Optimization Methods in Operations and Supply Chain Management

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 4563

Special Issue Editors


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Guest Editor
Department of Mechanical & Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada
Interests: reliability engineering; combinatorial optimization; statistical optimization and production scheduling

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Guest Editor
Guidhall School of Business and Law, London Metropolitan University, London N7 8DB, UK
Interests: optimization in operations and supply chain management; fuzzy decision making in business and management; quality management; sustainability
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent applications and developments of optimization methods in operations (both production and services) and supply chain management (SCM).

The business that competes in today’s world is based on the production of goods and the delivery of services based on customer needs and, at the same time, to be cost-effective (Eskandari et al., 2021). A supply chain is a collection of connected organizations under flows of material, information, and financial currents. These organizations may include institutions that produce raw materials and products, as well as those that supply services such as distribution, storage, wholesale, and retailing (Avakh Darestani and Hemmati, 2019). In fact, the application of optimization methods and techniques helps companies and individuals to implement optimal decision making in operations, production and supply chains at different levels. In this respect, this Special Issue serves as a forum for the development of novel and advanced algorithms and optimization methods applying existing ones for solving operations management and supply chain management problems.

Researchers are invited to submit their original and unpublished works on areas including but not limited to the following:

  • Optimization in supply chain management;
  • Application of heuristic and meta-heuristic algorithms in operations and production management;
  • Optimization algorithms in operations management;
  • Application of soft computing in supply chain management;
  • Decision theory in business and management;
  • Fuzzy sets theory in operations and supply chain management;
  • Application of meta-heuristic algorithms in production scheduling and sequencing;
  • Optimization in production scheduling and sequencing;
  • Application of fuzzy decision-making techniques in quality management;
  • Modeling and optimization in supply chain network models;
  • Fuzzy decision making in supplier evaluation and allocation problems;
  • Using decision making in lean manufacturing and production;
  • Optimization in inventory control and management;
  • Application of fuzzy decision making in sustainability in supply chain.  

Dr. Mani Sharifi
Dr. Soroush Avakh Darestani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization in supply chain management
  • application of heuristic and meta-heuristic algorithms in operations and production management
  • optimization algorithms in operations management
  • application of soft computing in supply chain management
  • decision theory in business and management
  • fuzzy sets theory in operations and supply chain management
  • application of meta-heuristic algorithms in production scheduling and sequencing
  • optimization in production scheduling and sequencing
  • application of fuzzy decision-making techniques in quality management
  • modeling and optimization in supply chain network models
  • fuzzy decision making in supplier evaluation and allocation problems
  • using decision making in lean manufacturing and production
  • optimization in inventory control and management
  • application of fuzzy decision making in sustainability in supply chain

Published Papers (2 papers)

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Research

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23 pages, 1396 KiB  
Article
An Application of a Decision Support System Enabled by a Hybrid Algorithmic Framework for Production Scheduling in an SME Manufacturer
by Athanasios C. Spanos, Sotiris P. Gayialis, Evripidis P. Kechagias and Georgios A. Papadopoulos
Algorithms 2022, 15(10), 372; https://doi.org/10.3390/a15100372 - 10 Oct 2022
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Abstract
In this research, we present a hybrid algorithmic framework and its integration into the precise production scheduling system of a Greek metal forming factory. The system was created as a decision support tool to assist production planners in arranging weekly production orders to [...] Read more.
In this research, we present a hybrid algorithmic framework and its integration into the precise production scheduling system of a Greek metal forming factory. The system was created as a decision support tool to assist production planners in arranging weekly production orders to work centers and other manufacturing cells. The functionality offered includes dispatching priority rules, bottleneck identification for capacity planning, production order reallocation to alternate work centers and planning periods, interchangeable scheduling scenarios, and work-in-process availability checks based on bill of materials (BOM) precedence constraints. As a consequence, a solid short-term production plan is created, capable of absorbing shop floor risks such as machine failures and urgent orders. The primary design ideas are simplicity, ease of use, a flexible Gantt-chart-based graphical user interface (GUI), controllable report creation, and a modest development budget. The practical application takes place in a make-to-stock (MTS) environment with a complicated multi-level production process, defined due dates, and parallel machines. A critical component is the integration with legacy applications and the existing enterprise resource planning (ERP) system. The method adopted here avoids both overburdening the existing information system architecture with software pipeline spaghetti, as is common with point-to-point integration, and overshooting implementation costs, as is often the case with service-oriented architectures. Full article
(This article belongs to the Special Issue Optimization Methods in Operations and Supply Chain Management)
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Review

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29 pages, 3561 KiB  
Review
Taxonomy of Scheduling Problems with Learning and Deterioration Effects
by Yenny Alexandra Paredes-Astudillo, Jairo R. Montoya-Torres and Valérie Botta-Genoulaz
Algorithms 2022, 15(11), 439; https://doi.org/10.3390/a15110439 - 21 Nov 2022
Cited by 4 | Viewed by 1791
Abstract
In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such [...] Read more.
In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such as learning, monotony, fatigue, psychological factors, etc. These effects can decrease or increase the actual processing time when workers execute a job. The academic literature has reported several modeling and resolution approaches to deal with the phenomenon in a variety of configurations. However, there is no comprehensive review of these research outputs to the best of our knowledge. In this paper, we follow a systematic approach to review relevant contributions addressing the scheduling problem with learning and deterioration effects. Modeling approaches for learning and deterioration effects, objective functions, and solution methods employed in the literature are the main topics for the taxonomy proposed in this review. A total of 455 papers from 1999 to 2021 are included and analyzed. Different areas of interest are presented, and some opportunities for future research are identified. Full article
(This article belongs to the Special Issue Optimization Methods in Operations and Supply Chain Management)
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