Local Search for a Cargo Assembly Planning Problem

Abstract

We consider a real-world cargo assembly planning problem arising in a coal supply chain. The cargoes are built on the stockyard at a port terminal from coal delivered by trains. Then the cargoes are loaded onto vessels. Only a limited number of arriving vessels is known in advance. The goal is to minimize the average delay time of the vessels over a long planning period. We model the problem in the MiniZinc constraint programming language and design a large neighbourhood search scheme. The effects of various optional constraints are investigated. Some of the optional constraints expand the model's scope toward a system view (berth capacity, port channel). An adaptive scheme for a greedy heuristic from the literature is proposed and compared to the constraint programming approach.

Modelling and Solving Online Optimisation Problems

“…We can use our framework to directly implement and solve sliding-window decompositions, simply by appropriately splitting the data. problem (CAPP) from the MiniZinc benchmark suite, 4 a simplified version of the problem described by Belov et al (2014). In CAPP, vessels arrive at different times.…”

“…In addition, many large offline optimisation problems can be better solved by decomposing them into smaller, simpler problems along a timeline. This popular approach is called sliding-window decomposition (Marquant, Evins, and Carmeliet 2015;Belov et al 2014), where the problem is decomposed into (usually overlapping) windows, each solved in increasing time order. In effect, this converts the offline problem into an online one, where each new window refers to some old parts of the problem (those overlapping with the previous window), and to some new (the rest in the new window).…”

Linear-time filtering algorithms for the disjunctive constraint and a quadratic filtering algorithm for the cumulative not-first not-last

“…These constraints are used in many applications. For instance, they are used to solve continuous casting scheduling problems [11], cargo assembly planning problems [5], university time tabling [12], and even carpet cutting problems [25].…”

Maximising Throughput in a Complex Coal Export System

“…Their work models the operations at the terminal in a greatly simplified way and does not consider channel traffic while including a more sophisticated model of the rail operations. Belov et al (2014) also tackles the integrated system, including in-terminal operations, coal arrival scheduling and channel traffic rules, using constraint programming. Since Thomas et al (2013) and Belov et al (2014) use time-indexed models, their approaches require a sufficiently high granularity to be of practical interest.…”

“…Belov et al (2014) also tackles the integrated system, including in-terminal operations, coal arrival scheduling and channel traffic rules, using constraint programming. Since Thomas et al (2013) and Belov et al (2014) use time-indexed models, their approaches require a sufficiently high granularity to be of practical interest. Since the time slots are typically smaller than one hour, and the planning horizons under consideration are typically of the order of weeks, such methods tend to be very computationally demanding.…”