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<div class="WordSection1">
<h1><a name="_fiuf14a6v0xy"></a><span lang="EN">Call for papers<o:p></o:p></span></h1>
<p class="MsoNormal"><b><span style="color:#1F497D;background:white;mso-highlight:white"><o:p> </o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:14.0pt;background:white;mso-highlight:white">Special Issue: Computational Methods for Supply Chain and Logistics Planning Problems<o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span style="background:white;mso-highlight:white"><o:p></o:p></span></b></p>
<p class="MsoNormal" style="text-align:justify"><span style="background:white;mso-highlight:white">Advances in computing power combined with methodological developments hold promise for obtaining optimal or near-optimal solutions for large-scale optimization
problems containing high degrees of uncertainty and complexity. The increased availability of real-time and transactional data also provides opportunities to gain a deeper understanding of the behavior and properties of dynamic systems, and to develop models
with greater fidelity.<o:p></o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="background:white;mso-highlight:white"><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="background:white;mso-highlight:white">Global supply chains are prime examples in this domain. They are prohibitively complex and dynamic, compounding existing challenges inherent in efficiently matching
supply and demand. Recent world events have revealed the vulnerability of numerous supply chains to external shocks and sources of uncertainty, which has led researchers in academia, with the support of government and industry initiatives, to rethink supply
chain planning approaches in order to improve their performance and resilience. This requires leveraging recent computational and methodological advances to address these emerging challenges for large-scale and integrated systems.<o:p></o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="background:white;mso-highlight:white"><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="background:white;mso-highlight:white">In light of these developments, this special issue is devoted to recent advances that leverage computational methods for dealing with high degrees of complexity
and uncertainty to address complex supply chain and logistics problems. The goal of this issue is to publish high-quality, concise articles with innovative developments that have the potential for application in practice. The objective is to disseminate the
knowledge generated via novel computational methods in the supply chain and logistics planning area.
<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b><span style="font-size:12.0pt">Topics<o:p></o:p></span></b></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="text-align:justify">This special issue of Optimization Letters is open to all areas of computational methods, including exact and heuristic solution approaches, mainly focusing on problems in supply chain management and logistics
planning. Topics of interest include, but are not limited to:<o:p></o:p></p>
<p class="MsoNormal" style="text-align:justify"><o:p> </o:p></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="text-align:justify;line-height:115%;mso-list:l0 level1 lfo1">
Approximate dynamic programming<o:p></o:p></li><li class="MsoNormal" style="text-align:justify;line-height:115%;mso-list:l0 level1 lfo1">
Sample average approximation methods<o:p></o:p></li><li class="MsoNormal" style="text-align:justify;line-height:115%;mso-list:l0 level1 lfo1">
AI/machine learning techniques, especially with the integration of optimization methods to speed up classical solution approaches, such as branch-and-cut, benders decomposition, column generation, etc.<o:p></o:p></li><li class="MsoNormal" style="text-align:justify;line-height:115%;mso-list:l0 level1 lfo1">
Reinforcement learning<o:p></o:p></li><li class="MsoNormal" style="text-align:justify;line-height:115%;mso-list:l0 level1 lfo1">
Parallel search algorithm development<o:p></o:p></li><li class="MsoNormal" style="text-align:justify;line-height:115%;mso-list:l0 level1 lfo1">
Estimation methods for optimal solutions<o:p></o:p></li><li class="MsoNormal" style="text-align:justify;line-height:115%;mso-list:l0 level1 lfo1">
Distributed optimization<o:p></o:p></li></ul>
<p class="MsoNormal" style="text-align:justify"><o:p> </o:p></p>
<p class="MsoNormal" style="text-align:justify"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-bottom:20.0pt;text-align:justify"><b>Submissions
<o:p></o:p></b></p>
<p class="MsoNormal" style="margin-bottom:20.0pt;text-align:justify">Manuscripts should be prepared using
<a href="https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.springer.com%2Fgp%2Flivingreviews%2Flatex-templates&data=05%7C02%7Ciefac.list%40mailhost.ces.clemson.edu%7Cf15796ec61d84bd633ff08dc16b8bc62%7C0c9bf8f6ccad4b87818d49026938aa97%7C0%7C0%7C638410229895453731%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=eVkK58QY%2Bz%2FuFuU3qWko0Ge6rTQy%2FoI7Jwn9QzMntNc%3D&reserved=0" originalSrc="https://www.springer.com/gp/livingreviews/latex-templates" shash="TJHq3VSwFoDCXFyfGGaM4YNSm7OrVwqPIs9fJiBjU3cmxjql1d2fO8TU+40pTqui2ikJ2CsLh0rWWsajG4DiaBENmt8ri12ElU3P9Sf2vI6voiRMibaKs17uzhAcPz7JkYqy54Wt3aY14FFVif3jM6GWRyK26r96MTdr9ywx/Ts=">Springer’s LaTeX macro package</a> and submitted through the journal's editorial manager
<a href="https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.editorialmanager.com%2Foptl&data=05%7C02%7Ciefac.list%40mailhost.ces.clemson.edu%7Cf15796ec61d84bd633ff08dc16b8bc62%7C0c9bf8f6ccad4b87818d49026938aa97%7C0%7C0%7C638410229895453731%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=t9OMpVmj2Dd2KSlpme5gvrlj0PN977cejp4%2F89VgGvM%3D&reserved=0" originalSrc="https://www.editorialmanager.com/optl" shash="aCCiQ/pg3y0D2ULpFg8B/yuyyg+5GDSl3YlyD4GTnLwKNrpwtAumL7qBygHMd7auOhNbJtbyp9yowE6UdJz/SaoV78Arpst16kTc9VMAuo2fv0a2jVjja6kVjQIr41kDvGehjOI/wRLVCcmvmvzSH9UFTyrNq36wHM4VrRL2uzs=">https://www.editorialmanager.com/optl</a>. Each manuscript will be peer-reviewed according to the editorial policy of
<i>Optimization Letters</i>. Articles should be original, unpublished, and not currently under consideration for publication elsewhere.
<o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:20.0pt;text-align:justify">Please select
<i><span style="background:yellow;mso-highlight:yellow">SI: CompSCL2023</span></i> for the article type when submitting your contribution. All papers will go through a regular peer review process.<o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:20.0pt;text-align:justify"><b>Important Dates
<o:p></o:p></b></p>
<p class="MsoNormal" style="margin-bottom:20.0pt;text-align:justify">Submission Deadline:<span style="background:yellow;mso-highlight:yellow"> March 1, 2024.
<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:20.0pt;text-align:justify"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-bottom:20.0pt;text-align:justify"><b>Guest Editors
<o:p></o:p></b></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="color:black;text-align:justify;line-height:115%;mso-list:l1 level1 lfo2">
<span style="color:windowtext">Joseph Geunes, Texas A&M University, College Station, TX</span><o:p></o:p></li><li class="MsoNormal" style="color:black;text-align:justify;line-height:115%;mso-list:l1 level1 lfo2">
<span style="color:windowtext">Bahar Çavdar, Texas A&M University, College Station, TX</span><o:p></o:p></li><li class="MsoNormal" style="color:black;margin-bottom:14.0pt;text-align:justify;line-height:115%;mso-list:l1 level1 lfo2">
<span style="color:windowtext">Reem Khir, Purdue University, West Lafayette, IN</span><o:p></o:p></li></ul>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><span style="font-size:8.0pt;color:#1F497D;mso-ligatures:none">Joseph Geunes<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:8.0pt;color:#1F497D;mso-ligatures:none">Professor and Marilyn and L. David Black Faculty Fellow<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:8.0pt;color:#1F497D;mso-ligatures:none">Associate Department Head for Graduate Affairs<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:8.0pt;color:#1F497D;mso-ligatures:none">Wm Michael Barnes '64 Department of Industrial & Systems Engineering<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:8.0pt;color:#1F497D;mso-ligatures:none">Texas A&M University<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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