Abstract:During recent years, horizontal collaboration in logistics has gained attention because of achieved potential benefits such as cost reduction, an increase in fulfillment rates, and a decrease in CO2 emissions owing to reductions in traveled distances. Successful real‐world cases, however, are rare since horizontal cooperation in logistics is not usually sustainable. This paper pays attention to this paradox of the lack of cases and discusses 16 identified practical issues that could explain this phenomenon. We… Show more
“…The result shows that the two main factors that affect the participation decision for collaboration are the expected benefit (positive effect from joining in collaboration) and the competitive intelligence risks (the problem of sharing information, possibly resulting in a loss of competitive advantage). Basso, D'Amours [54] found that the cost allocation for collaboration is the most studied practical issue. Furthermore, the study result indicates that trust and the coordination mechanism are significant variables for the implementation of logistics collaboration.…”
Section: Discussion and Policy Implicationsmentioning
This paper estimates the environmental, social and financial effects of logistics collaboration of the existing logistics companies in Seoul, Korea. The truck routing models for collaborative and non-collaborative deliveries are proposed to estimate the collaboration effects. Findings show that both major and minor companies can benefit from logistics collaboration by saving delivery costs and time through economies of scale. The results from the study further indicate that logistics collaboration can mitigate negative environmental impacts resulting from urban logistics by reducing the number of delivery trucks, and shortening delivery times and travel distances. Discussion of related challenges that must be addressed during the implementation of logistic collaboration is included as well.
“…The result shows that the two main factors that affect the participation decision for collaboration are the expected benefit (positive effect from joining in collaboration) and the competitive intelligence risks (the problem of sharing information, possibly resulting in a loss of competitive advantage). Basso, D'Amours [54] found that the cost allocation for collaboration is the most studied practical issue. Furthermore, the study result indicates that trust and the coordination mechanism are significant variables for the implementation of logistics collaboration.…”
Section: Discussion and Policy Implicationsmentioning
This paper estimates the environmental, social and financial effects of logistics collaboration of the existing logistics companies in Seoul, Korea. The truck routing models for collaborative and non-collaborative deliveries are proposed to estimate the collaboration effects. Findings show that both major and minor companies can benefit from logistics collaboration by saving delivery costs and time through economies of scale. The results from the study further indicate that logistics collaboration can mitigate negative environmental impacts resulting from urban logistics by reducing the number of delivery trucks, and shortening delivery times and travel distances. Discussion of related challenges that must be addressed during the implementation of logistic collaboration is included as well.
“…In recent years, the concept of collaboration has received increasing attention in production (e.g., Leng and Jiang 2018;Salamati-Hormozi et al 2018), logistics (e.g., Basso et al 2019;Guajardo et al 2018), and supply chain management (e.g., Herczeg et al 2018;Ponte et al 2018). In this context, collaboration is typically seen as a form of cooperation between two or more independent companies planning and executing jointly specific operations.…”
Section: Review Of Relevant Researchmentioning
confidence: 99%
“…The aim is to achieve mutual benefits, which can be related to cost reductions or the compliance with environmental regulations, for instance. Most commonly, cooperation can either take place between (competing) companies on the same stage of a supply chain (horizontal collaboration) or among partners that operate on different supply chain levels (vertical collaboration) (Basso et al 2019;Simatupang and Sridharan 2002).…”
Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration.
“…is problem relates to the complete set partitioning problem [6] in the Operations Research and Management Science (OR/MS) literature and is commonly formulated as a coalition structure generation problem [2] in cooperative game theory. Although there is a vast body of game theory literature dealing with coalition structure generation problems issues, the OR/MS literature is more sparse in this stream [4,7,8].…”
Genetic algorithms have proved to be a useful improvement heuristic for tackling several combinatorial problems, including the coalition structure generation problem. In this case, the focus lies on selecting the best partition from a discrete set. A relevant issue when designing a Genetic algorithm for coalition structure generation problems is to choose a proper genetic encoding that enables an efficient computational implementation. In this paper, we present a novel hybrid encoding, and we compare its performance against several genetic encoding proposed in the literature. We show that even in difficult instances of the coalition structure generation problem, the proposed approach is a competitive alternative to obtaining good quality solutions in reasonable computing times. Furthermore, we also show that the encoding relevance increases as the number of players increases.
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