair, and the duality gap. The proposed pth power Lagrangian method convexifies the perturbation function and guarantees the existence of an optimal generating Lagrangian multiplier vector. A condition for the existence of an optimal primal-dual pair is given for the Lagrangian relaxation method to be successful in identifying an optimal solution of the primal problem via the maximization of the Lagrangian dual. The existence of an optimal primal-dual pair is assured for cases with a single Lagrangian constraint, while adopting the pth power Lagrangian method. This paper then shows that an integer programming problem with multiple constraints can be always converted into an equivalent form with a single surrogate constraint. Therefore, success of a dual search is guaranteed for a general class of finite integer programming problems with a prominent feature of a one-dimensional dual search.     

Flow-shop scheduling with a learning effect

The paper is devoted to some flow-shop scheduling problems with a learning effect. The objective is to minimize one of the two regular performance criteria, namely, makespan and total flowtime. A heuristic algorithm with worst-case bound m for each criteria is given, where m is the number of machines. Furthermore, a polynomial algorithm is proposed for both of the special cases: identical processing time on each machine and an increasing series of dominating machines. An example is also constructed to show that the classical Johnson's rule is not the optimal solution for the two-machine flow-shop scheduling to minimize makespan with a learning effect. Some extensions of the problem are also shown.     

Single-machine scheduling with effects of exponential learning and general deterioration

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.     

Necessary and sufficient conditions of optimality for some classical scheduling problems

A scheduling problem is generally to order the jobs such that a certain objective function f(π) is minimized. For some classical scheduling problems, only sufficient conditions of optimal solutions are concerned in the literature. In this paper, we study the necessary and sufficient conditions by means of the concept of critical ordering (critical jobs and their relations). These results are meaningful in recognition and characterization of optimal solutions of scheduling problems.     

Bi-criteria scheduling problems: Number of tardy jobs and maximum weighted tardiness

Consider a single machine and a set of n jobs that are available for processing at time 0. Job j has a processing time p_j, a due date d_j and a weight w_j. We consider bi-criteria scheduling problems involving the maximum weighted tardiness and the number of tardy jobs. We give NP-hardness proofs for the scheduling problems when either one of the two criteria is the primary criterion and the other one is the secondary criterion. These results answer two open questions posed by Lee and Vairaktarakis in 1993. We consider complexity relationships between the various problems, give polynomial-time algorithms for some special cases, and propose fast heuristics for the general case. The effectiveness of the heuristics is measured by empirical study. Our results show that one heuristic performs extremely well compared to optimal solutions.     

Flow shop scheduling jobs with position-dependent processing times

The paper is devoted to some flow shop scheduling problems, where job processing times are defined by functions dependent on their positions in the schedule. An example is constructed to show that the classical Johnson's rule is not the optimal solution for two different models of the two-machine flow shop scheduling to minimize makespan. In order to solve the makespan minimization problem in the two-machine flow shop scheduling, we suggest Johnson's rule as a heuristic algorithm, for which the worst-case bound is calculated. We find polynomial time solutions to some special cases of the considered problems for the following optimization criteria: the weighted sum of completion times and maximum lateness. Some furthermore extensions of the problems are also shown.     

Single machine common flow allowance scheduling with controllable processing times

In this paper, we consider single machine SLK due date assignment scheduling problem in which job processing times are controllable variables with linear costs. The objective is to determine the optimal sequence, the optimal common flow allowance and the optimal processing time compressions to minimize a total penalty function based on earliness, tardiness, common flow allowance and compressions. We solve the problem by formulating it as an assignment problem which is polynomially solvable. For some special cases, we present an O(n logn) algorithm to obtain the optimal solution respectively.     

On the relations between different duals assigned to composed optimization problems

For an optimization problem with a composed objective function and composed constraint functions we determine, by means of the conjugacy approach based on the perturbation theory, some dual problems to it. The relations between the optimal objective values of these duals are studied. Moreover, sufficient conditions are given in order to achieve equality between the optimal objective values of the duals and strong duality between the primal and the dual problems, respectively. Finally, some special cases of this problem are presented.      

The -digraph optimization problem and the greedy algorithm

In this paper we present a new optimization problem and a general class of objective functions for this problem. We show that optimal solutions to this problem with these objective functions are found with a simple greedy algorithm. Special cases include matroids, Huffman's data compression problem, a special class of greedoids, a special class of min cost max flow problems (related to Monge sequences), a special class of weighted f-factor problems, and some new problems.     

Scheduling with release dates and preemption to minimize multiple max-form objective functions

In this paper, we study multi-agent scheduling with release dates and preemption on a single machine, where the scheduling objective function of each agent to be minimized is regular and of the maximum form (max-form). The multi-agent aspect has three versions, namely ND-agent (multiple agents with non-disjoint job sets), ID-agent (multiple agents with an identical job set), and CO-agent (multiple competing agents with mutually disjoint job sets). We consider three types of problems: The first type (type-1) is the constrained scheduling problem, in which one objective function is to be minimized, subject to the restriction that the values of the other objective functions are upper bounded. The second type (type-2) is the weighted-sum scheduling problem, in which a positive combination of the objective functions is to be minimized. The third type (type-3) is the Pareto scheduling problem, for which we aim to find all the Pareto-optimal points and their corresponding Pareto-optimal schedules. We show that the type-1 problems are polynomially solvable, and the type-2 and type-3 problems are strongly NP-hard even when all jobs’ release dates are zero and processing times are one. When the number of the scheduling criteria is fixed and they are all lateness-like, such as minimizing C_max, F_max, L_max, T_max, and WC_max, where WC_max is the maximum weighted completion time of the jobs, the type-2 and type-3 problems are polynomially solvable. To address the type-3 problems, we develop a new solution technique that guesses the Pareto-optimal points through some elaborately constructed schedule-configurations.     

非线性加工时间单机排序问题

讨论工件加工时间是等待时间的非线性增加函数的单机排序问题,目标函数为极小化完工时间和与极小化最大延误.基于对问题的分析,对于一般非线性函数的情况,给出了工件间的优势关系.对于某些特殊情况,利用工件间的优势关系得到了求解最优排序的多项式算法.推广了文献中的结论.     

Some single-machine scheduling problems with elapsed-time-based and position-based learning and forgetting effects

In this paper, a generalized model with past-sequence-dependent learning and forgetting effects is proposed. Both effects are assumed to be dependent on the sum of processing time as well as the scheduling position. Based on this model, we investigate and prove that some single-machine problems remain polynomially solvable with certain agreeable conditions. We further show that many models known in the literature are special cases of our proposed model. Several helpful lemmas are presented to analyze single-machine scheduling problems with various objective functions: makespan, total completion time, weighted completion time, and maximum lateness.     

Single-machine scheduling with deteriorating jobs and learning effects to minimize the makespan

This paper studies the single-machine scheduling problem with deteriorating jobs and learning considerations. The objective is to minimize the makespan. We first show that the schedule produced by the largest growth rate rule is unbounded for our model, although it is an optimal solution for the scheduling problem with deteriorating jobs and no learning. We then consider three special cases of the problem, each corresponding to a specific practical scheduling scenario. Based on the derived optimal properties, we develop an optimal algorithm for each of these cases. Finally, we consider a relaxed model of the second special case, and present a heuristic and analyze its worst-case performance bound.     

Learning effect and deteriorating jobs in the single machine scheduling problems

This paper studies the single machine scheduling problems with learning effect and deteriorating jobs simultaneously. In this model, the processing times of jobs are defined as functions of their starting times and positions in a sequence. It is shown that even with the introduction of learning effect and deteriorating jobs to job processing times, the makespan, the total completion time and the sum of the k$k$th power of completion times minimization problems remain polynomially solvable, respectively. But for the following objective functions: the total weighted completion time and the maximum lateness, this paper proves that the shortest weighted processing time first (WSPT) rule and the earliest due-date first (EDD) rule can construct the optimal sequence under some special cases, respectively.     

Production scheduling with supply and delivery considerations to minimize the makespan

In this paper we study a scheduling model that simultaneously considers production scheduling, material supply, and product delivery. One vehicle with limited loading capacity transports unprocessed jobs from the supplier’s warehouse to the factory in a fixed travelling time. Another capacitated vehicle travels between the factory and the customer to deliver finished jobs to the customer. The objective is to minimize the arrival time of the last delivered job to the customer. We show that the problem is NP-hard in the strong sense, and propose an O(n) time heuristic with a tight performance bound of 2. We identify some polynomially solvable cases of the problem, and develop heuristics with better performance bounds for some special cases of the problem. Computational results show that all the heuristics are effective in producing optimal or near-optimal solutions quickly.     

Coordinating multi-location production and customer delivery

We study two parallel machine scheduling problems with equal processing time jobs and delivery times and costs. The jobs are processed on machines which are located at different sites, and delivered to a customer by a single vehicle. The first objective considered is minimizing the sum of total weighted completion time and total vehicle delivery costs. The second objective considered is minimizing the sum of total tardiness and total vehicle delivery costs. We develop several interesting properties of an optimal scheduling and delivery policy, and show that both problems can be solved by reduction to the Shortest-Path problem in a corresponding network. The overall computational effort of both algorithms is O(n ^m2+m+1) (where n and m are the number of jobs and the number of machines, respectively) by the application of the Directed Acyclic Graph (DAG) method. We also discuss several special cases for which the overall computational effort can be significantly reduced.