Multiship Crane Sequencing with Yard Congestion Constraints

Crane sequencing in container terminals determines the order of ship discharging and loading jobs that quay cranes (QCs) perform, so that the duration of a vessel's stay is minimized. The ship's load profile, berthing time, number of available bays, and QCs are considered. More important, clearance and yard congestion constraints need to be included, which, respectively, ensure that a minimum distance between adjacent QCs is observed and yard storage blocks are not overly accessed at any point in time. In sequencing for a single ship, a mixed-integer programming (MIP) model is proposed, and a heuristic approach based on the model is developed that produces good solutions. The model is then reformulated as a generalized set covering problem and solved exactly by branch and price (B&P). For multiship sequencing, the yard congestion constraints are relaxed in the spirit of Lagrangian relaxation, so that the problem decomposes by vessel into smaller subproblems solved by B&P. An efficient primal heuristic is also designed. Computational experiments reveal that large-scale problems can be solved in a reasonable computational time

Diode area melting single-layer parametric analysis of 316L stainless steel powder

Diode area melting (DAM) is a novel additive manufacturing process that utilises customised architectural arrays of low power laser diode emitters for high speed parallel processing of metallic powdered feedstock. The laser diodes operate at shorter laser wavelengths (808 nm) than conventional SLM fibre lasers (1064 nm) theoretically enabling more efficient energy absorption for specific materials. This investigation presents a parametric analysis of the DAM process, identifying the effect of powder characteristics, laser beam profile, laser power and scan speed on the porosity of a single-layer sample. Also presented is the effect of process energy density on melt pool depth (irradiated thermal energy penetration capable of achieving melting) on 316L stainless steel powder. An analysis of the density and the melt depth fraction of single layers is presented in order to identify the conditions that lead to the fabrication of fully dense DAM parts. Energy densities in excess of 86 J/mm3 were theorised as sufficient to enable processing of fully dense layers

Supply chain hybrid simulation: From Big Data to distributions and approaches comparison

The uncertainty and variability of Supply Chains paves the way for simulation to be employed to mitigate such risks. Due to the amounts of data generated by the systems used to manage relevant Supply Chain processes, it is widely recognized that Big Data technologies may bring benefits to Supply Chain simulation models. Nevertheless, a simulation model should also consider statistical distributions, which allow it to be used for purposes such as testing risk scenarios or for prediction. However, when Supply Chains are complex and of huge-scale, performing distribution fitting may not be feasible, which often results in users focusing on subsets of problems or selecting samples of elements, such as suppliers or materials. This paper proposed a hybrid simulation model that runs using data stored in a Big Data Warehouse, statistical distributions or a combination of both approaches. The results show that the former approach brings benefits to the simulations and is essential when setting the model to run based on statistical distributions. Furthermore, this paper also compared these approaches, emphasizing the pros and cons of each, as well as their differences in computational requirements, hence establishing a milestone for future researches in this domain.This work has been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

On the use of simulation as a Big Data semantic validator for supply chain management

Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.This work has been supported by FCT (Fundacao para a Ciencia e Tecnologia) within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

Future Logistics: What to Expect, How to Adapt

As a result of global societal and economic as well as technological developments logistics and supply chains face unprecedented challenges. Climate change, the need for more sustainable products and processes, major political changes, the advance of “Industry 4.0” and cyber-physical system are some of the challenges that require radical solutions, but also present major opportunities. The authors argue that logistics has to reinvent itself, not only to address these chal-lenges but also to cope with mass individualization on the one hand while exploit-ing broad-fielded business applications of artificial intelligence on the other hand. An essential challenge will be to find a compromise between these two develop-ments – in line and in combination with the known triple-bottom line for sustaina-bility – that will define supply chains and logistics concepts of the future

Sourcing Flexibility, Spot Trading, and Procurement Contract Structure

We analyze the structure and pricing of option contracts for an industrial good in the presence of spot trading. We combine the analysis of spot trading and buyers' disparate private valuations for different suppliers' products, and we jointly endogenize the determination of three major dimensions in contract design: (i) sales contracts versus options contracts, (ii) flat-price versus volume-dependent contracts, and (iii) volume discounts versus volume premia. We build a model in which a supplier of an industrial good transacts with a manufacturer who uses the supplier's product to produce an end good with an uncertain demand. We show that, consistent with industry observations, volume-dependent optimal sales contracts always demonstrate volume discounts (i.e., involve concave pricing). However, options are more complex agreements, and optimal option contracts can involve both volume discounts and volume premia. Three major contract structures commonly emerge in optimality. First, if the seller has a high discount rate relative to the buyer and the seller's production costs or the production capacity is low, the optimal contracts tend to be flat-price sales contracts. Second, when the seller has a relatively high discount rate compared to the buyer but production costs or production capacity are high, the optimal contracts are sales contracts with volume discounts. Third, if the buyer's discount rate is high relative to the seller's, then the optimal contracts tend to be volume-dependent options contracts and can involve both volume discounts and volume premia. However, when the seller's production capacity is sufficiently low, it is possible to observe flat-price option contracts. Furthermore, we provide links between production and spot market characteristics, contract design, and efficiency.National Science Foundation (U.S.) (contract CMMI-0758069)National Science Foundation (U.S.) (contract DMI-0245352

Optimal static pricing for a tree network

We study the static pricing problem for a network service provider in a loss system with a tree structure. In the network, multiple classes share a common inbound link and then have dedicated outbound links. The motivation is from a company that sells phone cards and needs to price calls to different destinations. We characterize the optimal static prices in order to maximize the steady-state revenue. We report new structural findings as well as alternative proofs for some known results. We compare the optimal static prices versus prices that are asymptotically optimal, and through a set of illustrative numerical examples we show that in certain cases the loss in revenue can be significant. Finally, we show that static prices obtained using the reduced load approximation of the blocking probabilities can be easily obtained and have near-optimal performance, which makes them more attractive for applications.Massachusetts Institute of Technology. Center for Digital BusinessUnited States. Office of Naval Research (Contract N00014-95-1-0232)United States. Office of Naval Research (Contract N00014-01-1-0146)National Science Foundation (U.S.) (Contract DMI-9732795)National Science Foundation (U.S.) (Contract DMI-0085683)National Science Foundation (U.S.) (Contract DMI-0245352

Efficient GRASP+VND and GRASP+VNS metaheuristics for the traveling repairman problem

The traveling repairman problem is a customer-centric routing problem, in which the total waiting time of the customers is minimized, rather than the total travel time of a vehicle. To date, research on this problem has focused on exact algorithms and approximation methods. This paper presents the first metaheuristic approach for the traveling repairman problem