Practice Summary: Solving the External Candidates Exam Schedule in Norway

We developed a mixed-integer linear programming model to plan exam sessions for external candidates in the Vestfold region, Norway. With our model, the administration planned the last session of 2018, the two sessions of 2019, and the first session of 2020. The plans produced are of high quality and saved three weeks of person effort per session.

Hybrid Multiagent Collaboration for Time-Critical Tasks: A Mathematical Model and Heuristic Approach

Principal–assistant agent teams are often employed to solve tasks in multiagent collaboration systems. Assistant agents attached to the principal agents are more flexible for task execution and can assist them to complete tasks with complex constraints. However, how to employ principal–assistant agent teams to execute time-critical tasks considering the dependency between agents and the constraints among tasks is still a challenge so far. In this paper, we investigate the principal–assistant collaboration problem with deadlines, which is to allocate tasks to suitable principal–assistant teams and construct routes satisfying the temporal constraints. Two cases are considered in this paper, including single principal–assistant teams and multiple principal–assistant teams. The former is formally formulated in an arc-based integer linear programming model. We develop a hybrid combination algorithm for adapting larger scales, the idea of which is to find an optimal combination of partial routes generated by heuristic methods. The latter is defined in a path-based integer linear programming model, and a branch-and-price-based (BP-based) algorithm is proposed that introduces the number of assistant-accessible tasks surrounding a task to guide the route construction. Experimental results validate that the hybrid combination algorithm and the BP-based algorithm are superior to the benchmarks in terms of the number of served tasks and the running time.

A mixed integer linear programming model to support e-fulfillment strategies in warehouse-based supermarket chains

The use of the online channel has greatly increased the logistics costs of supermarket chains. Even the difficulty of managing order picking and delivery processes has increased due to the short delivery times and the preservation of perishable products. Against that backdrop, the proposed approach presents a mathematical model for planning the e-fulfillment activities with the objective of ensuring maximum efficiency. The linear programming model has been designed for e-grocers that prepare their online orders at central warehouses. The mathematical model determines both the time windows during which picking and transport should take place and the assignment of trucks to delivery routes. The allocation of online orders is performed taking into account the conservation requirement of each type of product and the availability of means. Considering this planning tool, managers can improve the decision-making process guaranteeing the quality of service while reducing the e-fulfillment cost for joint picking and delivery point of view. Motivated by a cooperation with a supermarket chain, results bring great insight based on the simulation of different logistics alternatives. Companies and researchers can compare the strategy of leveling the workload and the strategy of reducing the number of means, a common alternative in logistics outsourced to third parties. In addition, the different scenarios developed make it possible to determine the substantial savings achieved by modifying the delivery services and advancing the order preparation. As a result, managerial insights are identified highlighting the importance of efficient order planning to improve the profitability of online sales.

Optimization Approaches for Defining Storage Strategies in Maritime Container Terminals

In maritime container terminals, yards have a primary role in permitting the efficient management of import and export flows. In this work, a mixed 0/1 linear programming model and a heuristic approach are proposed for defining storage rules in order to minimize the space used in the export yard. The minimization of land space is pursued by defining the rules to allocate containers into the bay-locations of the yard, in such a way to minimise the number of bay-locations used and the empty slots within them. The main aim of this work is to propose a solution approach for permitting to the yard manager to compare yard storage strategies for different transport demands, in such a way to be able to evaluate and, eventually, change the storage strategy when the characteristics of the transport demand change. Computational experiments, based on both real instances and generated ones, are presented. All instances are derived by a case study related to an Italian terminal.

New Bounds on Roller Coaster Permutations

A roller coaster is a permutation \pi that maximizes the sum $\t(\pi) = \sum_{\tau\in X(\pi)}\id(\tau)$, where \(X(\pi)\) denotes the set of subsequences of \(\pi\) with cardinality at least \(3\); and \(\id(\tau)\) denotes the number of maximal increasing or decreasing subsequences of contiguous numbers of \(\tau\). We denote by \(\t_{\max}(n)\) the value \(\t(\pi)\), where \(\pi\) is a roller coaster of \(\{1,\ldots,n\}\), for \(n\geq 3\). Precise values of \(\t_{\max}(n)\) for \(n\leq 13\) were presented in \cite{AhmedTanbir}. In this paper, we explore the problem of computing lower bounds for \(\t_{\max}(n)\). More specifically, we present a cubic algorithm to compute $\t(\pi)$ for any given permutation $\pi$; and an Integer Linear Programming model to obtain roller coasters. As a result, we improve known lower bounds found in the literature for $n \leq 40$.