Intelligent Decision Support for Logistics and Supply Chain Management

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    On the Need for Random Baseline Comparisons in Metaheuristic Search
    ( 2018-01-03) Soper, Daniel
    A wide variety of organizations now regularly rely on established metaheuristic search algorithms in order to find solutions to otherwise intractable optimization problems. Unfortunately, neither the developers of these algorithms nor the organizations that rely on them typically assess the algorithms’ performance against a baseline random search strategy, opting instead to compare a specific algorithm’s performance against that of other metaheuristic search algorithms. This paper reveals the folly of such behavior, and shows by means of an optimization case study that simple random or nearly random search algorithms can, in certain circumstances, substantially outperform several of the most widely used metaheuristic search algorithms in finding solutions to optimization problems. The implications of the observed results for both organizations and researchers are presented and discussed.
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    Deep Multi-Agent Reinforcement Learning using DNN-Weight Evolution to Optimize Supply Chain Performance
    ( 2018-01-03) Fuji, Taiki ; Ito, Kiyoto ; Matsumoto, Kohsei ; Yano, Kazuo
    To develop a supply chain management (SCM) system that performs optimally for both each entity in the chain and the entire chain, a multi-agent reinforcement learning (MARL) technique has been developed. To solve two problems of the MARL for SCM (building a Markov decision processes for a supply chain and avoiding learning stagnation in a way similar to the "prisoner's dilemma"), a learning management method with deep-neural-network (DNN)-weight evolution (LM-DWE) has been developed. By using a beer distribution game (BDG) as an example of a supply chain, experiments with a four-agent system were performed. Consequently, the LM-DWE successfully solved the above two problems and achieved 80.0% lower total cost than expert players of the BDG.
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    An Intelligent Decision Support System for the Empty Unit Load Device Repositioning Problem in Air Cargo Industry
    ( 2018-01-03) Döppner, Daniel A. ; Derckx, Patrick ; Schoder, Detlef
    Unit load devices (ULDs) are containers and pallets used in the air cargo industry to bundle freight for efficient loading and transportation. Mainly due to imbalances in global air transportation networks, deficits and surpluses of ULDs are the result and require stock balancing through the repositioning of (empty) ULDs. Following a design science research approach, we (1) elaborate the hitherto uninvestigated problem class of empty ULD repositioning (EUR) and (2) propose an intelligent decision support system (IDSS) that incorporates a heuristic for the given problem and combines artificial intelligence (i.e., rule-based expert system technology) with business analytics. We evaluate the IDSS with real-world data and demonstrate that the proposed solution is both effective and efficient. In addition, our results provide empirical evidence regarding the positive economic and ecological impact of leveraging the potential of ULD pooling in multi-carrier networks.
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    A Decision Support System for Efficient Last-Mile Distribution of Fresh Fruits and Vegetables as Part of E-Grocery Operations
    ( 2018-01-03) Waitz, Martin ; Mild, Andreas ; Fikar, Christian
    Efficient last-mile distribution of fresh fruits and vegetables is a major challenge within e-grocery operations. This work presents a decision support system to jointly investigate the impact of various service offers on customer preferences and logistics operations. Results from a conjoint analysis surveying 531 end consumer are incorporated within an agent-based simulation. Delivery days, fees, time windows and discounts as well as guaranteed remaining shelf life of products at delivery are considered. To model shelf life and schedule deliveries, food quality models and vehicle routing procedures are further integrated within the system. Based on an e-grocery provider operating in Vienna, Austria, computational experiments investigate the impact of the offered delivery service on fulfilled demand, order volume and customer utility. Results indicate the importance of incorporating shelf life data within e-grocery operations and various potentials of considering customer preferences in logistics decision support systems.
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    RFID-Enabled Management of Highly-Perishable Inventory: A Markov Decision Process Approach for Grocery Retailers
    ( 2018-01-03) Siawsolit, Chokdee ; Gaukler, Gary ; Seepun, Sarun
    We address the challenge of managing perishable inventory. One study was conducted to analyze the effects of recapturing unsatisfied demand, and another to estimate improvements in operational metrics through delaying order placements. Our results indicate that significant profit improvements can be achieved under these scenarios, as evidenced by a greater than 30% median increase in profit margin.
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