Improved formulations, heuristics and metaheuristics for the dynamic demand coordinated lot-sizing problem

Coordinated lot sizing problems, which assume a joint setup is shared by a product family, are commonly encountered in supply chain contexts. Total system costs include a joint set-up charge each time period any item in the product family is replenished, an item set-up cost for each item replenished in each time period, and inventory holding costs. Silver (1979) and subsequent researchers note the occurrence of coordinated replenishment problems within manufacturing, procurement, and transportation contexts. Due to their mathematical complexity and importance in industry, coordinated lot-size problems are frequently studied in the operations management literature. In this research, we address both uncapacitated and capacitated variants of the problem. For each variant we propose new problem formulations, one or more construction heuristics, and a simulated annealing metaheuristic (SAM). We first propose new tight mathematical formulations for the uncapacitated problem and document their improved computational efficiency over earlier models. We then develop two forward-pass heuristics, a two-phase heuristic, and SAM to solve the uncapacitated version of the problem. The two-phase and SAM find solutions with an average optimality gap of 0.56% and 0.2% respectively. The corresponding average computational requirements are less than 0.05 and 0.18 CPU seconds. Next, we propose tight mathematical formulations for the capacitated problem and evaluate their performance against existing approaches. We then extend the two-phase heuristic to solve this more general capacitated version. We further embed the six-phase heuristic in a SAM framework, which improves heuristic performance at minimal additional computational expense. The metaheuristic finds solutions with an average optimality gap of 0.43% and within an average time of 0.25 CPU seconds. This represents an improvement over those reported in the literature. Overall the heuristics provide a general approach to the dynamic demand lot-size problem that is capable of being applied as a stand-alone solver, an algorithm embedded with supply chain planning software, or as an upper-bounding procedure within an optimization based algorithm. Finally, this research investigates the performance of alternative coordinated lotsizing procedures when implemented in a rolling schedule environment. We find the perturbation metaheuristic to be the most suitable heuristic for implementation in rolling schedules

An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control

Article asserts that spare parts inventory management is complex due to the combined impact of intermittent and variable demand patterns. This study proposes a novel nonparametric Bayesian forecasting approach with its roots in the empirical Bayes paradigm

Glass fiber reinforced ultra-high strength concrete with silica fume

Konvencionalni beton pojačan staklenim vlaknima (GFRC) ima široku primjenu u visokim zgradama, mostovima i radovima obnove. U ovom istraživanju razvijene su mješavine betona pojačanog staklenim vlaknima ultravisoke čvrstoće kako bi se smanjila veličina nosivih elemenata. Varijacija postotka staklenih vlakana bila je 0 %, 0,03 %, 0,06 %, 0,09 % i 0,12 %. U ovom istraživanju izrađeno je i ispitano deset greda, s dva omjera raspona i statičke visine (a/d) od 1,6 i 2. Svi testirani uzorci opterećeni su do sloma, no vrijednosti čvrstoće su varirale. Točnije, uzorci pojačani s 0,09 i 0,06-postotnim staklenim vlaknima pokazali su najveću savojnu i posmičnu čvrstoću. Rezultati dobiveni ovim istraživanjem mogu se iskoristiti za odabir optimalnih mješavina betona ultravisoke čvrstoće pojačanog staklenim vlaknima u zadovoljavajućim radnim uvjetima.Conventional glass fiber-reinforced concrete (GFRC) has wide applications in high-rise buildings, bridges, and renovation works. In this study, ultra-high-strength glass fiber reinforced concrete (UHS-GFRC) mixtures were developed to minimize the size of the structural members. The percentage of glass fiber was varied as 0%, 0.03%, 0.06%, 0.09%, and 0.12%. In this investigation, ten beams were cast and tested, with two span-to-effective-depth (a/d) ratios of 1.6 and 2. All the tested specimens attained their respective strengths; however, the strength values varied. Specifically, the specimens reinforced with 0.09% and 0.06% of glass fiber exhibited the highest flexural and shear strengths, respectively. The results obtained in this study can be utilized to select optimum mixtures of UHS-GFRC under satisfying service conditions

Investigating contingent adoption of additive manufacturing in supply chains

PurposeThe purpose of this research is to investigate the contingent adoption of Additive Manufacturing (AM) and propose a typology to evaluate its adoption viability within a firm's supply chain.Design/methodology/approachBy conducting semi-structured interviews of practitioners with deep knowledge of AM and supply chains from diverse industries, this research explores the contingent factors influencing AM adoption and their interaction.FindingsWhile the AM literature is growing, there is a lack of research investigating how contingent factors influence AM adoption. By reviewing the extant literature on the benefits and barriers of AM, we explain the underlying contingencies that enact them. Further, we use an exploratory approach to validate and uncover underexplored contingent factors that influence AM adoption and group them into technological, organizational and strategic factors. By anchoring to a selected set of contingent factors, a typological framework is developed to explain when and how AM is a viable option.Research limitations/implicationsThis study focuses on specific industries such as automotive, machine manufacturing, aerospace and defense. Scholars are encouraged to explore the contextual factors affecting AM adoption in particular industries to expand our findings. The authors also acknowledge that the robustness of their framework can be enhanced by integrating the remaining contingent factors.Practical implicationsThe developed typological framework provides a pathway for practitioners to see how and when AM can be useful in their supply chains.Originality/valueThis is the first paper in the supply chain management literature to synthesize contingent factors and identify some overlooked factors for AM adoption. The research is also unique in explaining the interaction among selected factors to provide a typological framework for AM adoption. This research provides novel insights for managers to understand when and where to adopt AM and the key contingent factors involved in AM adoption.</jats:sec

Ambidextrous humanitarian organizations

The COVID-19 pandemic disrupted life as usual around the globe. Efforts to control thespread of the virus with lockdowns and border closures pushed millions of people into foodand social insecurity. Most research on humanitarian organizations have been dominated bythe uncertainty and urgency of disaster response operations. However, some humanitarianorganizations also operate in long-term continuous aid programs where efficiency is the keygoal. We analyzed the operations of food banks in the Feeding America network and TheSalvation Army USA, and found them to be ambidextrous organizations. The ambidextroushumanitarian organizations like food banks and Salvation Army, focus on long-term continuousaid programs, specifically pertaining to the sustenance of the communities they serve,but also play a key part as first responders or as local agencies aiding in disaster relief andresponse. We propose a framework to analyze disaster, development, and sustenance aidsupply chains, and identify future research opportunities

Forecasting in humanitarian operations: Literature review and research needs

Forecasting research in the humanitarian context is scarce. In this literature review, ourgoal is not only to show why forecasting research is important for the humanitariansector, but also to identify what has been done so far, and where are the needs forfurther research. We conducted a structured literature search in Scopus, Web of Science,ABI Inform, and Google Scholar resulted in only 38 papers published between 1990 and2018. Based on our findings we highlight three case studies as exemplary research inforecasting within the humanitarian context and list seven future research streams withspecific research needs identified in each stream

Evaluation of joint replenishment lot-sizing procedures in rolling horizon planning systems

Joint replenishment problems are commonly encountered in purchasing, manufacturing, and transportation planning. Literature evaluates various algorithmic approaches for solving the joint replenishment problem in a static environment, but their relative performance in a dynamic rolling horizon system is unknown. This research experimentally evaluates nine joint replenishment lot-sizing heuristics and policy design variables when implemented in a dynamic rolling schedule environment. The findings indicate that a single algorithm does excel on both dimensions of schedule cost and stability. Hence, management must trade off these two performance metrics when choosing the best approach for their specific problem. Generally, metaheuristics provide the best cost replenishment schedule, but forward pass based heuristics yield the most stable schedules. The results also indicate that the choice of lot-sizing heuristic is the major cost performance driver in rolling planning systems, with policy design variables (frozen interval and planning horizon length) having little impact. While the simulated annealing heuristic of Robinson et al. (2007a) is the most effective solution procedure for the static joint replenishment problem, the perturbation metaheuristic of Boctor et al. (2004) produces lower schedule costs and greater stability in rolling schedule environments.Joint replenishment Lot sizing Rolling horizon