Mathematical programming and solution approaches for minimizing tardiness and transportation costs in the supply chain scheduling problem

Tamannaei, Mohammad; Rasti−Barzoki, Morteza

doi:10.1016/j.cie.2018.11.003

Cited by 33 publications

(15 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. , 17,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16 But in this placement, there are two unused positions between 16 (the last appearance of an element of B 5 ) and 8 (the first appearance of an element of B 4 ), which can contain the elements 2 and 3 of B 4 . By placing these two elements immediately after 16, we obtain a solution that satisfies our assumptions, and is at least as good:…”

Section: Notations and Problem Statementmentioning

confidence: 99%

“…Inexact techniques, on the other hand, can find reasonable suboptimal solutions in polynomial time. Inexact methods themselves can be grouped into two families: approximation algorithms, which guarantee to return a (suboptimal) solution that is within a certain factor of the optimal solution (see, e.g., [18], [19]), and heurstic/metahueristic approaches, which do not offer any performance guarantee but have been found to be very successful in solving NPhard problems (see, e.g., [3], [12], [13], [20]- [23]). It should be noted that some tractable cases of the TMP have been identified in the literature [24], [25].…”

Section: Introductionmentioning

confidence: 99%

“…Consider the TMP instance given by (1). For the subset B = {B 1 , B 2 , B 3 } of B, we have σ(B ) = 1,4,5,10,11,12,13,14,17 . Therefore, only four entries in the column corresponding to B need be computed, one corresponding to each of the sets {1}, {3, 4}, {7, 8, 9, 10}, and {16, 17}.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Novel Dynamic Programming Approach to the Train Marshalling Problem

Falsafain

Tamannaei

2020

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

Train marshalling is the process of reordering the railcars of a train in such a way that the railcars with the same destination appear consecutively in the final, reassembled train. The process takes place in the shunting yard by means of a number of classification tracks. In the Train Marshalling Problem (TMP), the objective is to perform this rearrangement of the railcars with the use of as few classification tracks as possible. The problem has been shown to be NP-hard, and several exact and approximation algorithms have been developed for it. In this paper, we propose a novel exact dynamic programming (DP) algorithm for the TMP. The worst-case time complexity of this algorithm (which is exponential in the number of destinations and linear in the number of railcars) is lower than that of the best presently available algorithm for the problem, which is an inclusion-exclusion-based DP algorithm. In practice, the proposed algorithm can provide a substantially improved performance compared to its inclusion-exclusion-based counterpart, as demonstrated by the experimental results.

show abstract

Section: Notations and Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Dynamic Programming Approach to the Train Marshalling Problem

Falsafain

Tamannaei

2020

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Tamannaei and Rasti-Barzoki [23] investigated the IPSVRP to minimize the total weighted tardiness, fixed and variable transportation costs. A Branch-and-Bound (B&B) based exact procedure and a meta-heuristic genetic algorithm (GA) were proposed to solve the problem [23].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Genetic Algorithm for Integrated Production and Distribution Scheduling Problem with Outsourcing Allowed

Izadi

Ahmadizar

Arkat

2020

IJE

View full text Add to dashboard Cite

In this paper, we studied a new integrated production scheduling, vehicle routing, inventory and outsourcing problem. The production phase considers parallel machine scheduling including setup times with outsourcing allowed and the distribution phase considered batch delivery by a fleet of homogenous vehicles with respect to holding cost of completed jobs. The objective of the Mixed Integer Linear Programming (MILP) formulated model is to minimize the total costs including production, outsourcing, holding, tardiness and distribution fixed and variable costs. Due to the nondeterministic polynomial time (Np)-hardness of the problem, we derive a number of dominance properties for the optimal solution and combine them with a Genetic Algorithm (GA) to solve the problem. To assess the efficiency and effectiveness of the proposed hybrid algorithm, we conduct the computational study on randomly generated instances. Sensitivity analyses showed the impacts of the parameters on the objective function were incorporated. In order to evaluate the significance of the differences among the results obtained by GA and GADP one-tailed paired t tests were performed and interval plots were depicted.

show abstract

“…In the TPIF1MP scheduling, where the release dates are given in ascending order starting from 1 to the number of jobs, the inaccuracy of finding the minimal total tardiness has the strongest negative impact. Therefore, this is almost the worst case, which defines the accuracy limit of the RPP-RAP heuristic (and other heuristics as well) and positively serves just as the principle of minimax guaranteeing decreasing losses in the worst conditions (the maximum of unfavorable states) [8,13,14].…”

Section: Introductionmentioning

confidence: 99%

A Sorting Improvement in the Heuristic Based on Remaining Available and Processing Periods to Minimize Total Tardiness in Progressive Idling-Free 1-Machine Preemptive Scheduling

Romanuke

2021

KPI SN

View full text Add to dashboard Cite

Background. In preemptive job scheduling, which is a part of the flow-shop sequencing tasks, one of the most crucial goals is to obtain a schedule whose total tardiness would be minimal. Total tardiness minimization is commonly reduced to solving a combinatorial problem which becomes practically intractable as the number of jobs and the numbers of their processing periods increase. To cope with this challenge, heuristics are used. A heuristic, in which the decisive ratio is the reciprocal of the maximum of a pair of the remaining processing period and remaining available period, is closely the best one. However, the heuristic may produce schedules of a few jobs whose total tardiness is 25 % greater than the minimum or even worse. Therefore, this heuristic needs a corrective branch which would further try to minimize total tardiness under certain conditions. Objective. The goal is to ascertain what is to be corrected in the heuristic so that the total tardiness value could be obtained lesser. The heuristic will be applied to tight-tardy progressive idling-free 1-machine preemptive scheduling, where the release dates are given in ascending order starting from 1 to the number of jobs, and the due dates are tightly set after the release dates. In this scheduling problem, the inaccuracy of finding the minimal total tardiness has the strongest negative impact, so this is almost the worst case, which defines the accuracy limit of the heuristic and positively serves just as the principle of minimax guaranteeing decreasing losses in the worst conditions. Methods. The heuristic sorts maximal decisive ratios by release dates, where the scheduling preference is given to the earliest job. To achieve the said goal, three other sorting approaches are presented and a computational study is carried out with applying each of the four heuristic approaches to minimize total tardiness. For this, two series of 266000 and 1064000 scheduling problems are generated. Results. The earliest-job sorting ensures a heuristically minimal total tardiness value in more than 97.6 % of scheduling problems, but it fails to minimize total tardiness in no less than 2.2 % of the cases. Nevertheless, a sorting approach with minimizing remaining processing periods produces a heuristically minimal total tardiness for almost any scheduling problem. If an exception occurs, this sorting approach “loses” to the other sorting approaches very little. Moreover, the exceptions are quite rare as it has been registered just a one scheduling problem (out of 31914 cases followed by a sole “win” of a heuristic version) whose minimal total tardiness is achieved by the earliest-job sorting. Conclusions. The best heuristic version is that one which uses the sorting approach with minimizing remaining processing periods. This, however, is confirmed only for the case where jobs do not have any priorities. The case when jobs have their priority weights is to be yet analyzed.

show abstract

Mathematical programming and solution approaches for minimizing tardiness and transportation costs in the supply chain scheduling problem

Cited by 33 publications

References 30 publications

A Novel Dynamic Programming Approach to the Train Marshalling Problem

A Novel Dynamic Programming Approach to the Train Marshalling Problem

A Hybrid Genetic Algorithm for Integrated Production and Distribution Scheduling Problem with Outsourcing Allowed

A Sorting Improvement in the Heuristic Based on Remaining Available and Processing Periods to Minimize Total Tardiness in Progressive Idling-Free 1-Machine Preemptive Scheduling

Contact Info

Product

Resources

About