In my game, I have modelled the production of items as a graph, where each node is an item (type) and the connections between them are processes to turn one or more material items into one or more products.

Now I want the player to be able to set a production goal, that is a desired number of items he wants produced of a give type.

The task is now for the computer to find the most efficient way of fulfilling the goal. There can be a number of material items already in store, and there can be a cost in time and energy for the processes. The cost in time and energy should be minimized.

Which search algorithm should I use? Neither A* nor BFS seem appropriate...

This could be quite a bit more complex than a simple path search (how complex is your production tree by the way?) so you might want to look into automated planning techniques. Although I never did anything major with them so I might not be able to give you much more advice than that.

I think I want to keep it simple to start with and not worry about scheduling.

Barring any other knowledge about your data structure, you are going to need to fully explore all possible paths of production in order to know which is the most efficient. Thus, Dijkstra's algorithm seems like a good bet to start with. Imagine your edge weights as material costs instead of road lengths and it should be easy enough to apply.

This is an interesting question. I need to get some sleep so I'll keep this short (apologies if it's a bit telegraphic), but I hope the following is helpful:

This sounds very similar to max-flow (but not quite identical).

If you have n conversion processes and T time steps, picture a graph with n T nodes. Each node represents doing a particular process at a particular time. Then, you link all the nodes from one time to all the nodes at the next time. I'm picturing a vertical column of nodes for each time, and those columns arranged in order from early times to late times from left to right.

What you're going to do is optimize over flows on the edges.

What are our constraints? Let's look at an individual node. Say you have a process with two inputs and two outputs. The conversion it does is,
INPUT: 2 units of input resource 1 and 3 units of input resource 2
OUTPUT: 4 units of output resource 1 and 7 units of output resource 2.

Say the amounts of input resources you put in are a and b, and the amounts of output resources are x and y. Then, introducing a new variable z to represent the total number of "trades" done, the above is summarized by the constraints,

z <= a/2
z <= b/3
x <= 4 z
y <= 7 z

so you have inequality constraints relating the flows on your edges. Additionally, all the flows need to be nonnegative, meaning that they go the right direction in time:

x >= 0
y >= 0
a >= 0
b >= 0 .

You also have some equality constraints: The flows into the nodes at the far left of the graph (the "earliest" ones) are fixed; these are the quantities you start out with. And the flows at the far right of the graph have "greater-than" constraints, to say that you want produced at least this much of each resource.

Finally, you have a cost, which is just a weighted sum of the flows.

So what does this boil down to?
- You have as many variables as you have edges [EDIT: plus nodes; you also have the "z" variables]
- You have a linear cost (a weighted sum)
- You have linear inequality constraints

You can solve this exactly as an integer linear program, or you can relax the answer to be real (instead of just integer), in which case you just have a standard LP.

Does this formulation leave anything out?

The linear programming approach looks suitable for this problem. I don't have much experience with LP though. I hope you will post again if you think of more.

With this solution, will it be possible to present the user with a production plan for each of his orders, that he can inspect (and possibly cancel)?
The production plan would be: "Produce x items a, y items b" and so on.

Combine solving the traveling salesman problem with the knapsack problem and you have two NP-hard problems -- at the same time. Don't be fixated on getting "the right answer". You won't. However, you can get something that is reasonable.

Dave,

I was afraid of that. But, keep the suggestions coming.

Investigate using swarm/particle theory. It solves Traveling Salesman in freakish time. It can be reworked to incorporate the bin-packing as well.

Also, this is probably a good place for either MCTS or a straight up GA. Anything that converges on a solution rather than trying to build one from scratch is a good bet.

I haven't used these techniques before. But they look great, and not too complicated. I guess it's time to learn something new.