Scheduling in a two-machine flow-shop for earliness/tardiness under learning effect

In the literature, earliness/tardiness (E/T) problem was known as weighted absolute deviation problem, and both tardiness and earliness is very important performance criteria for scheduling problem. While total tardiness criteria provides adaptation for due date (ignoring results of earliness done jobs), it deals with only cost of tardiness. However this phenomenon has been started to change with just-in-time (JIT) production concept. On JIT production, earliness is as important as tardiness. The phenomenon of the learning effect has been extensively studied in many different areas of operational research. However, there have been a few studies in the general context of production scheduling such as flow-shop scheduling. This paper addresses the minimization of the total earliness/tardiness penalties under learning effects in a two-machine flow-shop scheduling problem. Jobs have a common due date. We present mathematical model to obtain an optimal schedule for a given job sequence. We also present heuristics that use genetic algorithm and tabu search, based on proposed properties. Furthermore, random search was used for showing the significance of the study by comparison purpose. A new set of benchmark problems is presented with the purpose of evaluating the heuristics. The experimental results show that the performance of proposed approach is quite well, especially for the instances of large size.     

A due date density-based categorising heuristic for parallel machines scheduling

The effective management of shop floor resources is an important factor in achieving the goals of a manufacturing company. The need for effective scheduling is particularly strong in complex manufacturing environments. This paper presents an efficient due date density-based categorising heuristic to minimise the total weighted tardiness (TWT) of a set of tasks with known processing times, due dates, weights and sequence-dependent setup times for parallel machines scheduling. The proposed heuristic is composed of four phases. In the first phase, jobs are listed by the earliest due date (EDD). The second phase computes the due date gaps between listed jobs and categorises the jobs based on the due date density. In the third phase, the sequence of jobs is improved by a tabu search (TS) method. The fourth phase allocates each job to machines. The comprehensive simulation results show that the proposed heuristic performs better than other existing heuristics at a significantly reduced total weighted tardiness.     

A greedy heuristic and simulated annealing approach for a bicriteria flowshop scheduling problem with precedence constraints—a practical manufacturing case

This paper considers a flowshop scheduling problem with two criteria, where the primary (dominant) criterion is the minimization of material waste and the secondary criterion is the minimization of the total tardiness time. The decision maker does not authorize trade-offs between the criteria. In view of the nature of this problem, a hierarchical (lexicographical) optimization approach is followed. An effective greedy heuristic is proposed to minimize the material waste and a simulated annealing (SA) algorithm is developed to minimize the total tardiness time, subjective to the constraint computed for the primary criterion. The solution accuracy is compared with the optimal solution obtained by complete enumeration for randomly generated problem sets. From the results, it is observed that the greedy heuristic produces the optimal solution and the SA solution does not differ significantly from the optimal solution.     

A Tabu Search Algorithm for Single Machine Scheduling with Release Times,Due Dates,and Sequence-Dependent Set-up Times

In this paper, we present a tabu search algorithm that schedules N jobs to a single machine in order to minimise the maximum lateness of the jobs. The release times, due dates, and sequence-dependent set-up times of the jobs are assumed to exist. We modified the original tabu search method to be suitable for the scheduling problem. The proposed tabu search algorithm is composed of two parts: a MATCS (modified apparent tardiness cost with set-ups) rule for finding an efficient initial solution, and the tabu search method to seek a near optimal solution from the initial solution. The experimental results show that the tabu search algorithm obtains much better solutions more quickly than the RHP (rolling horizon procedure) heuristic suggested by Ovacik and Uzsoy.     

A GRASP heuristic for the multi-objective permutation flowshop scheduling problem

This paper presents a multi-objective greedy randomized adaptive search procedure (GRASP)-based heuristic for solving the permutation flowshop scheduling problem in order to minimize two and three objectives simultaneously: (1) makespan and maximum tardiness; (2) makespan, maximum tardiness, and total flowtime. GRASP is a competitive metaheuristic for solving combinatorial optimization problems. We have customized the basic concepts of GRASP algorithm to solve a multi-objective problem and a new algorithm named multi-objective GRASP algorithm is proposed. In order to find a variety of non-dominated solutions, the heuristic blends two typical approaches used in multi-objective optimization: scalarizing functions and Pareto dominance. For instances involving two machines, the heuristic is compared with a bi-objective branch-and-bound algorithm proposed in the literature. For instances involving up to 80 jobs and 20 machines, the non-dominated solutions obtained by the heuristic are compared with solutions obtained by multi-objective genetic algorithms from the literature. Computational results indicate that GRASP is a promising approach for multi-objective optimization.     

An efficient heuristic approach to total flowtime minimization in permutation flowshop scheduling

The problem of permutation flowshop scheduling is considered with the objective of minimizing the total flowtime. We present a constructive heuristic and two composite heuristics to solve the problem. The composite heuristics combine the simulated annealing method of Chakravarthy and Rajendran [Production Planning and Control 10 (1999)], the constructive heuristic of Nawaz et al. [Omega 11 (1983)] and the new heuristic. Computational analysis is carried out with the benchmark problems of Taillard [European Journal of Operational Research 64 (1993)]. The two composite heuristics produce better quality solutions than those produced by the composite heuristics of Liu and Reeves [European Journal of Operational Research 132 (2001)]. Statistical tests of significance are used to substantiate the improvement in solution quality.     

A drastic hybrid heuristic algorithm to approach to JIT policy considering controllable processing times

Job scheduling has always been a challenging task in modern manufacturing and the most real life scheduling problems which involves multi-criteria, multi-machine environments. In this research, the single-machine scheduling problem is studied in which job processing times are controllable, namely, they may vary within a specified interval. The goal of this research is to minimize total tardiness and earliness on a single machine, simultaneously. In this context, we first propose a mathematical model for the considered problem and then a net benefit compression–net benefit expansion heuristic is presented for obtaining the set of amounts of compression and expansion of jobs processing times in a given sequence. Two meta-heuristic approaches are then employed to solve medium-to-large-sized problems as local search methods. Thereafter, we apply a hybrid method based on our heuristic as well as these two meta-heuristics in order to obtain solutions with higher quality within lesser computational time. The addressed problem is NP-hard since the single machine total tardiness problem is already NP-hard. The computational results show that our proposed heuristics can effectively solve such Just-In-Time problem with a high-quality solution.     

求解置换调度问题的改进混合遗传算法

构造了求解极小化总完工时间的置换调度问题的改进混合遗传算法：先采用构造型启发式算法和随机方法共同产生初始种群，然后在选择、交叉和变异等遗传操作之前借助禁忌搜索算法寻找每个个体的局部最优解组成当前种群，再应用种群整体替换策略保存种群中的优秀个体构成新一代种群。改进混合遗传算法有机地结合了禁忌搜索算法的局部搜索性能和遗传算法的全局搜索性能。仿真实验表明，改进混合遗传算法具有比构造型启发式算法和禁忌搜索算法更好的鲁棒性和寻优性能。     

A fuzzy-neural approach for estimating the monthly output of a semiconductor manufacturing factory

The customer order scheduling problem (COSP) is defined as to determine the sequence of tasks to satisfy the demand of customers who order several types of products produced on a single machine. A setup is required whenever a product type is launched. The objective of the scheduling problem is to minimize the average customer order flow time. Since the customer order scheduling problem is known to be strongly NP-hard, we solve it using four major metaheuristics and compare the performance of these heuristics, namely, simulated annealing, genetic algorithms, tabu search, and ant colony optimization. These are selected to represent various characteristics of metaheuristics: nature-inspired vs. artificially created, population-based vs. local search, etc. A set of problems is generated to compare the solution quality and computational efforts of these heuristics. Results of the experimentation show that tabu search and ant colony perform better for large problems whereas simulated annealing performs best in small-size problems. Some conclusions are also drawn on the interactions between various problem parameters and the performance of the heuristics.     

Parallel machine scheduling with earliness-tardiness penalties and space limits

In order to maximize an availability of machine and utilization of space, the parallel machines scheduling problem with space limit is frequently discussed in the industrial field. In this paper, we consider the parallel machine scheduling problem in which n jobs having different release times, due dates, and space limits are to be scheduled on m parallel machines. The objective function is to minimize the weighted sum of earliness and tardiness. To solve this problem, a heuristic is developed which is divided into three modules hierarchically: job selection, machine selection and job sequencing, and solution improvement. To illustrate its effectiveness, a proposed heuristic is compared with genetic algorithm (GA), hybrid genetic algorithm (HGA), and tabu search (TS), which are well-known meta-heuristics in a large number of randomly generated test problems based on the field situation. Also, we determine the job selection rule that is suitable to the problem situation considered in this paper and show the effectiveness of our heuristic method.     

Solving non-permutation flowshop scheduling problems by an effective iterated greedy heuristic

Flowshop scheduling problems have received considerable research attention over the last five decades. This study proposes an iterated greedy heuristic for non-permutation flowshop scheduling problems. To validate and verify the proposed heuristic, computational experiments have been conducted on a well-known benchmark problem set, and the results are compared with 20 simple constructive heuristics and a state-of-the-art meta-heuristic performed on the same benchmark instances. In terms of both solution quality and computational expense, this study successfully develops an efficient and effective approach for non-permutation flowshop scheduling problems.     

A scheduling algorithm for the reentrant shop: an application in semiconductor manufacture

This paper addresses a heuristic algorithm that minimizes total weighted tardiness for reentrant flow shop problems with sequence-dependent set up time. The proposed algorithm consists of three phases: prior plan phase, initial solution generation phase and solution improvement phase. In the first phase, a reentrant order with cyclic process is divided into a number of jobs. An initial solution is obtained by a RATCS (revised apparent tardiness cost with setups) rule in the second phase, and the last phase employs a RHTS (rolling horizon tabu search) to improve the solution. The experimental results show that the proposed algorithms give very efficient schedules in terms of total weighted tardiness and computational effort.     

A heuristic method for the combined location routing and inventory problem

The combined location routing and inventory problem (CLRIP) is used to allocate depots from several potential locations, to schedule vehicles’ routes to meet customers’ demands, and to determine the inventory policy based on the information of customers’ demands, in order to minimize the total system cost. Since finding the optimal solution(s) for this problem is a nonpolynomial (NP) problem, several heuristics for searching local optima have been proposed. However, the solutions for these heuristics are trapped in local optima. Global search heuristic methods, such as tabu search, simulated annealing method, etc., have been known for overcoming the combinatorial problems such as CLRIP, etc. In this paper, the CLRIP is decomposed into two subproblems: depot location-allocation problem, and routing and inventory problem. A heuristic method is proposed to find solutions for CLRIP. First of all, an initial solution for CLRIP is determined. Then a hybrid heuristic combining tabu search with simulated annealing sharing the same tabu list is used to improve the initial solution for each subproblem separately and alternatively. The proposed heuristic method is tested and evaluated via simulation. The results show the proposed heuristic method is better than the existing methods and global search heuristic methods in terms of average system cost.     

An ant colony algorithm for scheduling in flowshops with sequence-dependent setup times of jobs

The problem of scheduling in flowshops with sequence-dependent setup times of jobs is considered and solved by making use of ant colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. A new ant colony algorithm has been developed in this paper to solve the flowshop scheduling problem with the consideration of sequence-dependent setup times of jobs. The objective is to minimize the makespan. Artificial ants are used to construct solutions for flowshop scheduling problems, and the solutions are subsequently improved by a local search procedure. An existing ant colony algorithm and the proposed ant colony algorithm were compared with two existing heuristics. It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithm and the existing heuristics, for the flowshop scheduling problem under study.     

Further study of minimizing total tardiness for the worker assignment scheduling problem in the identical parallel-machine models

Minimizing total tardiness in the model of an identical parallel-machine with nonpreemptive jobs of the worker assignment scheduling problem is further studied in this paper. More complicated simulation processes are established for this purpose. The specific worker assignment scheduling problem is solved in two parts of job scheduling and worker assignment. The S_PT, E_DD, S_lack (SES) heuristic is used for the job scheduling part while the largest marginal contribution (LMC) procedure is used to for the worker assignment part and then minimizes the total tardiness. From the new simulations conducted, the heuristics developed have again shown very convincing results quite efficiently.     

Minimization of maximum tardiness on unrelated parallel machines with process restrictions and setups

We consider the problem of scheduling N jobs on M unrelated parallel machines to minimize maximum tardiness. Each job has a due date and requires a single stage of processing. A setup for dies is incurred if the type of the job scheduled is different from the previous one on that machine. For each die type, the number of dies is restricted. Because of the mechanical structure of the machines and the fitness of dies to each machine, the processing time depends on both the job and the machine. In this paper, an efficient heuristic based on guided search, record-to-record travel, and tabu lists is presented to minimize maximum tardiness. Computational characteristics of the proposed heuristic are evaluated through extensive experiments, which show that the proposed heuristic outperforms a simulated annealing method tested and is able to prescribe the optimal solutions for problems in small scales.     

Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time

This paper addresses a new mathematical model for cellular manufacturing problem integrated with group scheduling in an uncertain space. This model optimizes cell formation and scheduling decisions, concurrently. It is assumed that processing time of parts on machines is stochastic and described by discrete scenarios enhances application of real assumptions in analytical process. This model aims to minimize total expected cost consisting maximum tardiness cost among all parts, cost of subcontracting for exceptional elements and the cost of resource underutilization. Scheduling problem in a cellular manufacturing environment is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no inter-cellular transfer is needed. Finally, the nonlinear model will be transformed to a linear form in order to solve it for optimality. To solve such a stochastic model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) algorithm, and an optimization rule will be proposed where SA and optimization rule are subordinate parts of GA under a self-learning rule criterion. Also, performance and robustness of the algorithm will be verified through some test problems against branch and bound and a heuristic procedure.     

Effective heuristics for the no-wait flow shop scheduling problem with total flow time minimization

The no-wait flow shop scheduling problem with total flow time criterion has important applications in industrial systems. Heuristics that explore specific characteristics of the problem are essential to find good solutions in limited computational time for many practical applications. This paper first presents two constructive heuristics, namely improved standard deviation heuristic (ISDH) and improved Bertolissi heuristic (IBH), by combining the standard deviation heuristic (Gao et al., Int J Adv Manf Technol 56:683–692, 2011) and Bertolliso heuristic (Bertolissi, J Mater Process Technol 107:459–465, 2000) with the procedure of the constructive heuristic of Laha (Int J Adv Manf Technol 41:97–109, 2009). Then, four composite heuristics, i.e., ISDH with local search, IBH with local search, ISDH with iteration, and IBH with iteration, are separately proposed using the insertion-based local search method and iteration operator to improve the solutions of the ISDH and IBH. Extensive computational experiments are carried out based on a set of well-known flow shop benchmark instances that are considered as no-wait flow shop instances. Computational results and comparisons show that the proposed composite heuristics perform significantly better than the existing ones, and the proposed composite heuristics further improve the presented constructive heuristics for the no-wait flow shop scheduling problem with total flow time criterion.     

Simple heuristic to minimize total tardiness in a single machine scheduling problem

This paper discusses a simple heuristic to minimize the total tardiness in a single machine scheduling problem. The problem of minimizing total tardiness in single machine scheduling is a combinatorial problem. Hence, heuristic development for such problems is inevitable. In this paper, an attempt has been made to develop a simple heuristic, alternatively called greedy heuristic, to minimize the total tardiness in a single machine scheduling problem with n independent jobs, each having its processing time and due date. Further, its solution accuracy is compared with the optimal solution of a set of randomly generated problems using an ANOVA experiment. From the ANOVA experiment, it is observed that the solution of the simple heuristic proposed in this paper does not differ significantly from the optimal solution at a significance level of 0.05.     

Heuristical job allocation in a flexible manufacturing system

Two heuristic algorithms are presented for solving the scheduling problem in a statically loaded Flexible Manufacturing System (FMS). The heuristics goal is to minimise the total cost resulting from the tardiness of jobs. Using the same heuristics, an iterative method is proposed to find an optimal makespan and the average lead time. Modifications required to handle the case of a dynamically loaded FMS are then presented. Simulation results show that the developed heuristics appear to out perform the other published techniques used in obtaining the schedules associated with minimum makespan, minimum average lead time and minimum cost of tardiness. Finally, the simulation of the dynamic case shows that the algorithms could be implemented locally on each station for the scheduling calculation.